Ground-based three-dimensional motion testing of space manipulators typically relies on active suspension-based gravity compensation systems. The design of such systems faces two fundamental challenges: first, how multiple suspension winch units can precisely track the dynamic trajectories of the corresponding suspension interfaces on the manipulator; and second, how to achieve optimal collision avoidance among the suspension mechanisms themselves during the tracking process. To address these challenges, this paper presents a multi-point suspension system endowed with kinematic redundancy for the trajectory tracking task, thereby ensuring precise tracking of the manipulator's complex three-dimensional motions. The key innovation of this work lies in formulating the internal collision avoidance constraints as safety distance functions and integrating them into the system states. These are then combined with the trajectory-tracking states to construct a unified state-extended system model that exhibits typical underactuated characteristics. For this model, and under the concurrent influence of external disturbances from both the manipulator's motion and the proximity to collision boundaries, a dedicated Model Predictive Controller (MPC) is designed. The results demonstrate that the proposed controller can generate an optimal coordinated collision-avoidance motion plan for the suspension winch units while maintaining precise trajectory tracking, thereby effectively solving the coordinated motion-planning problem for such complex underactuated systems. The proposed MPC achieves maximum tracking errors of 0.64 mm (X) and 0.13 mm (Z)-substantially lower than the 1.3 mm and 1.9 mm results listed in the comparative scheme-while delivering optimal collision avoidance, which is only suboptimally realized in the baseline.

Nonprehensile transportation represents a fundamental approach in robotic manipulation widely implemented in practical applications, where object dynamics constraints must be strictly maintained. However, existing approaches have certain limitations in operational reliability and control performance, particularly regarding execution efficiency and input adaptation. To address these limitations, we propose a novel shared teleoperation method for nonprehensile object transportation. The method reformulates constraints from object dynamics to robot kinematics level, eliminating the need for direct contact force control. It achieves autonomous orientation control through orientation feedforward smoothing while enabling shared position control based on user teleoperation inputs. Additionally, the coordination between position and attitude is ensured through input command optimization. The effectiveness of this method was evaluated through extensive trajectory tracking simulations and human subject experiments. The results demonstrate superiority over existing methods regarding operational safety, task efficiency, tracking accuracy, and input command adaptability.

The 7-DOF manipulator with multi-offset offers higher operational flexibility and a larger workspace, widely used in space on-orbit servicing. This paper proposes a novel inverse kinematics solution for 7-DOF multi-offset manipulators based on the concept of variable SRS equivalent manipulators and arm angle. First, a new definition scheme for shoulder and wrist points is proposed to address failures in traditional methods. Then, the concept of the variable SRS equivalent manipulator is proposed, solving the problem of existing arm angle based methods being inapplicable to the manipulator configuration studied in this paper. Subsequently, integrating this concept with arm angle method, the complex problem of solving seven joint angles within joint limits is transformed into solving a system of two nonlinear equations with two unknowns in the feasible domain. To address this nonlinear system, a hybrid algorithm combining an improved Levenberg-Marquardt method and an improved Particle Swarm Optimization algorithm is developed, augmented with an efficient initial value estimation strategy. The proposed inverse kinematics solution successfully introduces arm angle as a redundant parameter, filling the research gap in applying arm angle-based solutions to the studied manipulator configuration. Finally, verification and comparative experiments show that our inverse kinematics solution achieves higher success rates, efficiency, and safety in solving. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband-hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within +/- 0.01 degrees, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance.

Multi-object nonprehensile transportation in teleoperated robotic systems poses a dual control challenge: real-time trajectory tracking and simultaneous tray orientation control to satisfy object dynamic constraints. Existing approaches face limitations, including difficulty satisfying trajectory state constraints, excessive model dependency, inadequate adaptability to multi-object scenarios, and a lack of robust mechanisms for handling uncertain object parameters. To address these limitations, this work proposes a novel shared teleoperation framework for multi-object nonprehensile transportation, which enables shared control between human operators and the robotic system for object positioning; meanwhile, the robot autonomously controls object orientation to satisfy task constraints. The primary contributions are threefold: First, a theoretical analysis of dynamic constraints is developed, incorporating object position, inertial parameters, quantity, friction coefficients, and motion states. Furthermore, a virtual object-based dynamic constraint processing method is proposed for the first time, enabling simplified dynamic constraints to be directly utilized for trajectory planning. Second, a model predictive control-based trajectory smoothing algorithm with real-time dynamic constraint enforcement is designed, enabling dynamic coordination between user input tracking and orientation control. Third, simulation and experimental validation confirm that the proposed method successfully ensures dynamic constraints for all objects and achieves stable manipulation of nine different objects at accelerations up to 2.4 m/s2. Compared with the baseline method, the approach achieves a 72.45

With the growing demand for robotic operation in unstructured environments,fixed-configuration robots are increasingly limited in adaptability,fault tolerance,and task versatility.Modular self-reconfigurable robots,composed of standardized homogeneous or heterogeneous modules,can reorganize their topology to achieve morphological and functional reconfiguration.Since existing studies are commonly classified by geometric configuration or connection topology and therefore do not fully reflect the essential differences in reconfiguration mechanisms,this paper reviewed modular robots from the perspective of reconfiguration principles.They were categorized into four groups:non-self-reconfigurable,mobile self-reconfigurable,pose-transformation-based(translation/rotation)self-reconfigurable,and joint-motion-based self-reconfigurable systems.The mechanical principles,representative prototypes,control characteristics,and application scenarios of each category were systematically summarized,and their respective advantages and limitations in compactness,mobility,control complexity,energy efficiency,and environmental adaptability were compared.This review provides a reference for configuration design,strategy selection,and application optimization of future modular self-reconfigurable robots.

Initial attitude calibration is a critical yet challenging phase in hardware-in-the-loop (HIL) testing for space docking, often hindered by cumbersome procedures, safety concerns, and reliance on external equipment. This paper introduces a human–robot collaborative calibration method based on H∞ robust control. The core objective is to achieve symmetric pose alignment between docking mechanisms by allowing the operator to manually guide the test device, thereby rapidly obtaining initial attitude calibration results. An interactive model incorporating a time delay is established. Using H∞ synthesis, a stabilizing controller is designed to accurately track low-frequency operator commands while strongly suppressing high-frequency disturbances. Notably, the H∞ framework reconstructs an ideal interactive symmetry in human–robot collaboration by compensating for delays and disturbances. The solution to the Riccati equation within a game-theoretic framework effectively achieves symmetric optimization that balances tracking accuracy with safety constraints. Experimental results demonstrate that the method successfully compensates for system delays, enabling symmetric pose alignment while maintaining smooth and continuous motion of the docking mechanism. It also faithfully translates the operator’s low-frequency traction intent into motion. By retaining contact forces/torques within safe thresholds, the method balances interaction safety with operational precision, ultimately providing a reliable solution for initial attitude calibration in space docking HIL tests.

Transformable-wheeled robots exhibit efficient locomotion and obstacle negotiation through mode transformation, which underpins the development of the multimodal robot MTABot—a previously validated platform. However, existing literature primarily focuses on structural design, leaving autonomous mode transitions across varying terrains as a significant challenge. This paper presents a unified terrain-adaptive morphing and trajectory tracking approach for MTABot, utilizing the Nonlinear Model Predictive Control (NMPC) framework. This method eliminates the need for environmental recognition or prior training. Specifically, a segmented kinematic model for the transformable wheel has been developed, ensuring the feasibility of motion in both rolling and climbing modes. Additionally, a virtual ground attachment constraint is proposed to guide adaptive morphing for overcoming single or small obstacles. An online weight adjustment method for NMPC is introduced to synchronize wheel motion and overcome continuous large obstacles. Comprehensive experiments in multi-terrain composite scenarios and various obstacle-crossing tests validated the effectiveness of the proposed approach.

Seven-degree-of-freedom offset manipulators are typically large-scale space robot systems featuring two long links. The offsets at the shoulder, elbow, and wrist joints extend the joint motion range, thereby enhancing the manipulator’s workspace and dexterity to perform more complex missions. The two long links render the physical significance of the manipulator’s elbow highly pronounced, with the arm angle being closely coupled to the elbow joint. Arm angle parameterization is well-suited for configuration control of such manipulators; however, offsets pose a geometric challenge that hinders analytical redundancy resolution. This article focuses on the Experimental Module Manipulator (EMM) of the China Space Station and investigates its nullspace redundancy resolution approach via arm angle self-adaptation. First, a novel semi-analytical inverse kinematics solution via arm angle parameterization is proposed for the EMM, offering high computational accuracy and fast solving speed. Moreover, convex hulls of the EMM and the China Space Station are modeled for high-precision collision detection based on the Gilbert–Johnson–Keerthi distance algorithm. Subsequently, an obstacle avoidance strategy via arm angle self-adaptation is developed for the EMM to perform tasks safely in complex environments. Additionally, the effectiveness and practicality of both the arm angle parameterized inverse kinematics solution and the obstacle avoidance strategy are verified through comprehensive simulations. Finally, this research is applied to the EMM for successful accomplishment of on-orbit servicing to support the Shenzhou-16 astronaut’s extravehicular activities aboard the China Space Station, thereby achieving on-orbit experimental validation.

Satellite hovering missions involve an active propulsion phase for precise maneuvering and a subsequent passive dynamics phase wherein the satellite responds to external forces, such as from a manipulator. Therefore, a ground-testing method capable of seamlessly integrating these operational regimes is required. This paper presents a novel methodology that leverages the symmetry between active wheel-driven control and passive air-bearing dynamics to establish a unified testing platform. A mathematical model is established for the dual independent steering-wheel drive system, and an error model for tracking both the translational (position) trajectory and the rotational (attitude) trajectory of the satellite during hovering is derived. Based on this, a Model Predictive Control (MPC) scheme is designed to generate optimal driving speeds and steering angles for the wheels, ensuring accurate trajectory tracking while explicitly adhering to their driving and steering constraints. Furthermore, our work involves the integrated design of a gravity-compensated platform and its steering wheels, incorporating design methods to enhance air-bearing safety and a seamless switching method to maintain test continuity by minimizing transient disturbances. Experiments demonstrate that this integrated platform delivers both high-precision satellite trajectory tracking and high-fidelity passive air-bearing micro-gravity simulation for the active and passive phases of a satellite hovering mission.

Dual-arm motion is a critical capability for a multi-branch space robot in on-orbit missions. To address on-orbit motion challenges specific to SRS branch configurations, a constraint-integrated inverse kinematics (CIIK) method is proposed. This method employs joint points to equivalently represent the redundant spatial robot, which comprises dual arms and a torso. An analytical expression for the inverse kinematics is derived, ensuring both accuracy and computational efficiency in the solution. Constraints are imposed on the torso through spatial vectors to mitigate the impact of constraint computations on the efficiency of inverse kinematics solutions. Furthermore, a mapping method between joint points and joint angles is proposed, which can obtain effective and smooth joint angles during continuous motion. By comparing with widely adopted inverse kinematics methods, CIIK demonstrates superior performance in terms of computational efficiency, solve rate, and joint angle continuity under unconstrained mode. Simulations and physical experiments were conducted by applying constraints to the torso. The results indicate that CIIK does not increase computation time and exhibits good solving performance in constrained mode.

This paper introduces a unified planning framework for composite hybrid aerial/terrestrial precision manipulators (CHAT-PM), enabling energy-efficient trimodal navigation in complex environments. The proposed path search algorithm leverages terrain-specific heuristics and motion primitives to prioritize energy-saving terrestrial/inclined paths while dynamically selecting optimal motion modes. To address the computational complexity of trimodal dynamics, a unified nonlinear model predictive control (NMPC) approach is developed by integrating complementary constraints, thereby eliminating mixed-integer optimization and unifying aerial-terrestrial-inclined dynamics under a single framework. The dynamics model further incorporates disturbances from robotic arm operations, ground/slope contact forces, and aerodynamic effects, enhancing trajectory accuracy. Finally, the effectiveness and superiority of the proposed method are demonstrated by simulation results and real-world experiments.

Terrestrial-aerial robots, capable of swift aerial navigation and enduring terrestrial operations, possess significant potential for utilization in exploration and rescue missions. However, achieving their capability to negotiate diverse terrains with a high-power-efficient structure remains a formidable challenge. This letter presents a morphable terrestrial-aerial robot, named MTABot, which achieves three modalities through the deployment of two multifunctional appendages. These include: 1) rolling mode, 2) climbing mode, both achieved with transformable two-wheeled configuration, and 3) flying mode, achieved with a bicopter configuration. Moreover, the radius and sector angle of transformable wheel have been optimized to enhance the obstacle-climbing capability; the position of the robot's body center of gravity has been optimized to balance ground gripping capacity and flight dynamic response speed. Finally, the robot's multi-terrain overcoming capability is validated through obstacle-climbing experiments and continuous terrestrial-aerial transformation experiments, and the high power efficiency of robot is affirmed, demonstrating feasibility of the design.

From the optimization perspective, this article proposes a novel actual shape-based obstacle avoidance synthesized by velocity–acceleration minimization (ASOA-VAM) scheme that performs operational tasks safely in a complex environment utilizing redundant manipulators. Concretely, an actual shape-based obstacle avoidance (ASOA) strategy with a variable magnitude escape acceleration using the Gilbert–Johnson–Keerthi distance algorithm is presented. Trajectory tracking, the end-effector's errors feedback, and the joint multilevel physical limits (joint angle, -velocity, and -acceleration limits) avoidance are also incorporated into this optimization scheme. Meanwhile, the velocity–acceleration minimization (VAM) measure is developed. Combining the ASOA strategy with the VAM measure, the ASOA-VAM scheme is formed and further reformulated as a quadratic program (QP). Moreover, a recurrent neural network with theoretically provable convergence is designed to solve the QP online. Finally, simulations, comparisons, and experiments of a 7-degree-of-freedom manipulator with engineering applications illustrate the ASOA-VAM scheme's effectiveness, accuracy, superiority, and physical realizability.

Space robots play a significant role in on‐orbit servicing (OOS) missions, such as inspecting, capturing, refueling, and repairing satellites, assembling and maintaining large space infrastructure, and removing orbital debris. Over the past four decades, many space robot engineering applications and technology verifications for OOS have been accomplished. This article comprehensively reviews the advances by representative space robotic programs on space shuttles, outside/inside the International Space Station and the China Space Station, as well as on satellites, and the development trends of space robots are summarized. In addition, the primary key technologies and challenges are explored, including the following: 1) visual perception for noncooperative targets; 2) motion planning and control with a free‐floating base and flexibility; 3) multifunctional end‐effectors; 4) ground teleoperation with long time delays; and 5) high‐fidelity ground verification. Finally, the prospects for space robot future research are presented.

对于采用虚拟图形预测仿真克服天地不确定大时延的地面遥操作,为使任务专家准确监视、预测并遥控空间机械臂的运动,本文研究了Open Inventor软件开发包中虚拟相机的成像技术.分析了真实相机与虚拟相机的成像原理,推导了虚拟相机的透视投影和视口变换矩阵.依据二者的成像原理,对真实相机和虚拟相机的成像进行了一致性分析,并展开定量及定性验证.研究表明:基于Open Inventor开发包搭建的中国空间站及空间机械臂的仿真系统,可对空间机械臂的末端及肘部相机进行虚拟成像并准确模拟真实成像,为空间机械臂通过地面遥操作安全精准地完成在轨任务奠定了基础.

Imitating the visual characteristics of human eyes is one of the important tasks of digital image processing and computer vision. Feature correspondence of humanoid-eye binocular images is a prerequisite for obtaining the fused image. Human eyes are more sensitive to edge, because it contains much information. However, existing matching methods usually fail in producing enough edge corresponding pairs for humanoid-eye images because of viewpoint and view direction differences. To this end, we propose a novel and effective feature matching algorithm based on edge points. The proposed method consists of four steps. First, the SUSAN operator is employed to detect features, for its outstanding edge feature extraction capability. Second, the input image is constructed into a multi-scale structure based on image pyramid theory, which is then used to compute simplified SIFT descriptors for all feature points. Third, a novel multi-scale descriptor is constructed, by stitching the simplified SIFT descriptor of each layer. Finally, the similarity of multi-scale descriptors is measured by bidirectional matching, and the obtained preliminary matches are refined by subsequent procedures, to achieve accurate matching results. We respectively conduct qualitative and quantitative experiments, which demonstrate that our method can robustly match feature points in humanoid-eye binocular image pairs, and achieve favorable performance under illumination changes compared to the state-of-the-art.

In general, the traditional spacecraft semi-physical docking tests include the evaluation of docking and separation performance. However, these tests often rely on “specific” equipment, such as specially designed actuators and fast-response hydraulic systems, to meet the stringent dynamic response requirements of semi-physical testing. In this paper, a novel docking test platform is designed based on a general-purpose industrial manipulator using 3-D force and 3-D torque sensors. Different from the traditional solution, this novel platform is well-assembled and cost-effective. Furthermore, an actuation delay compensation method is introduced to improve the performance. Finally, the proposed method is evaluated using simulations. The results show that the novel method is with promising performance in terms of actuation delay compensation.

Purpose In the current teleoperation system of humanoid robots, the control between arms and the control between the waist and arms are individual and lack coordinated motion. This paper aims to solve the above problem and proposes a teleoperation control approach for a humanoid robot based on waist–arm coordination (WAC). Design/methodology/approach The teleoperation approach based on WAC comprises dual-arm coordination (DAC) and WAC. The DAC method realizes the coordinated motion of both arms through one hand by establishing a mapping relationship between a single hand controller and the manipulated object; the WAC method realizes the coordinated motion of both arms and waist by calculating the inverse kinematic input of robotic arms based on the desired velocity of the waist and the end of both arms. An integrated teleoperation control framework provides interfaces for the above methods, and users can switch control modes online to adapt to different tasks. Findings After conducting experiments on the dual-arm humanoid robot through the teleoperation control framework, it was found that the DAC method can save 27.2% of the operation time and reduce 99.9% of the posture change of the manipulated object compared with the commonly used individual control. The WAC method can accomplish a task that cannot be done by individual control. The experiments proved the improvement of both methods in terms of operation efficiency, operation stability and operation capability compared with individual control. Originality/value The DAC method better maintains the constraints of both arms and the manipulated object. The WAC method better maintains the constraints of the manipulated object itself. Meanwhile, the teleoperation framework integrates the proposed methods and enriches the teleoperation modes and control means.