Keywords: Automotive Systems, Planning and Navigation, Transportation Systems
Abstract: Real-time vehicle route and speed co-optimization play a vital role in improving its energy consumption with rapidly shifting traffic and environmental conditions (e.g., traffic jams, temperature, gust wind, etc.). In this paper, a novel vehicle eco motion planning (VEMP) method is proposed to optimize the vehicle route and speed simultaneously for minimizing its energy usage with a given origin-destination pair and a travel time limit. The proposed VEMP method is based on the modified Dijkstra algorithm and gradient descent speed optimization to find and update the optimal route and corresponding speed profile in real-time based on the changing traffic and driving environment information. Co-simulation studies are conducted for the developed VEMP method in MATLAB with the SUMO traffic model using a real-world map. The simulation results show that for the studied driving environment, the speed optimization in the VEMP method is able to reduce the energy usage by 2.27% over the case only using speed limits on a fixed route, and the VEMP can improve the total energy consumption by 23.83% over the fastest route created by Dijkstra algorithm. The simulation of a sudden traffic jam demonstrates the ability of real-time updating for the proposed VEMP method to handle sudden traffic situations such as vehicle cut-in.

Keywords: Vehicle Control, Motion Vibration and Noise Control, Control Application in Mechatronics
Abstract: Traditional hydraulic dampers dissipate the energy of perpendicular vibration as heat to improve ride comfort. Therefore, many studies have been done to recover the energy as electricity instead of wasting it. Most involve converting perpendicular vibration energy into rotational kinetic energy, which is regenerated as electric power using motors. In contrast, this paper proposes a novel idea of regenerating perpendicular vibration energy in electric vehicles (EVs) using drive motors mounted inside wheels. This approach aims to focus on the perpendicular forces generated by the driving force due to the suspension geometry and recover vibration energy that is potentially converted into rotational energy due to the structure. In this paper, a new model is proposed that considers the torque disturbance due to the vibration. Through simulations and experiments, the drive motors were confirmed to recover the vibration energy and a novel loss separation method to analyze this was proposed. To analyze the effectiveness, a suspension spring force regeneration control was applied. Analysis of the experimental results showed that the suspension spring force can be regenerated as electric power by drive motors.

Keywords: Automotive Systems, Planning and Navigation, Mobile Robots
Abstract: Planetary exploration rovers are required to estimate their position in GPS-denied environments accurately. Visual Odometry is one of the solutions in such environments. By tracking feature points in the image, it is possible to estimate the position with high accuracy, even in extreme environments. If all the feature points in the image are used for estimation, however, the computational cost increases. Especially in stereo camera methods, the calculation time required for stereo matching is highly extended. Therefore, this paper proposes a method for selecting feature points in the image before stereo matching. The accuracy will be diminished if the number of feature points is reduced. To address this issue, the feature points are selected separately for rotation and translation. As a result of verification using an actual rover, it is confirmed that the proposed method can reduce the computational cost by up to 33% compared to the conventional method.

Keywords: Robot Dynamics and Control, Unmanned Aerial Vehicles, Vehicle Control
Abstract: The wide utilization of Global Navigation Satellite System (GNSS) technology in Unmanned Aerial Vehicles (UAVs) has greatly improved the positioning accuracy of UAVs, thereby enhancing flight safety and expanding their applications. However, in densely forested areas or urban canyons where satellite signals are occasionally obstructed, GNSS signals can drift, and the positioning accuracy is compromised. This research paper addresses the special GNSS drift challenge by designing an integrated, custom-built control framework for UAVs, including a novel drift estimation and correction for tracking precision. The estimation and correction are based on the concept of dilution of precision (DOP), a term to quantify the effect of satellite geometry on positioning and timing precision. Experimental investigation mimicking a drone in urban canyon conditions confirms the effectiveness and demonstrates the promising features of the proposed design.

Keywords: Automotive Systems, Transportation Systems, Neural Networks
Abstract: The end-to-end paradigm has gained considerable attention in the field of autonomous driving due to its anticipated performance. However, prevailing end-to-end paradigms predominantly employ one-shot training using imitation learning, resulting in models lacking evolutionary capabilities and struggling to adapt to long-tail scenarios. Furthermore, addressing these long-tail scenarios necessitates end-to-end models to simultaneously exhibit the generalizability of environmental representations and the robustness of control policies. Therefore, this paper proposes an end-to-end autonomous driving model called GPCT, using a Generative Perception network and a Continuous-Time brain neural network, with a Policy-Reward-Data-Aggregation (PRDA) mechanism. Specifically, the generative perception network extracts two-dimensional and three-dimensional perceptual information from monocular camera inputs and undergoes distribution fitting and sampling to obtain environmental dynamics information. Subsequently, the sequential temporal environmental dynamics information is fed into continuous-time brain neural networks to output the control information. The end-to-end model is then applied to on-policy scenarios using the PRDA mechanism to collect data for further training and evolution. Data is collected within the Carla simulator, followed by model training, and the utilization of a multi-round PRDA mechanism for data collection and training to facilitate model evolution. The algorithm's performance improves by 63.85% after five evolution experiments. In the transfer experiments, the proposed algorithm achieves a route completion rate close to 100% and maintains a driving score of around 60%, even surpassing the performance of systems equipped with multiple cameras and LiDAR. Furthermore, under heavy fog conditions, the route completion rate remains at 85%, showcasing generalizability and robustness.

Keywords: Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics
Abstract: Energy consumption is receiving increasing attention due to environmental concerns. There is a high optimization potential concerning the energy consumption of industrial machines. Extensive research has been performed concerning geometry optimizations and motion profile optimizations towards energy-optimal point-to-point mechanisms. A co-optimization approach that integrates geometry and motion profile optimization in one architecture, outperforms methods that optimize motion profiles with suboptimal geometries, or vice versa. However, limited research focuses on co-optimizing geometry and motion profiles in one architecture. Therefore, a simultaneous, sequential, and nested co-optimization architecture is set up and compared. The most optimal motion profile and link lengths are determined for an industrial mechanism, resulting in a root mean square (rms) torque saving of 49.2%. To improve computational speed, a method that uses the derivation of a torque equation from three CAD simulations has been utilized. This method facilitates rapid convergence of the nested co-optimization. As a result, the major difference between the three co-optimization methods lies in the ability to converge rapidly to the minimum. This study demonstrates the nested co-optimization's capacity to identify an enhanced optimum, reducing the computational time by 74.4% compared to the simultaneous co-optimization and by 78.3% compared to three sequential iterations.

Keywords: Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics, Design Optimization in Mechatronics
Abstract: Robot-assisted fruit harvesting has been a critical research direction supporting sustainable crop production. One important determinant of system behavior and efficiency is the end-effector that comes in direct contact with the crop during harvesting and directly affects harvesting success. Harvesting avocados poses unique challenges not addressed by existing end-effectors (namely, they have uneven surfaces and irregular shapes grow on thick peduncles, and have a sturdy calyx attached). The work reported in this paper contributes a new end-effector design suitable for avocado picking. A rigid system design with a two-stage rotational motion is developed, to first grasp the avocado and then detach it from its peduncle. A force analysis is conducted to determine key design parameters. Preliminary experiments demonstrate the efficiency of the developed end-effector to pick and apply a moment to an avocado from a specific viewpoint (as compared to pulling it directly), and in-lab experiments show that the end-effector can grasp and retrieve avocados with a 100% success rate.

Keywords: Novel Industry Applications of Mechatroinics
Abstract: In this study, we designed a smart glove for measuring finger joint angles using a flexible circuit board. The glove design involves attaching a flexible printed circuit board with copper foil stickers applied to the joints of the fingers. On the back of the hand, a similar design using a flexible printed circuit board includes sleeves and coils. As the fingers bend, the copper foil pieces inside the sleeves slide, and the changes in coil impedance are measured to determine finger joint angles. Currently, the prototype smart glove has successfully calculated the angles of two joints in the right middle finger, achieving a resolution of 2° and a sampling frequency of 830 Hz.

Keywords: Novel Industry Applications of Mechatroinics, Actuators in Mechatronic Systems, Mechatronics in Manufacturing Processes
Abstract: This paper presents an advanced control error processing to enable the feedback position control of an industrial robot based on external sensor signals for dual-stage actuation. A measurement module comprising a magnetically levitated platform with an integrated 3D measurement tool is mounted as end effector on the industrial robot. The industrial robot is used to maintain the measurement platform within its limited actuation range, while actively tracking a sample in motion. The measurement module provides the short stroke but precision positioning of the measurement tool relative to a sample surface in 6-DoF. For a feedback controlled repositioning of the industrial robot’s tool center point based on the internal measurement platform’s position signals, a real-time interface is used. The implementation of the advanced control error processing successfully demonstrates the active tracking of a sample in motion, with residual tracking errors of 473 nm rms in sample-motion direction.

Keywords: Novel Industry Applications of Mechatroinics, Intelligent Process Automation, Modeling and Design of Mechatonic Systems
Abstract: Cultivated for its ornamental appeal, the Malabar chestnut demands precise planting for optimal growth, emphasizing the necessity of downward-facing seed germination points. Amidst a scarcity of agricultural labor, there is a growing demand for automated planting solutions. This paper presents an automatic planting machine for Malabar chestnut, utilizing deep learning image recognition to ensure proper seed orientation during planting. The machine incorporates a novel mechanism, leveraging high-speed pneumatic action and mechanical principles to guarantee accurate seed orientation. We provide insights into the architecture and training of the convolutional neural network-based recognizer, the design and analysis of the planting machine, and the system’s performance in field tests. Results from field tests affirm an 85% success rate in proper seed planting, achieving an average planting speed of one seed every 3 seconds.

Keywords: Novel Industry Applications of Mechatroinics, Tele-operation, Mechatronics in Manufacturing Processes
Abstract: Interest in integrating wireless communication capabilities into industrial Cyber-Physical Systems (CPS) has surged recently. Driven by advantages like ease of deployment, reduced maintenance costs, and asset mobility, CPSs now employ wireless communication for manufacturing control, logistics tracking, process control, and heavy machinery management. While wireless technologies such as IEEE 802.11, 5G cellular networks, and redundant IEEE 802.15.4 networks offer possible solutions, the challenge to adoption of these solutions for control systems lies in ensuring reliable and deterministic data delivery. Software-based private 5G implementations emerge as an intriguing solution, offering architectural flexibility, potential cost-effectiveness, and adaptability for diverse traffic types. We present a novel software-based 5G industrial wireless testbed with precision wireless channel measurement and control to support measurement-based research in the application of private 5G networks to these operational scenarios.

Keywords: Flexible Manipulators and Structures, Modeling and Design of Mechatonic Systems, Design Optimization in Mechatronics
Abstract: We propose a gravity compensation mechanism that combines practical compensation performance and a simple structure inspired by the skeleton of sauropods. Conventional gravity compensation mechanisms typically involve complex structures and design theories to be unaffected by variations in posture and fluctuations in self-weight induced by external loads. In considering a simpler structure, we focused on the simple gravity compensation functionality observed in sauropods, a particularly gigantic group of dinosaurs characterized by their long necks and tails, making them the largest terrestrial animals in history, and referred to their skeleton. Our proposed method emphasizes simplicity over strict compensation mechanisms, employing a straightforward structure of wires aligned with the articulated links. In this paper, we demonstrate the mechanism's compensation performance, showing how the gravity compensation ratio varies with different postures through simulation. Additionally, we fabricated a prototype to test the compensation effect, further verifying the effectiveness of our proposed mechanism.

Keywords: Control Application in Mechatronics, Flexible Manipulators and Structures, Robot Dynamics and Control
Abstract: In recent years, there has been a growing research focus on continuum robots due to their high flexibility and safety. Nevertheless, the inherent nonlinearity of the flexible structure of continuum robots has increased the complexity of their motion control. In this work, we propose a method based on model predictive control (MPC) to achieve the closed-loop motion control of a tendon-driven continuum robot. The robot has two bending degrees of freedom (DOFs) and the constant curvature model is used as the kinematic model. Selective laser sintering (SLS) technology is utilized to fabricate the entire continuum robot system, while a tracking camera is used to measure the robot position to provide the real-time feedback for the MPC controller. Experiments are also conducted, in which the continuum robot is actuated to move along different predefined plane trajectories. As a result, the position error of the MPC-based controller is much smaller than that of an open-loop controller, which demonstrates the good control performance of the proposed method.

Keywords: Flexible Manipulators and Structures, Actuators, Actuators in Mechatronic Systems
Abstract: Soft robots are growing in demand due to their inherent soft nature, compliance, and versatility. These attributes make them ideal for a large range of applications. To further expand the applications of soft robotics, we present a reconfigurable modular soft robotic actuator capable of easy reconfigurations and multi-actuator assembly for versatile tasks using a single actuator design. In this paper, we introduce a novel inter-unit connection mechanism that expands the reconfiguration capabilities of our soft modular robots. The mechanism includes magnets that can semi-permanently reverse their polarity on demand to achieve tunable magnetic connections without the need to supply constant electric current. The mechanism is made from a flexible material allowing articulated connections, which enables complex spatial configuration in a multi-actuator assembly. In addition, the mechanism allows various inter-unit connections, including air, mechanical, and electrical, that reduce the complexity of operation and reconfigurability. These connections enable a multi-actuator assembly to share a single actuation source, that simplifies the design and increases the functionality. We characterize the mechanical performance of individual actuators and the connection mechanism through experiments. Examples of structural configurations are presented to demonstrate the actuators’ reconfiguration capabilities.

Keywords: Compuational Models and Methods, Flexible Manipulators and Structures, Parallel Mechanisms
Abstract: Counter-bending is a bio-inspired passive behavior that has been observed in the whip-like flagella of many microorganisms and cells. Counter-bending beams passively bend toward and conform around applied forces. Counter-bending behavior is particularly interesting in soft robotic grasping as it offers passive adaptability to objects in contact. The mechanism behind counter-bending behavior has been proposed as models and inspired compliant grasper designs in previous works; yet, existing designs only realize 2D counter-bending, which limits the adaptability to one direction. 3D counter-bending fingers can expand their adaptability to objects with wider range of geometries, but a physical realization of 3D counter-bending beam has yet to be proposed. In this paper, we employ continuum topology optimization to search for a beam structure capable of 3D counter-bending. The topology optimizer 1) validates existing 2D counter-bending model, and 2) generates a 3D structure that provides insight into prototyping a 3D counter-bending beam. To validate the structure from optimization results, we prototype a 3D counter-bending beam using inextensible wires and soft elastomers, and we assemble 3D counter-bending fingers into an underactuated grasper to demonstrate the adaptability to objects with distinct geometries enabled by the counter-bending capability.

Keywords: Flexible Manipulators and Structures, Modeling and Design of Mechatonic Systems, Software Design for System Integration
Abstract: The complexity of deformable objects poses a challenge when attempting to replicate real-world behavior in simulation, which impedes the use of simulation as a testing environment for empirical applications. This study aims to create a data-driven model for seamlessly translating real-world deformable objects into simulation environments. Compressed soft balls are studied as an example of this strategy. Using machine learning, the model refines simulation parameters based on experimental data, such as forces and contours, allowing for highly realistic simulations and applications in areas such as manipulator manipulation interactions and reinforcement learning for task strategies.

Keywords: Flexible Manipulators and Structures, Fixture and Grasping, Service Robots
Abstract: This paper introduces a novel gripper that achieves both shape-adaptive grasping and variable stiffness. It is realized through an integration of the "soft gripper," capable of grasping objects while conforming to their shapes, and a toggle-linkage-based variable stiffness mechanism that does not hinder its grasping ability. Such features enable the gripper to securely manipulate and transport objects where soft/flexible grippers struggled, without being dependent on the shape of the object. Consequently, the proposed mechanism is expected to be capable of firmly grasping objects of various shapes. This paper details the idea, theory, prototype, and experimental validation of the proposed gripper. The paper particularly focuses on, it focuses on the compatibility of shape-adaptive grasping and variable stiffness for the prototype based on the idea, demonstrating its feasibility.

Keywords: Sensors and Sensing Systems, Modeling and Design of Mechatonic Systems, Compuational Models and Methods
Abstract: This paper presents the development of a five degree-of-freedom (DoF) cylindrical permanent magnet (PM) tracking system for estimating orientation and location. The system measures the effects of magnetic field changes caused by the PM through a tunnel magnetoresistance (TMR) sensor array. Closed-form solutions for the magnetic flux densities are derived, and the Gaussian‐Legendre quadrature method is applied to enhance computational efficiency. The Levenberg–Marquardt (LM) algorithm is employed to estimate location and orientation. These closed-form solutions were verified by finite element analysis software, and the LM methods were numerically validated. Experiments with a prototype confirm the analytical model’s accuracy in handling translational and angular displacements and demonstrate the validity of two cylindrical PMs’ location and orientation estimations.

Keywords: Human -Machine Interfaces, Sensor Integration, Data Fusion, Sensors and Sensing Systems
Abstract: This study introduces a sensorised device designed to address current shortcomings of post-stroke patient rehabili- tation assessment. The device interfaces with a platform allowing therapists to visually analyse real-time data and retrieve it for an in-depth analysis. The study details the development and characterisation of Pneumatic Sensing Chambers (PSCs) using Finite Element Analysis (FEA) and mechanical testing. After selection and integration, the sensors were incorporated into a compact, portable object with communication established via multiplexers and an Arduino Nano. The system was connected to a laptop using LabView for user interface. It allows concurrent measurement of finger forces during grasping movements for both hands, with real-time visualization and data retrieval support. The device offers a cost-effective, precise, and adaptable solution, with potential for further enhancements in precision, durability, and applicability in stroke rehabilitation scenarios. This tool has the capacity to significantly enhance rehabilitation strategies and aid in the recovery process.

Keywords: Sensor Integration, Data Fusion, Software Design for System Integration, Sensors and Sensing Systems
Abstract: In this study, we propose a water-sensitive urination detection system that detects urination not by physical disconnection of an ultra-high-frequency tag, as in the conventional method, but by a decrease in communication strength due to the influence of water during urination. The tag is attached to the outside of the diaper and keeps communicating with the antenna under the mattress. False urination detection due to changes in posture was addressed using a three-dimensional tag arrangement and an algorithm that identifies when the user turns over. System evaluation tests were conducted. The effectiveness of the system was demonstrated by measuring the urination detection rate using a simulated urination test with a tube. The detection algorithm was used both before and after the user turned over. The results show that the system can detect turning over with high accuracy and that the possibility of false detection is low. These findings suggest that a water-sensitive urination detection system based on the moisture-induced attenuation of communication strength can be used for urination detection.

Keywords: Image Processing, Sensor Integration, Data Fusion, Machine Vision
Abstract: This research addresses the significant challenge of noise in point cloud (PC) data, which undermines the accuracy of object recognition and pose estimation. We introduce a novel methodology that leverages systematic robotic camera movements for multiview PC acquisition, aimed at enhancing reconstruction accuracy in noisy environments. Using a Jaco robot arm outfitted with a Realsense D435 RGB-D camera, PCs of a globe valve are captured from multiple angles, focusing on a 60° area with 15° intervals. This setup results in a dataset of 81 PCs per iteration, with a total of three noisy PC datasets collected for analysis. The PCs are merged at 15° and 30° intervals, using the Color Iterative Closest Point (ICP) algorithm and refined through downsampling. The method is evaluated by measuring position and orientation accuracy using the Random sample consensus (RANSAC) algorithm across 216 instances-108 each at 15° and 30° intervals. The proposed methodology enhances the accuracy of pose estimation by 93% in both intervals, reducing mean errors in position to 2.35 mm and in orientation to 18.4°. This significant improvement underscores the effectiveness of our approach in mitigating noise in PC data for more precise object recognition and pose estimation applications.

Keywords: Sensor Integration, Data Fusion, Mobile Robots, Planning and Navigation
Abstract: This paper introduces a new dual monocular visual inertial odometry (dual-VIO) strategy for a mobile manipulator operating under dynamic locomotion, i.e. coordinated movement involving both the base platform and the manipulator arm. Our approach has been motivated by challenges arising from inaccurate estimation due to coupled excitation when the mobile manipulator is engaged in dynamic locomotion in cluttered environments. The technique maintains two independent monocular VIO modules, with one at the mobile base and the other at the end-effector (EE), which are tightly coupled at the low level of the factor graph. The proposed method treats each monocular VIO with respect to each other as a positional anchor through arm kinematics. These anchor points provide a soft geometric constraint during the VIO pose optimization. This allows us to stabilize both estimators in case of instability of one estimator in highly dynamic locomotions. The performance of our approach has been demonstrated through extensive experimental testing with a mobile manipulator testbed in comparison to running dual VINS-Mono in parallel. We envision that our method can also provide a foundation towards active-SLAM (ASLAM) with a new perspective on multi-VIO fusion and system redundancy.

Keywords: Sensors and Sensing Systems, Intelligent Sensors, Sensor Integration, Data Fusion
Abstract: This paper proposes a gasket-type bolt loosening detection sensor based on a surface acoustic wave (SAW) strain-sensitive unit. In order to improve the performance of the sensor, experiments, and simulations analyzed the influence of gel thickness on the strain-sensitive element of the SAW unit. Secondly, the influence of the thickness change of the secondary packaging structure on the packaging structure is studied, and the influence of friction change on the performance of the gasket sensor under different working conditions is discussed through experiments. Experimental and simulation results show that the gasket sensor proposed in this paper can effectively and accurately detect bolt looseness in real-time.

Keywords: Robot Dynamics and Control, Legged Robots
Abstract: Legged robots are well-suited for broad exploration tasks in complex environments with yielding terrain. Understanding robotic foot-terrain interactions is critical for safe locomotion and walking efficiency for legged robots. This paper presents a reduced-order mud reaction force model (MRF) for robotic foot-mud interactions. We focus on vertical robot locomotion on mud and propose a visco-elasto-plastic analog to model the foot-mud interaction forces. Dynamic behaviors such as mud visco-elasticity, withdrawing cohesive suction, and yielding are explicitly discussed with the proposed model. Besides comparing with dry/wet granular materials, mud intrusion experiments are conducted to validate the force model. The dependency of the model parameter on water content and foot velocity is also studied to reveal in-depth model properties under various conditions.

Keywords: Robot Dynamics and Control, Planning and Navigation, Mobile Robots
Abstract: Classical snake robot control leverages mimicking snake-like gaits tuned for specific environments. However, to operate adaptively in unstructured environments, gait generation must be dynamically scheduled. In this work, we present a four-layer hierarchical control scheme to enable the snake robot to navigate freely in large-scale environments. The proposed model decomposes navigation into global planning, local planning, gait generation, and gait tracking. Using reinforcement learning (RL) and a central pattern generator (CPG), our method learns to navigate in complex mazes within hours and can be directly deployed to arbitrary new environments in a zero-shot fashion. We use the high-fidelity model of Northeastern's slithering robot COBRA to test the effectiveness of the proposed hierarchical control approach.

Keywords: Underwater robotics, Robot Dynamics and Control, Vehicle Control
Abstract: The Multi-terrain Amphibious ARCtic explOrer (MAARCO) rover is an amphibious screw-propelled vehicle designed to traverse the Arctic terrains seamlessly. The propulsion system consists of two helical drives, similar to Archimedes' screw, that consist of hollow cylinder ballasts wrapped in auger or screw-shaped blades. In addition to moving on land and on water, the rover is also able to move underwater. The variable buoyancy offered by the ballasts, which can be flooded or emptied, and the thrust offered by the rotating helical blades enable the rover to operate underwater. In this paper, a dynamic model based on the Newton-Euler method is developed using the generalized underwater vehicle's dynamics equation of motion. The hydrodynamic forces considered on the underwater rover include added mass, viscous drag, buoyancy, and gravity. In addition to the hydrodynamic forces, the rover also experiences the thrust and buoyancy forces exerted by the helical drives. The dynamic model is used to test a control design for 3-dimensional trajectory tracking underwater. The errors in position and velocity are used to create a reference velocity error and a composite error as feedback to the controller. The results of the simulations show that the rover accurately tracks several 1-, 2-, and 3-dimensional trajectories using the controller in conjunction with the dynamic model.

Keywords: Robot Dynamics and Control, Modeling and Design of Mechatonic Systems, Control Application in Mechatronics
Abstract: Deriving a robot’s equations of motion typically requires placing multiple coordinate frames, commonly using the Denavit-Hartenberg convention to express the kinematic and dynamic relationships between segments. This paper presents an alternative using the differential geometric method of Exponential Maps, which reduces the number of coordinate frame choices to two. The traditional and differential geometric methods are compared, and the conceptual and practical differences are detailed. The open-source software, Exp[licit]TM, based on the differential geometric method, is introduced. It is intended for use by researchers and engineers with basic knowledge of geometry and robotics and aims to serve as a supportive resource during the study of differential geometric approaches. Code snippets and an example application are provided to demonstrate the benefits of the differential geometric method and assist users to get started with the software.

Keywords: Robot Dynamics and Control, Learning and Neural Control in Mechatronics, Network Robotics
Abstract: This paper addresses the problem of composite synchronization and learning control in a network of multiagent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The first layer is to facilitate each robotic agent’s estimation of the leader’s information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme.

Keywords: Robot Dynamics and Control, Actuators in Mechatronic Systems, Learning and Neural Control in Mechatronics
Abstract: The control of variable stiffness actuators (VSAs) is challenging because they exhibit highly nonlinear characteristics and are difficult to model accurately. In this study, we propose a machine learning-based tracking control approach combining Gaussian process (GP) learning and low-gain feedback control for VSAs subjected to unknown dynamics, where the GP model learns the inverse dynamics of agonistic-antagonistic (AA)-VSAs to feedforward control and provides the model fidelity by the predicted variance for the online adjustment of feedback control gains. It is shown that the tracking error is uniformly ultimately bounded and exponentially converges to a small ball under a given probability. Experiments on an AA-VSA named qbmove Advanced have validated the superiority of the proposed method with respect to tracking accuracy and generalization.

Keywords: Learning and Neural Control in Mechatronics, Design Optimization in Mechatronics, Automotive Systems
Abstract: Exploring the design space is a critical aspect of engineering and optimization, involving the search for the best configuration in complex systems with numerous options. In the system design process, it is essential to take into account a range of constraints related to architecture and component dimensioning, as well as requirements defined by standards or the current state-of-the-art. One of the main challenges in design space exploration is developing a control strategy tailored to each specific design, facilitating an objective comparison of different designs for closed-loop scenarios. Even though reinforcement learning offers promise as an automated solution for deriving control strategies, its trial-and-error methodology demands significant computational resources. To address this challenge, leveraging knowledge from similar design combinations, especially in larger design spaces, becomes beneficial. This study specifically targets the speed-up of automated derivation of control strategies within design space exploration using multi-task reinforcement learning. The work is applied to a safety-critical cross-domain motion system, comprising drive, brake, and steer systems. It further considers different driving scenarios and failure cases, enabling system performance assessment in normal and various failure modes within a limited time frame. With the proposed speed up of automated derivation of control strategies the overall effectiveness of design space exploration for multi-actuated and integrated system architectures is enhanced.

Keywords: Humanoid Robots, Actuators in Mechatronic Systems, Design Optimization in Mechatronics
Abstract: Highly dynamic motions, such as jumping, are crucial for biped robots to efficiently traverse challenging terrains and unstructured environments. This paper introduces an optimal design for an electrically actuated humanoid robot, specifically crafted to meet the demands of high dynamic jumping motions, with a focus on enhancing impact resisting, ruggedness, and power. Given the profound impact of the robot's actuation and transmission on diverse design specifications (e.g., speed, torque, overall mass, and compactness), special emphasis is placed on the design of joint actuators and the configuration of joint transmissions. The key characteristics achieved include low leg inertia, excellent joint back-drivability, and explosive power output ability, drawing inspiration from the characteristics of human jumping motion. Simulations and experiments of jumping motions validate the feasibility of the new design solutions. Recorded data indicates that the robot can execute explosive jumping motions, and the proposed leg structure can withstand significant impact forces, reaching up to 4.6 times the body weight during stable landing phases. Thanks to the new design of the leg actuator and linkage transmissions, the designed robot can achieve a vertical jump height of 0.5 m and a long jump of 1.0 m without the need for an energy storage system. Notably, through simulation comparisons, our up-shifting inertia design has the potential to reduce joint torque requirements by up to 32.2% during jumping motions.

Keywords: Design Optimization in Mechatronics, Actuators in Mechatronic Systems, Control Application in Mechatronics
Abstract: High-precision linear motor stages have been widely used for their excellent positioning accuracy and speed. However, core-type linear motor stages have performance limitations because of various nonlinear factors including cogging force, friction, and geometrical imbalance. This paper analyzes disturbances in velocity and position domains and trains a Two-Input-Single-Output (TISO) nonlinear model using the Gaussian process for the disturbance. With this, two state-dependent disturbances are appropriately removed. As a result, the control performance with a proposed controller is enhanced. Ultimately, this paper introduces three contribution points: 1) analysis of disturbances based on position/velocity, 2) design of TISO Gaussian process model, and 3) validation of proposed controller performance through simulation.

Keywords: Design Optimization in Mechatronics, Actuators in Mechatronic Systems
Abstract: The shank-free exoskeleton is proposed to increase the strength and endurance of the human during loaded walking. It interfaced with the human via back-waist belts and thigh cuffs, without physical coupling with the biological human shank. Instead of the traditional ankle-foot design, we employed a robotic shank driven by a linear actuator to achieve gravity support. The bilateral design of the exoskeleton was symmetrical, and the mechatronic design of the exoskeleton was described in detail. Metabolic expenditure measurement experiments were conducted on three able-bodied subjects walking with 27.1 kg of carried mass (including 15.5 kg of the exoskeleton), and the results exhibited an average metabolic reduction of 10.94%, comparing exoskeleton worn powered versus unpowered. This study introduces a potential way for load-carrying augmentation.

Keywords: Design Optimization in Mechatronics, Actuators in Mechatronic Systems, Actuators
Abstract: The need for optimized reduction gear is crucial for the development of high-performance construction vehicles. Previous research endeavored to optimize gearbox efficiency through the optimization of key parameters, including the number of teeth, profile shift coefficient, and center distance. Nonetheless, a significant portion of efficiency decrement stems from the phenomenon of slip occurring between the engaging teeth during meshing. In the present study, the tooth profile of an involute gear was subject to modification using a newly proposed profile crowning technique. The proposed involute-like profile crowning enhances the performance of the gear in terms of average efficiency, even under heavy loads causing deformation. Through transient finite element analysis, the optimal magnitude of this modification was determined. Initially, the theoretical determination of the reference gear train efficiency yielded a value of 98.37%, a result that was subsequently validated in the analysis assuming a rigid body. However, in practical scenarios, the presence of minor deformations at the tooth interface adversely affects the efficiency, resulting in a reduced value of 98.18% and 97.99% as indicated by the analysis. Assuming a constant coefficient of friction, the tooth modification improved the efficiency to 98.48%, 98.27% and 98.09% for rigid and flexible bodies respectively. Further investigation could be made from the analysis of the loss at the contacting surfaces of the gear teeth.

Keywords: Design Optimization in Mechatronics, Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems
Abstract: Hypersonic vehicle actuating system will endurance great thermal load when it re-enters the atmosphere. Pump-controlled loop pipe (PCLP) is widely used to dissipate its thermal load. PCLP is a kind of organic Rankine cycle (ORC). ORC is also commonly used in residual energy recovery. To address challenges to optimize multiple conflicting objectives at the same time in ORC, a multi-objective optimization (MOO) method for ORC is proposed, using energy consumption, total exergy loss, and compression ratio as objective functions. The unconstrained multi-objective evolutionary algorithm based on decomposition and fitness rate rank based multi-armed bandit (MOEA/D-FRRMAB) is enhanced to handle constrained MOO problem. An improved entropy weight-technique for order preference by similarity to an ideal solution (entropy weight-TOPSIS) approach is proposed for selecting the best solution from Pareto optimal solutions, which can obtain a set of real-time optimized parameters of system to achieve a comprehensive optimization effect, considering the system's thermal load.

Keywords: Human -Machine Interfaces, Service Robots
Abstract: People with visual impairment often face great challenges when shopping in supermarkets because they cannot accurately determine the location of the products they need and hard to understand their own location in the unknown area. To assist visually impaired people achieve daily shopping tasks, in this paper, we proposed a novel shopping cart-type robot system. This robotics system could achieve targeted and accessible navigation using a camera with ArUco markers embedded in the environment. Also, this system uses a passive control policy to guide users safely. Besides, this system provides verbal and haptic feedback to users to make them aware of where they will go. Using the proposed system, we completed 12 times validation experiments. The results show in all trials, the blindfolded users could find the desired spot guided by the system.

Keywords: Human -Machine Interfaces, Control Application in Mechatronics, Compuational Models and Methods
Abstract: In the context of a Human-in-the-Loop (HITL) system, the accuracy of reachability analysis plays a significant role in ensuring the safety and reliability of HITL systems. In addition, one can avoid unnecessary conservativeness by explicitly considering human control behavior compared to those methods that rely on the system dynamics alone. One possible approach is to use a Gaussian Mixture Model (GMM) to encode human control behavior using the Expectation-Maximization (EM) algorithm. However, relatively few works consider the admissible control input ranges due to physical or mechanical limitations when modeling human control behavior. This could make the following reachability analysis overestimate the system's capability, thereby affecting the performance of the HITL system. To address this issue, we present a constrained stochastic reachability analysis algorithm that can explicitly account for the admissible control input ranges. By confining the ellipsoidal confidence region of each Gaussian component using Sequential Quadratic Programming (SQP), we probabilistically constrain the GMM as well as the corresponding stochastic reachable sets. A comprehensive mathematical analysis of how the constrained GMM can affect the stochastic reachable sets is provided in this paper. Finally, the proposed stochastic reachability analysis algorithm is validated via an illustrative numerical example.

Keywords: Human -Machine Interfaces, Identification and Estimation in Mechatronics, Compuational Models and Methods
Abstract: Exoskeletons have the potential to enhance human performance, with the design of effective human-machine interaction (HMI) playing a crucial role in improving operability. However, the quest for optimal human-machine control remains an open field for further investigation. The key to success lies in establishing a flexible mode of communication between humans and machines, employing diverse methods and adjustments of parameters. Tailoring optimization to each individual user is pivotal for achieving significant advancements. This study presents a novel approach to human-in-the-loop optimization that leverages the correlation between brain activity and metabolic cost. This approach not only aims to reduce the energy expenditure of users during walking but also seeks to optimize gait patterns for healthy individuals across various walking environments. Our findings reveal that the μ and γ frequency bands display notable event-related synchronization (ERS) and event-related desynchronization (ERD) phenomena during walking. Notably, the γ band power is positively correlated with changes in inclined terrain. Pearson correlation indicates a stronger correlation between γ band power and metabolic cost than in other bands. This study thus built a regression model that links brainwave patterns to metabolic rates, enabling the prediction of current metabolic costs based on brain activity. This relationship could facilitate the realization of human-in-the-loop optimization, enhancing the walking economy.

Keywords: Human -Machine Interfaces, Machine Learning, Sensor Integration, Data Fusion
Abstract: Due to its non-stationary nature, surface electromyography (sEMG)-driven gesture recognition requires calibration to achieve satisfactory performance. Supervised domain adaptation is a commonly employed calibration technique. This technique involves combining one newly recorded repetition named calibration data of each gesture with all previously recorded data for each gesture under different conditions. This paper first quantified intra- and inter-conditions covariate shifts using the recognition error and Repeatability Index (RI), along with two newly introduced metrics: the non-overlapping index and the non-overlapping cluster count. Secondly, we extended the linear discriminant analysis (LDA) with the domain adaptation method by implementing a sample selection approach based on the repeatability index. We evaluated proposed techniques using two public datasets: the Multiday Ninapro DB6 and the Multiple Limb Position. Our domain adaptation approach with sample selection consistently outperformed the baseline model trained solely with calibration data in both datasets, showing improvements ranging from 0.7% to 3%. Compared to the domain adaptation without sample selection, our method demonstrated a 0.3% performance increase for the Multiple Limb Position dataset and achieved comparable results for the Ninapro DB6, underscoring the efficacy of RI-based sample selection in the domain adaptation.

Keywords: Human -Machine Interfaces, Biomechatronics, Actuators in Mechatronic Systems
Abstract: Pressure injuries in long-term care facilities present a significant problem for the well-being of bedridden patients and the overall cost of the healthcare systems. Mitigating risks of pressure injury formation might be possible through monitoring and control of the main extrinsic factors that cause them, including temperature, humidity, and normal and shear loads at the skin-support surface interface. An instrumented soft robotic pad system serving as an instrumented support surface is a potential solution. In this work, we present the design of two-degree-of-freedom soft actuators that when combined in a grid form an instrumented soft pad. The actuators have integrated humidity sensor, thermistor, and embedded force sensitive resistor (FSR). We investigate the optimal placement of the embedded sensors to monitor temperature, humidity, and applied normal loads during various actuation modes. We utilize a long short-term memory (LSTM) neural network to obtain estimated values of humidity and temperature at the expected contact interface, and also estimates of the normal loads exerted on the soft actuators under various actuation configurations that affect raw FSR sensor measurements. The developed system can be potentially used to monitor and mitigate pressure injuries risks factors in long-term care patients and enhance the quality of care of those patients.

Keywords: Human -Machine Interfaces, Virtual Reality and Human Interface
Abstract: The demand for wearable haptic devices has rapidly increased for various applications. However, many haptic devices interfere with the wearer's activities and movements. In addition, several haptic devices fail to elicit intuitive haptic sensations by adjusting to the natural posture of the wearer. To address these issues, we propose an elbow angle guidance system using a lightweight wearable fabric actuator. The proposed actuator is made of fabric and has two McKibben-type artificial muscles attached to it, rendering it extremely lightweight and facilitating the delivery of surface haptic sensations to intuitively induce elbow extension and flexion. The surface haptic sensation elicited by the fabric actuator is adjusted to natural body movements without interfering with the wearer's movements. Moreover, the proposed system measures and guides the elbow angle by changing the intensity of the surface haptic sensation delivered to users in real time. The accuracy of the proposed system is demonstrated through experiments involving human participants.

Keywords: Mobile Robots, Vehicle Control, Robot Dynamics and Control
Abstract: A ski-stunt maneuver is a type of aggressive vehicle motions in which a four-wheel vehicle runs on two wheels on one side, and the other two wheels are lifted in the air. It is a challenging task even for skilled car drivers to perform a ski-stunt maneuver. We present the safety-guaranteed motion control of autonomous ski-stunt maneuvers. Inspired by bicycle dynamics, a vehicle dynamic model is first built for ski-stunt motion. To prevent possible rollovers, a control barrier function is used in a model predictive control formulation to plan a safe motion trajectory. A motion controller is then designed to follow the safe trajectory with guaranteed balance. Ski-stunt maneuver initiation and switching strategies are also analyzed and designed. Extensive experiments are conducted using a scaled truck platform to demonstrate the control design. The experimental results confirm that the vehicle can successfully initiate the ski-stunt maneuver to safely navigate among obstacles and narrow passes and then switch to normal driving.

Keywords: Vehicle Control, Vehicle Technology, Transportation Systems
Abstract: Driving force control (DFC) has been shown as a successful method for improving the safety and comfort of in-wheel-motor electric vehicles (IWM-EVs). However, DFC for on-board motor (OBM) EVs faces many challenges due to the complexity and parameter uncertainty of the powertrain system, which includes gear, differential, and drive shaft. Aiming to solve the aforementioned issues, this paper presents a new DFC system for OBM-EVs with the force controller in the outer loop, and the motor speed controller in the inner loop. To achieve phase stabilization, the proportional-integral-derivative (PID) controller with a phase lead compensator is proposed for the inner loop. By integrating disturbance observer and least square algorithm, a novel adaptive driving force observer (A-DFO) is proposed to simultaneously estimate the driving force and the viscous friction coefficient of the drivetrain. A hardware-in-the-loop (HIL) experiment is introduced to show that it is possible to design DFC for OBM-EVs using IEM-EVs. Numerical simulations and Hardware-in-loop experiments show that the proposed system can accurately estimate and control the driving force. Especially, the tracking errors and vibrations are remarkably reduced in comparison with some existing control approaches.

Keywords: Mobile Robots, Robot Dynamics and Control, Vehicle Control
Abstract: The Multi-terrain Amphibious ARCtic explOrer or MAARCO is a screw-propelled vehicle designed to move seamlessly across the heterogeneous and diverse Arctic landscape. Its propulsion system consists of one or multiple pairs of helical drives (or Archimedes’ screws) that offer two modes of locomotion for straight-line motion while moving on land- Screw and Crab-crawl. In screw mode, the rover moves in a forward or backward direction by rotating the drives in opposite directions at the same speed. While in crab-crawl mode, the rover moves sideways by rotating the drives in the same direction at the same speed. This paper presents an algorithm for selecting between the two modes of locomotion for straight-line motion as a function of the terrain or substrate that the rover is traversing. The algorithm first determines the feasibility of both locomotion modes, which depends on the reaction forces exerted by the substrate on the blades and central cylinder (or ballast) of the drives. The algorithm is further applied for performing path planning. The algorithm considers several candidate paths that stretch over multiple substrates. Based on the amount of energy spent to go from the starting location to the final location, the algorithm chooses an optimal path. The amount of energy spent while traveling each candidate path depends on the mode of locomotion on each type of substrate and the energy spent to travel a prescribed distance in that substrate. Results show that the rover chooses crab-crawl mode if the substrate fails under the stresses exerted by the rover and vice versa. The path planning section of the algorithm shows that maximizing the distance traveled in crab-crawl mode while simultaneously minimizing the distance traveled in screw mode derives the path with the least amount of required energy.

Keywords: Vehicle Control, Planning and Navigation, Robot Dynamics and Control
Abstract: This paper presents an original approach to vehicle obstacle avoidance. It involves the development of a nonlinear Model Predictive Contouring Control, which uses torque vectoring to stabilise and drive the vehicle in evasive manoeuvres at the limit of handling. The proposed algorithm combines motion planning, path tracking and vehicle stability objectives, prioritising collision avoidance in emergencies. The controller's prediction model is a nonlinear double-track vehicle model based on an extended Fiala tyre to capture the nonlinear coupled longitudinal and lateral dynamics. The controller computes the optimal steering angle and the longitudinal forces per each of the four wheels to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Thanks to the optimisation of the longitudinal tyre forces, the proposed controller can produce an extra yaw moment, increasing the vehicle's lateral agility to avoid obstacles while keeping the vehicle stable. The optimal forces are constrained in the tyre friction circle not to exceed the tyres and vehicle capabilities. In a high-fidelity simulation environment, we demonstrate the benefits of torque vectoring, showing that our proposed approach is capable of successfully avoiding obstacles and keeping the vehicle stable while driving a double-lane change manoeuvre, in comparison to baselines lacking torque vectoring or collision avoidance prioritisation.

Keywords: Vehicle Control, Vehicle Technology, Transportation Systems
Abstract: This paper presents a new direct yaw moment control system for electric vehicles with two-input, two-output motor drives. The proposed system consists of two layers: yaw-rate control in the outer and driving force control in the inner. To optimize the driving force generation capability of the left and right tires during turning, a novel variable slip ratio limiter (VSRL) is developed for the driving force control (DFC). The VSRL algorithm is derived by analyzing the brush model of tire force characteristics concerning the sideslip angle of the vehicle body. The proposed system was evaluated using an actual electric vehicle prototype developed by Mitsubishi Motors. The experiments were conducted under extremely harsh conditions, such as sudden acceleration while cornering on the ice surface. Compared with the existing methods with conventional VSRL, the proposed system successfully enhances the yaw-rate tracking performance.

Keywords: Vehicle Control, Vehicle Technology, Automotive Systems
Abstract: This paper presents an Integrated Physics-Data-Based (IPDB) modeling and control scheme of the combined longitudinal-lateral vehicle dynamics. A nonlinear bicycle vehicle model is used to derive the linear parameter-varying (LPV) system representation, where four vehicle motion variables are considered as scheduling parameters. Taking advantage of kernels from LPV representation, the combined longitudinal-lateral dynamics are further expressed by the data snapshots of states, inputs, and scheduling parameters, which formulate the IPDB model. After that, the IPDB model is used to design a state-feedback gain-scheduling tracking controller to follow a reference trajectory. For validation purposes, the proposed modeling and control method is implemented on a QCar experimental platform. First, the IPDB model of coupled longitudinal-lateral dynamics is derived from experimental data and is further validated with excellent model accuracy under various driving conditions. Furthermore, an IPDB model-based gain-scheduling controller is synthesized and compared with the baseline Stanley controller in the experiment to track a given trajectory. The experimental results demonstrate that the IPDB model-based controller renders better tracking control performance.

Keywords: Fuel Cells and Alternative Power Sources, Educational Testbeds and/or Platforms
Abstract: Numerous battery management system (BMS) algorithms aimed at achieving state-of-charge (SOC) balancing have been proposed. This paper reports on the development and evaluation of an experimental platform for testing BMS algorithms. The platform we developed allows for simple parameter or physical quantity expression tweaks, making it easier to assess the performances of different various BMS algorithms. The hardware of the platform comprises a DSP chip (TMS320F28335), a custom-designed buck converter, various battery packs, and load resistors. By simulating circuit operations and analyzing battery output under load, an SOC versus open-circuit voltage graph is produced for estimation of the SOC. Employing cascaded PI controllers for the buck converter, the platform demonstrates its capability in power control and battery balance management through tests on a single battery system and on multiple battery systems. A BMS algorithm is selected for platform evaluation, affirming its effectiveness in maintaining SOC balancing among heterogeneous battery units.

Keywords: Technology Enabled Teaching of Mechatronics, Human -Machine Interfaces, Fault Detection and diagnosis in Manufacturing
Abstract: A key aspect of robotic systems include the ability to recover from an error and re-planning motion accordingly. The widespread acceptance of robotics in various sectors of society is inhibited by the degree of expertise required to program even the simplest error recovery strategies. Previous studies have addressed the complexity of robotics programming through user-interfaces, though they were not intended for novices or did not address error handling. This study discusses a framework developed that acts as an Interactive Robot Monitor and Control System (IRMCS), that allows non-expert users to interface, understand and recover from errors in a robotic process. The framework takes the form of a system that is geared towards informing a user of a robotic process through the means of an activity diagram that allows for visual and intuitive human-robot interaction to support incremental learning and error handling of simple pick and place tasks. To evaluate the effectiveness of this approach, we conducted a control experiment with four novice users. The results revealed that by using the developed system, novices were able to recover from errors and were unsuccessful in the control condition. Furthermore, their subjective evaluation showed that these non-expert users are highly receptive to understanding and successfully implementing error recovery strategies in robotics.

Keywords: Mechatronics-Enabled Teaching and/or Training, Virtual Reality and Human Interface
Abstract: This study investigates the auditory-based spatial cognition critical for blind soccer, where players rely entirely on sound due to the absence of visual cues. Blind soccer is characterized by three distinct auditory signals: static sounds originating from fixed positions, dynamic sounds produced by the ball's movement, and instructional sounds conveyed by guides. Recognizing these sounds simultaneously is essential for effective gameplay. Our research focuses on understanding how blind soccer players recognize these specific sound sources. We conducted an evaluation using a virtual acoustic space system enhanced with stereophonic technology to test the localization abilities for one versus three types of sound sources. The findings indicate that players with visual impairments demonstrated superior localization skills for both static and dynamic sounds compared to sighted players, whether they had experience in blind soccer or not. Furthermore, the study reveals a prioritization in localizing the ball's sound and the guide's instructions among the three types of sounds. This underscores the specialized auditory perception skills developed by blind soccer players, offering insights into training methods and the design of auditory-based assistance systems.

Keywords: Educational Testbeds and/or Platforms, Technology Enabled Teaching of Mechatronics, Learning and Neural Control in Mechatronics
Abstract: Data-driven control, which embraces artificial intelligence, machine learning, and experience-based inferencing architectures, has gained significant interest for its ability to provide robust optimization in model-free, nonlinear, and time-varying paradigms. Traditional systems, such as the haptic paddle, used to communicate system dynamics principles in undergraduate curricula, have yet to be adapted to the memory and processing requirements of data-driven control. In this work, we present a modular, open-source 3D printable friction-driven haptic paddle design, building on the designs proposed by the community, using commercial components and simple microelectronic packaging, to enable robust data-driven control for integration in undergraduate education. We make use of the RP2040 microcontroller, a small light-weight logic platform capable of fast online computation and robust memory storage for onboard data-driven control. To validate our design, we first develop an experimental model of the physical dynamics that shows that our 3D printed friction drive is comparable with friction driven paddles and capstan-cable driven paddles. Further, we demonstrate the utility of our design in explicating data-driven control by presenting the development of basic machine learning and reinforcement learning architectures for online, model-free robust control in the presence of time-variable plant dynamics in a trajectory tracking task that is well suited for implementation in undergraduate and introductory graduate system dynamics and controls curricula.

Keywords: Planning and Navigation, Human -Machine Interfaces, Mobile Robots
Abstract: Assistive walkers are essential for many individuals who require geriatric care or mobility aids for day-to-day activities. The functionality of these devices is not able to provide the care that is needed for those with different levels of physical disabilities or who are visually or mentally impaired. Developing an autonomous powered walker that can receive a user's intent and assist them in navigating obstacles would allow more people to have access to enhanced mobility. In this work, a control strategy is designed for obstacle avoidance using potential field generation and admittance regulation. The mechatronics hardware and software are developed to generate and implement a continuous real-time motion trajectory for the smart walker in response to the user's interaction torque and scanned locations of obstacles in the environment. The integration of the mecanum wheels, DC motors, microcontrollers, a rotating LiDAR scanner, and a mini-PC powered by an onboard battery allowed for the evaluation and testing of the obstacle avoidance algorithm and admittance control in real time. The experimental results showed that the autonomous walker can generate desired trajectories in response to the user's interaction using the admittance controller and navigate around the obstacles with real-time velocity updates for the motorized wheels.

Keywords: Planning and Navigation, Transportation Systems, Mobile Robots
Abstract: Multi-agent systems (MAS) have a wide range of applications, particularly in the field of cooperative transporting. In these tasks, MAS are often required to meet formation constraints. Therefore, path planning for MAS is a challenging task and needs to ensure that the path is short and collision free, while minimizing the adverse impact on formation maintaining. However, current research on MAS path planning mainly emphasizes reducing path length and optimizing planning efficiency, and limited attention is given to the preservation of formations. To address this gap, we propose the Multi-agent Formation Maintaining RRT* (MFM-RRT*) method for MAS with specific consideration of formation maintenance, which also enhances the computational and convergence speed of RRT*. Additionally, a novel cost function is proposed to compute the formation deformation cost based on the predetermined formation and environment information, thereby minimizing the deformation of multi-agent formation. In the simulations, we evaluate the performance of MFM-RRT* compared to several widely-used path planning algorithms. Moreover, we integrate MFM-RRT* with Attractive Potential Field (APF) to demonstrate its enhanced performance in maintaining formation in complex environment.

Keywords: Underwater robotics, Planning and Navigation, Mobile Robots
Abstract: This paper presents an obstacle avoidance path planning algorithm designed to generate smooth paths for underwater robotic systems that operate in dynamic environments. Using the kinematics of the system, an initial path is generated which is further optimized considering the constraints of the system and the environment. The correlation between path states is embedded into a kernel used throughout the optimization. This produces a more informative optimization process that leads to changes in one state based on all other states. However, the use of this correlation between path states may lead to an exhaustive computational effort for highly dimensional systems. Therefore, the proposed approach, named AmaxGPMP, introduces a strategy capable of reducing the needed information to develop these kernels while accurately describing the correlation among states, hence decreasing the computation time. The proposed path planner was tested in simulation and experimentally on a BlueROV2 Heavy vehicle that was modified to enable autonomous capabilities. The results demonstrate the ability of AmaxGPMP to successfully generate smooth, feasible, and safe behaviors for autonomous underwater vehicles.

Keywords: Planning and Navigation, Mobile Robots, Transportation Systems
Abstract: Safe navigation in a pedestrian-rich environment has gained a lot of attention in robotics research. Unlike classical motion planning with a static environment, pedestrian-rich scenarios are associated with a highly dynamic environment and safety risk. In this paper, we propose a novel local planner designed for differentially driven wheeled robots, directly inspired by the Dynamic Window Approach (DWA). Our model leverages convex optimization and differential drive kinematics to efficiently determine optimal velocity inputs as the robot moves in the human crowd. Our approach does not only account for the position but also the velocity of the pedestrians in the planning framework to facilitate safer navigation through dynamic pedestrian-dense environments. Through extensive simulation experiments, we demonstrate the superior effectiveness and safety of our method compared to DWA, showcasing significant enhancements in collision-free navigation success rates and computational efficiency through the use of convex optimization techniques. Code release: https://github.com/Leixinjonaschang/convex_op_planner

Keywords: Planning and Navigation, Mobile Robots, Learning and Neural Control in Mechatronics
Abstract: For productive operations involving multiple Automated Guided Vehicles (AGVs), it is essential to plan motions quickly that minimize the total sum of costs required for each AGV to perform transport tasks (Sum-of-Costs). However, calculating motions that achieve a superior Sum-of-Costs within a short period is challenging. This study introduces a strategy to accelerate motion planning for multiple AGVs utilizing Conflict-based Search (CBS). It incorporates two methodologies: (a) employing greedy search algorithms, and (b) integrating learned heuristics into the A* search algorithm. After implementing and simulating each method, the proposed integrated methodology showed significant efficiency for 15 AGVs. It achieved a 96% reduction in computation time and limited the deterioration of the Sum-of-Costs to 8%, compared to the CBS-based method. Furthermore, even with a strict CPU time limit of 60 seconds, the proposed method successfully planned motions for 20 AGVs, tasks that the baseline method failed to accomplish.

Keywords: Compuational Models and Methods, Neural Networks, Planning and Navigation
Abstract: This paper develops the Fractional-Order Zeroing Neural Network (FO-ZNN) model for addressing time-variant quadratic programming (TVQP) problems, marking an inaugural application of fractional calculus in neural models for robotic motion planning. Diverging from standard ZNN model, the FO-ZNN model incorporates a conformable fractional derivative definition that adheres to the Leibniz rule, which is commonly violated by other traditional fractional derivative definitions. In comparative analyses with the traditional ZNN model, the FO-ZNN model, parameterized with 0<α<1, achieves significantly accelerated convergence rates in TVQP scenarios, albeit with a marginal trade-off in accuracy. Conversely, when α is greater than 1, the FO-ZNN model not only enhances its accuracy but also exhibits augmented convergence speeds, outperforming in both time-invariant and time-variant QP challenges. In addition, the FO-ZNN model with differentiation perturbations demonstrates notable convergence attributes, showcasing its robustness to maintain bounded steady-state residual errors and to achieve convergence with increased γ value. Rigorous and empirical evaluations, including both simulations and physical experiments with a Flexiv Rizon robotic arm, validate the FO-ZNN's capability for accurate trajectory tracking and computational efficiency, highlighting its robustness in kinematic control.

Keywords: Machine Vision, Sensors and Sensing Systems, Mobile Robots
Abstract: The COVID-19 pandemic renewed interest in con- tactless vital signs monitoring using computer vision to efficiently screen for disease symptoms. These vital signs monitoring systems have been deployed in either surveillance camera systems or robotic systems. Despite initially promising results, there has been limited uptake. Surveillance cameras are static, which requires subjects to remain inside their field of view during measurement, thus limiting their capacity for continuous monitoring. Robotic systems are mobile and can autonomously track subjects during measurement, but they require expensive software and hardware and tend not to be scalable. In this work, we propose a cost-effective and scalable robotic solution using machine vision microcontrollers to capture photoplethysmography (PPG) information on ambulatory subjects. We characterize the performance of our camera system to design an optimized machine vision protocol to maximize the performance of the machine vision microcontroller for vital signs monitoring. We compared the heart rate estimation accuracy of our cost-effective solution against a state-of-the-art camera (FLIR Blackfly). Our solution achieves a mean average error of 5.0 BPM, comparable to the FLIR Blackfly’s mean average error of 4.7 BPM while keeping social distancing (at least 2 meters between cameras and subjects). The major contribution of this work is the design of a machine vision protocol that enables a cost-effective, scalable, and mobile system to achieve the same heart rate estimation accuracy as current state-of-the-art methods.

Keywords: Sensors and Sensing Systems, Humanoid Robots, Service Robots
Abstract: The sense of touch is fundamental to grasping and manipulating objects. Disabled prosthetic hands and robot dexterous hands need tactile sensors to provide tactile information to ensure that when grasping objects, the object will not be broken because the grasping force is too large, and the object will not slide because the grasping force is too small. At present, some tactile sensors have complex structures, high production costs, and are not easy to integrate. In this paper, a novel biomimetic 3D tactile sensor based on magnetic field is proposed. Most parts can be 3D printed, simple to manufacture, can measure three-dimensional forces, and can be easily integrated into prosthetic hands and robotic hands. Through experimental tests, the designed tactile sensor has a wide measuring range, can detect 15N normal force and ±6N tangential force, and has a small lag error, which can improve the tactile perception ability of the manipulator when grasping objects.

Keywords: Sensors and Sensing Systems, Image Processing
Abstract: This paper investigates causes of center detection errors in laser straightness measurements for precision positioning applications and proposes the calibration of these errors by utilizing the reversal method after reducing the center detection uncertainty. The uncertainty arising from unknown imaging errors, such as spatially varying pixel sensitivity and sensor pollution, is diminished by saturating the imaging sensor. The results demonstrate that the uncertainty can be reduced by a factor of 4.8, from 2.19 μm to 0.45 μm. This reduction justifies the application of the reversal method to calibrate the remaining center detection error, attributed to the shape of the laser beam cross-section in the image. Consequently, this enables straightness measurements with a repeatability of 0.34 μm.

Keywords: Sensors and Sensing Systems, Image Processing
Abstract: Curing is the key stage to determine the quality and economic benefits of tobacco leaves, which directly determines the income of tobacco farmers and the quality of cigarette raw materials. Proper control of temperature and humidity based on the condition of tobacco leaves plays a decisive role during the curing process. However, the exiting researched are mainly focused on the monitor of temperature and humidity due to high temperature, high humidity and pure black environment in bulk curing barn. Here we designed a novel integrated system for real-time monitoring of tobacco leaf images, temperature and humidity during curing process. TEC semiconductor cooling module, fill light and corrosion-resistant sensor housing were designed in hardware and an image color optimization algorithm was embedded in software. A control experiment was conducted using the image sensor before improved to validate the availability and feasibility of the novel integrated system. The results showed that the novel system was feasible to acquire the real-time and high quality images of tobacco leaves during the whole curing process, and the ΔE of the images decreased from 20 to 10 when the novel integrated system was applied. These results proved that the novel integrated system developed in this study was able to monitor the images, temperature and humidity during whole curing process, which has distinct advantages and potential application in the future of tobacco production.

Keywords: Sensors and Sensing Systems, Biomechatronics, Medical Robotics/Mechatronics
Abstract: Estimation/evaluation of muscle strength of limbs are needed as an evaluation index for clinical practice and sports training. The evaluation method of muscle strength using functionally effective muscule theory uses a two-dimensional musculoskeletal model of three pairs six muscles, in which the limb muscles are classified according to their functions, and enables the evaluation of muscle strength for each muscle group. In this study, we developed a new system to evaluate the muscle strength of each muscle group in a five muscle model that separates the bi-articular muscle strength of knee and ankle joint using the functionally effective muscular theory. The developed system consists of a device for measuring foot tip force in real time and a calculation algorithm for evaluating the muscle strength of five muscles from the measured data. The former one is a device that measures the foot tip force exerted at the foot by a force sensor attached to the boot. The latter one calculates the position of the exerted force in the boot from the foot tip force, and derives the muscle group torques of the five muscles, taking into account the variation of the exerted force position. In this paper, we show the validity of the developed system by comparing the results from Cybex measurements.

Keywords: Sensors and Sensing Systems, Intelligent Sensors
Abstract: With the rapid development of Internet of Things and Artificial Intelligence, long-term and wearable gait detection is of great significance for healthcare, rehabilitation and sports training. However, wearable gait detection devices are generally less flexible and only a small gait features can be detected, which limits their practical applications. Herein, we proposed a novel flexible, wireless and multifunctional electronic system with proximity and contact pressure sensing capabilities for daily wearable gait monitoring. The system mainly consists of two types of sensing modulus: proximity sensing units for the detection of periodic swing of legs and contact pressure sensing units for swing force sensing of the knee during walking, along with a wireless acquisition module and an APP for data visualization. In addition, the hardware of the system is lightweight and fully-flexible for comfortable wearing on the knees. Benefiting from above advantages, the developed multifunctional electronic system can realize the detection of gait cycles and quantitative extraction of gait characteristics such as gait steps, step pace, pressure changes at the knees, stride distance, spatial posture of legs, etc. Finally, multi-scenario long-term gait monitoring experiments were conducted to verify the potential application of flexible integrated electronic system for daily wearable gait monitoring.

Keywords: Artificial Intelligence in Mechatronics, Sensors and Sensing Systems, Robot Dynamics and Control
Abstract: Robotic painting, particularly in industrial and construction domains, has attracted considerable attention due to its precision and uniformity. However, current systems are constrained by inadequate precision and effectiveness in painting, particularly when applied to large-scale surfaces. This study introduces an advanced adaptive robotic painting system that incorporates a mobile manipulator (MM) designed to enhance both accuracy and efficiency in indoor surface painting through two innovative sub-modules: automated trajectory generation and MM adaptive control policy (ACP). Initially, to autonomously generate the accurate trajectory, we propose the Attention-aware Graph Network (AGN) for refining 3D surface model to significantly enhance the accuracy and efficiency of environment modeling. Following this, the RayCast 3D Mapping technique is introduced for precise projection of 2D images onto arbitrary 3D surfaces with its flexibility and adaptability. Furthermore, we introduce an MM ACP comprising a trajectory controller and a close-loop whole-body controller. This dual-controller system enables the MM to swiftly move to target poses and smoothly follow trajectories, with the capability to autonomously switch between control paradigms based on task requirements. In addition, Experimental results demonstrate that the proposed automated trajectory generation strategy, coupled with the MM ACP, significantly improves the accuracy of environmental perception and the efficiency of trajectory generation. Furthermore, the MM exhibits robust performance in both simulated and real-world settings, successfully executing fully autonomous painting tasks.

Keywords: Robot Dynamics and Control, Design Optimization in Mechatronics, Identification and Estimation in Mechatronics
Abstract: This article introduces an advanced methodology for optimal parametric design based on discrete-time analysis findings of a robust force control structure, integrating both a disturbance observer (DOB) and a reaction force observer (RFOB). While conventional continuous-time analyses are commonly employed to enhance system performance, offering guidance for stability and robustness improvement, they often fall short in explaining practical implementation instabilities. A significant knowledge gap persists in the systematic design approach for tuning the control system to meet specific desired behaviors. To bridge this gap, this paper thoroughly investigates the system's frequency response. Design constraints derived from this analysis process formulate the nominal parameter design as a minimization problem, subsequently solved using a genetic algorithm to determine optimal parameter values. Simulations illustrate the control system behavior, emphasizing the design constraints and optimal parameter design process. The proposed method is applied to a force-controlled spindle via calibrated experiments, providing robust validation. The results unequivocally confirm the effectiveness of the proposed design approach, achieving stability and robustness concurrently.

Keywords: Planning and Navigation, Robot Dynamics and Control, Learning and Neural Control in Mechatronics
Abstract: There has been a growing utilization of industrial robots as complementary collaborators for human workers in re-manufacturing sites. Such a human-robot collaboration (HRC) aims to assist human workers in improving the flexibility and efficiency of labor-intensive tasks. In this paper, we propose a human-aware motion planning framework for HRC to effectively compute collision-free motions for manipulators when conducting collaborative tasks with humans. We employ a neural human motion prediction model to enable proactive planning for manipulators. Particularly, rather than blindly trusting and utilizing predicted human trajectories in the manipulator planning, we quantify uncertainties of the neural prediction model to further ensure human safety. Moreover, we integrate the uncertainty-aware prediction into a graph that captures key workspace elements and illustrates their interconnections. Then a graph neural network is leveraged to operate on the constructed graph. Consequently, robot motion planning considers both the dependencies among all the elements in the workspace and the potential influence of future movements of human workers. We experimentally validate the proposed planning framework using a 6-degree-of-freedom manipulator in a shared workspace where a human is performing disassembling tasks. The results demonstrate the benefits of our approach in terms of improving the smoothness and safety of HRC. A brief video introduction of this work is available as supplemental materials.

Keywords: Robot Dynamics and Control, Control Application in Mechatronics
Abstract: This research aims to utilize a robot manipulator for metal cutting using a laser or plasma arc method by maintaining a constant distance (as small as 1mm) from the physical cutting surface. Sometimes, the position control bandwidth in practice is constrained by certain factors which limit its use in tightly bound motion. Therefore, this study proposes the robot end-effector's virtual force-tracking impedance control for motion stabilization by assuming a virtual surface with defined stiffness near the actual surface. Subsequently, the interaction force between the virtual surface and robot end-effector is then regulated to achieve the desired compliance. The selection of impedance dynamic parameters indirectly influences impedance control performance. Therefore, the closed-loop system with linear dynamics is constructed and utilized in particle swarm optimization (PSO) for desired parameter offline tuning. Because of linear dynamics, the tuned parameters may not be accurate. Subsequently, this uncertainty is compensated using the sliding mode control (SMC)-based impedance control. SMC is a nonlinear control that ensures finite time stability by compensating the perturbation effect. Finally, the simulations and experiment results validate that a stabilized motion and force tracking is achieved using the proposed impedance control while maintaining the constant distance near the real surface.

Keywords: Robot Dynamics and Control, Control Application in Mechatronics, Identification and Estimation in Mechatronics
Abstract: Compliant actuators are commonly utilized in physical interactions between humans and robots, and it is of great significance to focus on safety control issues. This article introduces a robust safe motion control (RSMC) framework that employs control barrier functions (CBFs) for robots driven by compliant actuators. Compliantly actuated robots are commonly subject to both matched and mismatched time-varying disturbances, including external environmental disturbances, imprecise link parameters, discontinuous friction, and unknown loads. These factors can have adverse effects on CBF-based safety control, resulting in safety violations and degraded control performance. To ensure safety robustness against disturbances, generalized proportional integral observers (GPIOs) are developed for higher-accuracy estimation of uncertainties and their higher-order derivatives. Subsequently, a new disturbance estimates-based high-order control barrier function (DE-HoCBF) is constructed by fully utilizing both the disturbance estimates and the upper bound of the estimation error. On the basis of the constructed DE-HoCBF, the RSMC law is established by solving a quadratic programming problem, which simultaneously ensures strict safety specifications and robustness against time-varying uncertainties. Experimental results are provided to validate the effectiveness of the proposed method.

Keywords: Motion Vibration and Noise Control, Robot Dynamics and Control, Service Robots
Abstract: Boom lifts are commonly utilized in various industries to provide safe and efficient access to elevated work areas. Recently, a boom-lift-mounted robot (BLMR) concept has been proposed, combining a boom lift and an industrial robot to facilitate enhanced levels of construction automation. However, boom lifts typically contain extendable large-scale, variable-stiffness structures, subject to complex nonlinear static deformation and dynamic motion/gust-induced vibrations. These issues hinder the BLMR's precise and safe operation. To address these issues, this paper proposed a static deformation compensation scheme and a vibration alleviation method based on the inertial measurement unit (IMU). The deformation exploits the measurement from a laser tracker, while the vibration compensation method synergistically exploits the extended Kalman filter of the IMU measurements at the tip and the robot motion’s dynamic contribution to vibration under a real-time feedback framework. A telescope-type BLMR prototype is built to verify the proposed method. The proposed method is compared with a time-varying input shaper concerning the extension-dependent natural frequencies and damping ratios. Enhanced accuracy of the BLMR operation has been experimentally illustrated.

Keywords: Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems
Abstract: Compared to traditional techniques, sensory tool holders can achieve higher sensitivity and accuracy in cutting force characteristics. However, existing methods require complex structure modifications to enhance sensor sensitivity, leading to weakened processing performance and expensive customization costs. Therefore, this study describes the mechanical design and optimization of a sensory tool holder with embedded piezoelectric sensors, which has an almost identical rigidity to that of a standard tool holder. A high-fidelity sensing model was developed by the integration of piezoelectricity and multi-axis stress analysis. Sensor locations and orientations were optimized within standard tool holders to achieve high sensitivity, accuracy, and cross-axis decoupling in specific directions. Static tests indicated that under optimized parameter configurations, the designed tool holder achieved a maximum cross-coupling error of approximately 4.6%, only half of existing researches; it also demonstrated better performance in sensitivity, linearity, hysteresis, and repeatability. The proposed model was verified through simulation, with a maximum sensitivity error of only 5.34%, confirming its applicability for sensor embedding optimization in various types of standard tool holders without the need for structure modification.

Keywords: Biomechatronics, Design Optimization in Mechatronics, Medical Robotics/Mechatronics
Abstract: This work presents the design, development and validation of a partially powered knee prosthesis that enhances key features of energetically passive microprocessor-controlled knees (MPKs). We introduce a novel mechatronic architecture which combines a compact electro-hydrostatic actuation (EHA) unit with a controllable hydraulic valve. The design rationale is focused towards the support of swing-related activities and, as such, does not require large amounts of torque. Furthermore, the proposed solution retains the backdrivability properties of conventional MPKs and enhances the versatility through a highly integrated actuation unit (active and passive sides share the same motion transmission system), which may lead to improved walking capabilities. To this end, a prototype is designed, built and validated experimentally on a test bench to verify its active and passive functionalities. Results highlight comparable passive features with respect to state of the art MPKs with the possibility to inject active power at the joint to support swing related activities during level ground walking and stairs ascent tasks.

Keywords: Flexible Manipulators and Structures, Design Optimization in Mechatronics, Modeling and Design of Mechatonic Systems
Abstract: Current robotic flexible medical tools employed in maxillary sinus surgery have shown certain limitations in dexterity and stiffness, resulting in large surgical incisions and potential unintended damage to patients. This paper presents a novel 4-DOF handheld nonconstant-curvature hybrid-structure robotic instrument (HNHRI) which is 3.5 mm in diameter and has significant improvement in both dexterity and stiffness. To enhance dexterity and stiffness, a hybrid-structure instrument with multiple layers and nonconstant curvatures is proposed. A compact and lightweight actuation system is designed to fulfill the requirements of handheld surgical device. A flexible section curvature optimization framework is introduced to enhance reachability and dexterity. Through bench-top experiments and simulation surgery, its performance is validated. The flexible section curvature optimization framework increases the reachability to target region to 100% and achieves an average dexterity index of 48% within the maxillary sinus. Compared to current robotic flexible instruments, bending and torsional stiffness are improved by 197% and 150%, respectively. Utilizing the HNHRI in maxillary sinus surgery offers notable enhancement in both dexterity and stiffness, which has the potential to substantially improve the efficacy and safety of the procedures. These advancements might reduce surgical incisions and minimize surgery-related damage, thereby improving the clinical outcomes for patients.

Keywords: Artificial Intelligence in Mechatronics, Design Optimization in Mechatronics, Intelligent Process Automation
Abstract: Setting optimum process parameters for complex manufacturing processes such as ultrasonic wire bonding is already challenging for one target variable. Due to numerous influencing physical factors, such processes often lack the necessary detailed physical models. Due to the lack of these models, such processes cannot be adjusted to an optimum using classic optimization methods. In manufacturing processes in particular, the process must be optimized with regard to several objectives such as process time or quality. A popular method for optimizing such processes without models is multi-objective Bayesian optimization. For this purpose, surrogate models in the form of Gaussian processes are used in combination with a multi-objective acquisition function. In real processes, parallel function evaluation offers advantages in terms of trial efficiency and scalability. In this paper, we present a new algorithm for batch-constrained Bayesian multi-objective optimization. With this algorithm, an arbitrary number of function evaluations per iteration can be specified, whereby a more efficient determination of the Pareto front approximation can be achieved in real applications. For this purpose, the Expected Hypervolume Improvement is extended by a term to consider a quality criterion. Using the ultrasonic wire bonding process as an example, we experimentally prove that the proposed framework is able to approximate the Pareto front of the system with only a few function evaluations. For this purpose, the process capability index should be maximized and the process time minimized.

Keywords: Design Optimization in Mechatronics, Fuel Cells and Alternative Power Sources, Vehicle Technology
Abstract: This paper considers the problem of optimal charging in rechargeable Li-ion batteries. Particularly, this paper considers the constant-current constant-voltage (CC-CV) charging strategy, which is a widely used strategy for Li-ion battery charging. At present, the charging current during the CC stage is set to a predetermined value such as 0.8 C-rate to minimize energy loss and temperature rise. Some recent works derived the optimal charging current for the CC-CV protocol in a discrete timescale model. The present paper relaxes the optimization for a continuous timescale. The proposed work in this paper builds on a previous work in which the total energy loss and the charging time of the CC-CV strategy were analytically derived. Based on this relation, a cost function was developed by assigning normalized weights for charging time and energy loss, respectively. In this paper, a Newton-Raphson-based optimization technique is implemented on the closed-form cost function under charging time and energy loss constraints. For the closed-form derivations, a straight-line OCV-SOC model popularly called the Unnewehr model was assumed. The proposed approach in this paper is evaluated using data collected from a battery simulator that was based on the characteristic parameters obtained from a cylindrical Li-ion battery.

Keywords: Design Optimization in Mechatronics, Control Application in Mechatronics, Actuators in Mechatronic Systems
Abstract: Elevation-Azimuth Photoelectric Survey Telescopes (Ele-Azimuth PST) working under long exposure station is very important for observing weak targets in space. The motion characteristics analyses including position and velocity of rotating targets show that the image stabilization system (IMSS) needs to have a self-locking capability, low temperature rise, anti-interference, high-speed switching and ultra-low-speed tracking performance. A dual motor IMSS with self-locking was adopted after comparison. In this work, in order to overcome the inherent uncertainties affecting the accurate tracking of a target in a dual motor drive gear of the IMSS, we introduce a compound control strategy including bias torque controller (BTC) in current-loop and active disturbance rejection controller (ADRC) in velocity-loop. Experimental results show that the proposed controller can effectively eliminate backlash of IMSS, and the fixed-point control accuracy can achieve 0.18arcsec. When the sinusoidal speed guidance is 0.068 sin (2π *0.01*t) (°/s) and position guidance is cos (2π *0.01*t) (°), the position error is 0.31 arcsec. After image stabilization, the imaging resolution can be guaranteed to reach within 0.5-pixel size under the 60s exposure time.

Keywords: Fixture and Grasping, Part Feeding and Object Handling 
Abstract: This article presents a novel industrial robotic gripper with a high grasping speed (maximum: 1396 mm/s), a high tip force (maximum: 80 N) for grasping, a large motion range, and a lightweight design (0.3 kg). To realize these features, the high-speed section of the quick-return mechanism and load-sensitive continuously variable transmission mechanism are installed in the gripper. The gripper is also equipped with a self-centering function. The high grasping speed and self-centering function improve the cycle time in robotic operations. In addition, the high tip force is advantageous for stably grasping and assembling heavy objects. Moreover, the design of the gripper reduces the gripper's proportion of the manipulator's payload, thus increasing the weight of the object that can be grasped. The gripper's performance was validated through kinematic and static analyses as well as experimental evaluations. This article also presents the analysis of the self-centering function of the developed gripper.

Keywords: Fixture and Grasping, Actuators, Rapid Prototyping
Abstract: Soft robots are becoming increasingly popular and are being used in different industries, such as retail, healthcare, food and beverages, and logistics. They are widely employed nowadays to manufacture innovative products. This area of robotics focuses on flexible and stretchable materials, such as silicone and other engineered soft materials, with mechanical properties close to those of living tissues. One active area of soft robotics is soft grippers that can grasp, move, and handle a wide variety of objects, even very delicate ones, without damaging them. The advantage of such grippers is that they can be safely operated near humans. This work reports on a soft gripper that is designed and developed based on 3D printed soft pneumatic fingers with localized bending joints. The introduction of localized bending joints enhances the deformation behavior at lower actuation pressures and provides a linear relationship between the input pressure and both the output deformation and tip force. This linearity is crucial for directly controlling the actuators using simple controllers. The actuators are manufactured using an open-source 3D printer using a thermoplastic polyurethane (TPU). Finite element modeling (FEM) is used in the design process to optimize and predict the behavior of the soft pneumatic actuators in terms of deformation and force output. The soft pneumatic fingers are demonstrated by showing that a gripper can grasp a wide variety of objects with multiple shapes, weights, textures, and stiffnesses successfully.

Keywords: Fixture and Grasping, Part Feeding and Object Handling , Mechatronics in Manufacturing Processes
Abstract: This paper explores the automation challenges and solutions for handling and packaging compressible stacks of items, specifically paper envelopes, in the envelope production and packaging industry. It introduces a new mechanical gripper design, along with a set of manipulation strategies, for secure, damage-free handling and effective packaging of envelope stacks. The development includes a custom pneumatic gripper tailored for envelope stacks, innovative strategies for precise stack extraction and placement of envelopes into cardboard boxes, and addressing friction-related issues during gripper retraction. Through experimental validation, the effectiveness of the proposed gripper design and strategies are demonstrated in preserving the integrity of envelope stacks. The research highlights the potential of these automation solutions in industries handling compressible items.

Keywords: Intelligent Process Automation, Fixture and Grasping, Human -Machine Interfaces
Abstract: In recent years, the automation of bin picking in factory environments has made significant strides. The automation of rigid objects, such as metal components, has been successfully implemented by leveraging 3D data. However, for deformable objects like wire harnesses, where the object's pose is uncertain, practical implementation is challenging. In most cases, manual intervention remains predominant. To overcome this problem, we propose a system wherein human operators teach a robot wire harness grasping actions through hand demonstrations. The process involves capturing human grasping of the wire harness and instructing the robot based on RGB-D images to learn the human grasped location and grasping posture. We notice that human tends to grasp specific regions with characteristic structures of wire harnesses. In order to learn such information, we propose a method to build a dataset for neural network training with few shot images. We form the problem as instance segmentation and augmentation of the training dataset is achieved by overlaying wire harness images onto various background scenes. Next, the obtained point cloud of grasping locations is aligned with the point cloud from the demonstration instances through point cloud registration. Using such information, the robot transfers the wire harness grasping pose during the demonstration to the current scenes. We evaluated the accuracy of grasping location segmentation and the success rate of wire harness grasping in real experiments.

Keywords: Image Processing, Part Feeding and Object Handling , Fixture and Grasping
Abstract: Nowadays, with the appearance of machine learning technologies, factory automation(FA) attracted much attention. The pose estimation process is commonly used in bin-picking, a kind of process in FA. The research of object pose estimation has been addressed by many researchers all over the world. This technology is adapted in many fields such as CG, VR/AR and robotics. In this paper, pose estimation of cylinder with point cloud data is focused. This is a complicated problem because there are cases that acquired point clouds can contain two types of aspect which are base and side of cylinder. This case highly affects the accuracy of pose estimation. For the problem, we proposed the geometric density-based clustering approach as the cylinder axis is the keypoint. The approach is comprised of three steps. Firstly, the probability density estimation of two Gaussian spheres from normal and cross product of point clouds is performed with directional kernel density estimation(KDE). Secondly, the dominant aspect out of base and side is chosen with the point-to-point matching process for estimate of the center point. Finally, aspect clustering is conducted with the in-out circle created by cosine similarity to use cylinder axis. Additionally, the center point is estimated as the average of point clouds or least square(LS) circle fitting depending on the dominant aspect. Consequently, we denoted the accurate one-shot pose estimation results at many cylinder dimension.