Keywords: Planning and Navigation, Machine Learning
Abstract: Robots and other autonomous systems need to understand how to move in complex and dynamic environments while avoiding or minimizing unwanted contact. With over 40 years of evolution, classical motion planning solutions have been hitting practical limits in solving many real-world environments due to their unpredictability as well as the curse-of-dimensionality. Even with today's best algorithms, we often experience unsatisfactory behaviors or performance: with robots taking many seconds or even minutes to think before they move, and even then, the movement may appear unusually roundabout and suboptimal. Higher-level considerations, including safety, responsiveness, and accounting for uncertainty can also add significant challenges.

Now, Machine Learning has arrived to the motion planning problem and promises to overcome the current limitations of our classical techniques and provide a transformative leap in autonomous planning and control. How does it manage to achieve this? In this talk, I will introduce our work in motion planning networks that started this path toward neural planners, breaking the mold of how robots should plan for navigation. In both simulation and real-world examples, we show how this research area has grown to solve multi-manipulator coordination, task and motion planning, kinodynamically constrained motion planners, autonomous driving, and more.

Keywords: Design Optimization in Mechatronics, Biomechatronics, Actuators in Mechatronic Systems
Abstract: The poster discusses design of a tensegrity leg that is composed of two revolute joints that are also passive in the transverse direction. The Actuated Tensegrity Revolute (AcTeR) Joint is able to have a range of motion of approximately 120 degrees. It comprises of two rigid links - one is a semi-circle, while the other is similar to the shape of a horseshoe. The structural integrity of the structure is maintained by strategically routing an elastic string between the two links such that both of them do not touch/interact with each other. This gives the AcTR joint its shape and the passive quality in the transverse direction. The passivity of the joint can be tuned by using different size elastic cables and tightening or loosening the cables. By changing the passivity of the joint, the AcTR joint can be used for different loads.

The novelty and advantage of the AcTR joint comes from the simplistic fabrication technique that provides it with complex attributes. It is simple in that it composes of two rigid links, one string to connect the links, and one belt and motor to actuate the joint. This bio-inspired structure allows for one to have a revolute joint that can also passively move in the transverse direction where the two rigid elements do not rub against eachother. In this sense the system has an embodied intelligence. The AcTR joint could lead to a system that more closely aligns with biology. This would be very helpful for traversing over unstructured environments.

Keywords: Biomechatronics, Rehabilitation Robots, Actuators in Mechatronic Systems
Abstract: In this study, we introduce a knee joint support suit applying shape memory alloy-based fabric-type artificial muscles (fabric muscles). An everyday pants-type strength-assist suit has fabric muscles attached to the location of the quadriceps to assist knee extension movements. We describe the performance of the fabric muscle that provides the assistive force, as well as the design process and composition of the suit.

Keywords: Human -Machine Interfaces, Humanoid Robots, Service Robots
Abstract: In recent years, some researchers investigated how to evaluate and improve human comfort in HRC scenarios. However, these research limits their comfort evaluation methods by merely using subjective ratings or simple statistical comparison approaches. There is a lack of a mathematical modeling approach to evaluate human comfort in HRC tasks. This study proposed an individual human comfort model using analytical approach, and further conduct comfort factor analysis. The proposed comfort model can be used to predict human comfort feedback given a set of robot motion parameters; thus, it will contribute to designing a more comfortable robotic system which will adapt its working style in HRC in the future. The five factors have been adopted and sufficient number of their combinations provide a comprehensive coverage of the scenarios that a human subject will potentially encounter in HRC tasks. Also, the results yielded provide a satisfying overall prediction accuracy of 81.33%. Thus, the comfort prediction model has been proved to be universally applicable and extendable to a wide range of HRC scenarios. The model has already been applied in a new study which is used to compute comfort rewards for an MDP model in HRC.

Keywords: Artificial Intelligence in Mechatronics, Sensor Integration, Data Fusion, Neural Networks
Abstract: A head orientation estimation solution is presented in this work. The proposed solution utilizes instrumented helmets that incorporate inertial measurement units (IMU), and the collected measurements are processed by an orientation estimation algorithm to compensate for bias and noise in the orientation measurements. The structure of the proposed dynamic estimation algorithm is motivated by complementary filters, and introduces a Convolutional Neural Network (CNN) model that is trained to capture a wide range of human head motion profiles. Experimental evaluation of the proposed head orientation estimation solution is presented in this work, and the obtained results are compared to both learning and non-learning-based solutions found in the literature. Test results demonstrate the potential advantages of the proposed CNN based solution for motion profiles with high acceleration disturbance that are characteristic of head motion.

Keywords: Control Application in Mechatronics, Machine Learning, Identification and Estimation in Mechatronics
Abstract: This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR); (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot’s operating speed with the use of the MIMO ILC.

Keywords: Machine Learning, Sensors and Sensing Systems, Identification and Estimation in Mechatronics
Abstract: This work presents a novel approach to classify the behavior of mobile sensors with the specific focus of detecting anomalous mobile sensor activity. In recent years, especially with the development of the Internet of Things (IoT), anomaly, or outlier, detection problems have become a key area for research in areas such as network security, system health monitoring, and navigation and obstacle avoidance in mobile systems such as factory-floor robots or autonomous vehicles. Mutual information is used to encode and quantify the expected behavior of a mobile sensor to identify the presence of anomalies. This technique benefits from straightforward implementation, and the concept can be extrapolated to other sensor measurements to create more robust detection schemes. Simulations are presented using measured data from a wireless sensor network to demonstrate application of the method. Results show that mobile sensor behavior that fall outside of a specific regime can be detected and systematically quantified.

Keywords: Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems, Rehabilitation Robots
Abstract: In this study, we introduce a multi-winding type gravity compensation mechanism for high torque compensation. The tendon-driven gravity compensation mechanism using a wire wound several times is compact and lightweight, and will be applied to joint compensation of wearable robots in the future. We verified the feasibility of the proposed compensation mechanism through experiments.

Keywords: Modeling and Design of Mechatonic Systems, Rehabilitation Robots
Abstract: We introduce a "lockable module" for controlling rotary degrees of freedom (DoFs) in a modular wearable robot system. We begin by outlining the biomechanics requirements, then present the brake design within the module. Subsequently, we demonstrate the integration of the module in a wearable system. Finally, we conclude our contributions and propose future research directions.

Keywords: Aerial Robots, Robot Dynamics and Control, Unmanned Aerial Vehicles
Abstract: The need for physical interactions and aerial manipulation has driven the demand for small multirotor vehicles with higher degrees of actuation and adaptability. This leads to the development of reconfigurable flying robots and modular flight platforms. In this work, we propose a modular vehicle comprising flight-capable quadrotors with passively deformable rotor arms as subunits. The foldable arms with loaded elastic components are designed to be stable in both folded and unfolded states such that the reconfiguration can be achieved passively through the manipulation of the propelling thrust. A docking mechanism is devised to permit multiple modules to combine during a mission without human intervention. Through a series of experiments, we show that a single robot with foldable arms is able to grasp a narrow structure for landing. As multiple modules are docked together, the thrust capacity of the robot is amplified and the foldable arms can be re-purposed as grippers for payload transport. Meanwhile, the increased number of rotors allows some propellers to tilt sideways, rendering the modular platform fully actuated.

Keywords: Aerial Robots, Unmanned Aerial Vehicles
Abstract: The article develops an impact-resilient aerial robot (s-ARQ) equipped with a compliant arm to sense contacts and reduce collision impact and featuring a real-time contact force estimator and a non-linear motion controller to handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions. Further, a new collision-inclusive planning method that aims to prioritize contacts to facilitate aerial robot navigation in cluttered environments is proposed. A range of simulated and physical experiments demonstrate key benefits of the robot and the contact-prioritized (CP) planner. Experimental results show that the compliant robot has only a 4% weight increase but around 40% impact reduction in drop tests and wall collision tests. s-ARQ can handle collisions while performing aggressive maneuvers and stabilize from high-speed wall collisions at 3.0m/s with a success rate of 100%. Our proposed compliant robot and contact-prioritized planning method can accelerate computation time while having shorter trajectory time and larger clearances compared to A* and RRT* planners with velocity constraints. Online planning tests in partially-known environments further demonstrate the preliminary feasibility of our method to apply in practical use cases.

Keywords: Aerial Robots, Transportation Systems, Control Application in Mechatronics
Abstract: Aerial grasping is beginning to revolutionize industrial applications through robotics in Industry 4.0. However, this sector still lacks a gripper mechanism effective in autonomous grasping of in-house cargo and simple enough for rapid generation and implementation on a variety of industrial drones. A novel four-bar linkage rigid gripper was developed to address these challenges. This gripper is constructed of lightweight multi-material 3D printed components facilitating rapid construction and designs. The linkage setup allows for easy scaling while modular end effectors optimize performance for varying gripping applications. Manual gripping tests along with autonomous pick-and-place missions were conducted to evaluate the overall performance. The results demonstrate viability and point towards design adjustments and robust control algorithms for improved autonomous grasping under ground effect. The gripper in this work was designed and tested on the COEX Clover Drone available in the host lab. Its design can be extended and adjusted to any other aerial vehicles in general.

Keywords: Unmanned Aerial Vehicles, Aerial Robots, Robot Dynamics and Control
Abstract: This paper presents a static-equilibrium oriented interaction force modeling and control approach of aerial manipulation employing uni-directional thrust (UDT) multirotors interacting with variously defined environments. First, a simplified system model for a quadrotor-based aerial manipulator is introduced considering parameterized work surfaces under assumptions, and then a range of meaningful manipulation tasks are utilized to explore the system properties in a quasi-static equilibrium state. An explicit interaction force model in relation with the aerial manipulator pose configuration and the environment parameter is derived from the static equilibrium analysis, based on which singularity is pointed out. Then a hybrid attitude/force interaction control strategy is presented to verify the proposed interaction force model, which involves high gain attitude control and feedforward plus feedback force control. This paper represents preliminary results. We study the properties of UDT-based aerial manipulators via specific tasks, and propose a novel framework for interaction force modeling and control aiming at maximizing the commercial values of UDT platforms for aerial manipulation purpose.

Keywords: Aerial Robots, Unmanned Aerial Vehicles
Abstract: Aerial manipulators coupled to UAVs can be beneficial for doing tasks in difficult-to-reach areas. Inspection is one of such most commonly required tasks conducted on aging infrastructure including bridges and tunnels. However, while employing an inspection UAV, the manipulator tip's angle of reach is often mechanically limited to only about the location of its attachment on the aircraft. In the proposed system, a design that allows a manipulator tip to reach all directions surrounding the UAV is developed. As the manipulator body, we propose a basic tiltable airframe design and employ auxiliary actuators to maintain the rotors' axis. The thrust direction of the rotors remains upright in the proposed design, which allows the rotor thrust to hold the maximum payload of the UAV even while tilted. We examine its efficiency and usefulness through experimental demonstration.

Keywords: Robot Dynamics and Control, Unmanned Aerial Vehicles, Control Application in Mechatronics
Abstract: This paper proposes a system framework for aerial manipulators to cooperatively transport a cable-suspended load. If the trajectory of the payload is considered as the control object, the entire system of the aerial manipulators and the slung load is redundant. Therefore, the null-space-based (NSB) controller can be presented to ensure the position/orientation of the slung load via the control for the quadrotors and robotic arm. The load trajectory and interactive force on the aerial manipulator during transportation are investigated. Additionally, the adaptive control method is presented in the inner controller to keep the control performance under dynamic uncertainties, interactive forces, and unknown load. To demonstrate the stability and efficacy of the proposed control structure, Lyapunov stability analysis, and numerical simulations are presented.

Keywords: Unmanned Aerial Vehicles, Planning and Navigation, Mobile Robots
Abstract: Illumination change, image blur, and fast motion dramatically decrease the performance of visual-inertial navigation systems (VINS). This paper presents a new illumination-robust visual-inertial odometry (IR-VIO) based on adaptive weighting algorithm with two-layer confidence maximization. First, to prevent the VIO performance degradation caused by poor image quality in complex scenes and ignoring the confidence differences of feature points, we develop a novel adaptive weighting algorithm on the multi-sensor layer and visual feature layer to better fuse multi-sensor information and maximize the overall confidence of VIO. Second, to solve the problems of image feature tracking difficulty and excessive image noise in illumination-changing scenes, an image enhancement algorithm is introduced to enhance consecutive images to the same brightness level, while a block noise removal algorithm with constraint protection mechanism is proposed to dynamically remove noise points. Finally, experimental results on the public dataset and real-world environments demonstrate that IR-VIO has superior performance in terms of accuracy and robustness compared with the state-of-the-art methods. Supplementary video is available at https://youtu.be/h9rmszxYHEk.

Keywords: Actuators in Mechatronic Systems, Control Application in Mechatronics, Mobile Robots
Abstract: This paper presents the concept of a spherical-motor-based visual tracking system. Unlike conventional gimbal systems consisting of serially articulated motors/gears for achieving multi-DOF negotiation of optical axis of a camera, the spherical motor is capable of providing three-DOF in one joint, thus greatly reducing the unwanted inertia and frictions of the rotating parts. The kinematic model relating the image projection motion and the spherical motor orientation is established for the omni-directional visual tracking configuration, based on which an image-based visual servo (IBVS) algorithm is derived. A cascaded visual tracking controller consisting of an IBVS control and a complementary H2-H∞ (C-H2-H∞) control is proposed for precisely controlling the spherical motor in presence of external disturbances. The capability of the proposed system for tracking a flying target is investigated and the performances are compared to a conventional gimbal system. The results demonstrate that the spherical-motor-based tracking system with the proposed controller can avoid singularities usually encountered in conventional articulated gimbals and provide fast and precise visual tracking of randomly flying targets.

Keywords: Mobile Robots, Sensors and Sensing Systems
Abstract: Visual odometry is a crucial technique for estimating a robotic vehicle's trajectory by analyzing images captured by its onboard camera when the vehicle's attitude cannot be retrieved. However, uncertainties such as modeling errors, measurement noise, mis-identification of feature marks, and the switching output arising from visual geometric constraints can all hinder accurate estimations. To address these challenges, this paper proposes a robust visual odometry that can be implemented in a sampled-data structure. Comprehensive simulations and experiments are conducted to demonstrate the effectiveness of the proposed design and to explore the relationship between design parameters and estimation performance. Additionally, tuning guidelines for visual odometry parameters are provided to help address these uncertainties effectively.

Keywords: Control Application in Mechatronics, Mobile Robots
Abstract: This work aims at designing a visual predictive control (VPC) scheme for a mobile manipulator equipped with two cameras. The task consists in accurately positioning the end-effector camera while starting few meter away from the desired pose with a tucked arm. Three challenges are addressed in this paper: the initial unavailability of the visual features, the arm singularities together with the closed-loop stability and the final positioning accuracy. The first one is dealt with by choosing image features extracted from both cameras and by suitably switching between them, the second one is tackled through a suitable manipulability measure introduced in the cost function, and the two last ones are fulfilled via the definition of an enhanced terminal constraint. The proposed approach has been validated experimentally on TIAGo robot. The obtained results show its relevance and its efficiency.

Keywords: Mobile Robots, Machine Vision, Planning and Navigation
Abstract: This research presents a method for improving the navigation capabilities of autonomous mobile robots (AMRs) in indoor environments. Indoor navigation is challenging due to the presence of various obstacles such as floors, walls, furniture, and doors. While depth sensing devices can effectively recognize geometric conditions in corridor environments, they struggle with reflective surfaces and slim objects. Our proposed solution is to fuse depth and RGB inspections using a dual-ResNet architecture in the visual detection ConvNet. This improves performance compared to traditional depth-only approaches. Field tests have shown that our system operates at a speed of 30 frames per second and guides the AMR through various corridor routes at 2 m/s, all on an embedded PC.

Keywords: Mobile Robots, Machine Vision, Vehicle Control
Abstract: This paper presents a novel navigation system for unstructured outdoor environments that does not rely on pre-existing maps. The system employs a responsive action design that combines a deep Convolutional Neural Network (ConvNet) for evaluating traversable regions based on RGB inputs, GNSS for global coordinates, and a compass. The Global Sense (GloS) module and the Traversable Region Abbreviation ConvNet (TRAC) work in tandem, with the former tracking the destination’s relative position and the latter determining the robot’s position within the traversable region. The action maker then executes Grand Direction and Local Maneuver simultaneously until the destination is reached. The system also uses deep learning-based semantic segmentation to analyze front-view images, which are then passed to the lightweight TRAC for real-time execution on an embedded system. Our experiments show that TRAC achieved an accuracy of over 70% at a frame rate of 30 fps. We have implemented the proposed system on a mobile robot and conducted field tests on a university campus, demonstrating the feasibility of map-free navigation with the proposed system.

Keywords: Control Application in Mechatronics, Vehicle Control, Modeling and Design of Mechatonic Systems
Abstract: This paper presents a road experimental exploration of control performance of magnetorheological (MR) suspension system under various road conditions. A series of practical MR dampers with single barrel structure are designed and installed on front and rear suspensions of a car. A generalization Bouc-Wen model and a feasible inverse model is constructed to portray the behavior of the MR dampers based on data obtained from an experimental with random displacement and current as input. Meanwhile, the response time of MR damper force is determined experimentally when the direction of stroke velocity and current change. An output feedback control strategy is proposed to reduce the vertical response of sprung and unsprung mass, as well as the roll and pitch. For commercially available, the required feedback signal are just relative velocity of MR dampers, the velocity of vehicle centroid, and the angular velocity of roll and pitch which are convenient to collect by commercial sensors like displacement sensor and IMU. To verify the feasibility of the system, a passenger car with four MR dampers suspension was tested on various road and the proposed controller was compared with the classical skyhook or ADD one. The experiment results indicate the better performance of ride comfort and road holding of vehicles can be achieved by the proposed MR suspension system.

Keywords: Vehicle Control, Automotive Systems
Abstract: In practical, a full vehicle system under random road excitation presents multiple-degrees-of-freedom vibration, which deteriorates the ride comfort and holding property. The present work proposes a novel control algorithm of semi-active magnetorheological (MR) suspension for full vehicle vibration suppression. The proposed controller derives the desired current for individual MR damper by mitigating the vertical motion of vehicle body and the body attitude adjustment. Superior to the published control algorithm which obtains the damping force, the proposed controller can avoid complex inverse model of MR damper and force tracing issue. The full vehicle in seven-degrees-of-freedom is established and its vibration mode analysis is conducted. The MR damper prototype is manufactured and its dynamic characteristics are tested. The semi-active control performance of MR suspension for full vehicle design is evaluated. The results show that the vertical, pitch and roll acceleration of the vehicle body center are greatly by the proposed controller.

Keywords: Virtual Reality and Human Interface, Actuators in Mechatronic Systems, Actuators
Abstract: The implementation of effective haptic devices has enabled fully immersive environments to be replicated or developed across many applications. Advances in these technologies utilise various methods and stimuli to develop these realistic sensations of touch, with the focus area on the hands and fingertips. In haptic devices, magnetorheological (MR) materials, namely MR elastomers (MREs), are yet to be fully explored, providing an opportunity for a novel application of an MRE haptic actuator. Through application of established MRE modelling techniques such as magnetic field simulations, a novel design of an MRE haptic actuator is developed. The device is then experimentally characterised, showing a maximum output force of 160 mN, increasing linearly with current supplied to the included electromagnet. Some paths towards optimisation are then explored for improving output force before investigation of an experimental approach to integrate with mixed reality technologies.

Keywords: Control Application in Mechatronics, Modeling and Design of Mechatonic Systems, Motion Vibration and Noise Control
Abstract: In this paper, a nonlinear stiffness structure based on curved surface design is proposed. In order to obtain good vibration isolation performance, the contact curved surface is designed by modeling method based on Lagrange equation and D'Alembert principle. The stiffness of nonlinear vibration isolation system with springs, rollers and curved surface is modeled. Through the proper design of the curved surface, the required stiffness is obtained. Then the parameters of the model are analyzed, and the influence of main structural parameters of the suspension on its vibration isolation performance is discussed. The dynamic models of the suspension are established under both of the force excitation and base displacement excitation and simulated in Simulink. Then a backstepping sliding mode controller is designed and applied in the model. After that, the semi-active strategy of controlling the electric resistance to ensure the electromagnetic force as close as possible to the ideal force is adopted. The vibration resonance, vibration transmissibility characteristics and vibration isolation performance of the passive and the semi-active system are compared.

Keywords: Actuators, Actuators in Mechatronic Systems, Rapid Prototyping
Abstract: Three-dimensional (3D) printing concepts that combine electrically conductive and electrically insulating materials opens up new opportunities for the design and manufacturing of electromagnetic actuators. While significant research has been conducted to 3D print antennas and planar circuits using silver nanoparticle inks, little focus has been given towards high power >1 W actuator applications. In this work, we design a novel 3D printed, centimeter-scale, multi-layer electromagnetic actuator consisting of syringe deposited silver nanoparticle ink on layers of copper-particle-filled polylactic acid (PLA) polymer filament. The Cu-PLA material is not only electrically insulating at moderately high temperatures but is also higher density and more thermally conductive than traditional polymer filaments. These features enable higher operating temperatures, higher burst forces, and longer sustained output. To demonstrate this concept, we first outline the design, material selection, and 3D printing process for a 16-layer, single trace electromagnetic coil. Then, models for the thermal characteristics, force distribution, and mechanical response are developed and compared with experimental results. Measurements show that the electromagnetic coil can produce up to 46 mN of force over 4 mm of stroke with 6.3 W of input power, and can operate indefinitely with 4.2 W of input power at 140 C without external cooling. Several applications are demonstrated including a small compliant joint gripper and a speaker. Finally, a fully-integrated, multi-material, single-print actuator and gripper combination is demonstrated to illustrate how this work can be used to create fully-operational single-print mechatronic and robotic systems.

Keywords: Actuators in Mechatronic Systems, Actuators, Design Optimization in Mechatronics
Abstract: This paper presents the design and control of a three degrees-of-freedom (3-DOF) magnetic levitation module for fine-positioning short-stroke actuators to be serially connected to high-acceleration long-stroke stages. The 3-DOF levitator module consists of two stator assemblies with iron-cores having actuating coils and permanent magnets (PMs) along the magnetic path. The levitating target is an U-shaped rotor with its weight passively compensated by the PM-biased flux and the lateral-direction force is balanced out by the symmetric structure.In such a magnetic levitator design, the PM-biased flux is superposed with the current-driven flux, enabling to control the reluctance forces in both the levitational and lateral directions in a decoupled manner to achieve active 3-DOF motion control.The control performance of the proposed 3-DOF magnetic levitator is experimentally validated to have RMS (root-mean-square) position tracking errors of 4.3 um for the translation motions and 5.97 udeg for the rotational motion. These control performances show a great potential of the magnetic levitation module to be utilized for fine-positioning short-stroke actuators that can overcome high inertial forces generated by serially-connected long-stroke actuators such as high-throughput linear stages and robotic arms.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Actuators
Abstract: Electromagnetic (EM) launchers (commonly known as coil guns) are a series of coils that use electromagnetic fields to accelerate a projectile at high speeds. Electromagnetic launchers are used in many magnetically driven applications that range from high-speed trains to kinetic energy weapons, and even futuristic orbital payload launchers. A traditional coil gun activates its series of coils with precise timing to pull the ferromagnetic projectile along the tube, accelerating it to high speeds. Traditional coil guns using ferromagnetic metals (iron) have very low energy efficiency, due to its coils always pulling/attracting the iron. This paper focuses on the research and experimentation of an exciting new variation of using a permanent magnet as the projectile. A permanent magnet has a much higher energy density than other ferromagnetic projectiles, and can be either pushed or pulled by a single coil set, therefore achieving a much higher efficiency than a traditional coil gun with a ferromagnetic projectile. A higher efficiency from an electromagnetic launcher with a permanent magnet can open up many more applications, and make electromagnetic launching competitive in the aforementioned applications: high speed trains, kinetic energy weapons, and futuristic orbital payload launchers. In this paper, the concept of a novel EM launcher with PM is introduced, and the theory and method of the EM launcher with PM are explained in detail. Additionally, simulation and experimental results with both iron and PM have been completed, compared and discussed.

Keywords: Modeling and Design of Mechatonic Systems, Control Application in Mechatronics
Abstract: In this paper we investigate and demonstrate independent control and manipulation of multiple levitated magnets using a single planar array of cylindrical coils. Tracked motion results are given for two levitated magnets where each magnet follows a motion trajectory in close proximity to the other. Stable levitation of both magnets together requires accurate modeling and real-time calculation of force and torque interactions between all coils and magnets, as well as between the two levitated magnets. We aim to further develop the concept of multiple magnet levitation to enable the use of magnets as robotic fingers to grasp and manipulate small objects.

An optical motion tracking system supplies the rigid-body position and orientation of the magnets as needed for feedback control, using three infrared emitters fixed to each magnet as markers. Each cylindrical magnet is controlled in three degrees of freedom in position and two degrees of freedom in rotation, leaving the rotation about the cylindrical axis uncontrolled. The forces and torques on the two magnets are generated by an array of 22 cylindrical coils, as a redundant control system. We plan to extend and improve these preliminary results to more complex motions and interactions through more sophisticated control and calibration methods.

Keywords: Actuators in Mechatronic Systems, Compuational Models and Methods, Modeling and Design of Mechatonic Systems
Abstract: This paper presents a design method based on distributed current source (DCS) that discretizes the permanent magnets (PMs) and electromagnets (EMs) into elemental current sources and derives the magnetic field and current-force models for design analyses of a 3-degree-of-freedom (3-DOF) planar motor with redundant inputs. The DCS models have been verified by comparing them with exact solutions and commercial finite element analysis (FEA). The results show that the DCS models are accurate (within 2.5% of exact solutions) and computationally efficient (a three-order improvement over FEA). As an illustration, the analytically derived DCS models are employed to analyze the geometrical constraints and parametric effects on the PM/EM layout and forces/torque performance of a 3-DOF planar motor. Using singular value decomposition, two designs are numerically evaluated. With the closed-form DCS models, the loci of the best/worst manipulability ellipsoids are graphically presented.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Actuators
Abstract: While the electromagnetic micropump has low-voltage driving characteristic, it requires continuous energy loss in the form of Joule heating during operations, which can be significantly problematic for battery-driven applications. This paper presents a design and control method of an energy-efficient electromagnetic bi-stable actuator for low-power micropump systems in battery-driven and low-power applications such as wearable drug delivery devices. The proposed actuator design achieves the magnetic bi-stability with the PM-biased flux, reducing the required power by providing zero-power passive latching force and by enabling reciprocating motions with only a short pulsatile current excitation. We also present in this paper the energy-efficient pulse control method using a relationship of the coil voltage and the mover velocity in order to achieve robust switching motions with minimized switching energy. The low-voltage low-power characteristic of the proposed actuator and the feasibility of the control method are experimentally validated. The measured minimum voltage and switching energy are 0.43V and 1.88mJ, respectively. The flow volume of the fabricated micropump prototype is measured to be 1.94ul per latching motion.

Keywords: Opto-Mechatronic Sensors, Mechatronics in Manufacturing Processes, Novel Industry Applications of Mechatroinics
Abstract: Abstract— The landscape of optomechatronics is viewed along the line of light vs. matter, photonics vs. semiconductors, and optics vs. mechatronics. Optomechatronics is redefined as the integration of light and matter from atom, device, system to application. The markets and megatrends in optomechatronics are further listed. The author then focused on optomechatronic technology in semiconductor industry as example and reviewed the practical systems, characteristics, and trends. Optomechatronics together with photonics and semiconductor will continue producing the computational and smart infrastructure required for the 4th industrial revolution.

Keywords: Sensor Integration, Data Fusion, Identification and Estimation in Mechatronics, Automotive Systems
Abstract: Correct radar data fusion depends on knowledge of the spatial transform between sensor pairs. Current methods for determining this transform operate by aligning identifiable features in different radar scans, or by relying on measurements from another, more accurate sensor. Feature-based alignment requires the sensors to have overlapping fields of view or necessitates the construction of an environment map. Several existing techniques require bespoke retroreflective radar targets. These requirements limit both where and how calibration can be performed. In this paper, we take a different approach: instead of attempting to track targets or features, we rely on ego-velocity estimates from each radar to perform calibration. Our method enables calibration of a subset of the transform parameters, including the yaw and the axis of translation between the radar pair, without the need for a shared field of view or for specialized targets. In general, the yaw and the axis of translation are the most important parameters for data fusion, the most likely to vary over time, and the most difficult to calibrate manually. We formulate calibration as a batch optimization problem, show that the radar-radar system is identifiable, and specify the platform excitation requirements. Through simulation studies and real-world experiments, we establish that our method is more reliable and accurate than state-of-the-art methods. Finally, we demonstrate that the full rigid body transform can be recovered if relatively coarse information about the platform rotation rate is available.

Keywords: Sensors and Sensing Systems, Modeling and Design of Mechatonic Systems, Compuational Models and Methods
Abstract: This paper presents the development of a non-ferrous metal magnetic/eddy current (NFM-M/EC) sensing system for simultaneous estimations of electrical conductivity and thickness for non-ferrous metal plates. For the physical field modeling, the distributed current source (DCS) method models the axisymmetric coordinate magnetic/eddy current fields to design the sensor. Sweep frequency analysis is applied on the excitation coil, and the anisotropic magnetoresistive sensor is used to detect the change in magnetic flux density caused by the induced eddy currents on the test plates. The effects of the frequency mapping method for estimations are numerically validated. Calibration between the model and experimental data by utilizing the mesh refinement method for frequency mapping is introduced to improve the accuracy of estimates efficiently. The solutions of the DCS method employed in the sensor are verified numerically by comparing the results from commercial finite-element analysis software. The proposed design, along with a prototype of the NFM-M/EC sensing system, is used on four different materials with varying thicknesses. The percentage errors of the electrical conductivity and thickness estimations are below 15% substantiate the NFM-M/EC sensing as a new alternative for non-destructive detection.

Keywords: Intelligent Sensors, Sensors and Sensing Systems, Machine Learning
Abstract: 点云的轻量级是一个基本问题 激光雷达在实际应用中的应用。收集的点云 户外往往有大量的地面点， 降低数据处理速度并影响 目标的分类和识别。论文 开发一种基于原理的地面提取方法 成分分析 （PCA） 和自组织映射 （SOM）。这 通过分析 独创的点云特征，提高统计能力 异常值去除滤波器，实现初始清洁 点云。过滤后的点云减少 通过 PCA 进行维度，并克服特征分类 加速后续点云时的难度 加工。此外，SOM实现了无监督学习 对于实用的点云，它执行高效 在稀疏和密集的位置进行地面提取，而不是 依赖于数据集的大小。实验 语义基蒂表明，检测精度 所提出的方法可以达到95%，并且还具有 令人满意的实时性能。

Keywords: Intelligent Sensors, Sensors and Sensing Systems, Artificial Intelligence in Mechatronics
Abstract: In this paper, we present a novel tactile-based method for detecting slippage in robotic manipulation, using a single piezoelectric sensor. The method combines spectral analysis (FFT) and deep learning (GRU) for improved efficiency and adaptability. We implement an automated data-collection process with accurate and unbiased labels of slip events. The proposed method is evaluated through an ablation study characterizing the influence of model hyperparameters and interaction settings. The results show a high classification accuracy of 98.70% at 100Hz and detection delays of 8.5 ± 23.7ms, demonstrating the relevance of our spectro-temporal pipeline. The proposed method has the potential to enhance the performance of robotic systems and increase their reliability in robotic grasping applications.

Keywords: Sensors and Sensing Systems
Abstract: This paper proposes a force sensor that integrates the sensing member, signal amplification, and analog-to-digital conversion all on a single printed circuit board. A specially designed raster copper pattern is directly fabricated on this printed circuit board, replacing the traditional process of adhering a strain gauge foil. The adhesion-free advantage increases the accuracy of this force sensor, making it suitable for mass production and cost-effective. The experiment results show that the linearity and full scale of the proposed bending force sensor are 99.3% and 20 N, respectively.

Keywords: Virtual Reality and Human Interface, Rehabilitation Robots
Abstract: Virtual reality (VR) is a powerful technology that provides a structured and safe environment for ADL training, allowing patients to have a similar experience as real-world training. However, a limited robot sensing system is proven reliable for such training. The effectiveness of current robot sensors was limited due to inherent technical problems, such as the installation challenges of encoders and IMU’s drifting, acceleration, and magnetic issues. Thus, we propose a novel and reliable sensing system consisting of absolute rotary encoders and visual-inertial sensors for the upper-limb exoskeleton in VR therapy. Our sensing system has demonstrated angle measurement for various robot joint types, including hinge, ball, and revolute joints along the limb’s longitudinal axis. Its sensing feedback can construct virtual arms that interact with virtual objects in the VR environment. In our experiments, with Vicon as the ground truth, our visual-inertial sensors achieved root-mean-square errors smaller than 2.3491° and a strong correlation (r≥0.9640,p<0.001). Additionally, the experiment result of seven healthy subjects indicated that subjects had similar muscle activations, joint ROM, and joint trajectories in the VR task with our sensing system, compared with the real-world task. Thus, our proposed sensing system can potentially be used in the upper-limb exoskeleton for VR therapy.

Keywords: Neural Networks, Robot Dynamics and Control, Rehabilitation Robots
Abstract: Estimation of dynamic uncertainties is a critical and fundamental problem when designing a control system for a robot. During robot-environment interaction, in addition to the internal dynamic model uncertainties, the external environment-exerted force will also enter the dynamics. For robot impedance control, an exact dynamic model of the robot is needed but usually not available. It has been shown that integrating an impedance controller with a disturbance observer can achieve accurate impedance control. However, it works only for robots in free motion but not robot-environment interaction. Although a disturbance observer is able to accurately estimate the dynamic uncertainties, the estimation is lumped uncertainties that contain all uncertainty sources including both the internal and the external disturbances. Without separating these two parts, the method of combining an impedance controller and an observer will result in the human-applied force being canceled instead of interacting with the robot. To solve this problem in this paper, we propose a framework for learning the internal disturbances and separating the external disturbances by integrating three entities: an impedance controller, a neural network (NN) model, and a disturbance observer. In the framework, the impedance controller provides compliant robot behavior, while the observer captures the lumped uncertainties, and the NN learns to separate the external disturbances. Simulation results of an application scenario with an obstructive virtual fixture demonstrate the effectiveness of the proposed framework.

Keywords: Rehabilitation Robots, Robot Dynamics and Control, Modeling and Design of Mechatonic Systems
Abstract: This paper presents a method for achieving Cartesian stiffness control at the endpoint of a 2-degree-of-freedom planar robotic arm by modulating joint stiffnesses. Planar robotic arms are widely applied for upper-limb rehabilitation through impedance control, but not generally in Cartesian stiffness control through joint stiffness. A modular robotic actuator with integrated controllers on a robot prototype enables the direct command of desired joint stiffness. A closed-form solution was derived through the Jacobian matrix to map the stiffnesses of a reference equilibrium. In addition, the prediction of the joint displacement corresponding to the endpoint motion is required for computing the needed joint stiffnesses. The proposed method is experimentally validated by recording the Cartesian force against the unidirectional displacement at different robotic arm configurations, showing a linear relationship. The results suggest that the proposed method has the potential for use in rehabilitation tasks when the direction of the endpoint displacement is predetermined. The method allows a precise control of the robotic arm’s stiffness, which can help in creating more efficient rehabilitation protocols on an easily accessible and affordable rehabilitation robot. Nonetheless, further work is needed to improve the accuracy and omnidirectional robustness of the control method. The study also highlights the importance of designing a robotic arm to satisfy stiffness requirements in addition to kinematic optimization for sufficient workspaces.

Keywords: Rehabilitation Robots, Actuators in Mechatronic Systems, Control Application in Mechatronics
Abstract: Robot-driving motors are frequently driven at high temperatures as their weight-to-torque ratio increases for various movements of robots. Such an increase in driving temperature reduces the magnetic flux density of the permanent magnet and the torque constant of the motor. Particularly in applications that mainly utilize feed-forward torque control without an additional torque sensor, this torque constant reduction leads to severe degradation of torque control performance. This research proposes a torque control method that compensates for the torque constant depending on temperature by identifying the relationship between magnet temperature and the torque constant. In addition, since it is difficult to measure the temperature of the rotor-attached magnet directly, lumped parameter thermal network(LPTN) and full-state observer are used for magnet temperature estimation. The condition for temperature convergence is presented, and the robustness of the proposed controller is verified through experimental results of torque error from 6.19% without compensation to 0.65% with compensation.

Keywords: Neural Networks, Biomechatronics, Rehabilitation Robots
Abstract: Ground reaction force (GRF) is a potentially useful control input for powered lower limb prostheses but accurate GRF measurement in real-time is challenging. The objective of this work is to evaluate the ability to estimate GRF from a minimal set of kinematic inputs (knee and ankle angle and angular velocities) during walking. Three artificial neural networks (ANNs) are evaluated for this purpose: nonlinear autoregression with exogenous input (NARX), delayed discrete recurrent neural network (DDRNN), and a self-organizing map with feedforward neural network (SOM-FFNN). Specifically, our work focuses on investigating the impact of ANN architecture and training/learning algorithms on the predication accuracy of GRF. First, ANN performance in open loop GRF estimation is investigated using treadmill walking data in a healthy participant at speeds from 0.79 to 1.9 m/s. Next, the effect of ANN estimated GRF is evaluated in a simulation of closed-loop powered prosthesis control with three levels of measurement noise. The results show that all ANNs are able to estimate GRF in open-loop with relatively low RMS, although SOM-FFNN performed the best with an average RMS of 4.85 N across all gait speeds. SOM-FFNN also showed the most robust performance in estimating GRF for trajectory tracking in closed-loop control, providing impetus for its further investigation in control of powered prostheses.

Keywords: Rapid Prototyping, Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics
Abstract: The present paper describes the design, fabrication and validation of a push-latch gripper produced via Additive Manufacturing, which is capable of performing planar grasps of objects with two opposite parallel surfaces. In particular, the gripper modes of operation are presented, along with an efficient virtual prototype of the system based on a Pseudo- Rigid Body approximation. Such model is proven to be considerably more computationally efficient as compared to the corresponding Finite Element simulation, while still accurately capturing the fundamental behaviors of the mechanism. Finally, quantitative performance assessments are reported to practically show how Fused Filament Fabrication of Nylon components can be an excellent approach for creating monolithic robotic mechanisms with embodied intelligence that can be effectively employed for pick and place operations. Furthermore, this work represents one further example of an alternative approach to mechanisms development that combines part minimization, faster design iterations, and high repeatability.

Keywords: Compuational Models and Methods, Design Optimization in Mechatronics
Abstract: The objective of this article is to present a comprehensive task analysis methodology that can provide guidelines for the design of dexterous robotic grippers. This methodology combines a human-centered gesture analysis and an object-centered grasp stability analysis. The former relies on a careful examination of a human operator’s hands gestures while performing a specific process, providing designers with tools that help specifying the number of fingers, the number of degrees of freedom, and the placement of tactile sensors. The latter exploits a grasp quality metric to compute the efforts required to handle the involved objects, providing guidelines for the specification of the actuation system. This approach is exemplified by defining technical specifications for the design of a multi-fingered robotic gripper intended to perform the tasks involved in a sterility testing process.

Keywords: Compuational Models and Methods, Modeling and Design of Mechatonic Systems
Abstract: This paper introduces a methodology for finding a solution to the inverse kinematic problem of underactuated manipulators composed of a three-link revolute joints planar mechanism with mechanical coupling. The proposed method consists in solving iteratively a set of algebraic equations defining the Inverse Kinematic Model (IKM) of a 3R mechanism whose rotational joints are considered independent. The respect of the mathematical constraint due to the mechanical coupling between certains axes is taken into account in the procedure by introducing an internal variable whose value is updated iteratively. The value of this internal variable is increased at each iteration until the coupling relationship is satisfied. The proposed methodology is applied to solve the IKM of a multi-phalanx robotic finger whose kinematics follows a human-like finger coupling between the intermediate and distal phalanges.

Keywords: Flexible Manipulators and Structures, Modeling and Design of Mechatonic Systems, Design Optimization in Mechatronics
Abstract: This paper presents a serial chain hinge support, a rigid yet flexible structure that improves the mechanical performance and robustness of soft-fingered grippers. Gravity can reduce the integrity of soft fingers in horizontal approach, resulting in lower maximum payload caused by a large deflection of fingers. To substantiate our claim we performed multiple experiments on the payload and deflection of the SofIA gripper under both horizontal and vertical approaches. In addition, we show that this reinforcement does not impede the original compliant behavior of the gripper, maintaining the original kinematic model functionality. Also, we showcase the proprioceptive and exteroceptive capabilities for two opposing manipulation problems: grasping small and large objects. Finally, we validated the improved SofIA gripper in agricultural and everyday activities.

Keywords: Robot Dynamics and Control, Control Application in Mechatronics, Machine Vision
Abstract: This study presents a novel approach for dynamically manipulating an object in a manner similar to standard pen spinning, utilizing a high-speed robot hand and visual feedback from a high-speed vision system. Firstly, the dynamics of both standard pen spinning and the robot hand are analyzed. Then, a pen spinning motion is developed for the robot hand based on these dynamics. The experimental result of dynamic manipulation like normal-type pen spinning indicates that even proposed simple motions of the robot hand can achieve the desired dynamic (unstable) manipulation without the necessity for complex feedback controls.

Keywords: Flexible Manipulators and Structures, Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems
Abstract: While numerous studies have been conducted, developing a compliant robotic gripper capable of replicating human hand grasping and manipulation capabilities is still challenging. This paper presents the design, fabrication, and preliminary testing of an anthropomorphic soft robotic gripper driven by twisted string actuators (TSAs). Termed as STAR–2, it is a second generation TSA-driven soft gripper from the Smart Robotics Laboratory at the University of Nevada, Reno. The novel design facilitated a monolithic structure comprising of a 3-degrees-of-freedom (DOF) thumb and four fingers each with 2-DOFs. On account of using tendon-based actuation and the large footprint required for the thumb, the design employed meticulously planned tendon routing within the monolithic structure. Preliminary results showed STAR–2's enhanced ability to demonstrate grasp taxonomies and dexterity over STAR–1.

Keywords: Aerial Robots, Vehicle Technology
Abstract: How would you like to climb into your personal aircraft, take off, and be whisked away to your destination? For recreation, you could soar over trees, rivers, and hillsides, marvel at the earth's beauty below, travel to locations not reachable by car, and relish in remote areas of nature. For work, you could dash high above commuter traffic, as the crow flies, arrive well rested and ready to get things done, and interact with colleagues while suppressing a grin. We at Opener are taking steps toward making this dream come true with the personal aerial vehicle called BlackFly. Classified as an ultralight, BlackFly can be flown today in non-congested areas. Taking off and landing vertically eliminates the need for a runway, and no pilot's license is required. In this talk, I'll describe what it means to be an ultralight vehicle, discuss the technological advances that came together to enable the creation of BlackFly, share some key considerations in the design and development of personal aerial vehicles, and summarize how far we've come. Throughout my talk, I'll share videos tracing Blackfly's evolution. So buckle your seat belt and get ready to take off: Watching BlackFly in action, you'll share in the thrill of three-dimensional freedom.

Keywords: Design Optimization in Mechatronics, Biomechatronics, Actuators in Mechatronic Systems
Abstract: The poster discusses design of a tensegrity leg that is composed of two revolute joints that are also passive in the transverse direction. The Actuated Tensegrity Revolute (AcTeR) Joint is able to have a range of motion of approximately 120 degrees. It comprises of two rigid links - one is a semi-circle, while the other is similar to the shape of a horseshoe. The structural integrity of the structure is maintained by strategically routing an elastic string between the two links such that both of them do not touch/interact with each other. This gives the AcTR joint its shape and the passive quality in the transverse direction. The passivity of the joint can be tuned by using different size elastic cables and tightening or loosening the cables. By changing the passivity of the joint, the AcTR joint can be used for different loads.

The novelty and advantage of the AcTR joint comes from the simplistic fabrication technique that provides it with complex attributes. It is simple in that it composes of two rigid links, one string to connect the links, and one belt and motor to actuate the joint. This bio-inspired structure allows for one to have a revolute joint that can also passively move in the transverse direction where the two rigid elements do not rub against eachother. In this sense the system has an embodied intelligence. The AcTR joint could lead to a system that more closely aligns with biology. This would be very helpful for traversing over unstructured environments.

Keywords: Biomechatronics, Rehabilitation Robots, Actuators in Mechatronic Systems
Abstract: In this study, we introduce a knee joint support suit applying shape memory alloy-based fabric-type artificial muscles (fabric muscles). An everyday pants-type strength-assist suit has fabric muscles attached to the location of the quadriceps to assist knee extension movements. We describe the performance of the fabric muscle that provides the assistive force, as well as the design process and composition of the suit.

Keywords: Human -Machine Interfaces, Humanoid Robots, Service Robots
Abstract: In recent years, some researchers investigated how to evaluate and improve human comfort in HRC scenarios. However, these research limits their comfort evaluation methods by merely using subjective ratings or simple statistical comparison approaches. There is a lack of a mathematical modeling approach to evaluate human comfort in HRC tasks. This study proposed an individual human comfort model using analytical approach, and further conduct comfort factor analysis. The proposed comfort model can be used to predict human comfort feedback given a set of robot motion parameters; thus, it will contribute to designing a more comfortable robotic system which will adapt its working style in HRC in the future. The five factors have been adopted and sufficient number of their combinations provide a comprehensive coverage of the scenarios that a human subject will potentially encounter in HRC tasks. Also, the results yielded provide a satisfying overall prediction accuracy of 81.33%. Thus, the comfort prediction model has been proved to be universally applicable and extendable to a wide range of HRC scenarios. The model has already been applied in a new study which is used to compute comfort rewards for an MDP model in HRC.

Keywords: Artificial Intelligence in Mechatronics, Sensor Integration, Data Fusion, Neural Networks
Abstract: A head orientation estimation solution is presented in this work. The proposed solution utilizes instrumented helmets that incorporate inertial measurement units (IMU), and the collected measurements are processed by an orientation estimation algorithm to compensate for bias and noise in the orientation measurements. The structure of the proposed dynamic estimation algorithm is motivated by complementary filters, and introduces a Convolutional Neural Network (CNN) model that is trained to capture a wide range of human head motion profiles. Experimental evaluation of the proposed head orientation estimation solution is presented in this work, and the obtained results are compared to both learning and non-learning-based solutions found in the literature. Test results demonstrate the potential advantages of the proposed CNN based solution for motion profiles with high acceleration disturbance that are characteristic of head motion.

Keywords: Control Application in Mechatronics, Machine Learning, Identification and Estimation in Mechatronics
Abstract: This work improves the positioning precision of lightweight robots with series elastic actuators (SEAs). Lightweight SEA robots, along with low-impedance control, can maneuver without causing damage in uncertain, confined spaces such as inside an aircraft wing during aircraft assembly. Nevertheless, substantial modeling uncertainties in SEA robots reduce the precision achieved by model-based approaches such as inversion-based feedforward. Therefore, this article improves the precision of SEA robots around specified operating points, through a multi-input multi-output (MIMO), iterative learning control (ILC) approach. The main contributions of this article are to (i) introduce an input-weighted complex kernel to estimate local MIMO models using complex Gaussian process regression (c-GPR); (ii) develop Geršgorin-theorem-based conditions on the iteration gains for ensuring ILC convergence to precision within noise-related limits, even with errors in the estimated model; and (iii) demonstrate precision positioning with an experimental SEA robot. Comparative experimental results, with and without ILC, show around 90% improvement in the positioning precision (close to the repeatability limit of the robot) and a 10-times increase in the SEA robot’s operating speed with the use of the MIMO ILC.

Keywords: Machine Learning, Sensors and Sensing Systems, Identification and Estimation in Mechatronics
Abstract: This work presents a novel approach to classify the behavior of mobile sensors with the specific focus of detecting anomalous mobile sensor activity. In recent years, especially with the development of the Internet of Things (IoT), anomaly, or outlier, detection problems have become a key area for research in areas such as network security, system health monitoring, and navigation and obstacle avoidance in mobile systems such as factory-floor robots or autonomous vehicles. Mutual information is used to encode and quantify the expected behavior of a mobile sensor to identify the presence of anomalies. This technique benefits from straightforward implementation, and the concept can be extrapolated to other sensor measurements to create more robust detection schemes. Simulations are presented using measured data from a wireless sensor network to demonstrate application of the method. Results show that mobile sensor behavior that fall outside of a specific regime can be detected and systematically quantified.

Keywords: Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems, Rehabilitation Robots
Abstract: In this study, we introduce a multi-winding type gravity compensation mechanism for high torque compensation. The tendon-driven gravity compensation mechanism using a wire wound several times is compact and lightweight, and will be applied to joint compensation of wearable robots in the future. We verified the feasibility of the proposed compensation mechanism through experiments.

Keywords: Modeling and Design of Mechatonic Systems, Rehabilitation Robots
Abstract: We introduce a "lockable module" for controlling rotary degrees of freedom (DoFs) in a modular wearable robot system. We begin by outlining the biomechanics requirements, then present the brake design within the module. Subsequently, we demonstrate the integration of the module in a wearable system. Finally, we conclude our contributions and propose future research directions.

Keywords: Unmanned Aerial Vehicles, Robot Dynamics and Control, Machine Vision
Abstract: The maintenance of visual features within the sensor field of view (FOV) poses a significant challenge for underactuated aerial vehicles like quadrotors, especially during aggressive maneuvers. However, existing image-based visual servo control (IBVS) methods rely on strict target visibility assumptions or impose excessive constraints on the quadrotor's agility to meet this requirement. Furthermore, the effectiveness of the visibility constraint defined in prior works remains unverified in aggressive flight tests. To address these issues, we present a robust IBVS scheme for quadrotors to perform aggressive maneuvers while ensuring target visibility. Based on the nonlinear model predictive control (NMPC) framework, we propose a novel underactuation compensation scheme to eliminate the need for a virtual camera frame, which enables us to formulate the true target visibility constraint. We then introduce a quaternion-based representation of spherical visual features to handle the nonsmooth vector field problem on the 2-sphere and derive its corresponding image kinematics. We validate our method through three challenging visual servo tasks where agile maneuvers are desired: fast landing, aggressive long-distance flight, and dynamic object tracking. Extensive simulation and experiment show that our method consistently achieves a target-visible rate of 100% in all image frames, even under a maximum pitch of 21.04 degrees. The results validate the effectiveness of our visibility constraint under large robot rotations and underscore its importance in enabling robust and aggressive flights.

Keywords: Unmanned Aerial Vehicles, Sensors and Sensing Systems, Machine Learning
Abstract: A real-time health structural diagnosis system for drones is becoming a key factor in developing safe drone operations and technologies. This study is dedicated to building one. Drone health status can be real-time diagnosed as “non-fault” or “potential fault” status. This study adds extra IMU sensors to collect the vibration signal of different drone structural faults and extracts the time domain and frequency domain features of the signal through feature engineering methods. Then, a Support Vector Machine (SVM) model is trained with those features. Feature selection and hyper-parameter tuning methods have been applied during model training to prevent model overfitting. This study also integrates a Window Sliding Technique and MAVLink to improve the real-time health diagnosis system.

Keywords: Sensors and Sensing Systems, Unmanned Aerial Vehicles, Machine Learning
Abstract: Localisation in GPS-denied environments is challenging and many existing solutions have infrastructural and on-site calibration requirements. This paper tackles these challenges by proposing a localisation system that is infrastructure-free and does not require on-site calibration, using a single active PTZ camera to detect, track and localise a circular LED marker. We propose to use a CNN trained using only synthetic images to detect the LED marker as an ellipse and show that our approach is more robust than using traditional ellipse detection without requiring tuning of parameters for feature extraction. We also propose to leverage the predicted elliptical angle as a measure of uncertainty of the CNN's predictions and show how it can be used in a filter to improve marker range estimation and 3D localisation. We evaluate our system's performance through localisation of a UAV in real-world flight experiments and show that it can outperform alternative methods for localisation in GPS-denied environments. We also demonstrate our system's performance in indoor and outdoor environments.

Keywords: Aerial Robots, Unmanned Aerial Vehicles, Sensors and Sensing Systems
Abstract: To successfully adhere to flight plans, aerial vehicles must keep track of their location in 3D space, which is usually reliant on external references such as GNSS which are susceptible to interference. To develop self-reliant onboard positionallocalization, a workflow using 360-degree panoramic images in an image-based localization system using a Deep Convolutional Neural Network is proposed. 360-degree panoramic images have the advantage that they take into account visual information from all angles. Model performance is also enhanced by generating synthetic data from a 3D model of the region of interest created via photogrammetry techniques. The performances of different training configurations are compared, and the configuration with mixed real and synthetic data exhibits the highest performance, an approximately 10 to 15 percent improvement over using solely real data. Additional image augmentations also further reduce the localization error by 8 to 15 percent.

Keywords: Identification and Estimation in Mechatronics, Unmanned Aerial Vehicles, Modeling and Design of Mechatonic Systems
Abstract: Recently force control for drones is receiving interest. One of the challenges to conduct force control for drones is the separation of wind disturbance from total disturbance. This paper focuses on the method which can apply fully actuated drones whose propellers are mounted in different directions. The wind disturbance estimation is conducted based on blade element theory. Our last proposed method has not considered the difference between the propellers' characteristics which can cause the estimation error. In this paper, we propose a new estimation method to improve the estimation accuracy compared with our conventional wind vector estimation method. The proposed method considers the model of each propeller and solves the non-linear equation using an optimization method. The proposed method is compared with the conventional method and its effectiveness is confirmed by simulations and bench experiments.

Keywords: Aerial Robots, Unmanned Aerial Vehicles
Abstract: This paper explores an aerial deployment of sensors which are intended to penetrate into the ground upon impact. This is made possible by the use of diving Samara Autorotating Wing (dSAW), explored in our previous work, which uses a single actuator to perform both a guided autorotation and diving towards the ground at terminal velocity. The versatile maneuvers of dSAW allows a special deployment method whereby the platform can navigate and glide towards intended location and perform a ground insertion of a sensor using its dive. In this work, the feasibility of such deployment is tested using an indoor test rig which accelerates sensors towards a test soil. The prototypes carrying sensors are dropped at a maximum of 15ms−1 into three different soil types and different levels of moisture and its moment of impact is captured by a high-speed camera at 5,000fps. The sensor selected is shown to survive all drop scenarios, hence demonstrating the feasibility of aerially deployed ground penetrating sensors

Keywords: Mobile Robots, Modeling and Design of Mechatonic Systems
Abstract: Tensegrity structures made from rigid rods and elastic cables have unique characteristics such as being shock absorbing, lightweight, inexpensive, easy-to-fabricate, and high weight to load-carrying capacity. As a result, they are an exciting addition to robotic systems as they can be used for many different purposes, including locomotion. Most currently researched tensegrity robots have used vibrations or motor-actuated cables to locomote. However, in this paper, we leverage tensegrity structures as wheels that can actively change their shape to expand or collapse. With the shape-changing capability, we show that a robot with two six-bar tensegrity wheels can reduce its width from 400 mm to 180 mm and simultaneously increase its height from 75 mm to 95 mm by changing the expanded tensegrity wheels to collapsed disk-like ones. The tensegrity wheels enable the robot to overcome steps with heights up to 110 mm and 150 mm with the expanded and collapsed configuration, respectively. We analyze the maximum step height that can be overcome by the robot and the force required to collapse the wheel, establishing design guidelines for robots with tensegrity wheels. The robot is also equipped with a bistable mechanism that can gradually store but quickly release energy, enabling the robot to jump onto obstacles up to 300 mm high. We demonstrate the robot's locomotion capability in indoor and outdoor environments, including various natural terrains, such as sand, grass, rocks, ice, and snow. Our results suggest that using tensegrity structures as wheels for mobile robots can enhance their capability to overcome obstacles, traverse challenging terrains, and survive falls from heights. When combined with other locomotion modes (e.g., jumping), such shape-changing robots can have broad applications for search-and-rescue after disasters or surveillance and monitoring in unstructured environments.

Keywords: Artificial Intelligence in Mechatronics, Control Application in Mechatronics, Robot Dynamics and Control
Abstract: We introduce a framework for cooperative manipulation, applied on an underactuated manipulation problem. Two stationary robotic manipulators are required to cooperate in order to reposition an object within their shared work space. Control of multi-agent systems for manipulation tasks cannot rely on individual control strategies with little to no communication between the agents that serve the common objective through swarming. Instead a coordination strategy is required that queries subtasks to the individual agents. We formulate the problem in a Task And Motion Planning (TAMP) setting, while considering a decomposition strategy that allows us to treat the task and motion planning problems separately. We solve the supervisory planning problem offline using deep Reinforcement Learning techniques resulting into a supervisory policy capable of coordinating the two manipulators into a successful execution of the pick-and-place task. Additionally, a benefit of solving the task planning problem offline is the possibility of real-time (re)planning, demonstrating robustness in the event of subtask execution failure or on-the-fly task changes. The framework achieved zero-shot deployment on the real setup with a success rate that is higher than 90%.

Keywords: Mobile Robots, Modeling and Design of Mechatonic Systems, Robot Dynamics and Control
Abstract: Tetrahedral robots have broad application prospects in the field of space exploration, showing the advantages of high adaptability to the environment and controllable motion trajectory. An open-chain tetrahedral robot is proposed in this paper. Based on the motion form of the robot, its equivalent plane mechanism is established and the kinematic model of the robot is derived. The dynamics of it in its deformation phase is modeled by using the Lagrangian formulation to solve for the required driving torques of its moving joints. The model was solved and analyzed, and the calculated outcomes were compared with the simulation results in ADAMS. The average relative errors between them did not exceed 10%, which mutually verified the correctness of the theoretical and simulation models. The causes of the errors are analyzed, providing an essential theoretical basis for the subsequent structural optimization and gait design of the robot.

Keywords: Novel Industry Applications of Mechatroinics, Control Application in Mechatronics, Mobile Robots
Abstract: In this paper, we propose a omnidirectional transport system with multiple dual-wheeled mobile robots and a LiDAR-based robot self-orientation estimation method. The proposed transport system applies active caster control to the dual-wheeled robots, enabling them to move the transport object in all directions. The angle of the robot with respect to the object is very important in the control of the robot used in this system. To measure this angle accurately, we propose a method to measure the structure of the lower part of the object using LiDAR and estimate the angle from the point cloud infomation. Through experiments, we confirmed that this estimation method works effectively in the proposed transport system.

Keywords: Space Robotics, Control Application in Mechatronics, Robot Dynamics and Control
Abstract: This paper deals with maneuver control for the autonomous docking of two small spacecraft in a rendezvous flight. Due to hardware constraints, maneuver control suppressing fuel consumption and computational cost is a significant issue for small spacecraft. Here, the maneuver control for the approaching motion used a model predictive control system. The spacecraft's maneuvers in two-dimensional plane motion were performed in a frictionless environment with air bearings to verify the control performance.

Keywords: Automotive Systems, Control Application in Mechatronics, Robot Dynamics and Control
Abstract: Bikebot manipulation has advantages of the single-track robot mobility and manipulation dexterity. We present a coordinated pose control of mobile manipulation with the stationary bikebot. The challenges of the bikebot manipulation include the limited steering balance capability of the unstable bikebot and kinematic redundancy of the manipulator. We first present the steering balance model to analyze and explore the maximum steering capability to balance the stationary platform. A balancing equilibrium manifold is then proposed to describe the necessary condition to fulfill the simultaneous platform balance and posture control of the end-effector. A coordinated planning and control design is presented to determine the balance-prioritized posture control under kinematic and dynamic constraints. Extensive experiments are conducted to demonstrate the mechatronic design for autonomous plant inspection in agricultural applications. The results confirm the feasibility to use the bikebot manipulation for a plant inspection with end-effector position and orientation errors about 5 mm and 0.3 degs, respectively.

Keywords: Machine Vision, Image Processing, Mechatronics in Manufacturing Processes
Abstract: In many industrial scenarios, such as automotive production lines, sheet stamping parts are widely used. However, due to the weak texture and strong reflections of these typical parts, pose estimation is hard to achieve, resulting in difficulties of grasping automatically. To deal with this problem, we propose a novel point pair feature (PPF) based pose estimation method to facilitate grasping. Firstly, a three-level structure downsampling method is introduced to seek the balance between the number of model points and significant features. Secondly, in order to reduce the interference of placement plane and other objects in the scene, a two-dimensional voting accumulator is constructed with weighted voting. Based on the voting results, the probability map is accordingly established, which guides keypoints sampling and voting again. Finally, edge points and model points are enrolled for pose verification to remove the wrong results. Our method is implemented in physical experiments, and the results show that the proposed method can be effectively applied to pose estimation of sheet stamping parts such as tire lock plates. Moreover, the ablation study demonstrates the criticality of each process.

Keywords: Machine Vision, Neural Networks, Artificial Intelligence in Mechatronics
Abstract: At present, deep learning objective detection method based on learning features suffer from low detection rates and poor accuracy rates in metal surface defect detection. This is primarily due to the fact that the detected images are mostly gray images with small features and low resolution, which makes the model inefficient to train and slow to converge. This paper proposes a BiSPD-YOLO metal surface defect detection model based on YOLOv5 to solve these problems. Firstly, this model uses SDP-Conv module to replace the traditional strided convolution and pooling to enhance the training of the network for low-resolution images; BiFPN is then used to replace PANet for multi-scale feature fusion. In this way, small features in the images can be better extracted; Finally, the original loss function of YOLOv5 is improved, and the SIOU function is used to optimize the training model. The testing results on the NEU-DET dataset after data augmentation indicate that the improved model mAP achieves 97.2%, which is 4.1% higher than the original model, and is superior to other mainstream models. Compared to the original model, the detection speed is basically unchanged, and can quickly and accurately detect metal surface defects in real time.

Keywords: Image Processing, Sensor Integration, Data Fusion, Mechatronics in Manufacturing Processes
Abstract: Extraction of features is a key process in image-based visual servo. However, existing image processing methods are difficult to segment the target foreground and cannot overcome distracting factors, such as background and illumination, resulting in reduced accuracy of feature extraction. Therefore, target foreground segmentation is a critical problem in image-based visual servo tasks. In this paper, a method for image foreground segmentation and visual servo control based on small data training is proposed. Semantic segmentation is achieved by training a small number of images. Focusing on the target artefact region and blurring the background are also achieved to avoid its influence on feature recognition, especially for industry parts. It is shown that recognition and segmentation under different lighting conditions can be obtained, reducing the interference of lighting on visual servo. Experimental results show that the proposed method is effective in visual servo control applications.

Keywords: Image Processing, Neural Networks, Machine Learning
Abstract: Wiring harnesses, i.e. a collection of electrical cables organized into branches, are vastly present in the automotive industry. Moreover, the number of wires and overall weight of automotive wiring harnesses are steadily increasing over time. Deformable wiring harness bags were introduced by manufacturers to simplify assembly operations. However, this task is still entirely performed manually by human labor. Despite the efforts, the degree of automation in wiring harness assembly is still close to zero. Due to the lack of task-specific datasets, modern state-of-the-art computer vision approaches are not commonly employed in the wiring harness industrial processes. In this work, we propose an approach to generate a dataset of a specific object of interest, i.e. deformable wiring harness bags, with minimal effort employing the copy and paste technique. The obtained dataset is validated on the semantic segmentation task in a real-world test setup, consisting of laboratory and automotive factory environments. An overall IoU of 53.8% and Dice score of 65.6% is obtained, demonstrating the capability of the proposed method.

Keywords: Machine Vision, Neural Networks, Image Processing
Abstract: Digital image correlation (DIC) is a widely used technique for full-field deformation measurement based on image processing. The high quality of images input for correlation is the most important guarantee for obtaining accurate physical field. In this paper, we propose a new method to deal with the negative effects of noise especially for deep learning-based DIC. Denoising convolutional neural network (DnCNN) block is introduced to deal with the noise before speckle patterns are fed into deep learning-based DIC. A new speckle pattern dataset, SPDataset, is created to train DnCNN on speckle features rather than on images with semantic information. The performance of networks trained on specific and blind noise level is compared and the possibility of network structure simplification is explored. The depth and performance of the network reach a good compromise at a layer number of 9. The denoising ability of the network for speckle patterns was confirmed qualitatively and quantitatively.

Keywords: Tele-operation, Fixture and Grasping, Mechatronics in Manufacturing Processes
Abstract: The manipulation of Deformable Linear Objects (DLOs) is a critical process in which the introduction of automation and autonomous systems is still marginal. In this paper, a novel teleoperation framework is proposed in which an intuitive manipulation of DLOs is achieved by means of visual aid. The proposed system could be deployed for manipulating DLOs in hazardous scenarios or for simplifying robot teaching tasks by allowing a faster demonstration time. Experiments are performed involving several subjects and their feedback is collected by means of a survey. The results show that the proposed teleoperation framework simplifies DLOs manipulation and reduces the task completion time by 20% on average.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Actuators
Abstract: Cable-driven serial robots have gained significant growth because of their compact size and low inertia characteristics. However, one major problem of cable-driven serial robots is motion coupling issue that one joint motion will cause movement of the other joints, resulting in a complicated control. In this paper, we proposed a novel method to decouple the joint motion by using a noncircular pulley. The length change of driving cables on a joint pulley due to the coupling issue is compensated by the noncircular pulley. The calculation process of the pulley profile, the mechanical design of the decoupling system, and control system of the cable-driven robot prototype are introduced. Experiments have been conducted to evaluate the performance of the motion decoupling method. The results demonstrate that the noncircular pulley can solve the motion coupling issue by keeping the cable length nearly constant with small errors.

Keywords: Control Application in Mechatronics, Identification and Estimation in Mechatronics
Abstract: An adaptive extended state observer (AESO)-based terminal sliding mode control (ATSMC) is proposed for PMSM systems with uncertainty in this paper. Firstly, in order to handle various uncertainties explicitly and effectively, to divide the uncertainty into structured uncertainty (i.e., parametric uncertainty) and remaining uncertainty (i.e., external disturbance and unmodeled dynamics) two types is carried out. The former is handled by introducing adaptive parameter estimation (APE), and the latter is considered as lumped disturbance and compensated by extended state observer (ESO). In this way, the cooperation between parameter adaption and disturbance observer is established and the estimation accuracy of ESO is guaranteed under the parameter perturbation. Then, a terminal sliding mode function with fractional order is used to achieve system state finite-time convergence. Thus, a compound nonlinear controller is obtained by integrating different mechanisms. The main advantages of this controller are only the output feedback used, and strong disturbance rejection ability. Experimental results show the efficiency of the proposed method.

Keywords: Actuators in Mechatronic Systems, Learning and Neural Control in Mechatronics, Robot Dynamics and Control
Abstract: Intelligent servo control significantly reduces the need to adjust control parameters, and is therefore widely used in robot joint control. However, existing intelligent servo control strategies for robot joints have problems of computational redundancy, limited prediction accuracy, and insufficient generalization capability. To solve these problems, this paper proposes a servo control strategy for robot joints that is based on the incremental Bayesian fuzzy broad learning system (IBFBLS). Firstly, we construct an intelligent servo control strategy with broad learning system on the basis of fuzzy rules to achieve good self-learning and generalization abilities. Secondly, the learning parameters of the control strategy are optimized by Bayesian inference to achieve precise joint servo control. Finally, the convergence of the control strategy is enhanced by combining it with Lyapunov theory to constrain the learning parameters of the proposed control strategy. The feasibility and superiority of the proposed control strategy are verified by simulation to compare it with existing intelligent servo control methods. In addition, experiments are conducted using robot joint test bed. Both the simulation and experiments verify that the proposed servo control strategy outperforms other servo control methods with respect to tracking accuracy, stability, and convergence. The root-mean square error in servo control of robot joints was 0.012%, which has been reduced by 55.56% compared to current state-of-art.

Keywords: Actuators, Rehabilitation Robots
Abstract: Recent studies proposed various robotic joint actuators with variable stiffness to enhance the physical human-robot interaction. However, these actuators were designed on the basis of the planar dynamic models, which limited the optimization of the structure and size of the actuator. This paper proposes a novel concept of incorporating a three-dimensional dynamic model in the design of variable stiffness actuators (VSAs), enabling more compact design of VSAs. A design of VSA is presented according to the proposed concept. The output torque and stiffness are modelled based on the dynamics of the actuator to identify the torque-deflection and stiffness-deflection relations. Simulation is conducted to analyse the dynamic behaviour of the proposed VSA. A prototype is created to evaluate the performance of the proposed VSA through experiments. The simulation results indicate that the proposed concept provides a reasonable principle for stiffness variation of VSAs. The torque estimation accuracy of the model is investigated by comparing the estimated torque with the torque measured by a torque sensor. The result illustrates that the model can estimate the output torque of the proposed VSA accurately. The dynamic behaviour of the proposed VSA is tested through the free vibration test.

Keywords: Actuators in Mechatronic Systems, Novel Industry Applications of Mechatroinics, Flexible Manipulators and Structures
Abstract: This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and therefore no extra input as compressed air is needed. Our system conducts modular design to provide good scalability, which has been demonstrated on a prototype with ten air channels. Each air channel of OpenPneu is equipped with both the inflation and the deflation functions to provide a full range pressure supply from positive to negative with a maximal flow rate at 1.7 L/min. High precision closed-loop control of pressures has been built into our system to achieve stable and efficient dynamic performance in actuation. An open-source control interface and API in Python are provided. We also demonstrate the functionality of OpenPneu on three soft robotic systems with up to 10 chambers.

Keywords: Actuators, Actuators in Mechatronic Systems, Humanoid Robots
Abstract: This paper presents a novel multi-degree of freedom actuator driven by three-phase inputs, referred to here as a spherical brushless direct current motor (SBLDC), and its closed-loop control system. The SBLDC can simultaneously generate both spinning and tilting torque for the orientation control of a rotating rotor using two independent BLDC drivers commercially available. The mathematical torque model is derived by three-phase inputs and rotor orientation. A drive method is developed based on the torque model to achieve desired three-dimensional torque. The closed-loop orientation control system based on cascade PID control is designed to validate the torque model and drive method. Finally, the performance of the SBLDC is demonstrated through the numerical simulation, which shows the compact three-phase spherical actuator's potential for precise three-dimensional orientation control.

Keywords: Sensor Integration, Data Fusion, Sensors and Sensing Systems
Abstract: Currently, many methods for assessing hand function in stroke patients are administered by humans, which can lack objectivity and make it difficult to achieve precise evaluations. In order to tackle this issue, we proposed a new assessment method that utilized hand movement data collected from the Leap motion device. By applying the independent sample T-test or Mann-Whitney U-test, we identified sensitive kinematic metrics from the 38 extracted metrics. We then used the principal component analysis (PCA) method to further analyze and rank the selected sensitive metrics. This processing enabled us to determine the most sensitive kinematic metrics that can distinguish differences in hand function between normal individuals and stroke patients. To validate the proposed method, we conducted an experiment with 15 volunteers. The results showed that MiddleMCP-Max was the most sensitive metric for distinguishing patients from normal individuals. The experimental results also demonstrated that the proposed method was effective, scientifically objective, and may be useful in assisting with the hand function evaluation of stroke-induced hemiplegia.

Keywords: Intelligent Sensors, Sensor Integration, Data Fusion, Sensors and Sensing Systems
Abstract: Event cameras are sensors that asynchronously measure the brightness change of each pixel with a microsecond-level time resolution. They have reduced motion blur and a dynamic response range of up to 140 dB, which allows them to handle extreme lighting conditions better than traditional frame cameras. Most of the research on event cameras has focused on dynamic vision, neglecting their potential applications in static scenes. This paper investigates how event cameras respond to stationary objects and provides a comprehensive theoretical analysis. We show that the event camera's output depends on the brightness of the objects and their circuit structure. We also derive a mathematical formula that relates the event camera's sensitivity to the number of absorbed photons. Furthermore, we propose a method to modulate the stationary output of event cameras.

Keywords: Biomechatronics, Sensors and Sensing Systems, Human -Machine Interfaces
Abstract: Enabling the robotic ability to sense tactile interactions is a complex and interdisciplinary challenge, as traditional pressure sensors made of hard and rigid materials struggle to replicate the soft and compliant interactions of mammalian somatosensory networks. This study investigates a novel matrixed contact sensor designed for use in soft robotics and biomedical prosthesis applications. The sensor is a coupled piezocapacitive-piezoresistive tactile sensor, optimized for scalability over various mechanical stimuli, including touch, pressure, bend, stretch, and proximity. By modifying the bulk geometry to include dielectric artifacts and integrating electroactive elastomeric composites, the soft and flexible sensor is fully mechanically compliant and conforms to skin behavior. The sensor has a maximum sensitivity of 0.028kPa-1, a resolution of 1.3%, and a bandwidth of 29Hz. The study characterizes the sensor's performance and identifies its static and dynamic response to aid in developing complex, closed-loop tactile feedback control systems toward realizing the biomimetic implementation of synthetic skin in soft-prosthetic devices.

Keywords: Biomechatronics, Sensors and Sensing Systems, Human -Machine Interfaces
Abstract: Explosive Ordnance Disposal (EOD) suits are widely used to protect human operators to execute emergency tasks such as bomb disposal and neutralization. Current suit designs still need to be improved in terms of wearer comfort, which can be assessed based on the interaction forces at the human-suit contact regions. This paper introduces a simulation-based modeling framework that computes the interaction loads at the human-suit interface based on a wearer’s kinematic movement data. The proposed modeling framework consists of three primary components: a) inertial and geometric modeling of the EOD suit, b) state estimation of the wearer’s in-suit movement, and c) inverse dynamics analysis to calculate the human-suit interface forces based on the simulated human-suit model and the estimated human movement data. This simulation-based modeling method could be used to complement experimental testing for improving the time and cost efficiency of EOD suit evaluation. The accuracy of the simulated interface load was experimentally benchmarked during three different human tasks (each with three trials), by comparing the predicted interface forces with that measured by commercial pressure sensors.

Keywords: Sensors and Sensing Systems, Human -Machine Interfaces, Intelligent Process Automation
Abstract: This paper presents the implementation of thermistor as a cost-effective alternative to resistance temperature detector (RTD) for the temperature measurement in the energy transfer station for chiller water application, and the impact of both RTDs and thermistors on the Delta T syndrome have been observed as well. An Energy Transfer Station application is developed in line with ISA-95 automation pyramid with temperature sensors as level-0, PLC as level-1 and HMI as level-2. The PLC Micro-800 system and PanelView 800 for Human Machine Interface (HMI) have been used for level-1 and level-2, respectively. The temperature transmitters are used as level-0 sensing devices for RTD and thermistors. The software development for the PLC programming and HMI graphics has been performed by using the Connected Components Workbench software.

Keywords: Actuators, Human -Machine Interfaces, Modeling and Design of Mechatonic Systems
Abstract: Novel actuators that can provide squeeze, vibration, and localized forces while remaining soft and comfortable are essential for next-generation augmented reality applications. Despite this need, there are currently few soft actuator topologies that can provide high forces and high bandwidths at low voltages and temperatures. In this work, we present a new type of soft electromagnetic actuator architecture for haptics. These low-cost, easy-to-manufacture, and conformal actuators are composed of a coil, magnet, thin film material, and water. Adding a thin ferromagnetic sheet further enables the creation of a latching actuator variant, which can improve force output while reducing power consumption. Each actuator combines low voltage (up to 2~V), high bandwidth electromagnetics with hydraulics to amplify force output. In addition to force amplification, the hydraulics provide cooling and thermal mass, which enable the actuator to be used safely in wearables for longer durations. In this work, we characterize the output forces, frequency response, efficiency, and thermal profiles of the prototype actuators. Results from tabletop experiments show that with hydraulic amplification, a non-latching actuator is able to exert 1.3~N of force at 4~A and a latching actuator is able to exert up to 5.2~N when preloaded with 3.7~N of compression. Furthermore, the prototype actuator has a bandwidth of 30~Hz when operating in air. After 120~seconds of continuous operation (1.3~W) in ambient air (26 °C), the maximum actuator temperature reaches 36 °C, making it safe for haptic applications. Using these designs, we develop a prototype wearable wristband device that can render body-grounded squeeze and vibration. By expanding on the operating principles described in this work, novel augmented reality applications can potentially become possible.

Keywords: Human -Machine Interfaces, Sensors and Sensing Systems, Identification and Estimation in Mechatronics
Abstract: Passenger vehicles are increasingly adopting the use of automated driving systems (ADS) to help ease the workload of drivers and to improve road safety. These systems require human drivers to constantly maintain supervisory control of the ADS. For safe adoption and ADS, the attention or alertness of the driver needs to be continuously monitored. Past studies have demonstrated pupil dilation as an effective measure of cognitive load. However, the raw pupil data recorded using eye trackers are noisy which may result in poor classification of the cognitive load levels of the driver. In this paper, an approach to reduce the noise raw pupil size data obtained from eye trackers used by ADS is proposed. The proposed approach uses a Kalman filter to filter out high-frequency noise that arises due to sudden changes in ambient light, head/body movement, and measurement noise. Data collected from 16 participants were used to demonstrate the performance of the model-based pupil-size filtering approach presented in this paper. Results show an objective improvement in the potential to distinguish changes in pupil size due to various levels of cognitive workload experienced by participants.

Keywords: Virtual Reality and Human Interface, Sensors and Sensing Systems, Modeling and Design of Mechatonic Systems
Abstract: This paper presents the Pinch Sensor, an elastic input device to sense the fine motion and pinch force of the index finger and thumb - the two most used digits of human hands for in-hand object manipulation skills. In addition to open and close, the device would allow a user to control a robotic or simulated two-finger hand to reorient an object in three different ways and their combinations. A unique design of elastic sensing provides the users a high degree of perception resolution, as well as the sensation of holding an object with a certain level of stiffness between the index finger and thumb. These characteristics help the users to fine control the pinch force while carrying out manipulation skills. The design features a small size that allows it to be integrated to a handheld controller. Commonly available off-the-shelf components for consumer electronics are used to achieve affordability and reliability.

Keywords: Human -Machine Interfaces, Modeling and Design of Mechatonic Systems, Fuzzy Logic
Abstract: One of the main challenges users of prosthetic hands face is the lack of haptic feedback, which can make it difficult for them to accurately perceive the shape, texture, and other characteristics of objects they are touching, resulting in heavy reliance on visual feedback. This can limit the user's ability to manipulate objects and interact with their environment effectively. In this research, we present a conceptual design of a mechanotactile haptic armband that has five fingers, each with two segments to provide different haptic profiles. The goal of this device is to provide haptic feedback to users of prosthetic hands, allowing them to experience a sense of touch and to more accurately perceive the shape, texture, and other characteristics of objects they are touching. To control the haptic armband, we have developed a control technique based on fuzzy logic, which maps the force sensed from a soft sensor to a force applied to the armband. Our results show that the haptic armband has the potential to improve the functionality and performance of prosthetic arms, enabling users to interact with their environment.

Keywords: Artificial Intelligence in Mechatronics, Sensors and Sensing Systems, Human -Machine Interfaces
Abstract: This paper presents an affordable stylus device with embedded inertial sensing for measuring dynamic kinematic data from handwritten signatures for the purposes of user identification. A set of spatiotemporal features are proposed for use in a simple multilayer perceptron classifier, and a brief user study is conducted for evaluation with promising results. In general, user authentication is a key component of securing digital information in cyber-physical systems. Current methods span alphanumeric passwords, multi-factor authentication, and biometric techniques, with each providing trade-offs between convenience, flexibility and security. This work presents a device that marries a kinematic trajectory unique to each person (handwritten signature) with digital authentication via a stylus type device. Handwritten signatures are ubiquitous for authenticating paperwork, credit card transactions, check deposits and ballot boxes to name a few. Oftentimes, handwritten signatures are executed and treated perfunctorily as a matter of routine with no genuine intention towards security or authentication. When authentication is requested of handwritten signatures, most often the only recourse is expert visual examination of the written pen strokes. While some methods have investigated the use of measuring temporal, inertial data from handwritten signatures as a mode of authentication, these were executed with either expensive haptic robotic devices or prototype, externally mounted sensors. This work enables dynamic inertial authentication methods of handwritten signatures in a low cost, seamless embedded stylus device.

Keywords: Fault Detection and diagnosis in Manufacturing, Machine Learning, Neural Networks
Abstract: Modern industrial systems are equipped with multi-sensor units, and building anomaly detection modules to monitor their collected data has become a vital task. Missing such abnormal patterns may cause producing faulty products, unwanted shutdowns in the production line, or even catastrophic damages. Sensor measurements of different natures with different sampling frequencies build a multivariate heterogeneous time series data. Conventional machine learning models fail to capture the temporal characteristics of such data. Deep learning models can address this thanks to their internal network architecture, yet training such models requires large datasets with adequate samples from all anomaly classes. This is not the case in real-world problems where class imbalance is a major issue. Tree-based ensembles are reported to have the dominant performance when dealing with structured tabular data. Inspired by this, we propose a supervised framework that combines an automatic feature engineering pipeline converting the time series dataset into its tabular counterpart with tree-based ensembles. The suggested method tackles class imbalance by generating synthetic anomalies using balancing techniques. Moreover, it allows handling heterogeneous multivariate data and augmenting categorical features with sensor measurements. Two real-world industrial datasets of relatively small size from robotized screwing processes are benchmarked, showing better results for the suggested framework compared to commonly used deep learning architectures.

Keywords: Fault Detection and diagnosis in Manufacturing, Machine Learning, Neural Networks
Abstract: In engineering applications, the performance of intelligent fault diagnosis models can be significantly impacted by noise interference in the signals. This paper aims to address this issue by analyzing the influence of noise interference on diagnosis models, focusing on the network structure and training methods. Based on the analysis findings, a wavelet-integrated residual network (WResNet) is proposed to improve the noise-robustness. WResNet integrates discrete wavelet transformation (DWT) into the residual network architecture to mitigate potential problems related to frequency aliasing caused by traditional down-sampling operations. By incorporating DWT, WResNet could reduce the impact of noise interference. In addition, a gradient-based adversarial training method is adopted for optimizing the loss function of WResNet. By minimizing the maximal risk for label-preserving fluctuations of input signals, adversarial training is able to enhance the stability of WResNet. The effectiveness of WResNet is validated by using the monitoring data from a motor with different signal-noise-ratio. The results show that compared with ResNet and the method that using wavelet transformation as a pre-processing step, WResNet is able to achieve higher diagnosis accuracy while owning better noise-robustness.

Keywords: Fault Detection and diagnosis in Manufacturing, Neural Networks
Abstract: Reliable machinery health monitoring using measured vibration signals requires a good readability of time-frequency (TF) images. However, conventional TF methods suffer from a limited time–frequency resolution and cross-term interferences, which limit their practical applicability in health monitoring. To address this issue, a TF image improvement method using deep learning is proposed in this paper. The proposed method employs a deep learning technique known as conditional generative adversarial network (cGAN) to convert a noisy low-resolution TF image of a bearing vibration signal into a noise-free high-resolution image such that the true frequency characteristics of measured signals may be revealed. In this paper, the cGAN model is trained using a simulation-based dataset generated from a bearing analytical model. The trained cGAN model is then utilized to improve TF images generated from real bearings under different fault and operating conditions. The results reveal that the proposed image improvement method generates high-resolution TF representations which are better than both the traditional TF images and those generated using TF reassignment methods.

Keywords: Fault Detection and diagnosis in Manufacturing, Intelligent Process Automation, Software Design for System Integration
Abstract: This study proposes a framework for inferring the causes of failures occurring in manufacturing systems from past Failure Mode and Effect Analyses (FMEAs) conducted on other systems to assist in inspecting and maintaining the systems. Among various manufacturing systems, a framework to search past FMEAs and the corresponding causes of the failure requires solving the following problems. First, the difference in products, equipment, and wording to represent them make it difficult to search the similar failure phenomenon from FMEAs. Secondly, the causes of failure highly depend on the process flow of the system until the failure occurs. Therefore, it is also hard to find appropriate failure causes from FMEAs without reflecting on the process. The framework solves the first issue by generalizing descriptions in past FMEAs based on structured concepts of manufacturing systems in an ontology before inference of causes to address. Furthermore, the framework analyzes the correspondence of the process flows between the target manufacturing system and past FMEAs using a process order model generated by SysML diagrams to solve the second issue. The comparison between the causes inferred by the proposed framework and by skilled experts for three typical failures in the manufacturing system and the interview with them about the plausibility of the inference results showed that more than 73 % of them were valid.

Keywords: Identification and Estimation in Mechatronics, Machine Learning, Neural Networks
Abstract: We demonstrate how Neural Networks can favorably be employed in the field of non-distructive testing using Full Waveform Inversion. The presented methodology discretizes the unknown material distribution in the domain with Neural Networks in an adjoint based optimization problem. To further increase efficiency, pre-trained neural networks are used to provide a good starting point for the inversion. This reduces the number of necessary iterations in Full Waveform Inversion to only one for specific, yet generalizable settings.

Keywords: Machine Learning, Neural Networks
Abstract: Regular structural inspections to detect cracks in ancillary structures are necessary to prevent fatigue cracks from compromising a structure’s safety and durability. Visual inspection is the most common method to inspect ancillary structures, but it is time-consuming, costly, and requires a great deal of experience. The inspection can benefit from implementing of deep learning because it can help save time and money, and monitor the ancillary structure more robustly. However, there is no comprehensive annotated dataset for deep learning to detect cracks in ancillary structures. In this work, a dataset containing 250 images collected from previous studies and 30 images collected from in-service ancillary structures was used. This dataset was divided in two annotated sets based on the labeling and bounding box approaches for deep convolutional neural networks (DCNN), AlexNet, and Faster RCNN (FRCNN). The annotated dataset of AlexNet contained 200 sub-images with fatigue cracks and 250 sub-images without fatigue cracks. Data augmentation, such as a change in the color, brightness, and crack orientation, was performed to increase the training size to 1400 images. Moreover, a realistic fatigue crack was superimposed on images of intact and in-service ancillary structures to increase the dataset size to 1500 sub-images. Then, the investigated models were applied for image-based crack detection in ancillary steel structures. The AlexNet model was trained in fully trained, transfer learning, and classifier modes. The performance of the models was made in terms of several metric, such as accuracy rate (ACC), true positive rate (TPR), and true negative rate (TNR). The results show that the values of TPR, TNR, and ACC were greater than 85% for all AlexNet models. Additionally, bounding box annotation was used to label fatigue crack as an object in 200 images with cracks. Next, FRCNN as an object detection algorithm was used to determine the location of cracks in ancillary structures. Compared to AlexNet, FRCNN can be used to determine the location of cracks in ancillary structures with a higher accuracy rate. The results show that this model, FRNN, outperforms AlexNet models with TRP, TNR, and ACC values of 0.87.

Keywords: Human -Machine Interfaces, Aerial Robots, Unmanned Aerial Vehicles
Abstract: The progress in robot autonomy has opened up opportunities for various applications, notably in autonomous navigation and mapping missions with mobile platforms. This motivates us to exploit such technologies to develop a human-robot collaboration system. Such a system improves task efficiencies and ensures the safety of human counterparts in search and rescue operations and site surveillance missions. In this paper, we present a robotic system of systems as a strategy for human-robot teaming missions in unexplored and unstructured environments. The system comprises a single human operator and multiple custom-built aerial robots equipped with various sensors for localization, mapping, and object detection. It enables the human operator to set operation modes and assign tasks to the robots individually or as a group via a human-robot interaction device, allowing the human operator to focus on critical mission objectives and decisionmaking. In each operation mode, the robot(s) navigates the environment autonomously while avoiding obstacles for a given set of waypoints. Additionally, a formation planning policy has been developed for group navigation and relative poses between the human operator and robots are estimated using UWB ranging and odometry measurements to improve the human operator’s IMU positioning accuracy. The robots are fitted with RGB-D cameras for object detection and real-time image streaming to the operator. Results from the deployment of the system in indoor settings are presented to demonstrate the feasibility of a human-robot collaboration system in an unknown environment.

Keywords: Human -Machine Interfaces, Planning and Navigation, Virtual Reality and Human Interface
Abstract: In highly automated driving vehicles, a human-vehicle interface might still be required for individualization and emergency intervention. We propose a tactical human-vehicle collaboration framework by leveraging the hand-landmark extraction algorithm and the augmented reality visual feedback. The proposed vision-based interface projects the gesture, as the driver's intention, onto the ground and feeds the projection back to the driver through the AR-HUD interface. The projected intention functions as a strategic decision or planning suggestion to the vehicle while collision avoidance, traffic rules compliance, and precise control are realized by the automation algorithm. The feasibility of the framework is validated through an integrated self-driving algorithm combining the risk field, learning-based trajectory prediction, and model predictive control. Comparisons with the conventional manual driving scheme demonstrate that high-level collaboration vastly reduces human physical burdens without compromising driving performance and driver mental workloads.

Keywords: Service Robots, Intelligent Process Automation, Genetic Algorithms
Abstract: Industry 5.0 sets off a new wave of the industrial revolution by highlighting human-centric intelligent manufacturing. Human-cyber-physical system (HCPS) is the cornerstone of Industry 5.0. It seamlessly integrates humans, cyberspaces, and physical assets to optimize the entire product lifecycle while ensuring the well-being of all stakeholders along the product value chain. Understanding the role of humans is of great importance. Disassembly plays a crucial role in achieving the sustainability required in Industry 5.0. The mass personalization of products requires the flexibility to accommodate frequent changes in disassembly. Human-robot interaction within the same workplace transcends boundary limitations and empowers flexibility for disassembly processes. This paper proposes a HCPS framework for human-robot interactive disassembly with two significant paradigms, namely human-in-the-loop (HitL) and human-on-the-loop (HotL). According to the HotL paradigm, a multi-objective optimization model for human-robot interactive disassembly is constructed considering the disassembly task complexity and operator ergonomics. An improved multi-objective hybrid grey wolf optimization approach is proposed to obtain the Pareto front that reveals the optimal human-robot interactive disassembly sequence. A HitL experiment for disassembling an automated vehicle control box is presented to illustrate the feasibility of the proposed method.

Keywords: Sensor Integration, Data Fusion, Biomechatronics, Rehabilitation Robots
Abstract: Squatting is a compound exercise that can provide strong stimulation to the entire lower limb and trunk, positively affecting cardiovascular functions, neural regulations, and hormone secretion. In addition, squatting is commonly involved in labor intensive tasks where potential injuries may happen, and assistive devices are needed. In this study, we developed a low-cost IMU data acquisition system for capturing human squatting movements, with a total cost of only ~500 UGD. We conducted experiments on normal squats and knee valgus squats. The data obtained is imported into the OpenSim software for analysis, and we obtain experimental results on joint angles, joint moments, muscle forces, and metabolism. The results show that incorrect squatting postures can increase the burden on the hip joint. Additionally, during the squatting process, the tibialis anterior muscle had the highest activation level, while the soleus muscle had the greatest force. Our experimental and simulation results provide guidelines for future design and optimization of exoskeletons to assist humans during squat motions while reducing the risk of injuries.