Keywords: Micro/Nano Manipulation, Micro-Electro-Mechanical Systems, Robot Dynamics and Control
Abstract: Active micromotors, which self-propels with optoelectronic guidance, hold great promise in biomedical applications such as immune-sensing and antibacterial, where motion accuracy and task efficiency are highly desirable. However, independent transport and cargo delivery for multiple micromotors is great challenging due to the time-varying self-propulsion velocity and nonlinear interactions with cargos. Here, we propose a novel data-driven adaptive control method for multiple micromotors, which enables independent control of micromotor motion and cargo delivery behaviors on demand. The motion and interaction states can be guided by programmable light patterns in optoelectronic systems. By dynamically adjusting parameters of light patterns, the state of multiple micromotors were decoupled, enabling individual trajectory and velocity for each micromotor. A data-driven motion controller was developed with consideration of system nonlinearity and random interference. The controller enabled online updating to learn micromotor differences, and providing individual features for optimal prediction to enhance the cargo delivery performance. The data-driven controller was validated by accurately controlling several micromotors with diverse reference velocities in a confined space with 10 collision-risk regions. The time-average velocity error was consistently within 1/2 of body length per second, and the success rate of cargo delivery was improved from 42% to 90% with optimal prediction.

Keywords: Biomechatronics, Flexible Manipulators and Structures
Abstract: Variable stiffness technology plays a key role in exploiting the advantages of soft robotics, however, few solutions can satisfy both large deformability and wide-range stiffness tunability. In this work, we present the variable tensile stiffness fiber (VTSF) based on crossing layer jamming, which enables both high stretchability and tensile stiffness tunability. By leveraging an X-shape crossing of layers and combining it with the outside silicone tube, the fiber can achieve fine stretchability and recovery. By applying a vacuum to the tube, friction between the layers generated by the atmosphere pressure allows for high tensile stiffness tunability. We establish the stretchability and mechanical models to illustrate the underlying principle of the VTSF and guide the design process. Our proposed fiber can achieve stretchability theoretically up to 300%, more than 100× tensile stiffness modulation, and a tunable maximum tensile force >25 N. We apply the VTSFs to the soft actuators and rigid-linkage joints to show the functionality. By combining the VTSFs with soft actuators, we verify their capability to achieve reprogrammable deformations and motions. Moreover, by applying the VTSFs to the rigid structures, we achieve both wide-range motion and high load capacity tunability of the soft-rigid coupled structures.

Keywords: Modeling and Design of Mechatonic Systems, Medical Robotics/Mechatronics, Flexible Manipulators and Structures
Abstract: Follow-the-leader (FTL) motion is essential for continuum robots operating in fragile and confined environments. It allows the robot to exert minimal force on its surroundings, reducing the risk of damage. This paper presents a novel design of a snake-like robot capable of achieving FTL motion by integrating fiber jamming modules (FJMs). The proposed robot can dynamically adjust its stiffness during propagation and interaction with the environment. An algorithm is developed to independently control the tendon and FJM insertion movements, allowing the robot to maintain its shape while minimizing the forces exerted on surrounding structures. To validate the proposed design, comparative tests were conducted between a traditional tendon-driven robot and the novel design under different configurations. The results demonstrate that our design relies significantly less on contact with the surroundings to maintain its shape. This highlights its potential for safer and more effective operations in delicate environments, such as minimally invasive surgery (MIS) or industrial in-situ inspection. A video demonstration of performance test on JammingSnake can be found at https://youtu.be/P2YTCPWHNfo.

Keywords: Service Robots, Human -Machine Interfaces, Control Application in Mechatronics
Abstract: This paper proposes a wheelchair-mounted tendon-driven robotic arm system based on upper limb posture control to address the conflict between the limited movement range and the weight of robotic arm in current wheelchair-mounted robotic arm systems. By utilizing the lightweight, compact design and multiple degrees of freedom of the tendon-driven robotic arm, the conflict between motion range and weight is resolved. The posture-based control method also meets the needs for fine operation tasks and is suitable for users with limited control abilities. First, a wheelchair-mounted tendon-driven robotic arm system was designed, then the control architecture of robotic arm was implemented. Finally, a physical platform was built to complete two daily assistive functions: object grasping and placement, as well as assistance with cleaning. These tasks were performed, achieving the expected assistive outcomes and validating the effectiveness of the tendon-driven robotic arm assistance and control method.

Keywords: Aerial Robots, Modeling and Design of Mechatonic Systems, Novel Industry Applications of Mechatroinics
Abstract: This paper presents a novel hydro-powered collaborative twin scrubber aerial cleaning robot that merges the advantages of aerial mobility and ground-based contact cleaning for safe and efficient surface maintenance. Inspired by impulse turbine principles, the system utilizes cleaning fluid for propulsion, eliminating multiple mechanical actuators and reducing its electronic footprint. High-velocity fluid ejection generates torque to drive lightweight scrubber pads for effective contact cleaning. The paper details the robot’s design, dynamic modeling, and a control strategy enabling friction-based locomotion. The proposed model and error-based controller are validated through simulations and experiments, demonstrating the feasibility of deploying a passively driven scrubber robot for planar surface cleaning.

Keywords: Sensor Integration, Data Fusion, Neural Networks, Sensors and Sensing Systems
Abstract: Relative measurement technology for cooperative objectives is a crucial foundation for autonomous flight and collaborative task execution in Unmanned Aerial Vehicle (UAV) swarms. To achieve onboard relative positioning of micro-UAV swarms in complex environments, this work proposes a lightweight Deep Learning (DL)-based Direction of Arrival (DOA) estimation method for miniaturized UAV-embedded microphone arrays. A Deep Neural Network (DNN) model consisting of one convolution block and four residual blocks is developed. The spatial covariance matrix of sound signals is reshaped as input feature vector to improve the generalization ability of the DNN model. We have conducted real-world experiments on a 2x2 cm UAV-embedded microphone array. Experimental results indicate that the proposed model achieves an average error of less than 10^circ in low signal-noise ratio (SNR) environment, demonstrating significant improvements over traditional methods and Convolutional Neural Network (CNN).

Keywords: Sensors and Sensing Systems, Neural Networks, Intelligent Process Automation
Abstract: In aircraft assembly, precise component docking is crucial, yet traditional manual alignment often results in extended assembly times and inconsistent outcomes. Recent advances in virtual assembly, relying on LiDAR-based 3D scanning, reduce both physical assembly attempts and potential damage to large aircraft parts. However, the extensive size and smooth geometry of these components require multiple LiDAR devices that produce large-scale and featureless point clouds, making accurate point cloud registration challenging. Conventional methods, including ICP-based variants, rely heav ily on favorable initial poses and are prone to local minima, while Transformer-based approaches often require high compu tational costs. To address these issues, we propose AeroMamba, a learning-based framework for efficient and high-precision registration of large-scale aircraft point clouds. It integrates a state-space model for local feature extraction, combing a Hilbert curve strategy to maintain spatial coherence by reordering raw point clouds in a one-dimensional sequence and a global feature module for robust representation. Finally, we use Singular Value Decomposition to compute transformations. Validations on a self collected fuselage dataset demonstrate AeroMamba’s superior accuracy, scalability, and computational efficiency, highlighting its potential for real-world aircraft assembly ap plications.

Keywords: Sensors and Sensing Systems, Applications of nano technology, Micro-Electro-Mechanical Systems
Abstract: This paper presents an analytical study of the vibration behavior of functionally graded (FG) nanoscale mass sensors with multiple mass-spring-damper attachments. Using the nonlocal strain gradient theory and Timoshenko beam assumptions, the effects of material gradation, attached mass-spring systems, and temperature on natural frequencies are analyzed. Results show that accounting for the nonlocal and strain gradient parameters is crucial for accurate frequency predictions, with the Timoshenko model consistently yielding lower frequencies for thicker beams.

Keywords: Sensors and Sensing Systems, Sensor Integration, Data Fusion, Vehicle Technology
Abstract: Driver drowsiness estimation is one of the important techniques concerning traffic safety. A lot of methods have been developed to evaluate drowsiness conditions of driver using vehicle performance, operation inputs, driver behavior, driver’s physiological signals, and fusion of these data. Field tests as well as experiments using driving simulators have yielded various datasets and research repositories in the literature. However, most of these researches considered only predefined driving situations, and for studies on driver behavior modeling most experiments were conducted with short durations, which may not induce enough drowsiness in subjects. This study aims to develop an experimental platform for driver drowsiness estimation in order to collect experimental data, which consists of a selected set of vehicle information, driver control inputs, and driver behavior data. In addition, electrocardiogram (ECG) signal is collected as a physiological channel. Different from previous studies, this study employs a simple car-following scenario in the experiments. The subjects are instructed to follow and keep a distance from a lead (front) car, which is maneuvered to stop with randomized sudden breaks. During the test sessions, the subjects need to concentrate to keeping the headway between two cars, and must pay attention to avoid collision. Such driving scenario is expected to induce fatigues and drowsiness in the subjects. Driving tests have been conducted with five subjects, and the experimental results verified the proposed method.

Keywords: Micro-Electro-Mechanical Systems, Design/control of MEMS-nano devices, Intelligent Sensors
Abstract: This paper presents the improvement approaches and experimental tests of a fused silica inductive vibrating ring gyroscope. By optimizing the external anchor of the ring resonator from a square to a circular shape and enhancing the metal film deposition process, the quality factor of the gyroscope was increased to 3.8 million. After mechanical trimming based on femtosecond laser ablation, the frequency splitting was reduced to 22 mHz. The device demonstrated a peak-to-peak angle-dependent bias of 0.161°/s under the rate-integrating mode, a scale factor repeatability of 1.60 ppm, and a high-speed rotation angle tracking error of 0.8°, highlighting its potential for high-speed inertial sensing applications.

Keywords: Control Application in Mechatronics
Abstract: Contour tracking is crucial in multi-axis motion control systems, requiring both multi-axial contouring and individual-axial servo performance. Existing methods such as cross-coupled control (CCC) and task coordinate frame (TCF) exhibit limitations in asymptotic tracking and system nonlinearity, respectively. This paper proposes a novel time-varying internal model principle-based contouring control (TV-IMCC) to enhance contour tracking performance by simultaneously reducing axial and contour errors. The TV-IMCC methodology includes an extended position domain framework with master-slave structures for contour regulation, and a time-varying internal model principle-based controller for axial tracking precision. A novel signal conversion algorithm within the extended position domain framework decouples the original n-axis contouring problem into (n-1) two-axis master-slave tracking problems, extending the class of contour candidates. With this, the TV-IMCC addresses time-varying dynamics in axial systems via the transformation between time and position domains. Stability analysis for the closed-loop system of the TV-IMCC is provided. Simulation and experimental results demonstrate enhanced contour tracking performance compared to existing methods, without strict requirements on master axis precision, making the TV-IMCC well-suited for multi-axis macro-micro motion systems.

Keywords: Machine Vision, Control Application in Mechatronics, Sensor Integration, Data Fusion
Abstract: Fruit harvesting robots often struggle with precise target localization, gripper alignment, and adaptability to harsh conditions when targets are occluded by leaves and stems. While various solutions have been suggested, the transferability of techniques is limited among different robots due to their unique hardware designs and sensor implementations. To address these challenges while preserving transferability, we propose a perception and alignment framework. It integrates a simplified perception method, Spherical Object Modeling (SOM), with hybrid visual servoing to achieve accurate target-gripper alignment. SOM effectively approximates the position and size of the fruits, enabling the controller to track the target even with partial observation. Evaluations of SOM at various occlusion rates show it achieves high precision (errors under 4%) and rapid processing times (under 1 ms), highlighting its overall robustness and efficiency. Our experiments with two fruits, melon and tomato, successfully demonstrate the framework’s capability to deal with different fruit types with several gains selection, noise and 39% occlusion.

Keywords: Vehicle Control, Mobile Robots, Control Application in Mechatronics
Abstract: This paper studies the path-following problem for autonomous ground vehicles (AGVs) where the modeling uncertainties and time-varying velocity are addressed. An observer-based robust switched linear-parameter-varying (LPV) energy-to-peak controller is proposed to improve the closed-loop robustness and path-following performance. Since the lateral velocity is unmeasurable, an observer-based output feedback framework is exploited. To overcome the wide variations of longitudinal velocity, the path-following system is modeled as a switched LPV system that consists of a series of LPV models to facilitate a switched control design. The energy-to-peak performance criterion is adopted to further limit the overshoot of the path-following error. Stability analysis is carried out mainly based on the average dwell time method for switched systems. Simulations and experiments demonstrate that, compared to conventional gain-scheduling methods, the proposed method can enhance path-following accuracy and robustness, especially in scenarios with a large range of varying velocity.

Keywords: Part Feeding and Object Handling , Mechatronics in Manufacturing Processes, Control Application in Mechatronics
Abstract: In this paper, we propose a new fabric manipulation method for the top layer of two layered pieces of fabric. The fine motion of the top fabric layer is controlled by rubbing the surface of the top fabric with the robot end-effector of a rigid conical rod with a hemispherical tip. The proposed method is inspired by a human skill to align fabric edges by rubbing the top fabric with his or her fingers. The top and bottom fabric edges can be precisely aligned through multiple rubbing motions controlled by the observed misalignment of the fabric edges. We perform the local edge alignment experiments of the cuff parts with three different fabrics along their longitudinal direction using a single manipulator with the conical rod end-effector. The experimental results show that the proposed method can accurately align the edges of the fabric.

Keywords: Parallel Mechanisms, Control Application in Mechatronics
Abstract: The increasing demand for machining larger workpieces has led to the development of larger machine tools. To enhance precision in driving these larger stages, parallel twin-drive systems, which utilize two motors, have been introduced. However, these twin-drive systems suffer from coupling forces between the motors, which degrade tracking performance. This study proposes a novel mode decoupling method to mitigate the effects of coupling forces and improve control accuracy in twin-drive stages. The system is modeled as a simple two-inertia system, and a virtual viscosity is introduced to decouple the dynamics into translational and rotational modes. Furthermore, a feedforward implementation of the virtual viscosity and a controller conversion method are developed to ensure compatibility with the actual control architecture of industrial machine tools. The proposed method is validated through simulations and experiments. Experimental results demonstrate a 21.9% improvement in tracking performance under machining conditions compared to the default controller, confirming the practical effectiveness of the proposed method.

Keywords: Rehabilitation Robots, Modeling and Design of Mechatonic Systems, Actuators
Abstract: In this paper, a lower limb powered exoskeleton was designed for rehabilitation assistance, aimed at aiding stroke patients in their rehabilitation to restore normal gait patterns. This exoskeleton was equipped with novel motorized linear actuators and magnetorheological (MR) dampers, which can enable the exoskeleton to provide the required assistance function precisely with lower energy consumption. In addition, two actuators installed at the waist were employed to drive the exoskeleton to turn left and right actively. We detailed the hardware design of the exoskeleton, including mechanical structures, novel linear actuator, MR damper and joint actuation units. Human testing on a healthy subject was conducted preliminarily to validate the functions of the exoskeleton. The results indicated that the developed exoskeleton’s hip, knee, and ankle joints can replicate the reference angles accurately during normal walking.

Keywords: Rehabilitation Robots, Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems
Abstract: Exoskeletons hold great potential to enhance human locomotion performance, but their development is often hindered by bulky, heavy, and obtrusive actuator and mechanism designs. Here, we present a compact and lightweight hip exoskeleton endowed with a custom high torque density actuator and two foldable mechanisms, namely foldable waist belt with self-alignment mechanism and foldable thigh brace with self-adjusting linear slider mechanism. Our model of actuator electromagnetic design considered four design parameters, including end winding length, stator teeth number, rotor pole pair number and gear ratio that are tailored for portable exoskeletons. Two foldable mechanism enhanced exoskeleton adaptability and user comfort. Benchtop experimental results demonstrated that our actuator can provide an 18 Nm peak torque with a packaging factor improvement of 27% in contrast with state-of-the-art actuators used in exoskeletons. The volume of our exoskeleton was reduced by 55% and the weight was reduced from 3.7kg to 2.7kg compared to our prior design. Preliminary human experiments demonstrated the feasibility of our exoskeleton to reduce metabolic rate during walking and stair climbing for young and older adults.

Keywords: Rehabilitation Robots, Modeling and Design of Mechatonic Systems
Abstract: The rehabilitation of wrist motor functions following neurological injuries, such as stroke, requires targeted, repeatable, and adaptive therapies to promote functional recovery and neuroplasticity. Robotic systems for rehabilitation have emerged as valuable tools, offering precise assessments of a patient's abilities and progress while accurately adapting training difficulty levels. Combined with real-time feedback, these systems enhance patient engagement, motor learning, and overall rehabilitation outcomes. However, despite considerable advancements in robotic rehabilitation technology, designing systems that replicate the human wrist's complex biomechanics, including human-like torque generation and a full range of motion, remains challenging. Constraints related to mechanical complexity, size, and portability pose additional barriers to widespread adoption in both clinical and home-based settings. This paper introduces a custom mechanical design solution implemented in a novel robotic end-effector specifically designed for human wrist rehabilitation, named EDUSA® PRO, which provides three actuated degrees of freedom to support complex wrist movements. We describe the design and development of a custom-designed motion system, which allows to meet both range of motion and torque values of the unimpaired human wrist. This goal is realized via the design of custom-made rails and rolls system that allows to replicate simultaneously movements along the three degrees of freedom, with smooth motion and without interference, and through the selection of a power transmission system purposely sized to achieve human torque values. Results demonstrate that EDUSA® PRO closely replicates the human wrist's capabilities while maintaining a compact and ergonomic design, making it one of the more competitive end-effector robots for upper limb rehabilitation on the market.

Keywords: Rehabilitation Robots, Humanoid Robots, Service Robots
Abstract: Upper-limb rehabilitation robot often link user with wearable exoskeleton or end-effector. But both of them have some inherent shortages. Facing over-constrained problem, exoskeleton pattern has strict demands for both construction design and control performance, which lead to complex controller and mechanical structure, and needs accurate measurement of user limb parameters. Single arm end-effector link pattern has a limit on the amount of DOF under control, that means the joint angle stats of upper-limb are often uncertain, so it is hard for upper-limb joints to get sufficient movements. Dual-arm end-effector link pattern is still strong link at present, robot and user fasten together. Though overcome shortages of single arm end-effector link pattern, it also has to face over-constrained problem as wearable exoskeleton. In order to overcome the shortages while hold the advantages, in this paper end-effector weak link pattern was proposed according to the pattern that physiotherapist bring patient’s limb to move hand-by-hand. A no fasten assistant arm is added based on single arm end-effector link pattern, and achieve humanoid rehabilitation training with dual arm cooperation. Through analysis, the shoulder and elbow joints of user can get sufficient rehabilitation training without over-constrained problem in this end-effector weak link pattern. Experiments with healthy subjects show that compared with single arm end-effector link pattern. End-effector weak link pattern can enlarge user’s shoulder internal/external rotation average range over 50° in this experimental rehabilitation motion, namely has more comprehensive rehabilitation training ability than single arm end-effector link pattern.

Keywords: Rehabilitation Robots, Machine Learning, Control Application in Mechatronics
Abstract: Upper limb rehabilitation exoskeletons offer a promising technological solution for neuromuscular function recovery. However, their clinical application remains challenging due to difficulties in effectively integrating therapists’ professional expertise. This paper proposes a demonstration-based therapist rehabilitation skill transfer learning paradigm, where general rehabilitation assisting policy is learned from demonstration through simulation-based learning, and ultimately deployed onto a real exoskeleton system. The learning framework consists of two components: a motion imitation module that simulates muscle-actuated movements using a two-level policy, and a reinforcement learning-based exoskeleton controller that adapts assistance based on patient states. These components work jointly within a human-exoskeleton coupled system to reproduce therapist-guided movements and force patterns. Demonstration data, including video and surface electromyography, are processed to generate biomechanically consistent reference motions and torque labels. The trained controller is validated through both single-joint and multi-joint rehabilitation tasks. Experimental results show that the system can accurately replicate therapist-like motion and joint torques, and demonstrates strong adaptability across subjects and task types.

Keywords: Biomechatronics, Robot Dynamics and Control, Control Application in Mechatronics
Abstract: The operating environment of snake robots is typically complex and harsh, often subjecting their actuators to high load conditions that can lead to joint failures. This paper presents an optimal robust path following control framework for snake robots operating under joint failures, based on improved lateral undulatory gait. Two joint failure scenarios are considered: free-joint failure (including head-joint free) and free-joint failure combined with an adjacent joint locked at 0 degrees. To tackle these scenarios, we develop improved gait patterns that increase the control degrees of freedom and adjust the inter-joint phase shift, enhancing the adaptability of the robot. The dynamic model is re-derived based on the modified gait, with simplifications made to reduce computational overhead. Model predictive control (MPC) is then applied to generate optimal control inputs while considering multiple constraints. Furthermore, to achieve robust path following control in the presence of unknown and variable friction forces and external disturbances, adaptive interaction is employed for real-time tuning of the MPC weights. Extensive simulation and experimental results, across various failure scenarios, validate the effectiveness of the proposed control architecture in enabling motion recovery and robust path following under joint failures.

Keywords: Biomechatronics
Abstract: 目的 分析冠状面、矢状面、 横向关节弯矩和关节冲量 通过调整步态来检测髋关节、膝关节和踝关节 通过改变 trunk 的姿态和 足底支撑，为 制定训练和康复计划正常和早期膝骨关节炎 （KOA） 人口。方法 10 例健康成年志愿者 招募，并使用 三维红外动作捕捉系统采集 正常步态 （NW）、躯干前倾步态的步态数据 （KW），穿着摇晃鞋 （NY），躯干前倾 穿着摇晃鞋 （KY）。数据已导入到 AnyBody，用于计算关节力矩和冲力矩 下肢。使用统计参数映射 比较步态之间的差异。结果 膝关节 内收力矩 （KAM） 和 KAM 冲量矩降低 KW、NY 和 KY 步态。在 KW 步态中，髋关节和膝关节矢状面 弯矩和矢状冲矩增加，并且 踝关节矢状面和冠状动脉冲动时刻增加。在纽约 步态、髋冠状面和水平冲

Keywords: Flexible Manipulators and Structures, Modeling and Design of Mechatonic Systems, Biomechatronics
Abstract: Soft robots, known for their adaptability, flexibility, and safety in interactions with humans, have garnered increasing attention due to their potential applications in diverse fields. Among various soft robotic designs, origami-inspired structures offer unique advantages such as lightweight construction and compactness through foldable mechanisms. This study introduces a novel enclosure and holding mechanism, inspired by the wing-folding structure of earwigs, which features a three-step folding process. The mechanism, constructed from sheet-like materials, demonstrated the ability to fold into a fan shape, allowing inward bending when pressure is applied to the base. Motion capture experiments revealed that the tip displacement reached up to 70 mm with a displacement angle of 77° during maximum indentation. Additionally, force measurements indicated a consistent inward force exceeding 0.15 N, with the highest force at the midpoint of the mechanism. The design effectively adapted to the shape of surrounding objects, suggesting potential applications in soft robotic grippers and adaptable holding devices. This study contributes to the development of bio-inspired, compact, and efficient soft robotic mechanisms for advanced grasping and manipulation.

Keywords: Biomechatronics, Control Application in Mechatronics, Human -Machine Interfaces
Abstract: Asymmetric weight-bearing frequently disrupts gait symmetry and increases muscular effort. This paper presents the development of a lightweight ankle exoskeleton and a human-in-the-loop (HIL) adaptive control strategy to mitigate these effects. The exoskeleton employs a modular design with remote actuation to minimize distal limb mass. The hierarchical control strategy estimates ankle joint moments in real-time using plantar pressure for proportional assistance, while a high-level Bayesian optimization algorithm iteratively adjusts bilateral assistance parameters to maximize a center of mass (CoM) velocity-based symmetry index (SI). Experiments with three healthy participants carrying a unilateral 15% body weight load demonstrated that the HIL optimization converged rapidly (average 2.8 min) to subject-specific, asymmetric assistance levels. Compared to unassisted loaded walking, the optimized assistance improved CoM velocity SI by 13.16%. Concurrently, bilateral plantar flexor muscle activation showed reductions of up to approximately 28%. These results highlight the potential of adaptive, personalized exoskeleton control to restore gait symmetry under asymmetric loading conditions.

Keywords: Biomechatronics, Legged Robots, Modeling and Design of Mechatonic Systems
Abstract: Climbing ladders is an important capability for robots, enabling their deployment in diverse environments. However, existing research primarily concentrates on locomotion along ladders, neglecting the critical transitions between horizontal and vertical movements. To address this issue, our work proposes a comprehensive strategy for enabling hexapod robots to move through ladders. The process of moving through the ladder is divided into three phases, corresponding control methods are designed, and the boundary conditions of the key phases are discussed. Particularly, the 2-2-2 gait and tripod gait are designed and implemented for the movement along the ladder, and a comparative analysis is conducted. For the Ladder-to-Ground (LTG) phase, the torque support distribution of each leg is discussed, resulting the derivation of the constraint conditions during movement. As the result, the hexapod robot, RHex-T3, is upgraded by enhancing the torque output capability of its front limbs and reducing weight, and experiments are conducted to demonstrate the feasibility and successful performance of the proposed strategy. The robot can climb ladders with varying spacings, inclinations, and even vertical orientations.

Keywords: Robot Dynamics and Control
Abstract: Pneumatic artificial muscles are extensively employed in soft wearable robotic systems to assist the movements of human joints. Nevertheless, conventional pneumaticartificial muscles make it difficult to change their configuration to adapt to different human joints. Moreover, a mismatching of assistive parameters and human motions causes poor adaptability or limited assistive performance. This study introduces a reconfigurable exomuscle system that employs the best evaluated parameters to assist hip flexion or ankle plantarflexion during walking. A set of the best evaluated parameters is determined from nine conditions with the objective of minimizing muscle activation in treadmill walking trials. Nine conditions represent different assistance timings of the xomuscle contraction and extension, which are triggered by foot plantar pressure. The system’s configuration can be altered from a series mode to a parallel mode within 7 mins. Assistive performance is validated through human subject testing of ten participants in both indoor and outdoor environments. The average reduction in metabolic cost for hip flexion assistance is 9.99%, whereas the average decrease for ankle plantarflexion assistance is 7.91% in treadmill walking. Results of an outdoor environment test indicate that metabolic costs can be reduced. The system offers an alternative method to facilitate the application of reconfigurable portable devices in real-world situations.

Keywords: Actuators in Mechatronic Systems, Novel Industry Applications of Mechatroinics, Actuators
Abstract: Contactless handling systems for substrates hold significant potential in enhancing chip manufacturing yields by allowing the use of thinner and larger substrates, eliminating the risks associated with physical contact. This article introduces a novel contactless force actuator, employing the active air-bearing working principle, designed with a compact structure to effectively actuate substrates. The actuator features a continuous deformable air-bearing surface composed of compliant-based actuation unit cells, ensuring ease of fabrication to meet tight air-bearing tolerances. A modular design with seven unit cells is designed and manufactured to validate the performance. The results confirm that the proposed contactless actuator can be used to levitate and actuate the substrate simultaneously, in which case the maximum actuation force in the x-axis is determined to be 90 mN and a 42.5-μm fly height in the z-axis is achieved.

Keywords: Intelligent Sensors, Modeling and Design of Mechatonic Systems, Vehicle Control
Abstract: This paper presents a wheel force sensing method based on spherical-joint force measurement. The force measurement uses strain gauges on L-shaped linkages connecting the wheel to the double wishbone suspension system. It does not require the wireless power supply and signal transmission because it does not rotate with the wheel. Using the measured joint-forces to solve the suspension mechanism configuration, the wheel position and orientation (relative to the vehicle) can be calculated, eliminating the need for position sensors. Through the continuous calculation of the position and orientation, the inertia force and moment can be estimated, which is used in the Newton-Euler formulation for wheel force calculation. Experimental results show the root mean square error of three-axis wheel forces≤8.26 N and mean error of three-axis wheel forces≤4.06 N, which validates the feasibility of the proposed method.

Keywords: Tele-operation, Robot Dynamics and Control
Abstract: Teleoperated manipulation under the human intelligence is an effective solution to confront complicated tasks in unknown environments. However, the uncertain contact in environments is the main challenge to achieve good teleoperation, and some inevitable issues such as nonlinearities, various uncertainties, constraints, and communication delays in local and remote robots should also be taken into account. In this article, a unified contact model is proposed to be the targeted environment interaction for remote robot, which can cover various conditions such as free motion and rigid contact by setting different model parameters. Subsequently, a hybrid motion/force controller is developed to cope with nonlinearities and various uncertainties by adaptive robust technique, thus guaranteeing system stability and good transient convergence to the unified contact model. Particularly, to handle misteleoperation or sudden change of conditions described in unified contact model, a model predictive control-based contact optimization method is developed to be the outer loop of hybrid motion/force controller, which plans the desired motion and force trajectories that meet the state and targeted interaction constraints. By the estimation and transmission of environment parameter, the environment dynamics is reconstructed in the local side, which provides the effective force feedback of remote contact environment for human operator. Since the signal transmitted is replaced by the estimated environment parameter, the power-cycle in communication channel is eliminated. This design can avoid the passivity stability problem caused by communication delays, so the stability and good transparency under delays can be guaranteed by the separate design of local and remote controllers. The comparative experiments are implemented and verify the effectiveness of proposed framework for teleoperated manipulation.

Keywords: Underwater robotics, Sensor Integration, Data Fusion, Artificial Intelligence in Mechatronics
Abstract: Underwater target localization uses real-time sensory measurements to estimate the position of underwater objects of interest, providing critical feedback information for underwater robots in tasks, such as obstacle avoidance, scientific exploration, and environmental monitoring. While acoustic sensing is the most acknowledged and commonly used method in underwater robots and possibly the only effective approach for long-range underwater target localization, such a sensing modality generally suffers from low resolution, high cost, and high energy consumption, thus leading to a mediocre performance when applied to close-range underwater target localization. On the other hand, optical sensing has attracted increasing attention in the underwater robotics community for its advantages of high resolution and low cost, holding a great potential particularly in close-range underwater target localization. However, most existing studies in underwater optical sensing are restricted to specific types of targets, thus lacking generalization capabilities. In addition, these studies typically focus on the design of estimation algorithms and ignore the influence of illumination conditions on the sensing performance, thus hindering wider applications in the real world. To address the aforementioned issues, this article proposes a novel target localization method that assimilates both optical and acoustic sensory measurements to estimate the 3-D positions of close-range underwater targets. The proposed sensing method integrates a large vision model with unique acoustic-based model prompt design to process multimodal sensor measurements, ensuring the generalizability and robustness of underwater target localization. A test platform with controllable illumination conditions is developed. Extensive experiments are conducted, the results of which validate the effectiveness of the proposed method.

Keywords: Aerial Robots
Abstract: Modular self-reconfigurable robots (MSRRs) offer enhanced task flexibility by constructing various structures suitable for each task. However, conventional terrestrial MSRRs equipped with wheels face critical challenges, including limitations in the size of constructible structures and system robustness due to elevated wrench loads applied to each module. In this work, we introduce an aerial MSRR (A-MSRR) system named BEnding-moment-free self-reconfigurable Aerial roboT moduLE (BEATLE), capable of merging and separating in-flight. BEATLE can merge without applying wrench loads to adjacent modules, thereby expanding the scalability and robustness of conventional terrestrial MSRRs. In this article, we propose a system configuration for BEATLE, including mechanical design, a control framework for multiconnected flight, and a motion planner for reconfiguration motion. The design of a docking mechanism and housing structure aims to balance the durability of the constructed structure with ease of separation. Furthermore, the proposed flight control framework achieves stable multiconnected flight based on contact wrench control. Moreover, the proposed motion planner based on a finite state machine achieves precise and robust reconfiguration motion. We also introduce the actual implementation of the prototype and validate the robustness and scalability of the proposed system design through experiments and simulation studies.

Keywords: Modeling and Design of Mechatonic Systems, Control Application in Mechatronics, Transportation Systems
Abstract: We present a development of a novel scaled testbed for studying aircraft tire-runway frictional interactions and braking performance under various pavement and wet conditions. The indoor testbed is built on a platform with a rotational arm to support an aircraft tire to travel on a circular, reconfigurable runway track. By designing the braking torque and the rotating arm motion, the testbed possesses the capability to emulate the dynamic characteristics of aircraft tire-runway interactions and braking maneuvers. Besides the mechatronic design of the testbed, we present the modeling and control of the platform subsystems to demonstrate the capability and performance for simulation of braking maneuvers with different pavement grooves and water film thicknesses. The dimensionless analysis and design are presented to represent the equivalent braking processes of a full-size aircraft. Experimental results demonstrate the effectiveness and efficacy of the testbed design in studying the frictional interactions between the aircraft tire and the runway pavement.

Keywords: Medical Robotics/Mechatronics
Abstract: This paper proposes a novel wireless localization system to locate magnetic robots or devices for clinical application. The primary challenge here is to precisely locate the robots in a human-body workspace while confirming systematic feasibility and reliability in clinical settings. Precisely, by the co-developing of cable-driven mechanism and flexible magnetic sensor printed circuits, a reconfigurable sensor array scheme is presented, which has regard for both sides about accuracy and practical viability. The sensor array configuration can be adjusted to accommodate different patient's body. Moreover, key technical issues, the configuration modeling approach of the cable-driven backbones and the sensors' coordinate transformation paradigm, are analyzed for the further construction of the magnetic modeling and the localization algorithm. Afterwards, the effectiveness of the developed system and methods are validated in the robotic arm platform and, notably, in-vivo animal environment. Results reveal that the localization accuracy can achieve 0.0088 m and 1.5081° in static cases, and 0.0126~0.0165 m and 3.4655~4.3316° with promotions of 42.73~55.65% and 29.80~63.54% in dynamic cases compared to the planar sensor arrays. Besides, in-vivo animal tests indicate applicability of the proposed system which realizes repetition accuracies with mm-level by 0.0059 and 0.0054 m, and localization accuracies by 0.00685 and 0.0419 m (two moving processes in animal esophagus). These results verify the feasibility and superiority of the proposed system, holding significant practical significance in addressing the challenge associated with magnetic localization tasks toward clinical scenarios.

Keywords: Medical Robotics/Mechatronics
Abstract: For stereotactic brain biopsy, some neurosurgical robots have been adopted to provide the level-1 autonomy (mechanical guidance). However, their surgical safety and collaboration with surgeon still remain challenges. To answer these problems, this article presents a robotic system with higher autonomy in this procedure. First, for level-2 (task) autonomy, a novel brain biopsy device is developed based on the requirements of surgical hygiene, task automation, and intuitive control. Second, for level-3 (conditional) autonomy, a safety-critical, collision-free, and workspace-constrained neuronavigation is achieved, with each proposed objective and constraint formulated by control Lyapunov functions (CLFs) and control barrier functions (CBFs). Specifically, for dynamic collision avoidance with surgeon, the Robo-centric ESDF (RC-ESDF) method is extended and integrated with the time-variant CBF. An optimal-decay CLF-CBF-based quadratic programming framework is utilized to unify and solve these tasks. For evaluation, simulations and experiments are designated and conducted. Results demonstrated that the proposed solution can realize the collision-free and workspace-constrained neuronavigation when the nearby surgeon performs surgical tasks, and offer the biopsy samples sufficient for histopathological examination. This research contributes to higher autonomy of brain biopsy robotic system, by bringing a facilitation of surgeons' operation, an alleviation of their physical/mental burden, and an enhancement of surgical safety.

Keywords: Mechatronics in Manufacturing Processes, Modeling and Design of Mechatonic Systems, Fixture and Grasping
Abstract: In this paper, we propose a Passive Actuator-Less Gripper (PALGRIP) for picking a piece of fabric from a stack of fabric parts and placing the picked fabric part. The picking of a piece of fabric from a stack is a simple but difficult process to automate. The proposed gripper can pick a piece of fabric from the stack by simply pressing the fingertips of the gripper against the stack. The fingers are closed and opened by the relative motion between the fingers and the housing of the gripper. The grasping motion of the gripper is generated by two mechanisms: a passive pinching mechanism and a self-locking mechanism. These mechanisms allow the fingers to perform opening and closing movements and to maintain the fingers in either open or closed state. The kinematics of the mechanisms are analyzed to design the gripper. The relation between the movement of the fingers and the force required to operate the gripper is also investigated through static force analysis and the experiment. Finally, experiments using PALGRIP are conducted, and the experimental results illustrate how the pick-and-place operations are carried out using the prototype of PALGRIP. The proposed gripper allows the robot to automate fabric pick-andplace operations easily by attaching it to the robot's endpoint.

Keywords: Tele-operation, Rehabilitation Robots, Design Optimization in Mechatronics
Abstract: Teleoperation has been widely used to expand human working capability in remote, unstructured, and dangerous environments, such as explosive disposal and rescue tasks. In such scenarios, both large-scale flexible obstacle avoidance and small-scale refined operations are often required. However, existing teleoperation devices rarely manage to meet both of these requirements at the same time. This article proposes a new exoskeleton capable of functioning in homogeneous and heterogeneous~(Homo-Hetero) teleoperation to achieve efficient and precise human-like teleoperation. Regarding hardware design, the exoskeleton fully considers the motion coordination of the human arm. It covers all degrees of freedom (DoF) of the human arm, including the sternoclavicular motion. A new elbow structure and special overall layout are implemented to reduce the motion inertia and enhance the range of motion (ROM).Regarding teleoperation control, a novel teleoperation framework is proposed that seamlessly switches between homogen

Keywords: Modeling and Design of Mechatonic Systems, Mobile Robots, Legged Robots
Abstract: The authors have developed a novel multi-modal robot named TraQuad, which integrates the features of legged and tracked robots. This robot aims to combine agility, maneuverability, traction, and efficiency for traversing various environments. Legged locomotion allows the robot to select optimal contact points on the terrain, while tracked locomotion enables faster movement over relatively simpler uneven terrains with greater efficiency. TraQuad can turn about its central vertical axis and execute sharp turns with a 0.25 m turn radius. It can climb steep slopes of 31° at a velocity of 0.9 m/s. Utilizing multimodal locomotion, it can climb rocks and overcome obstacles by either skipping or stepping on them. Climbing rocks 1.75 times the height of the tracks requires a peak torque of 5.14 Nm, whereas stepping on a block of the same height requires a peak torque of 8.15 Nm. Skipping a block 1.5 times the height requires a peak torque of 11.8 Nm. This demonstrates that climbing obstacles while maintaining contact with them is more economical than stepping on them, proving the viability of tracked-legged locomotion. These advancements highlight the potential of TraQuad as a robust solution for navigating diverse and challenging environments.

Keywords: Legged Robots, Robot Dynamics and Control, Learning and Neural Control in Mechatronics
Abstract: Quadrupedal jumping has been intensively investigated in recent years. Still, realizing controlled jumping with soft landings remains an open challenge due to the complexity of the jump dynamics and the need to perform complex computations during the short time. This work tackles this challenge by leveraging trajectory optimization and behavior cloning. We generate an optimal jumping motion by utilizing dual-layered coarse-to-refine trajectory optimization. We combine this with a variable impedance control approach to achieve soft landing. Finally, we distill this computationally heavy jumping and landing policy into an efficient neural network via behavior cloning. Extensive simulation experiments demonstrate that, compared to classic model predictive control, the variable impedance control ensures compliance and reduces the stress on the motors during the landing phase. Furthermore, the neural network can reproduce jumping and landing behavior, achieving at least a 97.4% success rate. Hardware experiments confirm the findings, showcasing explosive jumping with soft landings and on-the-fly evaluation of the control actions.

Keywords: Service Robots, Artificial Intelligence in Mechatronics, Machine Vision
Abstract: The field of robotic manipulation is having a major upgrade thanks to the recent breakthroughs in generalist artificial intelligence and the increasing demand for advanced automation at homes and workplaces. In terms of tooling for robotic manipulation, this work studies a unique configuration of robot end-effectors and skills. Tools as simple as a stick can fulfill a good variety of tasks in our daily lives when used creatively. Such tools often work with "aim-and-shoot" skills with which, as long as the tool is properly placed on the target object, the manipulation action is as simple as a single move. Other than benefiting affordability, reliability, and durability, the minimalism helps leverage the available ability of generalist AI on physical reasoning and enables them to reason on physics at sub-object level. Based on a cognitive analysis of ChatGPT, this paper introduces a prompt-based teaching pedagogy that allows novice users to easily teach AI to make reliable decisions for aim-and-shoot skills. In addition, new grounding techniques are presented for quantifying the AI decisions and facilitating visual servoing. The proposed methods are validated using two skills associated with a stick tool to fulfill three tasks.

Keywords: Novel Industry Applications of Mechatroinics, Artificial Intelligence in Mechatronics, Machine Learning
Abstract: Accurate prediction of surface roughness is im- portant for ensuring machining quality and advancing in- telligent manufacturing processes. In this study, we applied data-driven approaches using decision tree, support vector regression (SVR), and extreme gradient boosting (XGBoost) models to predict surface roughness based on key machining parameters. These methods were chosen due to their effective- ness in capturing nonlinear relationships in machining data. The multi-verse optimizer (MVO) was employed to fine-tune hyperparameters. To further enhance model performance and ensure physical interpretability, we employed a physics-guided loss function that constrains the machine learning models based on known physical phenomena. This approach ensures that the model predictions are more consistent with established physical laws. Data were collected from various machining parameters, including rotation speed, feed rate, cutting depth, force, and the abrasive size to ensure a comprehensive analysis of factors affecting surface roughness., and the result shows that SVR achieved the highest accuracy, with an R2 of 0.877 and MAPE of 0.119, followed by XGBoost, while the decision tree exhibited the lowest generalization ability. These results confirm that the integration of physics-informed techniques significantly enhances the model’s reliability and physical consistency in predicting surface roughness.

Keywords: Machine Vision, Artificial Intelligence in Mechatronics, Novel Industry Applications of Mechatroinics
Abstract: This paper proposes a load detection method for construction cranes using a boom-tip monitoring camera image sequence. Knowing the exact shape of a load is crucial for predicting and preventing accidents involving human workers. However, considering the vast variety of construction sites and loads involved, it is not always possible to know the shape and appearance of a load in advance. Therefore, we develop a method to detect various loads using a general segmentation model with temporal consistency analysis. To further improve the method, we propose using person motion trajectories to remove shadow regions and employing a multiple object tracker to allow for the evaluation of multiple initial candidates. We test the proposed method using real camera images and conduct an onsite load detection experiment.

Keywords: Artificial Intelligence in Mechatronics, Machine Learning, Machine Vision
Abstract: Battery recycling is becoming increasingly critical due to the rapid growth in battery usage and the limited availability of natural resources. Moreover, as battery energy densities continue to rise, improper handling during recycling poses significant safety hazards, including potential fires at recycling facilities. Numerous systems have been proposed for battery detection and removal from WEEE recycling lines, including X-ray and RGB-based visual inspection methods, typically driven by AI-powered object detection models (e.g., Mask R-CNN, YOLO, ResNets). Despite advances in optimizing detection techniques and model modifications, a fully autonomous solution capable of accurately identifying and sorting batteries across diverse WEEEs types has yet to be realized. In response to these challenges, we present our novel approach which integrates a specialized X-ray transmission dual energy imaging subsystem with advanced pre-processing algorithms, enabling high-contrast image reconstruction for effective differentiation of dense and thin materials in WEEE. Devices move along a conveyor belt through a high-resolution X-ray imaging system, where YOLO and U-Net models precisely detect and segment battery-containing items. An intelligent tracking and position estimation algorithm then guides a Delta robot equipped with a suction gripper to selectively extract and properly discard the targeted devices. The approach is validated in a photorealistic simulation environment developed in NVIDIA Isaac Sim and on the real setup (Fig. 1).

Keywords: Intelligent Process Automation, Artificial Intelligence in Mechatronics, Machine Vision
Abstract: This paper presents a cutting-edge robotic inspection solution designed to automate quality control in automotive manufacturing. The system integrates a pair of collaborative robots, each equipped with a high-resolution camera-based vision system to accurately detect and localize surface and thread defects in aluminum high-pressure die casting (HPDC) automotive components. In addition, specialized lenses and optimized lighting configurations are employed to ensure consistent and high-quality image acquisition. The YOLO11n deep learning model is utilized, incorporating additional enhancements such as image slicing, ensemble learning, and bounding-box merging to significantly improve performance and minimize false detections. Furthermore, image processing techniques are applied to estimate the extent of the detected defects. Experimental results demonstrate real-time performance with high accuracy across a wide variety of defects, while minimizing false detections. The proposed solution is promising and highly scalable, providing the flexibility to adapt to various production environments and meet the evolving demands of the automotive industry.

Keywords: Mechatronics in Manufacturing Processes, Control Application in Mechatronics, Rapid Prototyping
Abstract: Robotic wire arc additive manufacturing has been widely adopted due to its high deposition rates and large print volume relative to other metal additive manufacturing processes. For complex geometries, printing with variable height within layers offers the advantage of producing overhangs without the need for support material or geometric decomposition. This approach has been demonstrated for steel using precomputed robot speed profiles to achieve consistent geometric quality. In contrast, aluminum exhibits a bead geometry that is tightly coupled to the temperature of the previous layer, resulting in significant changes to the height of the deposited material at different points in the part. This article presents a closed-loop approach to correcting for variations in the height of the deposited material between layers. We use an IR camera mounted on a separate robot to track the welding flame and estimate the height of deposited material. The robot velocity profile is then updated to account for the error in the previous layer and the nominal planned height profile while factoring in process and system constraints. Implementation of this framework showed significant improvement over the open-loop case and demonstrated robustness to inaccurate model parameters.

Keywords: Modeling and Design of Mechatonic Systems, Design Optimization in Mechatronics, Novel Industry Applications of Mechatroinics
Abstract: This paper presents an advanced totem-pole bidirectional active front end (AFE) converter design for enhancing the performance of six-axis industrial robot controllers. Addressing the limitations of conventional diode rectifier-based systems, such as large harmonic currents, large volume for brake resistor, and volatile DC-link voltage, the proposed AFE converter offers high power factor, bi-directional energy flow, and tunable DC-link voltage. A detailed analytical loss model and hardware design guidelines for the totem-pole AFE is proposed, aiming to optimize energy conversion efficiency and improve power density. An advanced control strategy ensures stability and rapid response. Experimental results validate the effectiveness of the proposed AFE converter, demonstrating its potential to significantly enhance the efficiency and flexibility of automated production lines utilizing six-axis robots.

Keywords: Biomechatronics, Novel Industry Applications of Mechatroinics, Modeling and Design of Mechatonic Systems
Abstract: Flexible grippers based on handed shearing auxetics (HSA) have demonstrated potential advantages over pneumatic solutions with respect to lightweighting and energy savings. However, there is a paucity of systematic research on how material hardness and geometric thickness affect the grasping force and contact pressure distribution. To address this issue, this paper proposes a single servo gear coupling mechanism that can simultaneously drive four HSA cylinders to achieve a flexible bending grasp of delicate crops. The finite element analysis (FEA) method was employed to systematically investigate the impact of material parameters, including wall thickness and material hardness, on grasping force and pressure distribution. The findings of the study demonstrated that the incorporation of harder and thicker HSA cylinders led to a substantial enhancement in gripping force. However, this augmentation was accompanied by an increase in the concentration and localization of high pressure on the cylinder surface. In contrast, the utilization of softer or thinner designs resulted in a reduction in peak pressure and an enhancement in contact uniformity. Nevertheless, a compromise in the overall gripping ability was observed. The findings of this study underscore the necessity for a holistic balance of material and structural parameters to cater to distinct gripping requirements, thereby serving as a valuable reference point for future research and practical applications in this domain.

Keywords: Design Optimization in Mechatronics, Novel Industry Applications of Mechatroinics
Abstract: Operations in confined spaces are prevalent in large-scale manufacturing industries. The complexity of such environments and spatial constraints often necessitate unconventional working postures, impacting operational sustainability and posing potential health risks. Conventional assistive devices are often inadequate in these settings. This paper introduces a wearable and portable assistive device, supernumerary robotic limbs (SRLs), designed to provide support without hindering the user's natural limb function. We present the design and optimization of this novel bilateral SRL system specifically tailored for near-ground operations in confined spaces. The proposed device facilitates body weight support and active posture adjustments. By optimizing its mechanical structure, the system achieves improved torque density. Additionally, human-SRL collaborative experiments are conducted and the biomechanical analysis demonstrates that the proposed system reduces the user’s energy expenditure during near-ground tasks by up to 20%, validating the effectiveness of the proposed SRL system.

Keywords: Modeling and Design of Mechatonic Systems, Micro/Nano Manipulation, Novel Industry Applications of Mechatroinics
Abstract: Hybrid reluctance actuators are increasingly considered superior due to the remarkable motor constant, yet a challenge is the inherent nonlinearity of the large stroke reluctance actuator. Atomic force microscopy (AFM) is crucial for nanometric precision characterization and lithography in nanotechnology. However, conventional AFM suffers limitations of the piezoelectric stack's restricted stroke. This paper proposes a novel two-axis reluctance-actuated nanopositioner with enhanced linearity supporting large-scale AFM. To mitigate the reluctance actuator's inherent nonlinearity, a nonlinear compliant compensator is utilized. A two-step approach is presented to guide the design of both the reluctance actuator and nonlinear XY motion stage. Theoretical analyses and simulations validate that the proposed system achieves enhanced linearity and extended stroke, opening new avenues for large-scale AFM.

Keywords: Mobile Robots, Control Application in Mechatronics, Novel Industry Applications of Mechatroinics
Abstract: This paper studies the problem of designing a path following controller for the autonomous forklift in a warehouse. The proposed nonlinear model predictive controller (NMPC) tackles both the general need for efficient transportation between areas following a given path, as well as the specific requirement for autonomous forklifts to conduct precise fork positioning during pallet loading tasks to prevent damage to goods and pallets. To validate the efficacy of our approach, we conduct real-world experiments using a laboratory autonomous forklift prototype. The experimental results show that the proposed controller can achieve a good balance between accuracy and efficiency in general path following task, as well as achieving pose accuracy better than 1cm and angle accuracy within 0.5°in the pallet loading task.

Keywords: Planning and Navigation, Mobile Robots, Hybrid intelligent systems
Abstract: We present an autonomous exploration system for efficient coverage of unknown environments. First, a rapid environment preprocessing method is introduced to provide environmental information for subsequent exploration planning. Then, the whole exploration space is divided into multiple subregion cells, each with varying levels of detail. The subregion cells are capable of decomposition and updating online, effectively characterizing dynamic unknown regions with variable resolution. Finally, the hierarchical planning strategy treats subregions as basic planning units and computes an efficient global coverage path. Guided by the global path, the local path that sequentially visits the viewpoint set is refined to provide an executable path for the robot. This hierarchical planning from coarse to fine steps reduces the complexity of the planning scheme while improving exploration efficiency. The proposed method is compared with state-of-art methods in benchmark environments. Our approach demonstrates superior efficiency in completing exploration while using lower computational resources.

Keywords: Planning and Navigation, Neural Networks, Mobile Robots
Abstract: This paper tackles the problem of ensuring provably safe and computationally efficient l ocal o bstacle avoidance for robots navigating dynamic and spatially constrained settings. Traditional reinforcement learning algorithms often overlook the heterogeneity and interaction of human behaviors, limiting their effectiveness in real-world settings. To address this, we propose a neural network that integrates graph and attention mechanisms, enhancing the robot’s ability to interpret pedestrian characteristics and intentions. Additionally, we incorporate game theory into the reinforcement learning reward function, guiding the robot to learn more effective human-robot interaction strategies. Experimental results demonstrate that our approach enables robots to perform well in complex crowd navigation scenarios, adeptly handling pedestrians with varying goals and avoidance intentions.

Keywords: Genetic Algorithms, Planning and Navigation
Abstract: In the field of deep space exploration, land inspection and detection robots are a crucial tool for detecting the terrestrial environment on the surface of stars. With various complex terrain environments on the surface of different stars, traditional robots, such as wheeled robots, crawler robots, and legged robots, encounter the risk of overturning. The variable scale tetrahedral rolling robot with autonomous mobility is an innovative land inspection and detection robot that can achieve autonomous movement, continuous rolling motions and variable scale through its own spatial structure transformation. It has the advantages of high stability and flexibility. Path planning serves as a fundamental requirement for robots to achieve optimal movement. In contrast to traditional mobile robots, the trajectory formed by the tetrahedral rolling robot's movement is composed of triangular meshes. Currently, the path planning algorithms commonly used for mobile robots are not applicable to the path planning of tetrahedral rolling robot. To tackle the above problems and to meet the requirements of tetrahedral rolling robot's path planning, a genetic algorithm (GA)-based path planning approach is proposed. According to the mechanical structure of the robot, a form of rolling motion is proposed. Based on this motion principle, the trajectory as a triangular mesh is verified. Furthermore, the pose coordinates of the robot are established in a triangular grid map, while the transformation relationship between the triangular grid map and the Cartesian coordinate system is presented. According to the triangular mesh map, a path planning method based on GA is developed. Finally, the proposed method is verified experimentally by using MATLAB software. The experimental results illustrate that, the proposed method can realize the optimal path planning. The path planning method implemented in this paper lays a crucial foundation for the motion control of the robot. It provides a theoretical basis for robotic land exploration of complex environments in deep space exploration.

Keywords: Planning and Navigation, Mobile Robots, Compuational Models and Methods
Abstract: This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments comparing Ceres and g2o within Cartographer, Ceres outperformed g2o in terms of speed, convergence efficiency, and overall map clarity. Ceres required fewer iterations and less time to converge, producing more accurate and well-defined maps, especially in real-world mapping scenarios with the AgileX LIMO robot. However, g2o excelled in localized obstacle detection, highlighting its value in specific situations.

Keywords: Planning and Navigation
Abstract: Reliable positioning by Global Navigation Satellite System (GNSS) is essential for safe and large-scale land transport systems operations. However, the advent of GNSS navigation has led to a heightened vulnerability of GNSS services to the spoofing attacks. Based on the involvement of the Visual-Inertial Odometry (VIO) that remains unaffected by GNSS spoofing attacks, a GNSS spoofing detection solution is proposed with the enhancement by VIO. In this solution, a Kernel Density Estimation (KDE) strategy is adopted to model the distribution of difference between VIO and non-interfered GNSS positioning results. Real-time monitoring of the deviation in the current localization difference distribution is realized by the Kullback-Leibler (KL) divergence, with which the GNSS spoofing detection logic is designed. Real-world datasets of visual/inertial/GNSS-based positioning are involved to validate the solution through GNSS spoofing injection tests. Results under two typical spoofing attack scenarios demonstrate the advantages of the proposed solution in detecting deviation caused by the spoofing attack, illustrating the great potential in the VIO-enhanced multi-sensor fusion-based autonomous positioning systems.

Keywords: Humanoid Robots, Sensor Integration, Data Fusion, Planning and Navigation
Abstract: High-precision environmental mapping and accurate localization constitute critical prerequisites for mobile robot navigation. However, humanoid robots frequently exhibit motion blur during locomotion, which significantly degrades the localization accuracy of visual SLAM. Addressing the locomotion characteristics and capabilities of the humanoid robot GTX-III, this study proposes a multi-layer mapping method incorporating traversability maps and a localization solution based on multi-source sensor fusion. Building upon this foundation, an autonomous navigation platform for the humanoid robot GTX-III is developed. Experimental results demonstrate that the proposed approach effectively constructs hierarchical maps, achieves superior localization accuracy compared to conventional visual-inertial schemes, and successfully accomplishes autonomous navigation in custom-built complex environment.

Keywords: Modeling and Design of Mechatonic Systems, Control Application in Mechatronics, Robot Dynamics and Control
Abstract: This study presents the development of a two-wheeled self-balancing bicycle system using a reaction wheel for stability control. Due to the bicycle’s hardware complexity, a simplified test platform was first employed for physical modeling and designing with a Linear-Quadratic Regulator (LQR) controller for idea validation, while the actual full-size bicycle uses the full state feedback control for final implementation. Compared to conventional PID controllers, LQR outperformed superior response speed and stability. Although the LQR controller enhances system stability and performance by minimizing a weighted error in the state-space model, the frequent changes of motor speeds resulted in significant power consumption. Therefore, a novel Adaptive Equilibrium Point Gradual algorithm (AEPGA) is further applied to adjust the system’s equilibrium point to improve adaptability on environmental changes, such as imbalanced lateral payload. In addition to the simulation, the algorithms were also deployed to a real bicycle for performance comparison. Due to proposed AEPGA, the bicycle is capable of riding on campus uneven asphalt road with maximum 17km/h. Moreover, the imbalanced lateral payload experiments were also arranged in the laboratory with maximum 2kg imbalance weighs at one side, and the results showed that the proposed AEPGA outperformed the LQR with 67.456% total power efficiency reduction during 22s tests to show the advantage on power consumption on the reaction wheel. Finally, a 3D Lidar is utilized for the path tracking experiments inside building corridor and campus asphalt road, and the 77 m building corridor achieved 0.153 m mean absolute error (MAE) with 1.39 km/h average speed; the 190 m campus asphalt road achieved 0.283 m mean absolute error (MAE) with 2.31 km/h average speed.

Keywords: Hybrid intelligent systems, Machine Learning, Neural Networks
Abstract: The generation of industrial process flow charts is crucial in modern production, but traditional deep learning models struggle with inconsistent formats, terminology variations, and noisy data. To address these limitations, we propose a novel framework for industrial flowchart generation based on Multi-Modal Large Language Models (MLLMs), named IndustFlow. IndustFlow leverages MLLMs to convert industrial graphics into detailed, interpretable textual descriptions, improving the utility and comprehensibility of industrial data. Additionally, we introduce a Graph-Based Retrieval-Augmented Generation (GraphRAG) model, which integrates knowledge graph structures to generate precise and contextually enriched outputs. The framework also enables cross-domain knowledge alignment by bridging domain-specific terminologies and leveraging vast external knowledge sources. Finally, we enhance the logical reasoning and accuracy of flowchart generation through Group Relative Policy Optimization (GRPO), enabling reliable handling of complex industrial workflows. We collected thousands of noisy industrial documents to evaluate our fine-tuned model, achieving exceptional experimental results that outperform many advanced methods.

Keywords: Machine Learning, Neural Networks, Intelligent Sensors
Abstract: In recent years, significant progress in single-walled carbon nanotube (SWNT)-based flexible strain sensors has facilitated the development of low-cost, flexible, and wearable sensor networks for human motion sensing. However, due to the complex and nonlinear mapping between sensor outputs and human motion, accurately achieving digital human reconstruction from such sensor signals remains a challenging task. This paper proposes a spatiotemporal attention-based and physically constrained algorithm for digital human reconstruction using flexible sensor networks. An improved Transformer architecture is designed to model both temporal dynamics and spatial correlations among multiple sensors through a spatiotemporal attention mechanism. Furthermore, a composite loss function incorporating motion continuity and physical plausibility constraints is introduced to improve the smoothness and realism of predicted motion trajectories. The proposed method achieves real-time, end-to-end prediction of 35 human body joints from raw sensor data. Extensive experiments on a custom dataset demonstrate the effectiveness of the approach in terms of both accuracy and temporal stability. The results demonstrate its potential for real-world deployment in applications such as intelligent sports, rehabilitation, immersive digital human systems, and wearable human–computer interaction platforms.

Keywords: Machine Learning, Data Storage Systems
Abstract: This study addresses the inefficiencies of manual temperature control in hydrometallurgical slurries through intelligent transformation. An automated data acquisition system was developed to collect 1,153 operational records (Dec 4-25, 2024) encompassing key parameters: frame pressure, underflow pump frequency, slurry preheating temperature, steam valve opening, steam pressure, and equipment type. An XGBoost model was implemented to predict slurry temperature, achieving exceptional performance (R²=0.95, adjusted R²=0.95, MSE=9.44). Learning curve analysis confirmed model stability, with training and cross-validation scores exceeding 0.9. SHAP interpretability revealed steam valve opening (importance = 0.65) and heat exchanger type (0.24) as dominant factors, challenging traditional empirical strategies. This data-driven approach enables equipment-specific temperature optimization, advancing intelligent control in metallurgical processes.

Keywords: Fuel Cells and Alternative Power Sources
Abstract: 最近，质子交换膜燃料电池 （PEMFC） 已 在汽车中的应用越来越多 以及各种商业化场景。要解决 经验模型拟合精度的局限性 PEMFC，本研究采用了物理信息神经 提高输出电压精度的网络方法 在动态作条件下进行拟合。一 面向汽车的 PEMFC 耐久性测试用于 验证建议的方法。实验结果 显示拟合输出电压的相对误差 小于 0.5%。

Keywords: Artificial Intelligence in Mechatronics, Neural Networks, Machine Learning
Abstract: Deep neural networks (DNNs) are powerful tools with exceptional approximation capabilities across various applications. However, there is still a lack of substantial progress in attaining stable DNN-based robot feedback control. Current neural network-based Jacobian feedback control methods typically update weights for each specific robot task, emphasizing local task learning over general kinematics learning. There is currently no systematic way of learning the Jacobian matrix using DNNs and deploy it online for task space control in a stable manner without additional offline training for each task. This article introduces a DNN control system comprising a deep regression module and an online adaptation module. The regression module is trained offline with shuffled precollected data, and the online adaptation module is updated online with real-time data for specific tasks. This system enables DNNs to learn general robot kinematics using the regression module and adapt to new tasks online. The performance of the proposed method is demonstrated through experiments on tracking tasks performed by a UR5e robot manipulator.

Keywords: Legged Robots, Robot Dynamics and Control, Control Application in Mechatronics
Abstract: Designing a single mobile platform that can traverse diverse terrain both effectively and efficiently remains a significant challenge in robotics. In this work, we propose an optimized wheel-like walking (WLW) locomotion method that integrates terrain-based foothold planning to adapt to rugged environments. Our gait optimization framework incorporates multiple criteria, including tip-over stability, collision avoidance, leg–wheel mechanism kinematics, motion continuity, and energy consumption. Both the stance and swing phases of the leg trajectories are separately planned and optimized. We validate the proposed trajectory planning strategy on a leg–wheel transformable robot across various types of uneven terrain. Experimental results demonstrate that our approach effectively maintains body stability, minimizes foot–hip height variation, and stabilizes the body pitch near zero. Under equivalent conditions, the optimized WLW gait achieves better terrain adaptation, reduces energy consumption by up to 10%, and maintains minimal pitch variation, allowing the robot to remain nearly horizontal while moving forward on uneven terrain.

Keywords: Robot Dynamics and Control, Control Application in Mechatronics, Mechatronics in Manufacturing Processes
Abstract: Feedrate scheduling has significant impacts on motion efficiency, equipment vibration, and machining quality in robotic manipulation and computer numerical control machining. As the most effective methods for jerk-limited feedrate scheduling, however, optimization-based approaches face challenges such as high computational cost, artificial infeasibility, and feedrate oscillations. This paper proposes a triple linear programming (TLP) method for solving the non-convex 3rd-order problem. To avoid artificial infeasibility caused by convexification, an incremental linearization method (ILM) is developed to generate a feasible solution under non-stationary boundary conditions. Feedrate profiles are further adjusted to eliminate the oscillations caused by the discretization. In experiments on machine tools and robotic manipulators, the proposed method saves more than 10% of motion time than existing linear programming methods and reduces computational time by more than 80% than baselines based on sequential quadratic programming with better time-optimality. Furthermore, the proposed method outperforms baselines regarding feasibility and feedrate oscillations.

Keywords: Control Application in Mechatronics
Abstract: 一组异构的 Attitude 同步 航天器在轨道组装中起着至关重要的作用 超大型空间结构任务。在本文中， 通过采用 Backstepping 方法，分布式姿态 针对这些 无向树图下的异构航天器。一个 模式观察器和虚拟角速度分别为 引入以解决无法测量的问题 柔性附件的振动。通过使用 Lyapunov 理论 和级联系统的理论中，证明 提出的控制律可实现姿态同步 渐近和振动抑制。最后 给出了数值模拟以验证其有效性 拟议的控制法

Keywords: Robot Dynamics and Control, Control Application in Mechatronics, Mechatronics in Manufacturing Processes
Abstract: In many robotic applications that involve physical contact with the environment, robots are required to maintain a prescribed contact force along the normal direction of the contact surface, while tracking a reference trajectory in other directions. To accomplish these tasks without using costly force/ torque sensors, this paper proposes a sensorless hybrid force/ position control method. First, the robot dynamics along the normal direction of the contact surface is separated from other dimensions. Then the force regulation error is reduced by on-line adaption to the environmental stiffness based on the steady-state relation in the normal direction. In addition, sliding mode control is used for trajectory tracking in other directions. Experiments on a 6-axis industrial robot show that the force regulation errors can be reduced to a small level, regardless of a priori knowledge about the nominal environmental stiffness. Furthermore, the trajectory tracking is accurate and decoupled from the motion in the normal direction.

Keywords: Robot Dynamics and Control, Identification and Estimation in Mechatronics, Control Application in Mechatronics
Abstract: Non-contact manipulation is a promising methodology in robotics, offering a wide range of scientific and industrial applications. Among the proposed approaches, airflow stands out for its ability to project across considerable distances and its flexibility in actuating objects of varying materials, sizes, and shapes. However, predicting airflow fields at a distance – and the motion of objects within them – remains notoriously challenging due to their nonlinear and stochastic nature. Here, we propose a model-based learning approach using a jet-induced airflow field for remote multi-object manipulation on a surface. Our approach incorporates an analytical model of the field, learned object dynamics, and a model-based controller. The model predicts an air velocity field over an infinite surface for a specified jet orientation, while the object dynamics are learned through a robust system identification algorithm. Using the model-based controller, we can automatically and remotely, at meter-scale distances, control the motion of single and multiple objects for different tasks, such as path-following, aggregating, and sorting.

Keywords: Rehabilitation Robots, Robot Dynamics and Control, Compuational Models and Methods
Abstract: As prosthetic leg development advances, testing prosthetic legs becomes crucial for their continued improvement. This study presents the design and application of a gait simulator which consisting of a six-degree-of-freedom (6-DOF) robot arm and a treadmill for testing and evaluating lower limb prostheses. The walking gait of a unilateral above-knee amputee was recorded while walking on a treadmill using a motion capture system. The acquired gait data was subsequently input into a gait simulator, which was capable of reproducing the trajectory and kinematics of the affected thigh through the end effector of a robotic arm. Experimental results show that the joint angles of the prosthesis simulated in our gait simulator closely approximate those observed in the real-world prosthetic application. The feasibility of the gait simulator in testing and evaluation of the lower limb prosthesis was preliminarily validated, which will advance the related study in this field.

Keywords: Control Application in Mechatronics, Identification and Estimation in Mechatronics, Robot Dynamics and Control
Abstract: The non-minimum phase characteristics, uncertainties, and strong coupling of flexible link manipulators (FLMs) pose significant challenges to precise tracking control. This paper proposes a backstepping adaptive robust control based on reconstructed dynamics to achieve precise tip tracking. By employing a novel state reconstruction approach, the finiteorder dynamics of the FLM are decomposed into input-output dynamics and internal dynamics. Subsequently, a backstepping direct/indirect adaptive robust control (DIARC) law is synthesized to achieve coordinated integration of the inputoutput dynamics and internal dynamics, ensuring convergence of all system states. Within this scheme, a least square based adaptive law is adopted to estimate parameters accurately for addressing the effects of uncertainties and unknown disturbances in each virtual input channel, while effective robust feedback guarantees ultimate accuracy and robustness of the system. Comparative experimental results demonstrate that the proposed scheme exhibits superior tip tracking performance and reduced tip deflection compared to existing methods.

Keywords: Legged Robots, Robot Dynamics and Control, Compuational Models and Methods
Abstract: This paper presents a physics-informed data-driven methodology to construct a template for running legged robots. The approach leverages a library of candidate functions and a weight matrix to uncover the dynamic model behind the running robot's behavior, enabling the model to generalize beyond a specific trajectory. Instead of being restricted to a single predefined motion, the model can induce a variety of running behaviors across different conditions. The proposed methodology was validated through both numerical simulations and real-world experiments on a RHex-style robot. Results demonstrated that, even when derived solely on data from a single running cycle collected under one initial condition, the model was capable of inducing periodic, stable running behaviors in robots across a wide range of initial conditions.

Keywords: Aerial Robots, Unmanned Aerial Vehicles, Mobile Robots
Abstract: Abstract—This paper introduces LoadFlexBot, a novel air-ground robot integrating Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) systems to address the limitations of conventional UAVs, such as single-mode operation and limited endurance. The robot features a reconfigurable structure with four multifunctional appendages (each offering four degrees of freedom) and achieves a compact, lightweight design through innovative mechanical solutions, mechanical constraint mechanisms that enhance structural stability, and topology optimization of leg connection plates to maximize stiffness (a 42.9% improvement) under a 5% mass constraint. Kinematic and dynamic models, along with a decoupled control strategy, enable seamless mode switching between UAV (maximum speed of 2 m/s, 2.5 kg payload capacity), UGV (maximum speed of 5 m/s), and crouching (minimum height of 0.3 m) modes. Experimental results validate the robot’s capability to traverse 0.8 m obstacles in UAV mode, achieve 90% energy efficiency in UGV mode compared to flight, and demonstrate superior structural stiffness, load capacity, and operational flexibility compared to state-of-the-art systems like the M4 robot.

Keywords: Unmanned Aerial Vehicles, Vehicle Control
Abstract: Rotorcraft uncrewed aerial vehicles (UAVs) have been employed in coastal defense for surveillance tasks; however, the high energy consumption limits their operational range in wide-ranging environments. To address this challenge, a Lyapunov-based formation controller for fixed-wing UAVs is proposed to reduce energy consumption and further expand the surveillance coverage. Furthermore, the windy environment poses a considerable challenge to UAVs, especially those in close formation flight, as minor deviations from the desired position may lead to potential collisions. To mitigate these effects, the wind information estimated by the proposed sliding mode wind observer is incorporated into the formation controller. Finally, the effectiveness of the proposed controller is validated through the software-in-the-loop (SITL) simulation using the open-source PX4 autopilot.

Keywords: Control Application in Mechatronics, Identification and Estimation in Mechatronics, Actuators in Mechatronic Systems
Abstract: This paper proposes an indirect adaptive robust backstepping control method for trajectory tracking of input delay systems with unknown plant parameters and periodic disturbances. By modeling the delay as a transport PDE, the controller is established using the backstepping boundary control technique for PDEs. Periodic disturbances are compensated by its finite Fourier series expansion with unknown Fourier coefficients, and besides the compensation, the underlying robust control comprises the predictor feedback and nonlinear robust feedback, ensuring the robust stability in the co-existence of delay and uncertainties. Meanwhile, unknown plant parameters and Fourier coefficients are learned in batches to reduce parametric uncertainties. The adaptation law is constructed based on the actual system dynamics and totally independent from the design of underlying robust control law, which allows practical modifications such as the on-line explicit monitoring of signal excitation levels to improve the accuracy of parameter estimates in implementation. Comparative experiments on a voice coil motor setup confirm the effectiveness of the proposed controller.

Keywords: Robot Dynamics and Control, Mobile Robots, Actuators
Abstract: When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a certain extent, ensuring continuous operation. To this end, this paper introduces a model-free hierarchical control with fault accommodation (MFHCA) framework designed to address sensor and actuator faults in hydraulically powered HD-WMRs with independently controlled wheels. To begin, a novel mathematical representation of the motion dynamics of HD-WMRs, incorporating both sensor and actuator fault modes, is investigated. Subsequently, the MFHCA framework is proposed to manage all wheels under various fault modes, ensuring that each wheel tracks the reference driving velocities and steering angles, which are inverse kinematically mapped from the angular and linear velocities commanded in the HD-WMR's base frame. To do so, this framework generates appropriate power efforts in independently valve-regulated wheels to accommodate the adaptively isolated faults, thereby ensuring exponential stability. The experimental analysis of a 6,500-kg hydraulic-powered HD-WMR under various fault modes and rough terrains demonstrates the validity of the MFHCA framework.

Keywords: Actuators in Mechatronic Systems, Modeling and Design of Mechatonic Systems, Mobile Robots
Abstract: We have developed a compact and high-stretch ratio robot arm that can avoid interference with crops which are grown in SynecocultureTM. This robot arm has pitch axis rotation by parallel drive of convex. Compared with the existing arm, it expanded the vertical stroke from 300 mm to 1200 mm and can also extend the tip of the arm horizontally to 300 mm. Also, the Pitch axis angle has been improved from one-way operation from 0 deg to -90 deg to bi-directional operation from 25 deg to -90 deg.

Keywords: Parallel Mechanisms, Design Optimization in Mechatronics, Actuators in Mechatronic Systems
Abstract: Wave compensation platforms based on parallel mechanisms (PM) exhibit promising applications in marine engineering. The (6+3)-DOF 3-PPSPR kinematically redundant parallel mechanism (KRPM) is expected to become a wave compensation device for offshore operations. By employing the 3-PPSPR KRPM as the research target, the kinematic model is first constructed through the closed-loop vector method. Subsequently, a numerical search method is implemented to compute the workspace, considering linkage dimensions, joint travel, and geometric relationships. The influence between parameters and workspace volume is quantified through sensitivity assessments. Finally, a two-stage optimization strategy for multiparameter is proposed, and preliminary optimization results are derived. These findings establish the fundamental theoretical foundations for the dimensional synthesis of the 3-PPSPR KRPM.

Keywords: Modeling and Design of Mechatonic Systems, Control Application in Mechatronics, Actuators in Mechatronic Systems
Abstract: This paper presents the development of a novel multi-degree of freedom (DOF) motion simulator based on a spherical wheel system. Virtual reality (VR) has gained significant interest due to its broad range of applications, leading to the development of motion simulators. However, the existing simulators are limited in their motion range and speed of operation, and they lack a degree of freedom. In this paper, the design of a VR-spherical platform for an omnidirectional traveling simulator (VR-SPOTS) is presented to overcome the limitations of existing platforms. The proposed platform achieves high-speed operation with unlimited three-DOF rotation by combining a spherical wheel system and roller-gear assembly, resulting in a simpler mechanical structure. The kinematic and dynamic behavior of the platform is analyzed to establish the motion basis. In addition, a control strategy suitable for effective motion including orientation and velocity control is designed. Numerical simulations verify the feasibility and performance of the proposed design and control scheme, demonstrating accurate orientation and velocity control under various operating conditions. VR-SPOTS is expected to have broad applications in human-factor studies, rehabilitation, and human-machine interaction.

Keywords: Actuators in Mechatronic Systems, Identification and Estimation in Mechatronics, Automotive Systems
Abstract: Modeling of ElectroMechanical Brake (EMB) system is fundamental for the safety control of intelligent or autonomous vehicles. However, traditional physics-based EMB system modeling can be challenging due to requirement of precise parameters value and the switching among multi-stage working process of the EMB system. This paper proposes a hybrid physics-data driven modeling approach based on the Linear-Parameter-Varying (LPV) state space representation to describe the multi-stage working process of the EMB system in a unified model framework. The unified model starts with a traditional physical-based EMB system model, where multiple system parameters are presented, while the friction torque and the hysteresis braking force is modeled by switching functions; Then, the physical model is transformed into a quasi-LPV state space with motor speed appeared in the scheduling parameter vector; The system matrices are then identified through data, avoiding the precise value of each individual system parameters. The effectiveness of the unified model is then validated using experiment data, showing the smoothness and accuracy of the proposed model.

Keywords: Actuators in Mechatronic Systems, Parallel Mechanisms, Actuators
Abstract: The presented clutch mechanism works with textile-based materials having a rectangular shape. All mechanical components are 3-D printed in acrylonitrile butadiene styrene, a strong plastic polymer. It consists of a series of pivoted plates with rectangular apertures through which a webbing passes. Due to the push-pull effect of a small linear motor, the plates can move back and forth by gripping and releasing the webbing, respectively. The engagement and disengagement of the clutch is completely controllable. Moreover, once engaged, the clutch can adapt to the external load: its gripping force increases as the load increases due to the movable and adaptable arrangement of the motor fixations. Elastic bands, anchored to the motor base, allow the motor to return to its initial position, guaranteeing clutch disengagement even under loads. Four different tests are performed: a static tensile test up to 250 N, a cyclic tensile test from 0 to 200 N, an unlocking text under load, and a tensile failure test. Xomino shows prominent performance, as it can hold up to 350 N without breaking or slipping. It has been integrated in a hip exoskeleton prototype and validated on a subject to provide assistance while walking.

Keywords: Vehicle Technology, Human -Machine Interfaces
Abstract: Autonomous vehicle (AV) technologies have thrived with the continuous research efforts from the academia and industry. As AV mileage rapidly accumulates through the prevailing of advanced driver assistance systems and various experimental robotaxi services, passenger comfort remains a barrier to the broader acceptance of AVs. To date, there has not been an openly available dataset focusing on human comfort in AVs. This research presents MOCAV - a Multi-mOdality human Comfort database for Autonomous Vehicles. MOCAV contains the self-reported real-time comfort levels in simulated AV journeys of 20 participants along with the physiological signals and vehicular behaviors during the journeys. The dataset has empowered several peer-reviewed publications about comfort in AVs topics, including identifying factors of human comfort in AVs and quantifying human comfort in AVs. The outcomes of these studies generated suggestions for the comfort-aware designs of AVs and provided quantitative tools to investigate human comfort in AVs. This paper aims to introduce and share the dataset with the community to empower further research on human comfort in AVs.

Keywords: Human -Machine Interfaces, Sensors and Sensing Systems, Sensor Integration, Data Fusion
Abstract: In this study, an insole sensor measuring both pressure and shear force from the foot sole is used to predict the walking angle of a pedestrian on slopes. This wearable device, which is operable outdoors, is anticipated to be useful not only for daily health checks but also as a tool for monitoring motor function over time. By capturing variations in gait and foot dynamics across diverse surfaces, including flat, sloped, and stair environments, it can offer insight into how a user’s balance and coordination adjust in real-world settings. Prior research has demonstrated success in estimating surface types based solely on plantar pressure data, albeit limited to straight, instructed walking paths. This study expands on that by incorporating shear force to assess whether a pedestrian is walking straight or obliquely on slopes, addressing more realistic, spontaneous walking patterns. Looking ahead, this sensor technology has the potential to become a critical tool for early detection of gait abnormalities or balance issues that may be linked to age, neurological conditions, or recovery status post-injury. Such real-time monitoring could support clinicians in designing personalized rehabilitation programs, while users could benefit from proactive alerts to shifts in motor function that may warrant attention.

Keywords: Rehabilitation Robots, Machine Learning, Neural Networks
Abstract: 本研究为无法获得生物信号（如肌电图和足底压力）的患者提出了一种基于脑氧合信号的步态参数拟合和曲线重建方法。从志愿者那里收集脑氧合信号和步态数据，并针对不同的任务提取和减少多维特征。采用差分进化算法优化的随机森林模型开发步行启停意图识别模型 （准确率：94%，决策时间：-0.18s）。步态参数拟合模型集成了双向长短期记忆 （Bi-LSTM） 网络和注意力机制，并通过增量学习进行优化。适应性训练后，步幅和步频的 MAPE 分别为 2.98% 和 3.03%。重建连续的步态曲线，显示出与原始步行模式的强烈一致性。仿真实验验证了该方法的实时性能，算法延迟低于 160 毫秒，有效地驱动了正常行走的下肢外骨骼模型。这项工作为康复设备中的自主步

Keywords: Human -Machine Interfaces, Medical Robotics/Mechatronics, Rapid Prototyping
Abstract: Shoulder disorders, a significant subset of work-related musculoskeletal disorders (WMSDs), are a major cause of disability among industrial workers, driving the development of preventive shoulder exoskeletons. Passive exoskeletons are lightweight and portable but provide a fixed torque profile based on shoulder angle, which limits adaptability to diverse work tasks. In contrast, active exoskeletons offer task-specific assistive torque but are heavier and less energy-efficient, reducing their practicality in industrial settings. Hybrid exoskeletons present a promising solution by addressing the adaptability limitations of passive exoskeletons and the energy inefficiency of active exoskeletons. This study introduces a hybrid shoulder exoskeleton that integrates a magnetic spring-based counterweight mechanism with a quasi-direct-drive motor, achieving an 86.4% reduction in power consumption during arm elevation compared to a single actuator. The exoskeleton covers 90.3% of the shoulder’s natural range of motion and, in human performance tests, reduces anterior deltoid muscle activation by an average of 40% during overhead and waist-level tasks.

Keywords: Actuators in Mechatronic Systems, Human -Machine Interfaces, Flexible Manipulators and Structures
Abstract: Collaborative robots, or cobots, face ongoing safety challenges when operating in direct contact with human operators. This study explores how Series Clutch Actuators (SCAs) can enhance collision safety beyond mere detection and avoidance, focusing on strategies for mitigating sustained forces during and after collisions. Three distinct post-collision response strategies were implemented to enable a human moving the robot after an impact, while the robot remains stationary against gravity: partially engaged clutches (off-power torque-limit), minimally engaged clutches (clutch torque-limit equivalent to torque from gravity), and continuously slipping clutches due to motor input. We used the collaborative robot Nicebot-7 with SCA-joints. Unlike previous iterations of Nicebot, Nicebot-7 does not require passive gravity compensation. Experimental evaluations were conducted in different motion planes (horizontal and vertical), enabling an analysis of impact and escape forces. Results demonstrate overall low forces and that continuously slipping clutches minimize post-collision escape forces especially against gravity. This improvement is particularly relevant for quasi-static collisions, where users experience sustained compression (see Fig. 1). We demonstrate how cobots can become safer to operate, reducing risk to human partners.

Keywords: Human -Machine Interfaces, Mechatronics in Manufacturing Processes, Robot Dynamics and Control
Abstract: In traditional industrial robot trajectory planning, operators typically rely on teach pendant pen or pre-programmed instructions to define highly precise trajectories. This paper proposes a vision-aided human-robot collaboration (HRC) system to enhance the trajectory planning of multi-axis 3D printing for complex geometries. By integrating HTC VIVE trackers and a 3D camera, a novel HRC trajectory planning workflow has been proposed in this paper, which can capture human hand moving trajectories for the generation of a robotic toolpaths with high accuracy. The performance of the printing system was validated through the 3D printing of U-shaped and X-shaped plastic parts on a steel bracket. The combination of human creativity and robot precision enables the realization of more accurate and customized trajectory planning tasks.

Keywords: Design Optimization in Mechatronics, Mobile Robots, Flexible Manipulators and Structures
Abstract: In this paper we propose the design of a novel 3D-printed soft robot capable of traversing corrugated pipes, which are widely employed in subsurface agricultural drainage systems. The robot adopts an inchworm-like peristaltic motion for its bellow-structured body, and it utilizes a self-locking mechanism, realized by exploiting the anisotropic interaction of its reconfigurable fins with the pipe ridges, to achieve locomotion in the desired direction. A salient feature of the design is that the robot robustly moves by an integral multiple of the ridge-to-ridge distance per actuation cycle, allowing the robot to self-localize within the pipe. Experiments are conducted to characterize the stress-strain relationship of the 3D-printed compliant material (thermoplastic polyurethane (TPU)). The corresponding nonlinear elasticity model is used in finite element analysis (FEA)-based design of the bellow structure and the fins for achieving the desired compliance and stiffness anisotropy, respectively. Experiments on a robot prototype inside a four-inch drainage pipe have confirmed the utility of the FEA-based design in achieving sound anisotropic fin stiffness, and the robot's capability to travel a fixed number of ridge-to-ridge distances per actuation cycle, with the number determined by the bellow contraction/expansion stroke. A video summarizing these tests can be accessed at https://youtu.be/VJsw0x1Nl6k.

Keywords: Modeling and Design of Mechatonic Systems, Mobile Robots, Service Robots
Abstract: The ability to climb stairs is essential for mobile robots operating in real-world environments. In industrial settings such as oil and gas facilities, open-rise stairs are widely used due to their reliability and low cost. However, they pose significant challenges for wheeled robots. This study addresses the issue by proposing a novel design that combines four DC motor-driven wheels for ground mobility with a rear linear actuator and an additional wheel for stair climbing. During ascent, the actuator lifts the robot to help the front wheels roll over the steps and prevents tipping when the pitch angle exceeds safe limits. This design enables the robot to climb open stairs without compromising its maneuverability on the ground. In this paper, a wheeled stair climbing robot is designed and a static force analysis is performed in different climbing phases to validate its capability. The analysis also offers theoretical guidance for design and control. The robot was fabricated and tested, and the experiments confirmed its climbing performance on open and solid stairs.

Keywords: Modeling and Design of Mechatonic Systems, Aerial Robots, Unmanned Aerial Vehicles
Abstract: This paper presents TenMo, a lightweight tensegrity membrane wing for monocopters, integrating a prestressed membrane with discontinuous compression elements. This integration significantly enhances structural stiffness, adaptability, and weight efficiency compared to conventional rigid-wing designs made from balsa wood, foam, or 3D-printed composites. Tensegrity structures leverage tension-based equilibrium, offering high deployability and minimal mass—attributes particularly advantageous for aerospace applications. The design methodology employs dynamic relaxation for structural form-finding and analysis and a genetic algorithm for multi-objective optimization to effectively balance stiffness, vibration control, and mass efficiency. The membrane’s prestress naturally induces a cambered airfoil shape, which is beneficial for aerodynamic performance. A physical prototype constructed through straightforward assembly techniques demonstrates the practical feasibility of the proposed concept. Future work aims to enhance aerodynamic efficiency further and explore additional material and geometric configurations.

Keywords: Identification and Estimation in Mechatronics, Modeling and Design of Mechatonic Systems
Abstract: This paper investigates the impact of different pose error expressions and length units on the performance of robot kinematic calibration based on the product-of-exponential (POE) formula. Two models are derived: one expressing pose error in the base frame and another in the tool frame. A theoretical explanation is provided to show why the tool frame expression decouples orientation measurement and position errors, making it more robust to metric scaling. Simulations under various noise conditions and experiments with an Aubo i5 robot validate the hypothesis. The results demonstrate that the calibration model using tool frame pose error expression consistently achieves better accuracy and is less sensitive to changes in length units. This work provides significant insights for improving calibration effectiveness by selecting suitable pose error expression and length unit.

Keywords: Modeling and Design of Mechatonic Systems, Mobile Robots, Robot Dynamics and Control
Abstract: This paper introduces a semi-automated chassis architecture for the self-balancing mobile robot J4.Deliver, designed for multifarious logistic operations in both indoor and outdoor settings. Given the exigencies of high-speed traversal over heterogeneous terrains, rigorous NVH (Noise, Vibration, Harshness) mitigation is indispensable to preserve component integrity and augment maneuverability. A novel lateral stabilization mechanism, which dynamically modulates the robot’s posture during turns, effectively attenuates body roll and enables substantially higher cornering velocities with mitigated rollover risk. Furthermore, an advanced suspension system for dynamic mass and drive subsystem protection is proposed, safeguarding embedded electronics from deleterious impacts. Design optimization via simulation, corroborated by extensive field evaluations, demonstrates that J4.Deliver operates at speeds up to 50% higher than its predecessors, while the suspension system reduces impact accelerations by approximately 95%. Collectively, these innovations confer superior kinematic performance and cost efficiency relative to extant platforms.

Keywords: Humanoid Robots
Abstract: Compliant Bistable Beam Mechanisms (CBBMs), a subclass of Compliant Mechanisms (CMs), have become increasingly prominent in micro-scale and precision engineering applications due to their intrinsic bistability, monolithic fabrication compatibility, and zero-power holding capability. These features make them ideal candidates for integration in MEMS devices, sensors, actuators, and energy harvesting systems. However, the accurate modeling of CBBMs remains a formidable challenge due to the strong nonlinearities associated with post-buckling deformation and snap-through instability. This paper presents a comprehensive review of the current modeling approaches for CBBMs, including analytical, reduced-order, and numerical methods. Particular emphasis is placed on negative-stiffness parametric formulations, which enable efficient design and optimization workflows. Furthermore, a case study is introduced in which a Variable Constant Force Mechanism (VCFM) is designed by combining a Positive Stiffness Structure (PSS) with a Bistable Beam Structure (BSB). A novel FEA-based optimization method is proposed that leverages parametric negative-stiffness expressions to tune bistable characteristics. The method demonstrates fast convergence and accurate control over the force–displacement response, outperforming traditional black-box optimization strategies. A full topology optimization is also conducted to maximize stroke and force flatness.

Keywords: Micro/Nano Manipulation, Micro-Electro-Mechanical Systems, Applications of nano technology
Abstract: Micro/nanorobots have gained increasing attention worldwide owing to their promising potential in biomedicine. Benefiting from their small size and controllability, micro/nanorobots are ideal candidates for applications including targeted therapy, minimally invasive surgery, and drug delivery in physiological environments. However, the micro/nano-scale dimension hinders the ability and future application of miniature robots in the meantime. In recent years, swarm micro/nanorobotics has emerged as a rapidly developing interdisciplinary field. By simultaneously manipulating multiple micro/nanorobots, a micro/nanoswarm possesses larger delivery dose, better adaptivity to external environments, and better imaging contrast. Unlike macroscale robotic systems, implementing sensors or power supplies on micro/nanorobots is hard to achieve, which brings challenges for the control, feedback, and interagent communication of swarm micro/nanorobotics. In this review, we summarize state of-the-art research about micro/nanoswarm, including actuation, imaging, and automatic control. Effective driving strategies and feedback methods provide the foundation for practical application. With the assistance of advanced control algorithms, micro/nanoswarms are able to exhibit computational intelligence. Compared to manual control, micro/nanoswarm systems with high-level autonomy is able to conduct bio-tasks with better efficiency and precision. Moreover, the future challenges and directions for micro/nanoswarms are discussed. With this review, we aim to provide a comprehensive understanding and valuable guidance for swarm micro/nanorobotics researchers.

Keywords: Neural Networks, Rehabilitation Robots, Intelligent Sensors
Abstract: 机器人外骨骼的最新进展凸显了步态相位检测对增强活动能力的重要性。这个过程对于踝关节的正常功能至关重要，由于惯性测量单元 （IMU） 能够提供有关角速度和加速度的数据，因此越来越依赖于惯性测量单元 （IMU）。隐马尔可夫模型 （HMM） 等传统方法在捕获长期依赖关系方面面临挑战。为了解决这个问题，我们的研究提出了一种改进的步态相位检测算法，该算法将卷积神经网络 （CNN） 与长期和短期记忆 （LSTM） 网络相结合，有效地整合了先验值以更好地理解步态动力学。使用优化的 Harris Hawk 算法收集了 7 名参与者的数据，以训练和优化连接到他们脚上的支持向量机 （SVM） 设备。使用准确度、精密度、召回率和 F1 分数评估模型的性能，F1 分数为 93.3

Keywords: Compuational Models and Methods, Neural Networks
Abstract: For patients with motor impairments who cannot effectively collect foot pressure or EMG signals, a motion intention decoding method independent of these signals is needed for autonomous movement training. To improve human-machine interaction, early recognition of motion intention is essential to reduce control delays. For this purpose, Thirty-eight volunteers performed walking tasks. The cerebral hemoglobin signal was collected using functional near-infrared spectroscopy (fNIRS) during the whole process. Brain functional connectivity, network density, and clustering coefficient were calculated in five sub-bands (0.6–2.0 Hz, 0.145–0.6 Hz, 0.052–0.145 Hz, 0.021–0.052 Hz, and 0.0095–0.021 Hz). Furthermore, the AdaBoostClassifier was applied to establish a walking state discrimination model based on the correlation coefficients of brain regions and network parameters. Our study proposes a motion intention recognition method using brain oxygenation signals. It also explores brain network changes before and after motion intention occurs. In high-frequency bands ( 0.6~2.0 Hz, 0.145~0.6 Hz, and 0.052~0.145 Hz), network density and clustering coefficient decreased 1.698 seconds after walking began. These features are more useful for analyzing motion execution. In low-frequency bands (0.021~0.052 Hz and 0.0095~0.021 Hz), brain network topology changed 2.859 seconds before walking.This indicates that these signals help in early motion intention detection.The walking state discrimination model based on the AdaBoostClassifier can get the area under of curve (AUC) value of 0.914. These results confirm that brain oxygenation signals can enable anticipatory motion intention recognition.This provides a new method for human-machine interaction and autonomous movement control.

Keywords: Control Application in Mechatronics, Fixture and Grasping, Neural Networks
Abstract: Due to agricultural labor shortages, the need for autonomous harvesting methods has increased. Gradually, the field of robotics has ingrained itself in the agricultural sector. Unlike traditional robots, soft robotics provides compliant and flexible soft grippers that can interact with their surrounding environment with lower contact forces to handle delicate objects, such as ripe fruits and vegetables. This work focuses on an artificially intelligent 3D-printed soft robotic gripper with embedded pneumatic sensing chambers that can handle and categorize tomatoes during the harvesting process. The ripeness identification process involves two main stages. After touching a tomato with a closed-loop pressure/force control system, the respective pressure signal is fed to a neural network (NN) for classification by analyzing the series data. The proposed NN achieves an accuracy of 85.87%. This approach mimics human techniques for assessing the ripeness (i.e., stiffness) of produce, which involves gently touching the produce to identify its ripeness and then handling it based on the identified ripeness level. This soft gripper is ideal for harvesting fruits and vegetables as it can directly identify the ripeness level and apply the required pressure/force to preserve the quality of the produce.

Keywords: Modeling and Design of Mechatonic Systems, Rapid Prototyping, Opto-Mechatronic Sensors
Abstract: The growing demand for efficiency within the agrifood industry has driven the development of advanced robotic solutions. However, manipulating objects of different shapes, sizes, and weights, many of which are fragile, remains a challenge in the development of gripping devices. In fact, most designs on the market are task-specific, resulting in limited flexibility in terms of stroke and payload. Consequently, increasing interest is directed towards more versatile designs that can safely interact with one or several objects at a time, without causing damage. To fill this gap, this work introduces and validates, both virtually and experimentally, a four-finger sensorized gripper with a reconfigurable structure. The proposed design is capable of securely gripping objects of different shapes and weights using five reconfigurable grip configurations. It also enables multi-object handling by combining grip configurations with soft elements integrated into the palm. The sensing system that operates on the fingertips employs a multi-modal approach to optimize accuracy through the incorporation of tactile sensors, an Inertial measurement unit, and a dedicated connector for a Time-of-Flight proximity sensor module. Compared with the current state of the art, the developed gripper provides improved reconfigurability, versatility for multiobject grasping, and enhanced sensorization while maintaining competitive mechanical performance.

Keywords: Image Processing, Machine Vision, Sensors and Sensing Systems
Abstract: Defect detection in buried pipelines is critical for infrastructure maintenance. While deep learning-based methods have shown promising results, their performance heavily depends on large-scale annotated datasets, which are difficult to obtain for underground environments. On the other hand, traditional image processing techniques suffer from low robustness under complex backgrounds and varying illumination. To address these challenges, this study proposes a novel defect detection system that actively enhances defect visibility through a rotating point light source near the pipe wall. By leveraging the shadow and light reflection caused by the proposed light source, we design a gradient-based detection algorithm that identifies possible regions using polar coordinate transformation, directional Sobel filtering, and DBSCAN. A Darkness-Enhanced Contrast Score (DECS) is proposed to quantify the distinguishability of each possible region based on local contrast and shadow intensity. Experimental results on real corroded gas pipes demonstrate that the proposed system improves detection robustness without relying on machine learning and achieves high precision even under limited data conditions.