The theoretical capability of modular robots to recover their original configuration or function after disintegration caused by external impacts has been cited as an advantage for planetary exploration. The key to achieving self-recovery lies in addressing the stochasticity of disintegration. Here, a self-recovery strategy is proposed for modular planetary exploration robots. Firstly, the recovery process is analyzed to construct a strategy framework and provide the problem definitions and strategy assumptions. Secondly, by standardizing the selection criteria for meta-modules under the stochasticity of disintegration, non-mobile modules can acquire mobility through the meta-module method, thereby laying the groundwork for executable self-recovery. Finally, a comprehensive optimization model is proposed, which encompasses the module interactions arising from stochastic disintegration. By integrating selfrecovery characteristics with the simulated annealing algorithm, a solution method is designed to obtain self-recovery plans. Extensive hardware experiments were conducted, and the results demonstrate that the self-recovery strategy operates stably and executes successfully across various configurations and scenarios, thereby validating the feasibility and reliability. In this way, the self-recovery strategy and experiments could substantially advance the application of modular robots in space exploration, while also providing insights for other areas, such as assembly planning and applications involving non-mobile modular robots.

Trajectory planning plays a pivotal role in robotic motion planning, particularly in achieving time-optimal motion under complex dynamic constraints. Although the Time-Optimal Path Parameterization (TOPP) algorithm effectively addresses trajectory generation under joint torque constraints, classical methods often overlook third-order constraints. As a result, the generated trajectories, while torque-feasible, exhibit excessive jerk and poor dynamic stability, which limits their practical applicability. To overcome these limitations, this paper proposes a trajectory planning framework that simultaneously enforces torque and jerk constraints. Building upon torque-constrained TOPP, the method integrates a shooting-based strategy to identify switching points through bidirectional integration under jerk constraints and employs a Sigmoid-based fusion scheme to eliminate integration errors and ensure smooth transitions. The proposed approach is experimentally validated on a six-degree-of-freedom industrial robot. Comparative evaluations with the TOPP-RA algorithm demonstrate that the method significantly reduces both high-frequency vibrations during high-speed execution and residual oscillations after motion termination. Feedback from torque rate measurements, vibration sensors, and laser tracker data confirms faster settling and improved compliance, making the approach well-suited for complex industrial scenarios.

Exoskeleton technology holds significant promise within the human-centric paradigm of Industry 5.0 for mitigating work-related musculoskeletal disorders (WMSDs). However, existing systems often struggle with mismatched assistive torque and inefficient human-machine collaboration under dynamic loading conditions, largely due to insufficient motion intent recognition accuracy. This study proposes a dual-model-based multimodal fusion control strategy that integrates a bidirectional LSTM neural network (Bi-LSTM) with a transformer-based multi-task learning model (MTL) to enable real-time torque compensation and accurate prediction of dynamic load mass under varying conditions. The team developed a lightweight elbow joint exoskeleton prototype, leveraging multi-modal information to enhance assistive torque prediction accuracy. Experimental results show an 83.7% reduction in agonist muscle activation under a 3.5 kg load compared to conditions without the exoskeleton, underscoring its potential for industrial material handling scenarios.

In recent years, significant progress has been made in the prototype design and control methodologies of modular snake robots. However, there is still relatively little research on the potential enabled by the active morphological transformation of robots. This paper presents a novel modular snake robot capable of morphing into a bipedal configuration. The robot, ZBOT, is composed of some independent and homogeneous unit modules (named ZBot) connected in series. Each ZBot module has a dual-motor-driven 1-DoF rotational joint, which can rotate continuously, provide a large output torque and achieve backlash elimination. There are four connection orientations between adjacent modules. This paper proposes an articulation configuration, which enables the snake robot to achieve the active transformation from a snake form to a bipedal form. Meanwhile, through reinforcement learning (RL), movements including the stand-up gait are trained and verified in the IsaacSim/Lab simulation environment. This research will advance snake robots beyond surface-dependent locomotion, endowing them with more possibilities, unlocking greater potential for versatile applications.

Existing polar robots are constrained by limited energy supply, making it difficult to carry out long-term scientific exploration missions, which highlights an urgent demand for energy conservation. An energy-efficient multi-mode motion polar robot is proposed to address this challenge. Both increasing external assistance and reducing the driving force are critical for lowering energy consumption. A foldable sail is designed to provide external assistance. When unfolded, the sail generates assistive force. When folded, it maintains stability in extreme polar climates. The sail shape is designed based on a symmetrically extended NACA0018 airfoil, and the influence of different sail parameters on performance is discussed. The transformable tracks realize switching between traction and sliding modes through the separation of the track and teeth chain, using the sliding mode to reduce driving force. The effect of teeth parameter variations on traction performance is analyzed. The system kinematics and dynamics are model, and stability conditions are determined. Based on this, an energy-saving motion control framework for multi-mode motion is proposed. Finally, experiments are conducted to evaluate the energy-saving contribution of each independent mode under different configurations. Comprehensive experiments in multi-mode motion demonstrate an overall energy-saving rate of approximately 24%, verifying the effectiveness of the energy-saving motion control strategy. With its energy-saving advantages, this robot shows strong potential for enabling long-term scientific exploration in polar regions.

Preference-based optimizing methods have shown their advantages and potential in exploring individual, comfortable, and effective control strategies and assistance parameters of exoskeletons during locomotion. Research indicates that compared with naive wearers, knowledgeable wearers with abundant exoskeleton assistance experience have obvious advantages in speeding up the parameters exploration process and improving the assistant performance. However, there is no existing method that could utilize the human-exoskeleton locomotion interaction experience (HELIE) to assist naive wearers during the exploration process. In this work, we propose a novel preference-based human-exoskeleton locomotion interaction experience transfer (LIET) framework, which could speed up the exploration of human-preferred parameters and acquire more satisfying results for naive wearers via the HELIE acquired from knowledgeable wearers. In addition, based on the proposed LIET framework, we establish the mathematical expression of the HELIE transfer during exoskeleton assistance. This will promote the research that concerns utilizing HELIE for exoskeleton control parameters optimizations in the future. Finally, experiments demonstrate the proposed LIET framework could speed up the exploration process and acquire more satisfying optimized results for naive wearers.

Research has indicated that exoskeletons can assist human movement, but due to the influence of additional weight and challenges in control strategy design, only a few exoskeletons effectively reduce the wearers’ metabolic costs during running. This paper proposes an innovative and efficient hip-assisted running exoskeleton (HARE) designed to facilitate the flexion and extension movements of the joint along the sagittal plane. In the field of structural engineering, we propose implementing an active-passive combination constant force suspension system, hereinafter referred to as CFS, to effectively mitigate the impact of inertial forces during running. The decoupled transmission mechanism allows the CFS and assist mechanisms to operate independently, ensuring the tension of the cables. The flexible structural design can reduce the locomotion limitation on human bodies and reduce the additional energy burden on the body. In control strategy designing, the joint torque-generating strategy provides personalized assistance strategies for wearers to actively optimize the control parameters. Meanwhile, the safety control strategy based on abnormal gait recognition can ensure human safety. Experiments have shown that compared to not wearing exoskeletons, this device can reduce the energy consumption of the human body by 5.33 % at a speed of 9 km/h. This demonstrates its potential in human motion assistance processes.

The field of soft manipulators requires a more promising solution, including efficient structures and controllers. This article presents a novel cable–pneumatic hybrid-driven tapered soft manipulator (TSM) design and control scheme to enhance the performance in actual tasks. This article is the first to present the design with a Bowden tube as a driving tendon and propose a composite tendon with Bowden tubes and cable tendons (BTCTs). Leveraging the principles of hybrid-driven antagonism, the compact TSM integrates the composite tendon with BTCTs and pneumatically actuated tapered bellows. This new hybrid-driven form provides the TSM with excellent resistance to axial extension, tangential bending, and torsion, enhancing the stiffness of the TSM. The variable-stiffness range of the TSM was quantified in tests, including axial stiffness (0.57–10.77 N/mm), tangential bending stiffness (0.01–0.45 N/mm), and torsion stiffness (0.02–0.044 N$\cdot$m/$^\circ$) tests. A deep learning-based neural network approach was utilized to model the inverse kinematics of the TSM. For more precise motion control, using position and orientation feedback from the sensor at the tip, we have designed a closed-loop iterative feedback controller incorporating three algorithms. Experiments on spatial point positioning, trajectory tracking with different constraints, orientation control, and disturbance experiments were conducted on the TSM. Experimental results [spatial point positioning error (mean error of stable region: 0.17 mm), circular trajectory tracking error (mean and standard deviation (SD) of 100 trials: 0.87$\pm$0.57 mm), orientation control error (less than 1$^{\circ }$), and the performance in disturbance experiment] demonstrated that our approach has high control accuracy and strong robustness against external disturbances. We conducted experiments involving teleoperation control, collision-free precise operations in cluttered and constrained environments, and disturbance-adaptive board cleaning testing, ensuring both stability and safety during contact with humans. These experiments intuitively demonstrate the potential of this TSM for executing complex tasks in real-world environments, promising to become a safe collaborative assistant for humans in the future.

This paper presents the design and preliminary evaluation of a portable spring-based knee exoskeleton, the SpringExo, which is designed to provide assistance to the leg while minimizing interference with the natural leg movement. Traditional rigid exoskeletons are unable to accurately align with a user’s anatomical joints. In addition, the user’s natural motion pattern is likely to change due to the constraints of the rigid exoskeleton. Though some textile-based soft exosuits and cable-driven soft exoskeletons have been developed to achieve better alignment with human’s biological joints, forces applied by the cables have to be sustained by human skeleton and joints. SpringExo, in comparison, uses a coil spring which the user wears around the thigh and shank, and does not require alignment with the joints. The spring stores energy and provides minimal interference during elastic deformation. A key feature of the SpringExo is that the springs store energy during the flexion phase and release this energy to assist the knee extension in the extension phase. We conducted human subjects study to verify its biomechanical and physiological effects on the user during stair ascent. Results from a six-subject study showed that the device did not interfere with the natural joint angles and assisted knee extension during stair ascent. However, further redesign and optimization are needed on the actuation system to offset SpringExo’s drawback of hindering knee flexion.

This paper presents a real-time kinematic control strategy to realize fast tracking of redundant robot manipulators in a time-varying environment. An obstacle avoidance method based on the law of conservation of energy is proposed to adjust the motion states of robot manipulators in real time. This method defines that the total energy for the end effector consists of an energy toward object (ETO) and an energy around obstacle (EAO), and that the total energy for each critical point on manipulator composes a relative kinematic energy (RKE) and an energy memory (EM). The total energies remain constant at each sampling period, and the conversions between the ETO and the EAO or between the RKE and the EM are recognized to obey a distance-related S-function. Such considerations ensure the smooth movement of the manipulator and avoid collisions with obstacles. In real-time planning, an unsupervised single neuron PID model is raised to adaptively increase the convergence ratio of moving object tracking via the online learning of the principal component analysis. Then, combined with the dynamic obstacle avoidance method based on conservation of energy, the kinematic control strategy is established for redundant manipulators to track a moving object rapidly in the presence of multiple dynamic obstacles. Theory analysis and various contrast experimental results show that the proposed kinematic control strategy is feasible and has fast convergence.

Pathological tremor is defined as a roughly sinusoidal movement and usually occurs in the upper limb impacting individuals activities of daily livings. Functional electrical stimulation (FES) is proposed as a potential alternative for cancelling the pathological tremor. However, the feasibility and accuracy of FES depends on the estimation of amplitude and frequency of tremor signals measured by sensors. In this study, a novel algorithm incorporating a sliding fast Fourier transform (SFFT), an interpolation procedure and a limitation module of frequency range is developed to estimate tremor frequency and separate the tremor components from raw data. Based on the artificial signals and the actual tremor signals, the performance of the proposed algorithm is evaluated. The experimental results indicate that the developed algorithm could quickly adapt to the unknown dominant frequency and extract the tremor components with high accuracy. Therefore, this method could be employed in the tremor suppression by FES without affecting the voluntary movement.

This article presents an approach and a systematic design methodology to path following control based on motion decoupling for high-performance ducted fan unmanned aerial vehicles (UAVs). The decoupling is performed according to the principle of regarding the attitude motion as a virtual input of the lateral longitudinal flight dynamics. This allows the attitude and flight controllers to be designed individually without mutual interference. Considering that dynamics of the ducted fan UAV is uncertain, an estimation method of system function based on the smooth saturation function and the state variable integral is proposed. This method has the advantage of less computation while keeping the high estimation performance. The stability analysis and the simulation results showing the practical feasibility of the proposed control scheme to ducted fan UAVs are given.

This paper presented a novel compliant actuator used for lower limb exoskeletons. The compliant joint consists of a series elastic actuator (SEA) and parallel elastic (PE) unit. SEA has various advantages as the actuator of assistive exoskeletons, such as low output impedance, impact absorption, precise force control and high stability. We designed and fabricated a novel SEA as the primary joint actuator which is compact, adjustable and low-cost. Meanwhile an additional elastic unit is installed in parallel with the SEA to improve energy utilization by storing and releasing energy during motion cycles. An adaptive stable controller is designed to realize the joint following motion to a virtual limb. The algorithm can identify and compensate the undetermined contact stiffness between the joint output and the virtual limb. Finally, the performance of the actuator is evaluated through motion tracking and energy-conservation experiments. Preliminary results indicate the validity of the design and imply its potential usage in lower limb exoskeletons.

In the paper, a concept of novel self-reconfigurable robotic system made of the autonomous robotic modules has been reviewed. Each robotic module is made of simple structure and few degrees of freedom; however, a group of the modules is able to change its connective configuration by changing their local connections and has functionality of robotic system which is capable of generating complicated motions and accomplishing a large variety of tasks, such as: transportation, exploration, inspection, construction and in-situ resource utilization. Multimode locomotion and self-reconfiguration are the basic and essential abilities for the self-reconfigurable robotic system. Based on this concept, a new self-reconfiguration system, UBot robotic system that combines the advantages from the chain-based and lattice-based robots has been proposed. Each UBot module which is cubic structure based on universal joint has two rotational DOF and four connecting surfaces that can connect to or disconnect from adjacent modules. The smart structure and the reliable connecting mechanism of the modules make the robot flexible enough to complete multimode locomotion and self-reconfiguration. This paper demonstrates the design philosophy of the UBot module and a solution for multimode motions and self-reconfiguration using the UBot system. The system can complete motion in the modes of quadruped, chain and loop configuration. Besides, the system can deform from one mode to the other though self-reconfiguration. All the proposed methods have been verified though simulations and real hardware experiments.

The passive dynamic walking model, which can only depend on the gravity and its own inertia, presents stable, high-efficient, natural periodic gait on a slight slope. The stable periodic gait of the robot has a delicate balance of energy conversion, which makes the gait adjust itself as the parameters of the model change. In our work, the cell mapping method is combined with Newton-Raphson iteration to obtain the limit cycle of the periodic gait in the model, the track stability of the limit cycle is analyzed, and the eigenvalues change rule of Poincare Jacobi matrix is deduced. The influence of changing parameters on the gait is analyzed and discussed by simulations on the model with different sets of parameters. The result suggests that, the location of the center of leg mass too high or too low, foot radius increase or decrease, the slope or moment of inertia increase, will lead to the occurrence of bifurcation of the gait period and chaos; while the way the gait enters chaos from period doubling bifurcation, which results from different parameters change, obeys the law all the period doubling bifurcation share, that is, it has the same Feigenbaum constant. Furthermore, the dynamic features of the robot at the entrance of the chaos are obtained by the rule of the period doubling bifurcation of the gait; meanwhile, it can be found by the analysis of the gait features in the chaos area that there is also certain periodic law in the chaotic gait.

Significant research have been devoted into the field of multi-robot teleoperation system over Internet. However, the low safety and efficiency due to time delay and complexity between multiple robots and operators prevent the teleoperation technology from developing. Specially, the possible collision between multiple robots has a great effect on the Internet-based multi-operator-multi-robot teleoperation system. In this paper, a task planner based on Petri net theory was proposed to assist multi-operators coordinate multi-robots. The conception of implicit elementary operation and explicit elementary operation were introduced to realize modeling and automatic plan of the system. In the multi-operator multi-robot system built by us, a supervisory controller was designed based on the place invariants not only to lead two operators' manipulation with virtual environment but also to restrict the state of two robots' motion during their work. An experiment via network was carried out to evaluate the validity of the task planner for the medical research of highly dangerous virus such as SARS, bird flu etc. The experimental results show that the safety and efficiency of the system can be improved using the task planner

A closed loop self-calibration method for Delta mechanism and 3-RRR mechanism used in a double parallel haptic device was presented. This approach blocks successively one joint of each parallel mechanism using a simple lockup device and reforms the 3-DOF mechanism to be a 2-DOF one. The parameter errors of the mechanism are identified according to the differences between the sensor output of the redundant chain and nominal model output. Both the condition number and singular value of the error propagation Jacobian are synthesized as a new selection principle of calibration set such that the calibrating process is insensitive to the noise and the calibration precision is increased efficiently. Experiments show that the ratio of maximal error before and after calibrating is 9.6, namely, the setting accuracy of the two mechanisms are increased about 10 times and the error of re-position is less than 0.1 mm and 0.1deg