Search Results (99)

Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article

Intention prediction is essential for enabling intuitive and adaptive control in upper-limb exoskeletons, especially in dynamic industrial environments. However, the suitability of different cues, sensors, and computational models for real-world industrial applications remains unclear. This systematic review, conducted according to PRISMA guidelines, analyzes 29 studies published between 2007 and 2024 that investigate intention prediction in active exoskeletons. Most studies rely on motion capture (14) and electromyography (14) to estimate joint torque or trajectories, predicting from 450 ms before to 660 ms after motion onset. Approaches include model-based and model-free regression, as well as classification methods, but vary significantly in complexity, sensor setups, and evaluation procedures. Only a subset evaluates usability or support effectiveness, often under laboratory conditions with small, non-representative participant groups. Based on these insights, we outline recommendations for robust and adaptable intention prediction tailored to industrial task requirements. We propose four generalized support modes to guide sensor selection and control strategies in practical applications. Future research should leverage wearable sensors, integrate cognitive and contextual cues, and adopt transfer learning, federated learning, or LLM-based feedback mechanisms. Additionally, studies should prioritize real-world validation, diverse participant samples, and comprehensive evaluation metrics to support scalable, acceptable deployment of exoskeletons in industrial settings. Full article

This paper studies the dynamic parameters identification problem of load and linkages of a serial robot in the presence of model uncertainty. The dynamic parameters of load and linkages of a serial robot have been identified through a combination procedure, which is useful for different platforms of serial robot systems. The purpose of this paper is to propose a dynamic parameter identification method for a serial robot based on a dual quaternion. Using the information of the force and torque of the load obtained by the six-dimensional force sensor installed on the end-effector of the robot, the dynamics parameter identification matrix of the load is derived, which also uses the information of motion speed and acceleration of the end-effector. On the other hand, the analysis of the dynamic relationship between adjacent linkages and the joints is based on dual quaternion algebra, and the identification matrix for the dynamic parameters and the difference values of associated linkages are derived, as well. The combination procedure of the method is flexible in the application of dynamic parameters identification for a serial robot using a dual quaternion. Furthermore, the proposed DQ (dual quaternion)-based method in this paper has the advantage of lower cost compared with the RBFNN (radial basis function neural network)-based method. The effectiveness of the proposed dynamic parameter identification method for a serial robot has been verified by relevant experiments. Full article

This paper proposes an adaptive human–robot concurrent control scheme that achieves the appropriate gait trajectory for a robotic leg prosthesis to improve the wearer’s comfort in various tasks. To accommodate different wearers, a neuromuscular-force-based impedance method was developed using muscle activation to reshape gait trajectory. To eliminate the use of sensors for torque measurement, a disturbance observer was established to estimate the interaction force between the human residual limb and the prosthetic receptacle. The cost function was combined with the interaction force and tracking errors of the joints. We aim to reduce the cost function by minimally changing the control weight of the gait trajectory generated by the Central Pattern Generator (CPG). The control scheme was primarily based on human-in-the-loop optimization to search for a suitable control weight to regenerate the appropriate gait trajectory. To handle the uncertainties and unknown coupling of the motors, an adaptive law was designed to estimate the unknown parameters of the system. Through a stability analysis, the control framework was verified by semi-globally uniformly ultimately bounded stability. Experimental results are discussed, and the effectiveness of the adaptive control framework is demonstrated. In Case 1, the mean error (MEAN) of the tracking performance was $3.6°$ and $3.3°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance were $2.3°$ and $2.8°$, respectively. In Case 2, the mean error (MEAN) of the tracking performance is $2.7°$ and $3.1°$, respectively. And the minimized mean square errors (MSEs) of the tracking performance are $1.8°$ and $2.4°$, respectively. In Case 3, the mean errors (MEANs) of the tracking performance for subject1 and 2 are $2.4°$, $2.9°$, $3.4°$, and $2.2°$, $2.8°$, $3.1°$, respectively. The minimized mean square errors (MSEs) of the tracking performance for subject1 and 2 were $1.6°$, $2.3°$, $2.6°$, and $1.3°$, $1.7°$, $2.2°$, respectively. Full article

With the acceleration of industrialization, deformable mechanisms that can adapt to complex environments have gained widespread applications. Joints serve as carriers for transmitting forces and motions between components, and their stiffness significantly influences the static and dynamic characteristics of deformable mechanisms. A variable stiffness joint is crucial for ensuring the safety and reliability of the system, as well as for enhancing environmental adaptability. However, existing variable stiffness joints fail to meet the requirements for miniaturization, lightweight construction, and fast response. This paper proposes a piezoelectric-actuated variable stiffness miniature rotary joint featuring a compact structure, monitorable loading state, and rapid response. Given that the piezoelectric stack expands and contracts when energized, this paper proposes a transmission principle for stiffness adjustment by varying the pressure and friction between active and passive components. This joint utilizes a flexible hinge mechanism for displacement amplification and incorporates a torque sensor based on strain monitoring. A static model is developed based on piezoelectric equations and displacement amplification characteristics, and simulations confirm the feasibility of the stiffness adjustment scheme. The mechanical characteristics of various flexible hinge structures are analyzed, and the effects of piezoelectric actuation capability and external load on stiffness adjustment are examined. The experimental results demonstrate that the joint can adjust stiffness, and the sensor is calibrated using the least squares algorithm to monitor the stress state of the joint in real time. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

To address the shortcomings of existing robot collision detection algorithms that use six-dimensional force sensors, a force compensation algorithm based on Kane’s dynamics is proposed, along with a collision detection algorithm that uses the six-dimensional force sensor data combined with the robot’s outer surface equations to derive the robot body’s collision point coordinates. Firstly, a collision detection model for a joint-type collaborative robot is presented. Secondly, based on Kane’s dynamics equations, a force compensation model for the joint-type collaborative robot is established and the corresponding force compensation algorithm is derived. Thirdly, a collision detection algorithm is derived, and an example using a cylindrical joint robot with a link’s outer surface equation is used to solve the collision point. The collision is categorized into nine cases, and the coordinates of the collision point are solved for each case. Finally, force compensation and collision detection experiments are conducted on an AUBO-I5 joint-type collaborative robot. The results of the force compensation show that the comparison curves for forces/torques in three directions are consistent, and the relative error is below 5.6%. The collision detection results indicate that the computed collision positions match the actual collision positions, thus verifying the correctness of the theoretical analysis of the force compensation and collision detection algorithms. The research results provide a theoretical basis for ensuring safety in human–robot collaboration. Full article

Robotic cadaveric testing provides a controlled approach to studying knee ligament biomechanics under continuous motion, addressing limitations in static or mechanical loading testing. Our study describes an alternative method for soft-tissue strain measurement, followed by an investigation of this method on knee ligament strain and joint kinematics using a six-degree-of-freedom robotic system equipped with force and torque sensors. Six cadaveric knee specimens underwent controlled 90° flexion cycles, with uniaxial strain gauges sutured to the ACL, PCL, MCL, and LCL for strain measurement. Results indicate that the LCL exhibited the highest extension at 1.63 mm, while the ACL showed minimal extension at 0.09 mm. The MCL were at −0.76 mm and PCL at −1.76 mm contraction, suggesting a stabilizing function under flexion. Varus torque at 2.18 Nm at 90° flexion correlated with LCL strain, and PCL translation variability reflected its multi-planar engagement. These findings confirm ligament-specific strain responses under dynamic loading, highlighting that the LCL and PCL undergo the most significant length changes. Full article

Ground reaction forces (GRFs) are the primary interaction forces that enable a legged robot to maintain balance and perform locomotion. Most quadruped robot controllers estimate GRFs indirectly using joint torques and a kinematic model, which depend on assumptions and are highly sensitive to modeling errors. In contrast, direct sensing of contact forces at the feet provides more accurate and immediate feedback. Beyond force magnitude, tactile sensing also enables richer contact interpretation, such as detecting force direction and surface properties. In this work, we show how tactile sensor information can be used inside the feedback of the control loop to achieve compliance of legged robots during ground contact. The three main contributions are (i) a fast and computationally efficient 3D force reconstruction method tailored for spherical tactile sensors, (ii) a tactile admittance controller that adjusts leg motions to achieve the desired GRFs and compliance, and (iii) experimental validation on a quadruped robot, demonstrating enhanced load distribution and balance during external perturbations and locomotion. The results show that the peak ground reaction forces were reduced by 55% while balancing on a beam. During a locomotion scenario involving sudden touchdown after a fall, the tactile admittance controller reduced oscillations and regained stability compared to proportional–derivative (PD) control. Full article

The accurate detection and identification of collision states in industrial robot environments is a critically important and challenging task. Deep learning-based methods have been widely applied to collision detection; however, these methods primarily rely on dynamic models and dynamic threshold settings, which are subject to modeling errors and threshold adjustment latency. To address this issue, we propose MomentumNet-CD, a novel collision detection method for industrial robots that leverages backpropagation (BP) neural networks. MomentumNet-CD extracts collision state features through a momentum observer and constructs an observation model using Mahalanobis distance. These features are then processed by an optimized three-layer BP neural network for accurate collision identification. The network is trained using a modified Levenberg–Marquardt algorithm by introducing regularization terms and continuous probability outputs. Furthermore, we developed a comprehensive acquisition system based on the Q8-USB data acquisition card and the QUARC 2.7 real-time control environment. The system integrates key hardware components including a MR-J2S-70A servo driver, ATI six-dimensional force/torque (F/T) sensor, and ISO-U2-P1-F8 isolation transmitter, and the corresponding software module is developed through MATLAB/Simulink R2022b, which achieves the high-frequency real-time acquisition of critical robot joint states. The experimental results show that the MomentumNet-CD method achieves an overall accuracy of 93.65% under five different speed conditions, and the detection delay is only 12.16 ms. Compared with the existing methods, the method shows obvious advantages in terms of the accuracy and response speed of collision detection. Full article

Joint actuators are the key components in the innovation and iterative optimization of the robots, with a significant impact on both the performances of robots and manufacturing costs. Conventional industrial collaborative robots often use high-precision position and torque sensors, which are not cost-effective or energy-efficient in specific applications like assistive exoskeletons, legged robots, or wheeled robots. Alternatively, we propose a triboelectric-assisted sensory actuator that balances lightweight design, performance, and affordability for large-scale applications. The actuator is composed of a high-power density motor, a low reduction gearbox, and integrated with a rotational triboelectric sensor, which leads to high dynamic performances and low power consumption. The feasibility of the prototype is initially verified by characterizing the angular positioning accuracy and the back drivability. Experiments indicate that the rotational triboelectric sensor is able to accurately detect the angular displacement of the actuator with the self-generated signals. Overall, a highly integrated actuator module with the actuation and sensing circuit is fabricated as a compact design ready for assembling a complete intelligent robot. This actuator holds great potential as a cost-effective, energy-efficient, and versatile solution for modern robotics, crucial for advancing this field and improving human convenience. Full article

The combine harvester, as a multi-component machine comprising a cutting table, a conveyor, a threshing cylinder, and other components, experiences significant stress and bolt failures in cutting table-conveyor structures due to inherent excitation and the cutting table’s cantilevered design. To address bolt loosening monitoring in the critical joint, this paper designed a Wheatstone bridge circuit-based wireless monitoring system and a multi-channel Wheatstone bridge sensor, enabling multi-bolt monitoring on combine harvesters. Utilizing LoRa wireless communication, the system effectively overcomes the wiring complexity and deployment difficulties of traditional agricultural machinery bolt monitoring systems. The Wheatstone bridge sensor can precisely monitor pre-tightening forces up to 150 kN for M12–M24 bolts. A calibration test based on dynamic time warping (DTW) accurately fitted the sensor’s response to pressure and displacement with determination coefficients of 0.9780 and 0.9753. Then, a validation test focusing on connection bolts revealed a 95.12% overlap between the simulated measurement range and the calibration range under pre-tightening conditions. Furthermore, fitting curves for simulated measurements against tightening torque and angle yielded coefficients of determination of 0.9945 and 0.9939, which demonstrated accurate fitting of pre-tightening conditions and defined the monitoring range of 3.02 × 10¹² to 3.49 × 10¹². Finally, combined with simulation results, a field performance test confirmed the sensor’s ability to detect minute 5% pre-load reductions, achieve 200 ms data transmission to a host computer, and maintain lossless data transmission over 1.2 km. This sensor and system design provided a valuable reference for bolt loosening monitoring in combine harvesters and other agricultural machinery. Full article

The main goal of this paper is to develop a simplified model of the human body motion system that enables its application for movement simulations and the analysis of the kinematic and dynamic parameters occurring during the performance of activities. The model is established on the basis of the modified Hanavan model and consists of rigid solids with simple geometry that are connected mostly with spherical joints. Based on anthropometric data from the literature, a complete set of equations parameterizing the dimensions and mass of each segment was formulated. The equations depend on only two body measurements (height and mass). The model is built in the Adams system as a 3D numerical dynamic model and tested using data gathered with an IMU sensors system. A volunteer lifting an object with a bent spine from the ground with both hands is used for this purpose. Three angles (from the IMUs) are applied to each model’s joint to best simulate human movement and to analyze the angular displacements, velocities, and torques. These results are consistent with theoretical expectations and assumptions, thus proving that reproducing human movements with the developed model is possible and that it also allows various parameters of the human body to be obtained. Full article

Decreasing the position error and control torque is important for the coordinate control of a modular unmanned system with less communication burden between the sensor and the actuator. Therefore, this paper proposes event-trigger reinforcement learning (ETRL)-based coordinate control of a modular unmanned system (MUS) via the nonzero-sum game (NZSG) strategy. The dynamic model of the MUS is established via joint torque feedback (JTF) technology. Based on the NZSG strategy, the existing coordinate control problem is transformed into an RL issue. With the help of the ET mechanism, the periodic communication mechanism of the system is avoided. The ET-critic neural network (NN) is used to approximate the performance index function, thus obtaining the ETRL coordinate control policy. The stability of the closed-loop system is verified via Lyapunov’s theorem. Experiment results demonstrate the validity of the proposed method. The experimental results show that the proposed method reduces the position error by 30% and control torque by 10% compared with the existing control methods. Full article

Although integrated joint torque sensors in robots dispel the need for external force/torque sensors at the wrist to measure interactions, an inherent challenge is that they also measure the robot’s intrinsic dynamics. This is especially problematic for delicate robot manipulation tasks, where interaction forces may be comparable to the robot intrinsic dynamics. Therefore, the intrinsic dynamics must first be experimentally estimated under no-load conditions, when the measurement only consists of torques due to the transmission of the robot actuator, before external interactions may be measured. In this work, we propose an approach for identifying and predicting the intrinsic dynamics using linear regression with non-linear radial basis functions. Then, we validate this regression on a wheel-bearing turning task, in which its friction is a measure of quality, and thus must be accurately measured. The results showed that the bearing torque measured by the joint 7 torque sensor was within an RMS error of 11% of the torque measured by the external force/torque sensor. This error is much lower than that before our proposed model in compensating the intrinsic dynamics of the robot arm. Full article