The exploration of challenging terrains requires robotic systems with advanced mobility and adaptability. However, maintaining stability and maneuverability while traversing obstacles remains a significant challenge for such systems. Redundantly actuated wheel-legged rovers present a promising solution by introducing additional degrees of actuation, enabling more optimized motion strategies and enhanced overall performance. This paper focuses on enhancing the obstacle-crossing capability of these systems by analyzing the dynamic stability criteria and actuation patterns, using the TAWL rover as demonstration. Firstly, the dynamic models of the TAWL rover’s individual leg and the whole body are established, and the dynamic stability criteria are introduced. Secondly, the actuation patterns of the redundant system are analyzed to identify feasible actuation patterns and evaluate associated energy consumption. Thirdly, enhanced obstacle-crossing strategies are proposed, consisting of two components: increasing the rover’s maximum obstacle-crossing height and improving its stability when traversing specific obstacles. Finally, the proposed method is validated on the TAWL rover. The results demonstrate that the proposed method enables the rover to achieve maximum obstacle-crossing height and exhibit superior stability margin while traversing obstacles.

Space manipulators are crucial for conducting various space missions. To accurately simulate these operations on Earth, this paper presents a full-physical simulation system and corresponding method based on disturbance moment identification, addressing the issue of incomplete gravity unloading in space dexterous operations. Full-physical simulation is the comprehensive modeling of real-world physical interactions such as motion, forces, and collisions in a virtual environment with high fidelity and accuracy. The system’s hardware configuration is introduced first. Then an innovative full-physical method is proposed mainly consisting of the modeling and optimization of disturbance moment (force). The disturbance moment (force) model is optimized to enhance full-physical simulation accuracy. The control framework gives the system framework and signal flows. Numerical simulations are done to verify the optimization process. Interior point method is utilized to decrease the disturbance moment enormously and to reduce the largest joint moment significantly. Multi-objective particle swarm optimization is then implemented to achieve optimal unloading forces. Finally, experiments confirm the effectiveness of the proposed full-physical methodology from two aspects: the verification of the identification method and that of optimization method.

This paper addresses the high-precision motion control problem for an asymmetric hydraulic cylinder electro-hydrostatic actuator (EHA) subject to the challenges under partial and noisy state feedback. A novel composite control scheme is proposed, which centers on a series-connected filtered extended state observer (SFESO) integrated with a prescribed performance fast terminal sliding mode controller (PPFTSMC). For complex nonlinear systems like EHA, a single extended state observer struggles to simultaneously estimate both unmeasured velocity and unknown lumped disturbance with satisfactory accuracy. The proposed SFESO cascades two structurally similar FESOs to simultaneously estimate both the unmeasured velocity and the unknown lumped disturbance with high accuracy. This design requires only two parameters to tune, and offers the advantages of low structural complexity and inherent noise attenuation. Based on these estimates, a fast terminal sliding mode controller is synthesized within a prescribed performance framework. Unlike conventional prescribed performance control that constrains only the tracking error, this work innovatively applies the performance bounds directly to the sliding variable, proactively suppressing chattering while ensuring fast, smooth, and overshoot-free convergence. The stability of the entire closed-loop system is rigorously proven via Lyapunov analysis. Both the simulation and experimental results validate the effectiveness and superiority of the integrated SFESO-PPFTSMC strategy for EHA control.

In this paper, we investigate potential singularity issues in prescribed performance control induced by actuator faults and propose a global nonmonotonic prescribed performance scheme for strict-feedback nonlinear systems. This scheme allows for parameterized relaxation of constraint boundaries in the presence of actuator faults. Unlike existing prescribed performance control schemes that rely on redesigning nonmonotonic rate functions to modify boundary relaxation properties, the proposed method introduces a novel adjustment module into the prescribed performance function, enabling the parametric design of nonmonotonic boundaries. Moreover, the proposed method simultaneously addresses the removal of the initial feasibility condition and the imposition of asymmetric constraints by introducing a global asymmetric design with an error correction module. This module enables flexible conversion from asymmetric to symmetric boundaries at a predetermined time instant, thereby reducing transient overshoot while maintaining steady-state tracking precision. Simulation results validate the effectiveness and superiority of the proposed scheme.

This article proposes a singularity-free prescribed performance control (PPC) scheme for nonlinear systems with actuator faults involving unknown positive odd integer powers. Unlike conventional fault-tolerant control schemes, where the exponent of the faulty input is fixed to 1, this work generalizes it to an unknown positive odd integer, directly coupling faults with higher-order input power characteristics. Moreover, departing from existing PPC methods, the study employs a Vandermonde matrix to construct a prescribed trajectory, transforming the original tracking error constraints into constraints on the deviation between the tracking error and the prescribed trajectory. This approach prevents input divergence even when the tracking error approaches the constraint boundary, effectively resolving the singularity issue observed in existing barrier function-based PPC schemes. Simulation results validate the effectiveness and superiority of the proposed scheme.

3-RRR spherical parallel mechanisms (SPMs) are capable of producing three-DOF spherical motion, which can find applications in various fields. This paper investigates the kinematics and workspace performance of a particular class of 3-RRR SPMs, characterized by coaxial input-joint axes, coplanar distal-joint axes, and six orthogonal links. First, two novel formulations are proposed for the forward kinematics (FK) of this robot class, both resulting in linear univariate polynomials that significantly streamline the FK formulation and reduce the computational complexity. Moreover, this robot class exhibits minimal and physically interpretable singularity loci in both the orientation workspace and the input joint space, greatly facilitating singularity avoidance and path planning. Furthermore, local and global dexterity indices are employed to analyze the mechanism’s performance, from which the architectural parameters yielding optimal dexterity are identified. Finally, the robot class demonstrates a considerably large tilt-torsion orientation workspace when the architectural parameters and link shapes are appropriately selected, allowing for infinite torsional motion when pointed within almost a hemisphere. The foregoing features make the robot class potentially promising in a wide range of applications.

This article presents a comprehensive dimensional design and optimization strategy for a redundantly actuated space quadruped climbing robot. Our approach focuses on non-dimensional optimization, utilizing a dimensionally normalized Jacobian and a design space dimensionality-reduction method for both the leg mechanisms and the overall robot structure. A key component of this strategy is a novel performance evaluation framework, the dominant joint-workspace partitioning framework (DJ-WPF). The DJ-WPF uses a joint participation index to identify dominant actuators for different regions of the workspace, enabling the selection of optimal region-specific actuator sets. Using this framework, we generate performance atlases to visualize key indicators and identify high-performing design regions. An optimal set of non-dimensional parameters is then determined using a weighted averaging method. Finally, these parameters are scaled to physical dimensions under practical constraints. The resulting optimized design achieves a condition index and a minimum payload index that are 50.19% and 79.3% of their theoretical maximums, respectively, while reducing computational cost.

In space missions, particularly in on-orbit servicing (OOS) missions, many tasks involve non-cooperative targets. To ensure the safety and precision of such missions, complete identification of the target’s inertia parameters is essential. This paper proposes a novel method for identifying the inertia parameters of a non-cooperative target, introducing an innovative approach to position and velocity estimation based on a time-of-flight (TOF) camera. The paper first describes the physical configuration of the system, followed by the overall identification process of the target. Subsequently, all inertia parameters are reviewed, and the associated data processing procedures are presented. The (angular) momentum of both the satellite and the manipulator is calculated to make preparations for subsequent identification steps. The motion parameters of the target are estimated using the Kalman filter (KF) and extended Kalman filter (EKF), with newly designed models for position and velocity. Furthermore, a novel full-parameter identification method is proposed, building upon the preceding motion estimation process. Simulations show that the identification errors of all inertia parameters are less than 0.3%, which validates the correctness and effectiveness of the proposed methods.

The rod-fastening rotor system is the core rotating component of gas turbines and aero engines. The rod-disk joint structure of the rod-fastening rotor system is subjected to tie rod bolt loosening under complex operating conditions. Because of the uncertainty of fault occurrence, the model must possess diagnostic capability under unseen working conditions. Domain generalization methods are commonly employed to address such problems. They typically utilize multi-condition data or generate data from a single condition to train the model, enabling it to generalize to unknown operating conditions. However, difficulties in collecting complete multi-condition data and limitations in generating data, such as conflicting attributes and limited categories, make it challenging to ensure high-quality data for model training. To address these issues, this paper establishes a dynamic model of the rod-fastening rotor system to obtain high-fidelity simulation fault data, ensuring the physical accuracy, richness, and diversity of the source domain data. Meanwhile, a novel signal-domain generalization method called simulation data-driven attention fusion network with multi-similarity metric is proposed for tie rod bolt loosening in the rod-fastening rotor system. The attention feature fusion strategy is utilized to achieve the comprehensive representation of local and global features efficiently. Moreover, a multi-similarity loss based on deep metric learning is introduced to comprehensively consider the self-similarity and relative similarities between features, enabling class-level optimization of features. The proposed method is validated to have superior diagnostic performance in two cross-domain tasks compared with other methods. The proposed method can provide valuable insights into fault diagnosis in the rod-fastening rotor system.

This paper proposes an anti-saturation prescribed-time control scheme for free-flying space robots (FFSRs) subject to system uncertainties, external disturbances, input saturation, and output constraint. Initially, the control scheme is developed based on a newly constructed stochastic model, introducing stochastic neural networks (SNNs) to approximate the lumped stochastic factor that encompasses system uncertainties and external disturbances. Subsequently, a novel self-adapting non-monotonic prescribed-time function is proposed, integrating input saturation as an adaptive variable to dynamically adjust the constraint boundaries. This integration enables the constraint boundaries to adaptively expand in a non-monotonic manner in response to the occurrence of input saturation. Moreover, the constraint boundaries are designed as tunnel-shaped to prevent overshoot. The proposed control scheme ensures that all closed-loop signals are semi-globally uniformly ultimately bounded in probability, with the tracking error stabilized within a prescribed time. Finally, simulation results validate the effectiveness and superiority of the proposed scheme.

Wheel-legged planetary rovers possess superb locomotion capabilities. This article combines an offline predefined motion planning library with online path planning, integrating energy consumption and probabilistic aspects of the robotic system. The primary focus is on addressing the planning challenges in dense environments, where the distance between any adjacent obstacles is smaller than the width of the prototype. Therefore, it is necessary to consider the interaction between the prototype and the environment. First, the generalized function set theory and the configuration topology theory are utilized to mathematically describe the motions of multilimbed systems. Based on the representation, an offline planning library is established. Second, the Markov-decision-process-based path planning method is extended by incorporating the platform's geometry and locomotion capabilities. The concept of "limb-travel relevant nodes" is introduced. To address the numerous iteration problems, the informed value iteration algorithm is proposed. Third, a multilayered map is evaluated to further enhance computational efficiency. Finally, the proposed algorithm is implemented on the terrain adaptive wheel-legged rover. Experimental results demonstrate that the proposed algorithm is capable of finding the optimal path with high computational efficiency, and it exhibits excellent adaptability on nonuniform maps.

Path planning is of great research significance as it is key to affecting the efficiency and safety of mobile robot autonomous navigation task execution. The traditional gray wolf optimization algorithm is widely used in the field of path planning due to its simple structure, few parameters, and easy implementation, but the algorithm still suffers from the disadvantages of slow convergence, ease of falling into the local optimum, and difficulty in effectively balancing exploration and exploitation in practical applications. For this reason, this paper proposes a multi-strategy improved gray wolf optimization algorithm (MSIAR-GWO) based on reinforcement learning. First, a nonlinear convergence factor is introduced, and intelligent parameter configuration is performed based on reinforcement learning to solve the problem of high randomness and over-reliance on empirical values in the parameter selection process to more effectively coordinate the balance between local and global search capabilities. Secondly, an adaptive position-update strategy based on detour foraging and dynamic weights is introduced to adjust the weights according to changes in the adaptability of the leadership roles, increasing the guiding role of the dominant individual and accelerating the overall convergence speed of the algorithm. Furthermore, an artificial rabbit optimization algorithm bypass foraging strategy, by adding Brownian motion and Levy flight perturbation, improves the convergence accuracy and global optimization-seeking ability of the algorithm when dealing with complex problems. Finally, the elimination and relocation strategy based on stochastic center-of-gravity dynamic reverse learning is introduced for the inferior individuals in the population, which effectively maintains the diversity of the population and improves the convergence speed of the algorithm while avoiding falling into the local optimal solution effectively. In order to verify the effectiveness of the MSIAR-GWO algorithm, it is compared with a variety of commonly used swarm intelligence optimization algorithms in benchmark test functions and raster maps of different complexities in comparison experiments, and the results show that the MSIAR-GWO shows excellent stability, higher solution accuracy, and faster convergence speed in the majority of the benchmark-test-function solving. In the path planning experiments, the MSIAR-GWO algorithm is able to plan shorter and smoother paths, which further proves that the algorithm has excellent optimization-seeking ability and robustness.

To address the issues that univariate phase space reconstruction does not consider the spatial information between different scanning points and the need for baseline attractors during detection, a baseline-free nonlinear laser ultrasonic imaging method of fatigue cracks with micron-scale width using multivariate phase space reconstruction is proposed. This method selects appropriate parameters for univariate phase space reconstruction at each scanning point. Subsequently, a more accurate attractor is reconstructed using the multivariate from the five adjacent scanning points, and the correlation dimensions of the reconstructed attractor are finally extracted for imaging fatigue cracks. This proposed method is validated on a laser ultrasonic detection platform, and experimental results indicate that this method can achieve baseline-free imaging of fatigue cracks, and the imaging performance surpasses that of the univariate phase space reconstruction method. This study provides guidance for high-precision laser ultrasonic detection of fatigue cracks.

This paper explores the dynamic modeling and performance analysis of a wheeled-legged rover. The studied terrain-adaptive wheel-legged (TAWL) rover possesses 20 active joints and 4 passive springs. The end-effector of each leg exhibits 3R1T motion characteristics, along with an attached active wheel. For the dynamic modeling, the Newton–Euler formulation and the principle of virtual work are utilized to calculate the forces acting on each link and the torque of the driven joints. The dynamic model of each individual leg is first established. Then, the dynamic model of the whole body corresponding to different supporting and swinging legs is derived. For the performance analysis, the dynamic stability coefficient of the rover is determined. The stability coefficient around a certain supporting edge is defined as the ratio of the total moment exerted on that edge to the standard state’s total moment. The stability coefficient of the system is defined as the minimum coefficient value around all supporting edges. In order to verify the proposed modeling and analysis method, numerical simulations are conducted. The presented modeling approach can serve as the fundamentals for the optimization and control of the rover in future work.

This paper proposes a set of multifunctional end-effectors for space robots, designed to expand the application scenarios of space robots in spacecraft maintenance and repair tasks. The end-effectors comprise the robotic tool changer and three types of maintenance operation tools: the Maintenance Tool for Capturing Thruster (MTCT), the Maintenance Tool for Capturing Handle (MTCH), and the Maintenance Tool for Cutting Operation (MTCO). These tools can establish mechanical and electrical connections with the space robot via the robotic tool changer to perform satellite capture, hatch opening, and cutting tasks. This paper also introduces the robot admittance control algorithm for the docking of the robotic tool changer and the tools, followed by a corresponding simulation. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Space manipulator plays a crucial role in various space tasks. To reproduce the real experimental environment and simulate the space missions on the ground, full-physical simulation method is essential. This paper proposed an identification and elimination strategy for the disturbance moment and force which come from the insufficiency of the gravity unloading system in the full-physical simulation process. The system structure is described briefly first. Then, the forward kinematics of the 7-DOF manipulator is elaborated for the modeling and analysis of the disturbance moment and force. An identification method for the disturbance force and moment is proposed. The problem of the disturbance force and moment is then transformed into an optimization problem which possesses multiple optimization targets. A new elimination strategy for the disturbance moment and force is proposed. The strategy first converts the problem into a single-objective problem and the optimized input are taken as the initial value for the subsequent multi-objective optimization stage. Simulations are done to verify the feasibility of the proposed method which successfully eliminates the disturbance moment and force.

7-DOF manipulator has a redundant freedom which allows it to execute various complex tasks. This paper proposes a method for identifying inertia parameters of non-cooperative targets and deriving dynamic characteristics of the whole system. Firstly, the framework of the inertia identification process is introduced. Each joint of the manipulator is locked. Then by offering the manipulator base with an initial velocity, the manipulator will collide with the target. With depth camera equipped, the velocity and angular velocity of the target is thus detected and utilized for identification of inertia parameters of the target and the whole system. Secondly, inertia parameters needed to be recognized are analyzed and an identification method is proposed. Assisted with relevant sensors and other measurement devices, the momentum and angular momentum of the manipulator system are known, and the inertia parameters of the target are derived and calculated by conservation of momentum with least square method. Simulations are done to verify the correctness of the algorithm. Finally, after the target is captured, the total inertia parameters of the whole system are analyzed.

In few-shot fault diagnosis tasks in which the effective label samples are scarce, the existing semi-supervised learning (SSL)-based methods have obtained impressive results. However, in industry, some low-quality label samples are hidden in the collected dataset, which can cause a serious shift in model training and lead to the performance of SSL-based method degradation. To address this issue, the latest prototypical network-based SSL techniques are studied. However, most prototypical network-based scenarios consider that each sample has the same contribution to the class prototype, which ignores the impact of individual differences. This article proposes a new SSL method based on pseudo-labeling multi-screening for few-shot bearing fault diagnosis. In the proposed work, a pseudo-labeling multi-screening strategy is explored to accurately screen the pseudo-labeling for improving the generalization ability of the prototypical network. In addition, the AdaBoost adaptation-based weighted technique is employed to obtain accurate class prototypes by clustering multiple samples, improving the performance that deteriorated by low-quality samples. Specifically, the squeeze and excitation block technique is used to enhance the useful feature information and suppress non-useful feature information for extracting accuracy features. Finally, three well-known bearing datasets are selected to verify the effectiveness of the proposed method. The experiments illustrated that our method can receive better performance than that of the state-of-the-art methods.

Wheeled-legged rovers combine the flexibility of legged robotic systems and the energy efficiency of wheeled mobile systems. This paper presents a framework that allows energy-efficient and highly maneuverable locomotion for wheeled-legged rovers. Firstly, terrain geometry is first obtained by the perception system. It is modeled and classified as small relatively regular terrains. Then, the configuration topology of the rover and motion characteristics of end-effectors are utilized to express complex gait patterns and redundant motion combinations. During in-situ exploration, the decision-making process could be simplified by establishing the mapping relationship between terrain classes and locomotion strategies. Finally, trajectories are generated to achieve planned motions while maintaining stability and low energy consumption. The approach is applied to the Terrain-Adaptive Wheeled-Legged (TAWL) rover. Experiments show that the rover is able to autonomously choose appropriate gaits and has great maneuverability and stability when navigating through challenging terrains. The swing amplitude is reduced by about 83.3

Trading-arm suspension vehicles (TAV) often encounter difficulties when navigating uneven terrains due to their unique mechanisms and coupling dynamics, rendering them susceptible to compromised ride comfort and significant posture changes. To address these challenges, we propose a decentralized posit ion-based impedance control (PIC) approach, which integrates posture stability metrics with impedance characteristics through a centralized skyhook model. Furthermore, we introduce the generalized momentum (CM) method to estimate the external torque at the arm joint induced by road disturbances, acting as the mapped Cartesian external force in the PIC loop. Through simulation experiments, we verify the efficacy of the proposed controller and juxtapose its performance against traditional PD suspension controllers. The results attest to the substantial enhancement in ride comfort and posture stability afforded by the proposed controller, thereby presenting a compelling control solution for TAV applications.