Keywords: Robot Dynamics and Control
Abstract: This paper considers the stabilization of the paddle juggling, i.e. the hitting task of a ball with a table tennis racket. The rebound phenomenon between the ball and the racket is firstly shown in 2-dimensional space. The continuous trajectory design for the racket is secondly introduced to realize the iterative hitting the ball. With the assumption where the trajectory tracking is achieved, the discrete phenomenon at the rebound is thirdly modeled with the parameters of the continuous trajectories as the new input. The LQ servo controller is then designed which stabilizes the hitting task with reducing the disturbances due to the tracking errors. Numerical simulations are shown to verify the effectiveness of the proposed controller that consists of the regulations of the hitting position and apex.

Keywords: Robot Dynamics and Control, Aerial Robots
Abstract: In this paper, we study the robustness of minimally-actuated 3D flapping wing hovering systems under aerodynamic modeling uncertainties. Our goal is to evaluate and compare the vehicles' robustness under commonly used control scheme and provide guidelines for control design stage. For this purpose, we first develop a 3D flapping wing dynamical model and find a desired hovering flight mode based on the nominal estimate of the aerodynamic characteristics. Then, we parameterize the wing kinematics in a novel way to allow the system to generate recovering aerodynamic forces. Using this parameterization, the control inputs are defined as the variable-time segments of the wingbeat cycle. We use the discrete LQR framework to design three controllers, each focused on a different objective: i) least control input change, ii) fast convergence rate and iii) least state residue. To study the robustness, we apply the controllers to the nonlinear system with aerodynamic uncertainty and through exhaustive simulations, we find the range of uncertainties that lead to stable response. The results show that controller (i) has the largest tolerance for drag and lift coefficient uncertainties. Moreover, this controller is robust on the uncertainties from lift offset phase over a wide range (up to 90 degrees) but has narrower tolerance to drag offset phase changes, especially towards the positive offset angles. Considering inherent uncertainty associated with the quasi-steady models, the results suggest to use a slightly larger than nominal value (upper bound) aerodynamic coefficients for control design to enjoy a greater tolerance (more robustness). This is true for both the magnitudes as well as the offset angles used in lift and drag functions.

Keywords: Aerial Robots, Unmanned Aerial Vehicles, Mobile Robots
Abstract: Aerial robots are well suited for a variety of tasks. Especially in remote locations they can make use of their fast speed, independence of the ground condition and stable hovering capabilities. In uncontrolled environments, such as disaster sites, robots require flexibility to cope with a variety of challenging work environments. This work serves as the continuation of our research on a three-arm aerial manipulator system, developed in our lab. In this work, the manipulator system is used to show the realization of advanced aerial tasks. A method for rope fastening and removal tasks which can be completed by using the aerial robot, as well as a method for flying through narrow spaces using the robotic arms as tools to prevent accidental impact is proposed. Grippers suitable to conduct these tasks are developed and the concept of the proposed tasks is verified through several experiments.

Keywords: Unmanned Aerial Vehicles, Modeling and Design of Mechatonic Systems
Abstract: Waterborne vessels have advanced in many ways over the last two decades. However, their usage and deployment are still labor and expertise-intensive. They need to be transported by a surface vessel to the deployment area, which causes a substantial increase in transportation and logistics costs. In this paper, a modular design approach to the universal robotic housing is presented for off-the-shelf UAVs that enable them to operate on the water surface using the thrust force transferred from the UAV rotors through a clutch mechanism. The transformable robotic housing is outfitted with a waterproof cover sliding back and forth on its frame by a servo motor integrated link mechanism. The waterproof cover opens and closes depending on whether the UAV unit is airborne or on the water surface. Compared to the custom-built UAV-USV hybrid vehicle or collaborative operations of two individual vehicles, the proposed housing, which is easily reproducible and disposable, can be straightforwardly scaled to accommodate off-the-shelf UAVs of any size. Initial experiments have been conducted in an in-lab testbed and demonstrated a promising validation toward its practical applications.

Keywords: Aerial Robots, Unmanned Aerial Vehicles, Mobile Robots
Abstract: This paper proposes a mechanical system that can easily engage, disengage and move along a cable, such as a power line or similar objects. The system is attached on top of a multi-rotor UAV and used to suspend the UAV from a cable. The device moves along the cable by using two actuators that drive the sheaves of the mechanical pillar, enabling power-efficient movement along the cable. This significantly increases the deployment duration for a UAV used in a possible inspection task. The targeted usage case for this work is the assistance of inspection personnel of power lines to monitor and inspect high-altitude cables on a daily basis. The ability to suspend a UAV from a cable and operate it from the ground would minimize the risk of injury due to falling and also avoid the risk of electric shock compared to traditional manual inspection methods. The mechanical system is developed with the goal of a simple structure, low cost and ease of operation in mind. The effectiveness and reliability of the system is analysed and attested in field experiments.

Keywords: Flexible Manipulators and Structures, Aerial Robots, Fixture and Grasping
Abstract: In this paper, we propose a novel mechanism for attaching payloads to pole-like structures, titled GRASPER (GRavity ASsisted Payload EntangleR). Inspired by the tangling-based weapon 'bolas', the 'pendulum catch' experiment and driven by SWaP (size, weight and power) constraints imposed on aerial platforms, we developed GRASPER as an alternative to arm-based grippers for load transportation and attachment tasks specific to pole-like structures. It works with two-bodies; a counterweight and a payload that are released onto a pole at height. Being a passive attachment system, it is lightweight, simple to use, surprisingly robust and functions remotely at a distance; a plus for rotorcraft endurance and safety. To study GRASPER, we first define a model accounting for the influence of gravity, rotational dynamics and the Capstan effect on a two-body system released from rest. The time-evolution of the system is simulated numerically, then compared against real-world experiments across variables such as mass, string length and counterweight types, with design implications for GRASPER derived thereafter. Finally, we demonstrate its feasibility of implementation on a typical quadrotor for aerial payload deployment applications.

Keywords: Design Optimization in Mechatronics, Unmanned Aerial Vehicles, Robot Dynamics and Control
Abstract: Up until recently a sequential approach has been pursued for model-based design of dynamic mechatronic systems, where first the system is optimized for static performance measures after which its functionality is enhanced by optimizing its control trajectory. However, this impedes finding systems with concurrent optimal design and trajectory. Therefore multi-disciplinary integrated design methods – co-design – have appeared that treat design and trajectory optimization at the same time. In this paper we examine two strategies for co-design, namely the simultaneous approach and the nested approach, for the optimal design of a quadcopter to perform a predefined task. To enable the former, Bayesian optimization is introduced in the nested framework to account for the computational cost. In regards to the latter, we rely on a flatness-based description of the dynamics of the aerial vehicle. Comparison of the two approaches with a sequential approach shows the added value of co-design to the design phase of dynamical systems and the impact of objective and constraint function formulation.

Keywords: Aerial Robots, Planning and Navigation, Intelligent Sensors
Abstract: Unmanned aerial vehicles (UAV) are capable of operating tasks in challenging environments of high risk for humans. However, these tasks usually require UAVs to plan motion in environments where GPS is unavailable. This paper implements a UAV system of motion planning and obstacles avoidance in GPS-denied indoor environments with the visual-inertial odometry (VIO) approach. Collecting measurements from stereo cameras and IMU, the visual-inertial odometry estimates the states of the UAV by jointly minimizing visual and inertial residuals and solving the nonlinear optimization problem. We utilize the minimum snap method to generate feasible trajectories based on the estimated states provided by VIO and an obstacle map of the indoor environment. In order to verify the effectiveness of the proposed system, we perform experiments on a UAV platform and the results show that the UAV accurately follows the planned trajectory and avoids obstacles without the aid of GPS.

Keywords: Automotive Systems, Vehicle Control, Control Application in Mechatronics
Abstract: Vehicle transient motion control plays an important role in reducing energy consumption for both hybrid and electric vehicles since vehicle acceleration and deceleration can be optimized based on driving environment. In this paper, nonlinear quadratic tracking (NQT) control is used for optimal acceleration and minimal principle is applied to deceleration to optimize energy recovery, where acceleration control generates the propulsion torque based on the current powertrain status and the error between vehicle speed and given reference provided by the connected system based on the surrounding traffic; and the deceleration (braking) control optimizes regenerative brake to maximize the recovered energy while obeying speed and braking distance constraints. Both control strategies are designed for real-time applications and updated online to respond to the rapid changing traffic environment. Co-simulation models, consisting of SUMO traffic and Simulink control models, are developed for validating the proposed control strategies through computer-in-the-loop (CIL) and Hardware-in-the-loop (HIL) simulations. CIL simulation results show that in the eco-acceleration mode, the proposed strategy can save 6.8% energy over the rapid acceleration and brake control recovers 50.8% more energy than the vehicle driven by SUMO simulated human driver. HIL simulations further confirms reduction of energy consumption and adaptability to a changing traffic environment in real-time.

Keywords: Vehicle Control, Learning and Neural Control in Mechatronics
Abstract: This paper presents a safety guaranteed control method for an autonomous vehicle ski-stunt maneuver, that is, a vehicle moving with two side wheels. To capture the vehicle dynamics precisely, a Gaussian process regression model is used as additional correction. We construct a probabilistic exponential control barrier function (CBF) to guarantee the planar motion safety. The CBF and the balance equilibrium manifold are enforced as the constraints into a safety critical control form. Under the proposed control method, the vehicle can avoid the collision safely and maintain the balance for autonomous ski-stunt maneuvers. We conduct numerical simulation validation to demonstrate the control design. Preliminary experiment results are also presented to confirm the learning-based motion control using a scaled autonomous truck for autonomous ski-stunt maneuvers.

Keywords: Automotive Systems, Vehicle Control, Fuzzy Logic
Abstract: In this paper, the linear speed tracking control problem of a single-wheel module (SWM) operating in an off-road environment is discussed. The approach is an extension to the authors’ previous work for angular speed control based on sliding mode control methodology. The linear speed tracking is performed by incorporating an adjusted reference angular speed. This reference speed is constructed utilizing a proportional control method. Further, two different approaches for tire slippage suppression are proposed. Both methods provide a corrected reference angular speed such that tracking it limits the tire slippage growth. The first method utilizes a pre-defined slippage limit, while the second method bounds the slippage by considering a range of characteristic slippages corresponding to different tire-terrain attributes. The second method is based on fuzzy logic control (FLC) and specifically is designed according to Mamdani fuzzy inference system. The efficacy of the controlled system is evaluated through numerical simulations. It is shown that the closed-loop system is able to robustly track a reference linear speed while it suppresses the tire slippage. Nonetheless, only the method based on FLC is able to address varying tire-terrain conditions and possible terrain transitions without compromising the system performance.

Keywords: Planning and Navigation, Automotive Systems, Vehicle Technology
Abstract: Precise map combination is the key solution to generate largescale maps for autonomous vehicles. The current state-of-art of LIDAR based SLAM technologies does not allow to achieve this demand because of dealing with a massive number of vehicle positions and the sparsity of point clouds to represent environments. In this paper, we fully design a unique Graph Slam framework to combine maps based on node strategy. A node encodes a set of LIDAR frames and represents accurate road surfaces in a grayscale image. The nodes images are identified in the Absolute Coordinate System (ACS) to facilitate the detection of map-combiner events between maps. The relative-position errors at the detected map-combiner events are significantly compensated by matching the dense nodes’ images using Phase Correlation technique. These compensations are reflected in ACS by a cost function designed to combine maps with maintaining the consistency of the road surfaces in the image domain. To prove the reliability of the proposed system, the world’s longest tunnel has been scanned two times. In addition, two combination formulas are investigated to simulate the creation and updating of collected maps by different agents. The experimental results have verified the robustness, accuracy and outperformance of the proposed framework against an accurate GNSS/INS-RTK system with improving the global position accuracy.

Keywords: Mobile Robots, Service Robots, Vehicle Control
Abstract: This paper deals with autonomous navigation through orchards. It proposes a strategy based on a sensor-based model predictive control law coupled with a spiral-based framework allowing to deal in a similar manner with the two main tasks: the in-row navigation and headland maneuvers. The Model-based Predictive Control scheme allows to deal with stability, boundaries of the actuators and offers a solution to switch between the different tasks. Simulation results based on ROS and Gazebo are presented at the end of the paper.

Keywords: Mobile Robots, Robot Dynamics and Control, Control Application in Mechatronics
Abstract: Automated guided vehicles (AGVs) have been utilized widely on transporting objects in factory or logistic to reduce the demands on human workers for the sake of increasing efficiency and reducing cost. However, the presence of uncertain faults on driving motors could degrades the processes and may cause danger during operation. In this paper, a three-step Fault Detection Isolation Reconfiguration (FDIR) is proposed to ensure that the transportation mission can be completed in the presence of unknown actuator faults. In the first step, an observer is designed for an AGV to determine whether a fault has occurred or not, while the second step utilizes a bank of observers to determine the type of fault. The third step involves reconfiguring the controller according to the isolation results. In the proposed approach, the FDIR method is addressed in the presence of dynamic uncertainties for both detection and isolation that make the proposed approach superior to the previously developed method. Finally the simulation results are presented to illustrate the effectiveness of the proposed FDIR method for omnidirectional AGV systems.

Keywords: Vehicle Control, Vehicle Technology
Abstract: The engine cooling fan drive is crucial for engine as it ensures safe and efficient engine operation. In heavy duty mobile machine the engine cooling fan consumes considerable amount of engine power. To meet high cooling demand a hydraulic fan drive is usually employed. A simple and cost-effective way is to use two fixed displacement hydrostatic units and a pressure relief valve where it has severe valve throttling losses. A more efficient way is to use a variable displacement pump to drive a fixed displacement motor. It eliminates valve throttling however the drive efficiency is still relatively low due to variable pump displacement control. Therefore an engine cooling fan drive system with a power split hydraulic transmission was proposed in previous study. The hydraulic transmission is a compact unit with both pumping and motoring functions. The fan speed is controlled by adjusting the transmission outlet pressure. Although previous results showed the system has relatively higher drive efficiency than conventional hydraulic fan drive at different fan speeds, the control design and system dynamic performance have not yet been studied. Therefore in this paper the control design of the cooling fan drive system with power split hydraulic transmission is studied. A feedforward controller based on transmission characteristics is designed to achieve better system performance. A hydraulic fan drive test bench is developed to evaluate system control design. The system dynamic performance with feedforward and feedback controls are compared through both simulation and experimental studies.

Keywords: Vehicle Technology, Mobile Robots
Abstract: Small mobile robots have been studied for various purposes such as exploration and research. For small mobile robots, there is a trade-off between miniaturization and performance in overcoming steps, and it is desirable to improve the performance without increasing the number of actuators. Various methods such as crawlers, wheels, and leg transfers have been developed to achieve a high step-clearing performance without increasing the size of the mobile robot. Among these methods, we focused on the use of eccentric wheels. By eccentrically rotating the center of the wheel, the robot can traverse higher steps than that is capable by a normal wheel. Although this is a simple mechanism, the eccentric wheel has a disadvantage in that its traveling efficiency on flat roads is inferior to that of a normal wheel. In this study, we developed Passive offset adjusting wheels that automatically deforms into an eccentric wheel only when stepping over a bump and remains in the normal wheel state at other times. Experiments were conducted using a prototype rover with a wheel diameter of 120 mm, and it was confirmed that the eccentricity of the wheel was 50% higher than that of a normal wheel.

Keywords: Control Application in Mechatronics, Mobile Robots
Abstract: This paper considers semi-autonomous excavation done by heavy-duty bulldozers with an onboard front-mounted blade. The goal is to gradually manipulate the blade digging depth to maximize the amount of excavated soil while considering the approaching terrain shape, traction, and the mobile base inclination. With the proposed system, the operator provides the target load on the machine and the blade is controlled semi-autonomously, while the operator can focus on driving the mobile base. To reduce large load variations caused by terrain shape irregularities affecting the blade, our system uses an onboard lidar to generate an elevation map of the terrain, updating it online as the machine moves. From the discrete map, we generate a well-behaved function describing the terrain shape. This provides a nominal blade elevation path that is modified online based on the measured load on the machine to maneuver the blade gradually, avoiding large gradient changes. The resulting Cartesian elevation profile is used as a reference to a blade position controller that counteracts the mobile base inclination. A commercial bulldozer with no modifications to its original hydraulics was used as an experimental platform, and the results verify the efficacy of the proposed methods in challenging outdoor conditions.

Keywords: Motion Vibration and Noise Control
Abstract: A resonance and an antiresonance peak characterize many industrial mechanisms dynamics driven by a Permanent Magnet Synchronous Motor (PMSM). The presence of the resonance peak can lead to vibrations and instability of the system. On that account, advanced methods exist to tune the speed Proportional Integral (PI) controller based on adaptive or fuzzy theory. However, those methods require expertise in control theory and are not available in commercial drives. For that, this paper proposes a Bode-based method for PI parameters selection in combination with a notch filter that can be easily set in any industrial drive. The proposed method is compared with conventional tuning methods in a physical setup.

Keywords: Modeling and Design of Mechatonic Systems, Robot Dynamics and Control, Identification and Estimation in Mechatronics
Abstract: In this paper, the design of a novel dual-drive ``checkerboard" gantry is introduced with symmetric layout, rigid structure and decoupled X-Y motion. However, its double redundantly-actuated configuration and the strong inter-axis coupling force challenges the coordinate motion and real-time force distribution between parallel axes. With consideration of stiffness of bearings, the dynamical model revealing the inter-axis coupling effect due to asymmetric load from the moving platform is established. Thereby, the force allocation between pairs of parallel axes is naturally solved by upcoming controller design. To compensate disturbance due to both structural and unstructural uncertainties, a 2-degree-of-freedom control scheme is proposed. It takes both tracking error and the model prediction error to update the dynamical model in real time, thus achieves effective feedforward control without setting up the deadzone. Meanwhile, a rule is developed to tune the proportional-integral-derivative feedback term in the task-space control from its joint-space counterpart. Additionally, the robust integral of signum of error term is proposed with gain adaptation, so that bandwidth of control is fine adjusted according to the velocity tracking error. Real-time experiments on the actual testbed indicate that both improvement of planar motion precision and prevention of parameter drifting over a large workspace are successfully achieved compared with conventional control methods.

Keywords: Mechatronics in Manufacturing Processes, Intelligent Process Automation, Human -Machine Interfaces
Abstract: A Supervisory Control and Data Acquisition (SCADA) system employed to monitor system processes became one of the fundamental components of in the automation area. With the emergence of digital twin concepts in Industry 4.0, this paper proposes an architecture to merge SCADA systems with key facets of digital twin with a case study on FESTO MPS processing station. We propose an OPC UA client-based architecture that employs Ignition software, which has built-in OPC UA modules, in order to create a digital twin of station’s sensors and actuators. Monitoring and controlling of the system was enhanced by connecting mobile devices to the network and adding a Raspberry Pi controller connected with traditional microcontrollers. The example implementation indicated that client-based architecture for digital twins and devices is feasible Interoperability allows to connect devices of interest with low-latency, low CPU overhead utilization, and with minimal added hardware and software extensions.

Keywords: Network Robotics, Robot Dynamics and Control, Control Application in Mechatronics
Abstract: This paper presents topological sorting methods to minimize the multiplicative depth of encrypted arithmetic expressions. The research aims to increase compatibility between nonlinear dynamic control schemes and homomorphic encryption methods, which are known to be limited by the quantity of multiplicative operations. The proposed method adapts rewrite rules originally developed for encrypted binary circuits to depth manipulation of arithmetic circuits. The paper further introduces methods to normalize circuit paths that have incompatible depth. Finally, the paper provides benchmarks demonstrating the improved depth in encrypted computed torque control of a dynamic manipulator and discusses how achieved improvements translate to increased cybersecurity.

Keywords: Learning and Neural Control in Mechatronics, Control Application in Mechatronics, Artificial Intelligence in Mechatronics
Abstract: By learning a variable impedance control policy, robot assistants can intelligently adapt their manipulation compliance to ensure both safe interaction and proper task completion when operating in human-robot coexisting environments. In this paper, we propose a DMP-based framework that learns and generalizes variable impedance manipulation skills from human demonstrations. This framework improves robots‘ adaptability to environment changes(i.e. the weight and shape changes of grasping object at the robot end-effector) and inherits the efficiency of demonstration-variance-based stiffness estimation methods. Besides, with our stiffness estimation method, we generate not only translational stiffness profiles but also rotational stiffness profiles that are ignored or incomplete in most learning variable impedance control papers. Real-world experiments on a 7 DoF redundant robot manipulator have been conducted to validate the effectiveness of our framework.

Keywords: Motion Vibration and Noise Control, Control Application in Mechatronics, Human -Machine Interfaces
Abstract: Vibrotactile haptic sensations are commonly achieved by a Linear Resonant Actuator (LRA) or an Eccentric Rotating Mass (ERM). ERMs dominated the early years of the vibrotactile haptic market in large part due to their simplicity and low cost. An ERM produces output proportional to the square of its angular velocity. To achieve a desired force output, the device must accelerate to the associated velocity. In typical open-loop methods of ERM control, this acceleration takes some finite time, a limit on responsiveness. This limitation has led to increased emphasis on LRAs as manufacturers seek improved responsiveness in devices such as smart phones, game controllers, and interfaces for virtual reality systems. Another limitation of ERMs is that they cannot decouple the amplitude of output from the frequency of vibration, despite both being relevant to haptic perception. This work addresses these two deficiencies by presenting a novel closed-loop system comprised of two ERMs. Output is controlled by cascaded velocity and phase control loops--the velocities of the motors controls the output frequency, and the relative phase controls the output amplitude, decoupling the two outputs. Also, changes in relative phase can be performed faster than starting up the motor from rest, improving response time. In this implementation, the switching time from a cancellation state to maximum output is faster than thirty milliseconds. Also introduced are novel counter-balances to cancel parasitic torque, and learning algorithms that leverage symmetry and accelerometer measurements to calibrate the relative alignment of the eccentric masses with sensors.

Keywords: Sensors and Sensing Systems, Modeling and Design of Mechatonic Systems, Control Application in Mechatronics
Abstract: This paper newly proposes a force control method for soft object manipulation by multi-joint three-fingered robotic hand. In this study, the soft object is made of polyurethane resin, which enables the force control method based on strain of the grasped object to work well. First, we mention the design of anthropomorphic robotic hand, which contains eight degrees of freedom as the joint and five DC motors in total. A tendon-driven mechanism and its fine wiring enable grasping force to be 14.0N at maximum. This robotic hand has an index/middle finger and a thumb to realize stable and robust grasping motion, in which an optimal arrangement of the tendon is designed in all three fingers. In addition, this study designs a novel small sensor capable of estimating the Young's modulus of a contacting soft object, in which we formulate an estimation algorithm based on Hertzian contact

theory. The Young's modulus measurement system has been optimized for a humanoid hand and realized with less than a 10% measurement error in verification experiments. By using the small sensor, we demonstrate that soft object manipulations can be easily achieved with a three-fingered robotic hand on the basis of inner-loop force feedback control with the strain reference of a soft object. Finally, we show secure dynamic responses by the three-fingered hand, in which strain output is able to successfully follow stepwise target signal regardless of the viscoelastic repulsive force of soft objects.

Keywords: Medical Robotics/Mechatronics, Identification and Estimation in Mechatronics
Abstract: The diagnostic procedure that involves attaching electrodes at distinct points on the human head to detect and analyze electrical signals of brain activity is known as Electroencephalography (EEG). Previously,we proposed an algorithm for computerized EEG electrode position prediction that is substantially faster and more accurate than the current traditional method of calculating and marking these positions. This paper proposes an alternate and revised approach for estimating the optimal electrode position that is more robust, effective and less susceptible to errors. This new approach utilizes the 3D ellipsoid model of human heads and is based on the Earth's Map projection and location identification. Similar ideas are adapted for predicting the optimal positions of the EEG electrodes' placements on the human head. The proposed approach is implemented and compared to the results from the algorithm in our previous work. The findings from the experimental studies show that the proposed approach is more efficient than the algorithm described in our previous work, with better RMSE under simulated angular tilts, more resilient with 70% more exact prediction under simulated noise and accurate with an average RMSE of around 2 cm or less.

Keywords: Medical Robotics/Mechatronics, Fixture and Grasping, Image Processing
Abstract: Surgical instrument sorting, sterilization, and inspection in the hospital are highly time-consuming and labor-intensive due to the sheer volume and variety of tools. It is not easy to find the automation replacement since the diversity and similarity of these instruments bring numerous challenges to their identification, and hence the problem remains largely unresolved. In this paper, we design a system incorporating supervised deep learning networks and conventional methods to realize surgical instruments' fast and robust identification to facilitate robot picking. Our approach overcomes the difficulties of manually determining the region of interest (ROI) for surgical instruments. We fasten this process through a proposed labeling strategy and hence avoid the high manual labor and time costs. Two types of surgical instruments datasets are created through the design for the first time and are openly available. Robot experiments are performed to demonstrate the effectiveness of our strategy in facilitating surgical instrument automation.

Keywords: Medical Robotics/Mechatronics, Actuators in Mechatronic Systems, Rehabilitation Robots
Abstract: Physical assistive robotic devices have demonstrated many desirable advantages in rehabilitation and augmentation, allowing for efficient and effective performance of activities of daily living (ADLs). However, active wearable devices that provide resistive capabilities to enable strength training have been difficult to develop. This is partially due to the required high force outputs during strength training. Developing soft, compact, compliant and lightweight robotic devices that exhibit both assistive and resistive capabilities is an even tougher challenge. This paper presents an Active Wearable Assistive and Resistive Device (AWARD) that is capable of providing assistance and resistance to the motion of fingers, the first of its kind. The device was actuated by six twisted string actuators (TSAs) with four TSAs using both stiff strings and conductive supercoiled polymer (SCP) strings and a further two TSAs using only stiff strings. Force sensitive resistors were used on the fingertips in assistive and resistive modes. The AWARD showed a maximum peak force of 13.55 N in finger extension and a maximum peak force of 8.66 N in finger flexion.

Keywords: Medical Robotics/Mechatronics, Flexible Manipulators and Structures, Modeling and Design of Mechatonic Systems
Abstract: This article presents a unique multiphysical analytical modeling framework and solution algorithm to provide an effective tool for design of magnetically steerable robotic catheters (MSRCs) experiencing external interaction loads. Particularly, in this study, we are interested in design and fabrication of a MSRC with flexural patterns for treatment of peripheral artery disease (PAD). Aside from the parameters involved in the magnetic actuation system and the external interaction loads acting on the MSRC, the considered flexural patterns have a critical role on the deformation behavior and steerability of the proposed MSRC. Therefore, to optimally design such MSRC, we utilized the proposed multiphysical modeling approach and thoroughly evaluated the influence of involved parameters on the performance of the MSRC via two simulations studies. We also conducted experimental studies in a free bending condition and in the presence of different external interaction loads on two custom-designed MSRCs to thoroughly evaluate the efficacy of the proposed multiphysical model and solution algorithm. Our analysis demonstrates the accuracy of the proposed approach and necessity of utilizing such models to optimally design a MSRC before fabrication procedure.

Keywords: Medical Robotics/Mechatronics, Control Application in Mechatronics, Micro-Electro-Mechanical Systems
Abstract: We discuss optical and actuator design considerations in development of a miniature reflection confocal microscope (RCM) based on parametrically-resonant MEMS micro-mirror scanning. Microscope form factor is intended for real-time imaging of the brain in small animals, at subcellular resolution. Candidate designs start with a combination of lens sequence, micro-mirror size, and scanning angle, within packaging considerations for eventual implantation on a freely-behaving mouse. Design optimization focuses on maintaining the diffraction-limited resolution over large field-of-view (FOV), while ensuring large feasible micro-mirror operating frequency. After investigation of optical configurations and required mirror performance over tradeoffs such as working distance (WD) and numerical aperture (NA), we demonstrate a system providing 1.5µm lateral and 6µm axial resolution with 350×350µm FOV and 303 µm WD.

Keywords: Micro/Nano Manipulation, Medical Robotics/Mechatronics
Abstract: This study presents the preliminary development of separating parasites with a robotics approach. Cryptosporidium is a parasite that causes sickness in people and animals. Cryptosporidium oocysts, which are approximately 5 μm in size, contain four haploid sporozoites. It is currently unproven whether these ’siblings’ are genetically identical or not. Common methods of selective cell isolation were considered for separation and the pick-and-place with microaspiration technique was selected due to the ability to work with low volume samples that would be the four sporozoites from a single oocyst. The drawbacks of high labour and time cost were offset by considering automation. The forming of the micropipettes was developed to produce the best tip for separating the oocysts and sporozoites. The optimum flow rate of the syringe pumps and the optimum angle of the micropipettes during microaspiration were intensively tested and found to be 12 μl/min and 45◦ respectively. The manual process was able to separate the Cryptosporidium oocyst and sporozoites. The separation process was found to be possible to automate with separating silica microbeads using computer vision. Overall, the findings of this paper are that parasites can be separated with micro-aspiration approach and the process can be automated to make the process viable within laboratories.

Keywords: Sensors and Sensing Systems, Medical Robotics/Mechatronics, Sensor Integration, Data Fusion
Abstract: With the proliferation of low cost magnetic sensors, magnetic field-based localization systems promise portable and affordable noncontact sensing solutions for tracking the tip of a catheter, such as a NG tube or a ventriculostomy catheter, inside the human's body. Currently, these localization systems are constrained by a short tracking range of up to 150 mm from the sensor center. In this paper, we present a compliant non-invasive system that enables extendable long-range tracking in real time using a permanent magnet -- to be embedded to a catheter -- and an array of sensors. When the catheter being inserted into the body, the magnet creates a magnetic field which can be detected by proximic sensors. To achieve high accuracy in long-range sensing while keeping the computational requirements reasonable, the sensors are first configured using the best horizontal and vertical offsets identified from numerical simulation. Next, only the magnetic flux densities measured by axial sensing channels of a sensor positioned in close proximity of the magnet, which we refer to as attentive sensing channels, will be considered for localization process. Two threshold schemes for identifying attentive sensing channels are proposed and evaluated. Finally, a novel nonlinear-based localization algorithm is used to estimate the magnet position based on the magnetic flux density detected by attentive axial sensing channels. The results of numerical simulation and real experiments show that our proposed approach warrants long range localization of magnet position with good localization accuracy, resource conservation and high robustness.

Keywords: Medical Robotics/Mechatronics, Intelligent Sensors, Sensor Integration, Data Fusion
Abstract: The spatial orientation of rotating objects is typically measured by utilizing inertial measurement units and requires temporal integration of angular velocities. The integration of the angular velocities accumulates the measurement noise and results in erroneous orientation estimation, thus necessitating real-time error compensation. In this study, an integral-free 3D orientation estimation framework based on stereo-accelerometry and sensor fusion is proposed and validated. Afterward, a wearable device, micarp for robot-assisted interventional surgery was designed, prototyped, and investigated for accuracy and real-time performance. To achieve real-time performance, an artificial neural network was trained and implemented in the wearable device. The comparison of the results of the proposed method with a representative complementary filtering method showed superior performance. The proposed method had a mean-absolute-error of 1.724, a measurement range of 180, and a real-time sample rate of up to 117 Hz. In the end, the feasibility of integrating the proposed device with a representative robotic intervention system was investigated. The proposed wearable device showed the capability of robust capturing of multiple successive rotations for an arterial cannulation task on a vascular model.

Keywords: Control Application in Mechatronics, Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics
Abstract: To improve the tracking performance of a magnetic levitation system in the presence of time-varying disturbances, a model predictive control (IEIDO-based MPC) scheme based on improved equivalent input disturbance observer is proposed in this paper. In order to enhance the accuracy of observer estimation, an IEIDO which can estimate both system state and time-varying equivalent input disturbance is proposed. The estimations of IEIDO are combined into the MPC design to predict the output position. Following this, an explicit analytical form of the optimal predictive controller is calculated to reduces the computational burden in practical applications, as well as the robustness against lumped input disturbance.Stability analysis shows that the closed-loop system is asymptotically stable and results of simulations show the effectiveness of the proposed method, less fluctuation of position compared with the conventional EID approach and Luenberger observer based integral MPC (LO-based IMPC).

Keywords: Control Application in Mechatronics
Abstract: This paper concerns the problem of designing a modified repetitive-control (RC) system for a class of servo system with nonintegral-chain form and unknown nonlinear dynamics, whose relative degree may be less than the system order. Our solution focuses on the unique features of equivalent input disturbance (EID) and extended state observer (ESO) proaches. An improved ESO is designed to estimate the EID together with the states by exploiting the known information of the plant model and the incorporation of the EID estimate into an RC law compensates for the influences of the actual disturbances. Both the stability criteria and optimization algorithm for controller parameters are provided. Finally, an application to speed control of a brushless DC motor system demonstrates the validity of the method.

Keywords: Human -Machine Interfaces, Service Robots
Abstract: This study investigated the impressions people have when receiving instructions by using two robots with different appearances that automatically move at different distances within a workspace and give instructions to subjects. In particular, we clarified how factors such as the appearance of the robots and the sense of distance, including personal space, influence when task instructions are given by the robots through　active communication from the robots in subjects in two age groups. The results of the experiment showed the likeability of the robot and the impression of distance from the robot when the robot gave instructions to a person, as well as the impression of warmth or discomfort toward the robot. The change in the impression of the robot throughout the experiment was also shown. The results also showed differences in impressions of the distance between different age groups, and differences in the final likeability of the robot through the interaction between the person and the robot between the two different age groups.

Keywords: Human -Machine Interfaces, Sensor Integration, Data Fusion, Virtual Reality and Display
Abstract: Indoor localization methods provide pose information in their own virtual coordinate systems. Adjusting these custom virtual spaces to the real physical spaces can be a complex and high cost (manpower, equipment) procedure. Verifying whether the adjusted poses accurately reflect the poses in the real space or not is also a difficult task. This paper proposes the application of the WYSIWYG (What-You-See-Is-What-You-Get) style for indoor localization systems as a tool for physical Human-Robot Interaction (pHRI) experiments. To realize this, we have constructed a system using floor projection and a self-developed virtual to real coordinate system adjustment tool with an easy to use, intuitive user interface. The calculated real positions are shown in real-time in the same space where the real tracked objects are. Therefore an operator can instantly verify and adjust the virtual-real coordinate transformation parameters, effectively minimizing the cost of calibration and possibly increasing accuracy. Our proposed system is capable of gathering positions from indoor localization systems and providing the transformed real-space pose information via different methods including a common standard interface, in the form of Robot Operating System (ROS) messages. For the proof of concept system we have used the trackers of the HTC Vive system as a localization data source and a consumer grade projector resulting in a low-cost solution. Our preliminary experiments show that the proposed WYSIWYG system provides a suitable environment for pHRI scenarios, and can also provide secondary functions e.g. intention projection, augmented reality applications etc. Furthermore, as a tool it can facilitate the conversion of simulated HRI scenarios into real physical experiments.

Keywords: Identification and Estimation in Mechatronics, Design Optimization in Mechatronics, Control Application in Mechatronics
Abstract: Disturbances are inevitable in control practice. Many methods have been reported for disturbance rejection. Among them, equivalent-input-disturbance (EID) approach is effective for both matched and mismatched disturbances. However, since the disturbances are usually unknown, conventional methods cannot be used to analyze the control performance of disturbance rejection, especially the dynamic performance. In this paper, by treating the Luenberger observer as the role of an ideal system, an auxiliary variable, i.e., the output error betwween the disturbed system and the ideal system, is introduced to aid the disturbance-rejection performance analysis of EID approaches. The relation between the control performance and the output error are revealed. Then, a nonlinear EID estimator is constructed to speed up the disturbance-rejection control. The finite-time dynamic performance and uniformly ultimately bounded steady-state performance are guaranteed. Further, a design algorithm is developed for the NEID-based closed-loop control system. Finally, by comparing with a conventional linear EID-based method, simulation results illustrate the validity and superiority of the developed method.

Keywords: Motion Vibration and Noise Control, Sensors and Sensing Systems, Identification and Estimation in Mechatronics
Abstract: This paper presents a new method to estimate an along-wind load that contains a mean component using an equivalent-input-disturbance approach. An along-wind force contains both mean and fluctuating components. However, most studies estimate only fluctuating components. Moreover, these studies assume that the damping matrix is a Rayleigh one. In contrast, this paper presents a method that estimates both the mean and fluctuating components using velocity response. Furthermore, this method does not require that the damping coefficient is Rayleigh damping. The numerical verification verifies using an 11 degree-of-freedom(DOF) model of a seismic-isolated building. The results presented that the presented method accurately estimates an along-wind load.

Keywords: Modeling and Design of Mechatonic Systems, Actuators in Mechatronic Systems
Abstract: Currently, progress in soft robotics is contributing to advancements in the field of robotics. A soft robot has several advantages over a traditional robot including portability, durability, and increased flexibility. A suitable design of the actuators in a robot is crucial for increasing the robot's flexibility. A suitable design of the actuators present in a robot is crucial for increasing the robot's flexibility. This study aims to develop a soft rat robot based on pneumatic actuators that can interact with real rats. The pneumatic actuator design developed by Suzumori et al. in 2017, the flexible micro actuator (FMA), is chosen as the main actuator to drive the robot. This actuator is rodlike, made using silicone fiber, and driven by filling high-pressure air into a specially designed container. An actuator based on the FMA design with a diameter of 25 mm is fabricated to form a soft rat robot that can simulate the motions of a real rat— rearing and rotating left and right. Finally, the properties of the pneumatic actuator are tested and analyzed to optimize the structure of the robot.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics
Abstract: This paper presents the design and testing of a two degree-of-freedom (DOF) magnetic levitator which is capable of controlling the levitational- and lateral-direction forces in a decoupled manner using a single-body actuator. Our 2-DOF levitator is composed of an U-shaped iron-core, an internal permanent magnet (PM), and a pair of actuating coil windings. A 1-inch steel ball is used as a levitated target, and its position is measured in real-time by dual fiber optic sensors. The two perpendicular forces are generated and decoupled by the combination of the PM-biased flux and the coil-driven flux in the common and differential current-excitation modes. We present our control strategy for the decoupled 2-DOF stabilization, and experimentally validate the control performance of our 2-DOF actuator. Using our actuator and the decoupled 2-DOF control, we achieve the significant improvement on the stiffness and the damping of the levitated target in the transportation direction, thereby achieving 82 % reduction in the lateral deviation and 99 % reduction in the settling time as compared to a general 1-DOF magnetic levitation system. Such promising results show a significant potential of our single-body 2-DOF levitator for the high-throughput non-contact object transportation.

Keywords: Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems, Compuational Models and Methods
Abstract: The Time Sensitive Network (TSN), currently being standardized by the IEEE 802.1 working group, has been getting much attention for transporting time-triggered (TT) data flows in a smart manufacturing factory. In a TSN, several mechanisms (synchronization, frame scheduling, gate control, etc.) are available to guarantee deterministic and very low delay for periodical TT flows. %TSN requires transmission of data flow in internet must follow pre-arranged schedule. Previous studies on guaranteeing deterministic low delay for TT flows focus on modeling the scheduling constraints. However, without considering routing issue and network topology design, a TSN's capacity may be limited due to link congestion. %Also it is possible that the data flows passing the congestion nodes can not satisfy the maximum end-to-end latency. Thus, the data flows not only lose the feature of TSN, but also the scalability of manageable data flow quantity for the internet is seriously affected. %On the other hand, even using the superior network topology to decide the routing paths of the data flows’, it still needs to design a network topology with low-cost and well thought out routing algorithm. We believe that a good TSN design, in particular for supporting TT flows, should include the following three parts: topology design, routing decision, and scheduling. This paper establishes an optimization model to construct a hierarchical interconnecting-tree network, taking flow demand, switch bandwidth, and geography factor into consideration. It is capable of constructing a minimized-cost TSN in a smart factory while also allowing for enhanced network resilience at the expense of extra cost.

Keywords: Compuational Models and Methods, Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems
Abstract: This article presents our nonlinear hybrid layer modeling (NHLM) method to capture the heavy saturation in the iron-cored linear permanent-magnet synchronous motors (LPMSMs). In the high-performance iron-cored LPMSM, significant nonlinearities are observed in the motor forces and associated ripples. In order to accurately model such nonlinearities, we introduce our hybrid approach of the Maxwell model and the flux-tube model for the uniformly-layered moving PM track region and toothed-structure of the armature stator region, respectively. We also propose our novel leakage reluctance adaptation method to predict the amount of leakage fluxes so that the nonlinear force-increasing-rate of the motor force is taken into account with the high accuracy. Our NHLM is validated by the equivalent 2D FEM, comparing the magnetic fields and motor forces. We observe the fidelity of our proposed NHLM with the accuracy of higher than 97 %, while showing much faster computation time by multiple-orders of magnitude compared to the FEM.

Keywords: Modeling and Design of Mechatonic Systems, Flexible Manipulators and Structures, Software Design for System Integration
Abstract: The ongoing trend from mass-produced to mass-customized products with batch-sizes as small as a single unit has highlighted the need for highly adaptable robotic systems with low down-time for maintenance. To address these demands, this work proposes the development of a novel reconfigurable collaborative robot (cobot), which has the potential to open up many new scenarios within the rapidly emerging flexible manufacturing environments. As the technological contribution, we present a complete hard- and software architecture for a quickly reconfigurable EtherCAT-based robot. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. As the theoretical contribution, we propose a method to obtain in an automatic way the kinematic and dynamic model of the robot and store it in URDF format as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. While the paper focuses on reconfigurable manipulators, the proposed concept can support arbitrary serial kinematic tree-like configurations. We demonstrate the contributions with examples of: (a) how the topology of the robot is reconstructed and the URDF model is generated, (b) a Cartesian task application for a cobot built with the basic modules, demonstrating the quick reconfigurabilty of the system from a 4 degrees of freedom (DOF) robot to a 5-DOF robot, in order to satisfy new workspace requirements.

Keywords: Design Optimization in Mechatronics, Humanoid Robots
Abstract: In industrial applications, a manipulator is usually required to perform multiple pre-defined tasks on a fixed installed pose. Due to the ununiform dexterity distribution, the manipulator may work in awkward configurations, and some tasks may even exceed the reachable workspace. Thus, the performance of the tasks can be improved by optimizing the installation pose (IP) of the manipulator. However, there are complex nonlinear relationships between the IP and the tasks, which makes the optimization problem challenging. In this paper, a new method is proposed to optimize the IP of the manipulator for multiple tasks. Firstly, the paths of the end effector are discretized into poses for different tasks. Then the optimization model is established, which is applicable to different indexes and constraints. Finally, the whale algorithm is improved to solve the optimization model. To verify the effectiveness of the proposed method, two case studies of the robotic inspection task and the unhooking the freight train task are given. Simulation results show that the proposed method can give good performance for complex tasks.

Keywords: Part Feeding and Object Handling , Novel Industry Applications of Mechatroinics, Planning and Navigation
Abstract: Multi-arm systems can perform complex and difficult tasks, such as manipulating a heavy or large object, that cannot be accomplished by a single manipulator owing to workspace and payload limitations. However, the motion planning problem for performing such a task is challenging because of the need to consider the closed-chain constraint. This paper proposes an efficient motion planner that considers the closed-chain constraint based on a probabilistic roadmap. The proposed planner utilizes the following strategies. First, the planner obtains feasible nodes by randomly sampling the object pose, followed by computing the inverse kinematics (IK) solution of the multi-arm. This can directly find a collision-free node satisfying the closed-chain constraint. Second, the planner repeatedly updates the new IK solution of the multi-arm for the start and goal object pose. The IK solution is computed as close as possible to the joint configuration of the neighbor node. Consequently, the planner is more efficient than the existing methods that generate a node by sampling the joint configuration with projection method and have one pair of the start and goal node. Therefore, the planner can efficiently compute the path for object manipulation using a multi-arm under a closed-chain constraint. The effectiveness of the proposed planner is validated by comparison with the existing planners in several scenarios. A video clip of the experiments in various scenarios can be found at https://youtu.be/PR9aFf3juu4.

Keywords: Novel Industry Applications of Mechatroinics, Modeling and Design of Mechatonic Systems
Abstract: To facilitate mechanics testing in special environment, this paper presents a magnetic levitation bending testing device (MLBTD), where a specimen can be bent while being levitated. To control the levitation, a mathematical model was built for the plant. In addition, electromagnetic analyses were conducted to get the parameter values of the plant model. Furthermore, since the specimen needs to bear a bending force while being levitated and the bending force may affect the stability of the levitation, a control method that fixes levitation stiffness and levitation damping of the maglev system was proposed. Simulation results demonstrated that the fixed stiffness-damping control can maintain the stiffness and damping at constant values well even though the bending force was applied to the specimen. Finally, experiment results demonstrated that the fixed stiffness-damping control method allow MLBTD withstand a bending force of up to 8.5N.

Keywords: Novel Industry Applications of Mechatroinics, Neural Networks, Sensors and Sensing Systems
Abstract: Surface roughness plays an important role in grinding; it can represent the grinding quality of machined parts. In previous research, analytical models and empirical models have been used to predict surface roughness. This research presented surface roughness prediction models based on linear regression and artificial neural networks of several types of model structures, then applied different features as model inputs, including force data and force data after statistical processing. After conducting the prediction model, a self-developed grinding machine was used to collect the force data for model training and testing, and the mean absolute percentage error was used to evaluate the prediction performance. In the end, a neural network of three hidden layers was marked as the best model, which was useful for surface roughness prediction during grinding.

Keywords: Mobile Robots, Mechatronics in Manufacturing Processes, Novel Industry Applications of Mechatroinics
Abstract: This paper describes the development of a two-wheeled mobile robot that polishes steel structures. The robot has ring-shaped permanent magnets on its wheels, and can move freely on flat walls, slopes, and ceilings. Although pneumatic devices are generally used as the power source for buffing tools, it is preferable for the robot to be able to use them wirelessly; therefore, an electric motor was used for the buffing drive unit. The driving system of the robot was equipped with two brushless motors, and the differential drive allowed the robot to move in a straight line and turn on the spot. The motors for buffing and the drive unit were controlled using a microcontroller mounted on the robot, and batteries were installed to enable wireless use. In the prototype, commands were sent from a PC to perform forward, backward, and turning movements. The experimental results of the buffing and running performances of the prototype are reported in this paper.

Keywords: Artificial Intelligence in Mechatronics, Mechatronics in Manufacturing Processes
Abstract: In the multistage manufacturing process, the dimensional errors generated in previous stage will affect the dimensional errors in the next stage. Considering that the machining process is a typical time-varying nonlinear system, the determination of dimensional errors and the analysis of stream of errors have great challenges. The analysis and inference approaches based on theoretical model and data-driven model have some shortcomings in accuracy. In order to solve the above problems, an improved semiparametric model integrating engineering knowledge, mechanism model and measurement data is proposed. Transfer error, system error and random error among multistage machining process are decomposed and analyzed. Besides, the uncertainties within transfer coefficient and system error are quantified and calibrated through Bayesian framework. The effectiveness of the model is verified through the multistage machining process of turboshaft. The deviation between inference results and measurement results is 6.9026μm. Compared with the traditional parametric model, nonparametric model and semiparametric model, the proposed improved semiparametric model shows higher inference accuracy.

Keywords: Intelligent Process Automation
Abstract: Parts produced by powder bed fusion (PBF) additive manufacturing often suffer from defects due to non-uniform temperature distribution. To address this problem, the authors have recently proposed SmartScan, an intelligent (i.e., model-based and optimization-driven) approach for generating scan sequences that substantially improve temperature uniformity. However, the current version of SmartScan can only be applied to laser scanning of simple geometric patterns with uniform-length features. This paper extends SmartScan to complex geometries with a finite set of feature lengths. Simulations and experiments involving laser marking of AISI 316L stainless steel plates demonstrate that, compared to heuristic sequences, the proposed SmartScan approach improves temperature uniformity by up to 7.95 times and reduces mean deformations by up to 1.5 times.

Keywords: Learning and Neural Control in Mechatronics, Mechatronics in Manufacturing Processes
Abstract: In this paper, we develop a predictive geometry control framework for droplet-based additive manufacturing (AM) based on a physics-guided recurrent neural network (RNN) model. Because of its physically interpretable architecture, the model’s parameters are obtained by training the network through back propagation using input-output data from a small number of layers. Moreover, we demonstrate that the model can be dually expressed such that the layer droplet input pattern for (each layer of) the part to fabricated now becomes the network parameter to be learned by back-propagation. This approach is applied for feedforward predictive control in which the network parameters are learned offline from previous data and the control input pattern for all layers to be printed is synthesized. Sufficient conditions for the predictive controller’s stability are then shown. Furthermore, we design an algorithm for efficiently implementing feedback predictive control in which the network parameters and input patterns (for the receding horizon) are learned online with no added lead time for computation. The feedforward control scheme is shown experimentally to improve the RMS reference tracking error by more than 30% over the state of the art. We also experimentally demonstrate that process uncertainties are compensated by the online learning and feedback control implementation.

Keywords: Novel Industry Applications of Mechatroinics, Fixture and Grasping, Neural Networks
Abstract: Automation has been adopted and realized in many industrial fields in recent years. However, it has been barely implemented in the field of dealing with living creatures. Therefore in this paper, we propose a robotic system for grasping and transporting jellyfish to automate operations involving living creatures. We created a wire-driven robotic gripper specialized for grasping jellyfish that is very soft with an extremely low friction coefficient. We trained a YOLOv5 model for recognizing jellyfish and obtain the grasping position. In experiments, we used gel jellyfish fabricated using a 3D gel printer instead of living jellyfish. Experiments were conducted to evaluate grasping range and height of the gripper. Results indicated that the grasping height limit was 15 mm from tank bottom, and the grasping range was a circular area with a diameter of 140 mm. Finally, we conducted an experiment to automatically detect, grasp, and transport jellyfish from one tank to another with the proposed robotic gripper and deep learning based recognition method.

Keywords: Artificial Intelligence in Mechatronics, Intelligent Process Automation, Machine Learning
Abstract: In the field of aerospace, aviation turbine shafts with the slender and thin-walled shape are important parts in aero engines. The machining accuracy of aviation shafts has been playing a crucial role in the whole assembly process. Focused on the multi-stages machining accuracy of aviation shafts, this paper provides a foreknowledge perception and decision method in aviation shafts manufacture. In this paper, in-site machining process concerning aviation turbine shafts has been analyzed as well as extracting the key detection features. Based on the proposed hierarchical criterion of aviation shaft qualification states, this paper establishes a hidden Markov model of machining quality conforming to the actual aviation shaft machining conditions by collecting the industrial in-site data. Furthermore, a data-driven model of aviation shaft multi-process inspection data generated by qualification state hidden Markov model is established by means of neural network model. Consequently, the foreknowledge perception of subsequent process machining accuracy can be realized by the inspection data of the current machining process, laying the foundation for the prediction of machining accuracy of multiple processes in aviation turbine shafts manufacture.

Keywords: Learning and Neural Control in Mechatronics, Control Application in Mechatronics, Neural Networks
Abstract: This paper proposes a method for controller approximation via neural network in the presence of parametric perturbations. The neural network is based on long short-term memory blocks and is trained to approximate a numerical optimal control law, solved for different parameter values. Using this approach, the obtained approximate control law learns to generate the control inputs based on different optimal control solutions for different parameters: as compared to training the neural network only based on the optimal control law defined for the nominal parameters, the overall system performance greatly improves when parameter variations are present, and does not degrade when the nominal parameters are used for testing. The proposed approach is validated experimentally on an inverted pendulum with dual-axis reaction wheels.

Keywords: Learning and Neural Control in Mechatronics, Neural Networks, Artificial Intelligence in Mechatronics
Abstract: Neural networks have been extensively used in robot control for various applications because of their powerful capability in approximation of nonlinear functions. However, existing literature on feedback control of robots mainly focuses on shallow networks where the analysis is developed for the output weights only and the linearity in parameters is often a requirement. This is due to the fact that convergence analysis is difficult for deep networks. Since stability and convergence are critical in robot control, our main aim is to develop a theoretical framework for using deep networks in robotics in a safe and predictable manner. In this paper, we use a deep network to approximate the Jacobian matrix of a robot with unknown kinematics. An analytic layer-wise deep learning framework is proposed where the deep network is progressively built and trained, and the convergence of the tracking error is guaranteed during the online learning process. The experimental results for tracking control tasks performed on an industrial robot are given to illustrate the effectiveness of the proposed method.

Keywords: Artificial Intelligence in Mechatronics, Machine Learning, Biomechatronics
Abstract: This paper presents a bio-inspired machine learning framework, which aims to mimic the human hearing functionalities, for industrial acoustic monitoring. It involves firstly modelling the functionality of the cochlea, which is an essential part of the inner ear. This is accomplished by extracting important time-frequency information of the acoustic signals through cochleagrams. Then, to emulate more closely the neural activities in the brain when processing information, a bio-plausible Spiking Neural Network (SNN) is applied for pattern recognition. Finally, the proposed method is verified with acoustic data collected from machine bearings with healthy and faulty conditions. The initial feasibility study has demonstrated the viability and the efficacy of the proposed “machine hearing” approach for industrial acoustic monitoring applications.

Keywords: Service Robots, Robot Dynamics and Control, Parallel Mechanisms
Abstract: Cleaning tasks for vertical structures, such as building façades and walls on construction sites are dangerous; there is a high risk of fall accidents among workers engaged in such tasks. Hence, research on the automation of wall cleaning tasks based on robotics technology has been actively conducted in recent years. However, existing wall-cleaning robots have limitations, such as poor mobility performance or the need for additional infrastructure for operation. In this study, we designed a novel rope-driven wall-cleaning robot Edelstro-M2 with two innovative characteristics. First, both vertical and horizontal movements on the wall are possible by implementing a dual rope climbing mechanism and parallel kinematics. Second, except for a rope fixing arrangement, additional infrastructure such as winch and building management unit (BMU) systems are not required for operation. A prototype model was developed, and real-world experiments were conducted to verify the mobility and cleaning performance of the robot.

Keywords: Robot Dynamics and Control, Legged Robots, Flexible Manipulators and Structures
Abstract: This paper presents a method to achieve circular stepping motion by a single actuator on a microrobot leg mechanism. Five-bar linkage with different bending strength flexible hinges have two different resonance frequencies in the first and second axis. While force actuates on the first and second axes of the leg mechanism simultaneously and the frequency is between two resonance frequencies, motions in the first and second axis have a phase difference. Due to phase differences, the mechanism can perform a circular stepping motion. This method can reduce the required number of actuators and driving circuits of one leg to achieve stepping motion and decrease the complexity of the mechanism and size of the microrobot. This method is verified by the four legs microrobot, which is 70 mm in length, 40 mm in width, 20 mm in height, and 6.72 g in weight. Four pairs of coil and magnet actuators on four legs are controlled by a 7 V and 23 Hz alternating current (AC) signal. Microrobot can walk at a speed of 13.4 mm/s, equal to 0.187 body-length/s.

Keywords: Robot Dynamics and Control, Human -Machine Interfaces, Compuational Models and Methods
Abstract: This paper proposes an integral design for sensor-less collision detection of serial robots. The idea is to use a generalized momentum based observer to estimate the disturbance joint torques caused by the external forces and compare the estimated values with the corresponding thresholds. This method includes the robot dynamic model identification, observer implementation and thresholds determination. Based on our previous work, the robot dynamic model is established and identified to provide accurate parameters for the observer. In order to implement the observer, an automatic differentiation method is proposed, which uses the identified parameters directly to estimate the disturbance joint torques. When setting thresholds, a varying bandwidth filter is designed to suppress the effect of the friction uncertainties and velocity-dependent thresholds are given. Experiments illustrate that the proposed method improves the detection sensitivity and avoids false alarm simultaneously.

Keywords: Tele-operation, Robot Dynamics and Control, Control Application in Mechatronics
Abstract: Ensuring the safety of a teleoperation system is crucial, and optimization-based control approaches are an effective solution to ensure that the robot satisfies the multiple safety constraints. However, the control input reshaping via optimization can make a system non-passive. Therefore, this study proposes a new optimization method for ensuring both the safety and passivity of a teleoperation system by integrating it with the task scaling method. Additionally, the energy tank method is implemented to modulate the trade-off between dissipativity and control performance. The experiments demonstrated that the proposed method ensures the safety and passivity of the teleoperated system for all robot configurations and any operator’s command inputs.

Keywords: Legged Robots, Robot Dynamics and Control
Abstract: We report on the development of a dynamic model of the complex 11-linkage and closed-chain leg-wheel module using a multibody dynamics method. This novel and hybrid mechanism has two degrees of freedom and is capable of transforming between wheeled mode and legged mode in less than 100 milliseconds. Having a dynamic model is the first step for monitoring or controlling the status of the module, and the ground reaction force is of particular interest because it determines the locomotion quality of the legged system. The performance of the model is compared with that of one derived using commercial software as a double check. In addition, the dynamic behavior of the model was further compared with that of the empirical leg-wheel module using a very dynamic leaping test. In the quasi-static validation, the percentage error between commercial software and the model is 2.76%. In the dynamic validation, the simulation and experiment show the same trend.

Keywords: Compuational Models and Methods, Actuators in Mechatronic Systems, Machine Learning
Abstract: Ultrasonic diagnosis is a noninvasive internal examination method requiring a high level of operational skill. Previous studies have proposed supports using machine learning, but further development is required. We propose a haptic ultrasound probe with a sensitive haptic feedback function to address these demands. Furthermore, an automatic operation support system that clusters touching conditions is proposed by integrating haptic and ultrasound image information. The proposed guidance system is validated by experiments using a prototype haptic ultrasound probe.

Keywords: Micro/Nano Manipulation, Medical Robotics/Mechatronics
Abstract: Microrobotic swarms offer great promise in performing targeted delivery tasks with environmental adaptability. One of the significant challenges to apply microswarm for in vivo applications is medical imaging-based localization. In this article, we propose an optimized actuation strategy to enhance the ultrasound imaging contrast of a reconfigurable colloidal microswarm. A dynamic ultrasound contrast of the microswarm is observed. It depends on the coordination between the frequency of applied magnetic field and the temporal resolution of ultrasound imaging. Taking advantage of the dynamic contrast, optimal driven frequency (fop) is analyzed to obtain an enhanced ultrasound contrast of microswarms at different depths, which is experimentally validated at imaging depths of 3–7 cm. Based on the modeling and experimental results, pattern transformation of the microswarm is performed to further enhance the ultrasound contrast. The change of the imaging contrast during pattern transformation is experimentally investigated and analyzed, demonstrating a good agreement with the analytical results. Moreover, the microswarm is localized ex vivo at depths of 3.4–6.5 cm, and the minimal dose of nanoparticles is reduced due to the pattern transformation-enhanced imaging contrast. The reversible pattern transformation also provides morphological adaptability. A microswarm can navigate and simultaneously exhibit reversible pattern transformation in a narrowed channel. The optimized strategy of enhancing the ultrasound contrast of the colloidal microswarm provides a potential approach for utilizing microrobotic swarms in medical-imaging-guided in vivo tasks with a real-time localization capability.

Keywords: Actuators in Mechatronic Systems, Flexible Manipulators and Structures, Fixture and Grasping
Abstract: When a machine or robot is operating under positional control, it is difficult to visually confirm the reaction of the robot to the force applied to an object or the stiffness or mass of the object to be interacted with, based on visual changes in the robot's appearance. When a human and a robot cooperate or work in the same space, the interaction will be smoother and safer if we can visually understand the robot's state. In addition, soft robot technology that can flexibly adapt to people and various external environments is currently being researched. In this study, we aim to develop a technology that can communicate the status of the soft robot that can deform based on the target object by explicitly changing its appearance. We specifically attempt to visualize the internal stress of the soft robot by amplifying the color change caused by the photoelastic effect using origami and kirigami structures. In addition, as a feasibility study, we developed a soft robot hand that visually expresses the mass and stiffness of the object to be grasped．

Keywords: Actuators, Actuators in Mechatronic Systems
Abstract: Soft robots with intrinsic softness are expected to be used for delicate object manipulation and/or human-collaborative applications. However, fluidic-driven soft actuators need a pump system with a pump, compressor, tube, and valves, which makes the system bulky and rigid. Some researchers developed built-in centralized fluidic pumps for soft robots, but the output performance is limited since their specifications and carried energy are fixed. To enable the pump system to be soft and increase the output performance, we propose a decentralized reconfigurable soft pump module using permanent magnetic elastomers (PME). With this soft smart material, the system can be fully soft. Inspired by the ‘calf muscle pump,’ our soft pump module can be freely re-configured to squeeze the working fluid and increase the flowrate as an additional power source. We designed the structure of the module, built the modeling, and conducted various experiments to verify its feasibility. We confirmed that the module can generate force up to 12.76 N and flowrate about 3.74 ml/s, which shows its capability to convey working fluid. After being attached to the soft tube, with sequence control, the reconfigured pump can generate peristaltic motion and convey the working fluid. The success of the pump module indicates its possibility used for increasing the output performance of the soft robots.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Rehabilitation Robots
Abstract: High-performance prostheses are crucial to enable versatile activities like walking, squatting, and running for lower extremity amputees. State-of-the-art prostheses are either not powerful enough to support demanding activities or have low compliance (low backdrivability) due to the use of high speed ratio transmission. Besides speed ratio, gearbox design is also crucial to the compliance of wearable robots, but its role is typically ignored in the design process. This paper proposed an analytical backdrive torque model that accurately estimate the backdrive torque from both motor and transmission to inform the robot design. Following this model, this paper also proposed methods for gear transmission design to improve compliance by reducing inertia of the knee prosthesis. We developed a knee prosthesis using a high torque actuator (built-in 9:1 planetary gear) with a customized 4:1 low-inertia planetary gearbox. Benchtop experiments show the backdrive torque model is accurate and proposed prosthesis can produce 200 Nm high peak torque (shield temperature <60°C), high compliance (2.6 Nm backdrive torque), and high control accuracy (2.7/8.1/1.7 Nm RMS tracking errors for 1.25 m/s walking, 2 m/s running, and 0.25 Hz squatting, that are 5.4%/4.1%/1.4% of desired peak torques). Three able-bodied subject experiments showed our prosthesis could support agile and high-demanding activities.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Compuational Models and Methods
Abstract: Dielectric elastomers (DEs) are polymeric multifunctional materials that can be used to develop lightweight electrostatic actuators. Among other, DEs allow developing coil-free loudspeakers, in which the acoustic diaphragm and the actuator are embedded into a single DE membrane, whose deformations are driven by electrostatic stresses. In this paper, we present a simulation analysis of a DE loudspeaker with the aim of highlighting the effect of relevant design parameters (namely, the DE membrane thickness, diameter, and geometric aspect ratio) on the acoustic response. For the sake of illustration, we make reference to a simple loudspeaker layout, which does not require any mechanical or pneumatic biasing elements, and only consists in a DE membrane pre-loaded off-plane. Based on a validated finite element multi-physics model of the system, we discuss how the system response (namely, eigenfrequencies and generated sound pressure level) varies with the design parameters. The presented results point out thresholds and trends that are relevant for the choice of DE loudspeakers' design parameters.

Keywords: Actuators, Identification and Estimation in Mechatronics, Rehabilitation Robots
Abstract: This paper presents a novel parameter identification method for series elastic actuators (SEAs). Conventionally, such devices are equipped with (linear or rotary) encoders to measure the displacement of both the motor and the load sides. However, in some applications (e.g. wearable devices), it is difficult to mount a load-side encoder, due to cost or manufacturing issue. MEMS accelerometers have recently attracted considerable attention in this field, due to their low-cost and add-on feature. The proposed method replaces the load-side encoder with a MEMS accelerometer and relies on disturbance observers (DOBs). DOBs are famous for their ability to nominalize the plant by feedforward of the computed equivalent disturbance. Instead, the equivalent disturbance is used here for parameter identification, in an iterative fashion. The identification process is performed with two ad hoc closed-loop motor position tracking experiments, one for identifying the motor-side parameters and the spring stiffness, and one for the load-side parameters, exploiting the orthogonality of position, velocity, and acceleration signals. A theoretical analysis is provided for the applicability of the method, also when tracking errors and noises are present. Experimental results are provided to validate the proposed method.

Keywords: Identification and Estimation in Mechatronics, Modeling and Design of Mechatonic Systems, Legged Robots
Abstract: Incorporating realistic actuator dynamics in robotic simulations is an important detail for a successful simulation-to-reality transfer. But real actuation chains are often complex and impossible to model with analytical methods alone. Although it is feasible to reverse-engineer the actuator dynamics from hardware measurements, this requires the completed robotic system to be already available. To enable the inclusion of realistic actuator dynamics in robot models also during the design phase or for initial controller tuning, this work presents an alternative hands-on approach for actuator characterization. Based on actuator measurements taken independently of the overall system integration, a model expression for the actuator is derived. This can be added to the simulation of any robotic system. To showcase this concept, we present the workflow for a robotic leg with a Series Elastic Actuation chain. We create a simulation of the leg incorporating the derived actuator model and show its validity through comparison with analogous hardware. The observed motor and link dynamics of both cases show close correspondence without increasing the needed computation times with respect to a simulation without actuation. Thus, the proposed method offers a promising approach to include realistic actuator dynamics during the design and development process of robotic applications.

Keywords: Identification and Estimation in Mechatronics, Flexible Manipulators and Structures, Sensor Integration, Data Fusion
Abstract: Soft robots have the potential to overcome some of the limitations of conventional, rigid manipulators, especially in those applications where safe interaction and high flexibility are required. One critical issue concerns the need for estimating the position, the configuration, or even the shape of the soft robot without introducing bulky external sensors. Soft robots based on dielectric elastomers (DEs) offer a potential solution to this problem, due to their self-sensing feature. Thanks to self-sensing, the information on DE displacement can be estimated online via electrical measurements, performed simultaneously with high-voltage actuation, and used to potentially reconstruct the full configuration of the robot. This work presents a first investigation towards this system-level self-sensing concept, by choosing a bendable soft robotic module driven by rolled DE actuators as case study. An extended Kalman filter is implemented based on a physical model, and used to estimate the full module configuration via DE-level information only, i.e., the actuators lengths available through simple electrical measurements. The effectiveness of the architecture is evaluated by means of camera-based experiments, showing that an accurate and robust estimation of the system configuration variables can be effectively achieved.

Keywords: Actuators in Mechatronic Systems, Identification and Estimation in Mechatronics, Modeling and Design of Mechatonic Systems
Abstract: Magneto-rheological clutches (MRCs) are potential to provide jointed robots with the capability of behaving compliantly and safely. However, before achieving those behaviors, the MRCs should be well modeled to capture their complicated nonlinear dynamics including rate-dependent hysteresis and creep phenomena. This paper develops a multi-state fractional-order MRC model that captures such complicated dynamics. Compared to the classical models, this new model offers better estimation accuracy for the hysteresis and creep phenomena simultaneously. To further improve the modeling performance, an observer based on the fractional-order model and the super-twisting algorithm (STA) is designed to compensate for the model uncertainties. With an implicit Euler integration method, the observer can exactly estimate the model uncertainties and then further improve the model prediction. The effectiveness of the proposed fractional-order model and observer is demonstrated with experiments and comparisons with the fractional-order Bouc-Wen model.

Keywords: Identification and Estimation in Mechatronics, Sensors and Sensing Systems, Machine Learning
Abstract: Robotic haptic object identification is the process to identify objects out of a given object set using a robotic hand equipped with tactile and finger-joint displacement sensors. When taking measurements by grasping the object, the uncertainties in the pose of the object relative to the hand will adversely affect the identification accuracy. Each tactile sensor measures contact in its locality thus a change in object contact locations relative to the robotic grasping hand significantly affects the tactile measurements. In object identification, statistical properties of the uncertainties in the collected measurements are generally obtained a priori, allowing the probabilities of an object to be estimated for improved accuracy. The problem of object pose uncertainty typical in robotic grasping results in multiple peaks in the probability distribution of the resulting tactile measurements. The peaks are associated with whether or not the (locality of) tactile sensor on the robotic hand is in contact with the object due to the variations in object pose. As such, in this paper, a Beta mixture model allowing multiple peaks in the distribution is proposed to represent this object pose uncertainty (relative to the robotic hand) in place of the conventional Gaussian model used in the literature. The method was experimentally validated and demonstrated to be effective in capturing the uncertainties and improving the accuracy of the haptic object identification.

Keywords: Identification and Estimation in Mechatronics, Robot Dynamics and Control
Abstract: In recent decades, the increased space debris around Earth's orbit has become a severe problem due to the increased risk of collisions with spacecraft. Therefore, the development of technology to remove this debris is one of the most urgent issues. This study takes up a real-time method of estimating the decommissioned spacecraft's periodic attitude motion using video taken from the chaser spacecraft. According to this study, it is difficult to accurately estimate the strong non-linear motion in 3-Dimension and construct the mathematical model of low calculation cost using image processing in real-time. In terms of high estimation accuracy and low calculation load, the proposed method visually tracks the feature points of the target spacecraft, and the attitude motion of the spacecraft is estimated by Extended Kalman Filter using minimum variables. The minimum variables are expressed using a quaternion, in which both the posture direction and angular speed are instantly known, and the singular point is accessible. The proposed method has the assumption that the target is in the nutation motion or the geometry is known. This article reports numerical simulation examples in which the proposed method estimates the nutation motion.

Keywords: Service Robots, Artificial Intelligence in Mechatronics, Neural Networks
Abstract: We propose a food arrangement framework for a robot to automatically serve meals. We start from the premise that anybody knows how to arrange food. Based on this, we use a Convolutional Neural Network (CNN) to evaluate how good a food arrangement is. The CNN is trained using a dataset gathered through Amazon Mechanical Turk (AMT), where people are asked to choose the best food arrangement between a pair of pictures. The food arrangement and rearrangement is done entirely virtual through image processing. The initial food placement is random and evaluated by the CNN. If this evaluation is under a given threshold, the position of some ingredients will be changed according to the rearrangement algorithm we developed. For this algorithm we tested two different strategies for finding a relocation together with two different approaches for deciding which food to relocate. After the rearrangement is done, the CNN will evaluate again the food arrangement. The previous steps will be repeated until the food arrangement evaluation is beyond the given threshold. The resulting arrangement will be given to the robot for its actual execution. We evaluated our framework using two different sets of meals. We demonstrate that a UR3 robot is capable of serving a steak meal using a spatula-like end-effector.

Keywords: Artificial Intelligence in Mechatronics, Learning and Neural Control in Mechatronics, Robot Dynamics and Control
Abstract: Dynamic movement primitives (DMPs) allow complex position trajectories to be efficiently demonstrated to a robot. In contact-rich tasks, where position trajectories alone may not be safe or robust over variation in contact geometry, DMPs have been extended to include force trajectories. However, different task phases or degrees of freedom may require the tracking of either position or force -- e.g., once contact is made, it may be more important to track the force demonstration trajectory in the contact direction. The robot impedance balances between following a position or force reference trajectory, where a high stiffness tracks position and a low stiffness tracks force. This paper proposes using DMPs to learn position and force trajectories from demonstrations, then adapting the impedance parameters online with a higher-level control policy trained by reinforcement learning. This allows one-shot demonstration of the task with DMPs, and improved robustness and performance from the impedance adaptation. The approach is validated on peg-in-hole and adhesive strip application tasks.

Keywords: Learning and Neural Control in Mechatronics, Artificial Intelligence in Mechatronics, Machine Learning
Abstract: Efficient and effective learning is one of the ultimate goals of deep reinforcement learning (DRL), although the compromise has been made most of the time, especially for the application of robot manipulations. Learning is always expensive for robot manipulation tasks and the learning effectiveness could be affected by the system uncertainty. In order to solve the above challenges, in this study, we proposed a simple but powerful reward shaping method, namely Dense2Sparse. It combines the fast convergence advantage of the dense reward and the noise/uncertainty isolation merit of the sparse reward, to achieve a balance between the learning efficiency and the effectiveness, which makes it suitable for robot manipulation tasks. We evaluated our Dense2Sparse method with a series of ablation experiments using the deep learning-based state estimator with system uncertainty. The testing results show that, in both simulation and real robot implementation, the proposed Dense2Sparse method is capable of getting a higher expected reward and success rate compared with the ones using standalone dense or sparse reward, and it also has a superior tolerance for system uncertainty.

Keywords: Network Robotics, Wed-based Control of Robotic and Automation Systems, Hybrid intelligent systems
Abstract: In this study, we propose task planning framework for multiple robots that builds on a behavior tree (BT). BTs communicate with a data distribution service (DDS) to send and receive data. Since the standard BT derived from one root node with a single tick is unsuitable for multiple robots, a novel type of BT action and improved nodes are proposed to control multiple robots through a DDS asynchronously. To plan tasks for robots efficiently, a single task planning unit is implemented with the proposed task types. The task planning unit assigns tasks to each robot simultaneously through a single coalesced BT. If any robot falls into a fault while performing its assigned task, another BT embedded in the robot is executed; the robot enters the recovery mode in order to overcome the fault. To perform this function, the action in the BT corresponding to the task is defined as a variable, which is shared with the DDS so that any action can be exchanged between the task planning unit and robots. To show the feasibility of our framework in a real-world application, three mobile robots were experimentally coordinated for them to travel alternately to four goal positions by the proposed single task planning unit via a DDS.

Keywords: Planning and Navigation, Automotive Systems, Vehicle Control
Abstract: This paper addresses the posture constraints problem in multi-vehicle motion planning for specific applications such as ground exploration tasks. Unlike most of the related work in motion planning, this paper investigates more practical applications in the real world for non-holonomic unmanned ground vehicles. In this case, a strategy of diversion is designed to optimize the smoothness of motion. Considering the problem of the posture constraints, a postured collision avoidance (PCA) algorithm is proposed for the motion planning of the multiple non-holonomic unmanned ground vehicles. Two simulation experiments were conducted to verify the effectiveness and analyze the quantitative performance of the proposed method. Then, the practicability of the proposed algorithm was verified with an experiment in a natural environment.

Keywords: Rehabilitation Robots, Virtual Reality and Display, Hybrid intelligent systems
Abstract: With the increasingly serious aging situation, more and more elderly people are physically disabled. In addition, the current rehabilitation resources have the problems of shortage and uneven distribution, coupled with the impact of COVID-19 in early 2019, most patients have been greatly restricted from going to the rehabilitation center for training. To solve these problems, we propose a “Kinect-based 3D Human Motion Acquisition and Evaluation System for Remote Rehabilitation and Exercise” which uses the Kinect3 camera to obtain human motion with an error rate of only 3%. Then we use Unity to create a humanoid virtual model and interactive scene and synchronize the real body motion to the virtual model with an average error less than 1%. At the same time, our system provides reliable and highly accurate methods for evaluating actions based on angles and trajectories. What’s more, users don’t need to wear any wearable devices when using the system. It is a mark-less motion acquisition system, which reduces the cost and improves the usability and scalability of the system. And the interactive virtual scenes also increase the training motivation of users.

Keywords: Rehabilitation Robots, Biomechatronics
Abstract: The functional electrical stimulation hybrid rehabilitation robotic system has been proved to have a positive effect on the gait rehabilitation of stroke patients in recent years, but the functional electrical stimulation walking assistance strategy combined with the exoskeleton robot is still one of the current research focuses. In this paper, a trajectory-adaptive walking assistance strategy based on functional electrical stimulation for exoskeleton is proposed, which can automatically adjust the electrical stimulation output current intensity according to the joint angle trajectory error during walking. Moreover, a small-sized four-channel functional electrical stimulation device was developed, which could be integrated inside the exoskeleton, providing a hardware platform to verify the feasibility of this walking assistance strategy. The gait data of ten healthy subjects were measured, and their joint angles during walking were used as a reference for natural gait. And another six healthy subjects were recruited to participate in the experiment. The correlation coefficient of knee joint angle between walking with exoskeleton and natural gait was used to evaluate the effectiveness of the walking assistance strategy. The mean correlation coefficient of all subjects during walking with electrical stimulation was 0.78 ± 0.1164, which increased by 0.13 compared with the condition without electrical stimulation. With the assistance of functional electrical stimulation, the mean value of range of movement of knee joint increased by 9.18 °, the mean value of the maximum knee joint angle increased by 4.69 °, and the mean value of the minimum knee joint angle decreased by 2.80 ° compared with no electrical stimulation. These results demonstrate that the walking assistance strategy can adapt to different users and have the effect of gait correction, making the user's gait more similar to the natural gait when walking with the exoskeleton.

Keywords: Rehabilitation Robots, Medical Robotics/Mechatronics, Biomechatronics
Abstract: During robot-assisted neurorehabilitation, the therapy protocol and the tuning of the robot assist parameters depend on the operator’s or the therapist’s experience, and the ideal intervention methods remain unknown. A major obstacle to investigating the feasibility of rehabilitation protocols and optimal assist parameters is the difficulty of conducting human research, therefore, to find the optimal intervention methods, investigative research based on animal tests is required. In this paper, we propose a rehabilitative exoskeleton robot for locomotor training in rats as a research tool in neuroscience and robot-assisted neurorehabilitation. The main design feature of the presented exoskeleton robot is the ability to produce synergistic movements between the limb joints similar to intact rats. The assist mechanism in this work with two degrees of freedom uses a combined four-and-five-bar mechanism to simulate the gait patterns of quadrupedal locomotion. We present the design and development of the assist mechanism, and preliminary experiments to verify its function by generating gait movements and evaluating their covariation planes. The results showed that the mechanism can produce three joint angles with inter-joint synergies similar to those of rodents, implying that the mechanism is valid for implementation in a rodent-size exoskeleton robot. The future outlook of this project is to implement a sensing modality for the animal's voluntary will and perform demonstration experiments in vivo.

Keywords: Human -Machine Interfaces, Robot Dynamics and Control
Abstract: This study aims to improve the maneuverability of power assist systems based on guidance force field. We have proposed an artificial potential method to generate a guidance force field on an arbitrary work trajectory to guide the operator and manipulator to reduce the workload of the operator when using a position-controllable industrial manipulator as a PAS. Experiments showed that the proposed guidance force field contributed to a reduction in the workload of the operator. Individual differences were observed in the maneuverability of the operator under the guidance force field. In this paper, we propose a mathematical model of the maneuverability of the guidance force field and a method to obtain the optimum spread of the guidance force field for each operator.

Keywords: Rehabilitation Robots, Modeling and Design of Mechatonic Systems, Medical Robotics/Mechatronics
Abstract: This paper proposes a new 3-layer elastic cloth actuation mechanism in assistive suit with adjustable structure, based on a 2-layer non-adjustable structure, to increase assistive force on lower back muscle group and reduce pre-tension when user pulls the rubber belt located on the back to receive higher assistive force. The pretension acted on the user will result in faster body fatigue. An experiment involving the measurement of muscle activities is implemented to evaluate the change in assistive force in lower back comparing to the current model of 2-layer nonadjustable prototype. The experimental results show that the new 3-layer structure successfully increase the assistive force in lower back muscle group.

Keywords: Rehabilitation Robots, Modeling and Design of Mechatonic Systems, Robot Dynamics and Control
Abstract: Hemiplegia is the most common sequelae of stroke, which will lead to the loss of mobility in patients. Rehabilitation robots can efficiently assist patients in performing rehabilitation training. This paper designed an exoskeleton based on a series elastic actuator (SEA) to assist patients with post-stroke hemiplegia for knee rehabilitation. Due to the use of SEA, the exoskeleton can achieve low impedance and precise force control. The mechanical limit can improve the safety of the device, and the bracket can reduce the wearing burden on the human body. Unlike traditional rigid drives, our design improves compliance of human-robot interaction. The spring can provide cushioning for increased safety when the patient suffers an accidental shock. In this paper, the design process is introduced and the exoskeleton model is analyzed. We implement the force control based on the integral separation PID algorithm and conduct experiments and analysis on the impact resistance of the exoskeleton.

Keywords: Modeling and Design of Mechatonic Systems, Control Application in Mechatronics, Sensors and Sensing Systems
Abstract: Lasertrackers with interferometric distance measurement can be used for precise distance determination. Here, we present a decentralized distributed multi-lasertracker-system, which applies the method of triangulation to determine the absolute distance. The trinangulation requires a calibration procedure that reveals transformation matrices between the lasertrackers, distributing the measurement in the network. To our knowledge, our measurement system is the first decentral- ized and distributed multi-lasertracker-system that can accurately track a moving target with a linear quadratic regulator and integral action. For absolute distance measurement, we confirm the distribution of triangulation in the network in a final real-time approach.

Keywords: Modeling and Design of Mechatonic Systems, Sensors and Sensing Systems, Compuational Models and Methods
Abstract: This paper presents an electromagnetic (EM) tracking system design for location and orientation estimations. Consisting of a sensing coil and a field generator formed by six excitation coils, the system tracks the sensing coil within the influence of the change in the magnetic field produced by the field generator. Closed-form solutions are formulated for the electromotive force (EMF) induced in the sensing coil, and the effects of translational and angular displacements are investigated. For the inverse calculation, artificial neural network (ANN) models are proposed to estimate the location and orientation of the sensing coil based on analytical EMFs. The closed-form solutions are verified by Finite Element Analysis (FEA), and the ANN models are also numerically validated. Experiments are conducted with a simplified prototype composed of one excitation and one sensing coil. The analytical model is first verified for both translational and angular displacements, and the proposed ANN models for inverse calculations are also validated by emulating the relative spatial relations between each excitation coil and the sensing coil.

Keywords: Modeling and Design of Mechatonic Systems, Identification and Estimation in Mechatronics, Artificial Intelligence in Mechatronics
Abstract: Motion control and automation can benefit from models that accurately predict the behavior of mechatronic systems to enhance efficiency and performance. Uncertain nonlinear physical phenomena however hinder to fully capture the system behavior with classical physics-based models. The emerging availability of data has caused a surge in the use of data-driven techniques, yet it is expensive to acquire a sufficiently rich data-set of a given system. This paper presents a hybrid model that integrates physical laws, in the form of ordinary differential equations (ODE), and neural network layers, as the unknown ODE substructures, for uncertain mechatronic systems. We focus to enhance the hybrid model's predictive capabilities for systems that are subject to multiple unknown phenomena and partial state observations. To discover these unknown dynamics we use the framework of direct multiple shooting. This allows to formulate the problem of aligning observed time-series with model responses as a constrained optimization problem. Furthermore, this formulation allows to deal with incomplete state observations, which would otherwise be unattainable with classical approaches. We apply and validate the proposed methodology to identify the friction in both joints of an acrobot of which only measurements in one joint are available. Numerical experiments show that our model can discover detailed representations of the friction characteristics in both joints and has accurate multistep predictive capabilities.

Keywords: Automotive Systems, Modeling and Design of Mechatonic Systems, Rapid Prototyping
Abstract: The contribution at hand presents a novel flexible matrix headlight modeling approach for rapid prototyping that is not based on ray tracing and focuses on parameters which are meant to be intuitive and interpretable. The model parameters are directly linked to the beam pattern of the headlamp. The model is designed to generate illuminations of arbitrary matrix headlights that contain color information and inhomogeneities, making it possible to virtually prototype headlight design concepts in the pre-development phase of the system. The modeling approach is verified by comparing the generated intensity distribution with a real one of low and high resolution systems currently under development. The model has a mean absolute error of about one percent of the maximum intensity for all tested systems.

Keywords: Part Feeding and Object Handling , Fixture and Grasping, Modeling and Design of Mechatonic Systems
Abstract: Laboratory automation provides a more reliable solution while potentially reduces human participation. Volume and dropping time are precisely controlled by liquid handling robots. This paper presents our built pipetting robot suitable for life sciences and pharmaceutical laboratories dealing with many liquids and micropipettes of different scale ranges. The SCARA manipulator having four degrees of freedom is capable to move the pipet’s microtip to any desired position and the gripper’s orientation can be maintained while the robot is grasping a micropipette from a hanger or returning it back. The compact robotic arm provides sufficient workspace for the number of test tubes and clean microtips. A Roh’Lix transmission is used for intrinsically safe vertical motion. The gripper and the pipetting stand are custom designed to allow automatic changing of commercial micropipettes. Instead of manually adjusting the rotary knob scale to set the pipetting volume, a micro linear actuator is applied to compress the pipetting button with precise displacement. The liquid volume varying against the displacement of the micro linear actuator was validated experimentally. A contaminated microtip is released by using another micro linear actuator before installing a new one.

Keywords: Modeling and Design of Mechatonic Systems, Mobile Robots, Robot Dynamics and Control
Abstract: Spherical rolling robots (SRR) have been a promising avenue for the exploration of unstructured environments with variable topologies. The advantages include the ability to move fast, robustness to collision and a lower number of actuators. However, to finally be used in real missions and applications, they need to have a high maneuverability and have sufficient inner space to house a proper payload for the intended application, such as cave and tunnel exploration, without compromising on the performances. With barycentric spherical robot, adding mass with a payload may become challenging, as the location of the center-of-mass (CoM) is critical for the locomotion. In this paper, we propose a novel barycentric spherical robot with two degrees-of-freedom (DoF) named Autonomous Robotic Intelligent Explorer Spheres (ARIES). The motion of this SRR is generated by a cylindrical actuated joint acting like a 2-DoF pendulum. This design allows us to have a nearly empty upper hemisphere inside the spherical shell, which is dedicated to payloads adapted to the application. The full kinematics and dynamics are presented, and simulation results are included. The control scheme implemented is detailed. We conducted an experimental evaluation of the ARIES with different trajectories, as well as discussed practical considerations and future improvements.