Motion planning is a critical component of autonomous decision-making in intelligent robots, unmanned aerial vehicles, and self-driving systems, where both path quality and planning efficiency are essential. However, conventional path-planning algorithms often suffer from slow convergence, redundant node generation, and limited path quality. To address these limitations, this paper proposes DVFA-RRT*, a progress-driven hybrid sampling and staged extension algorithm for three-dimensional (3D) trajectory planning and obstacle avoidance. The proposed method introduces a goal-distance-based progress metric that adaptively regulates both sampling-strategy selection and the associated probability distribution. A two-stage extension strategy is then developed to balance goal-directed exploitation with global exploration. Specifically, a goal-biased extension strategy integrated with visibility-fan-based obstacle avoidance is used to accelerate convergence, while improved APF-guided exploration with an RRT*-based fallback enhances robustness in cluttered environments. Finally, greedy shortcutting, interpolation-based densification, and B-spline smoothing are applied to refine the generated path and enhance trajectory smoothness. Experiments conducted in five 3D simulation environments demonstrate that DVFA-RRT* generates higher-quality initial paths, requires fewest nodes, and exhibits stronger adaptability across the tested scenarios, thereby improving the overall path-planning performance.

Electromagnetic de-tumbling has emerged as a promising non-contact approach for mitigating the rotational motion of space debris and defunct satellites because of its inherent safety and controllability. In practical missions, however, the target often undergoes nutational motion and bounded positional offsets relative to the service spacecraft, which makes rapid and accurate prediction of electromagnetic torque more difficult. This paper presents a corrected approximate analytical model for calculating the electromagnetic torque acting on a nutating conducting spherical shell in a magnetic dipole field within a bounded offset domain. A mathematical model describing the relative position and electromagnetic interaction between the spherical shell and the magnetic dipole is first established. Finite-element simulations are then conducted to obtain the spatial distributions of the three torque components and to provide numerical benchmarks. Based on these results, approximate analytical expressions and polynomial correction terms are derived for torque prediction. The corrected analytical solutions show high accuracy and good consistency with the numerical results, providing an effective theoretical basis for subsequent dynamic analysis, real-time control, and rapid torque prediction.

This paper proposes a trajectory planning strategy based on Lyapunov-based Model Predictive Control (LMPC) for liquid slosh suppression. The approach ensures that the space manipulator transfers the liquid-filled target to the desired position in three-dimensional space while effectively suppressing liquid slosh. First, an equivalent mechanical model of the liquid-filled target is established based on a 3D-rigid-pendulum. According to the dynamic relationship between the target and the pendulum, a trajectory planning strategy is introduced to effectively suppress the pendulum oscillation. Then, based on this strategy, a trajectory generator based on LMPC is designed, where auxiliary constraints are constructed according to the prominent characteristics of Lyapunov nonlinear backstepping control to ensure that the pendulum inside the spherical tank reaches the desired state at different trajectory planning phases, and the recursive feasibility of these auxiliary constraints is proven. Furthermore, to ensure that the space manipulator tracks the desired trajectory generated by LMPC accurately, a trajectory tracking controller is designed based on the backstepping method, and the stability of the system is proven. Finally, the feasibility of the proposed LMPC-based trajectory planning strategy is verified through numerical simulations.

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.

This article proposes a constrained visual servoing method for the safe and reliable capture of noncooperative tumbling satellites using a free-floating space robot. The adapter ring serves as both the capture interface and the visual feature. However, during the close-range fine servoing phase, only a limited portion of the adapter ring is visible within the hand-eye camera's field of view due to its large size, leading to reduced servoing accuracy. To address this issue, a three-line structured light system is introduced in this phase to augment visual features and complement the long-range guidance provided by the monocular camera. This scheme enables visual servoing from long to close range. Moreover, the target's tumbling motion, the ring's geometric properties, and the coupled dynamics between the manipulator and the base pose significant challenges to the reliability and safety of the servoing process. To mitigate these issues, a set of constraints is formulated, including hard constraints to ensure that visual features remain within the camera's field of view and to avoid obstacles, as well as soft constraints to maintain continuous communication with the ground station and minimize impact forces at the moment of capture. To manage these constraints and regulate the system in real time, a motion planner employing nonlinear model-predictive control is developed, supported by differential models of visual features. Furthermore, a low-level joint controller based on quadratic programming ensures accurate tracking of planned trajectories while adhering to torque limits. Simulation results validate the effectiveness of the proposed method.

Accurate motion prediction of free-tumbling satellites is crucial for the success of capture operations. This paper proposes a two-step method to estimate the motion states and parameters of such satellites, thereby enabling precise long-term motion prediction. This paper begins with a measurement of the system’s degree of observability, quantified through the Empirical Observability Gramian (EOG). Based on this measurement, a batch processing algorithm is first employed to estimate the satellite’s constant parameters offline. Subsequently, an online filtering algorithm, utilizing a minimal state set, fine-tunes these parameters and estimates the motion states in real time. This integrated approach significantly enhances both convergence properties and estimation accuracy, particularly for systems with poor observability. Utilizing the predicted long-term motion of the satellite, a composite evaluation metric is formulated to identify the optimal capture point and moment. The base pose of the space robot is then adjusted to ensure that the optimal capture point lies within the manipulator’s dexterous workspace, which is determined through a pre-constructed capability map. The effectiveness of the proposed method is demonstrated through both simulation and experimental results.

The rapid accumulation of space debris poses a serious threat to operational spacecraft, with the capture and removal of rapidly tumbling non-cooperative targets being a primary challenge. Non-contact electromagnetic de-tumbling technology is a promising solution due to its enhanced safety. This paper addresses the issue of torque modeling and validation in the electromagnetic de-tumbling process for a specific configuration involving a magnetic dipole and a spherical shell under a symmetrically distributed magnetic field. Based on the principles of electromagnetic induction, an approximate analytical expression for the electromagnetic eddy current torque on a rotating spherical shell within a dipole magnetic field is first derived. A high-fidelity finite element model is then established, which reveals a systematic discrepancy between the initial theoretical model and numerical simulation results. A distance-dependent power-law correction factor is introduced to calibrate the theoretical model, significantly improving its accuracy and reducing the average error to 1.5 percent. Finally, a ground-based experimental platform is designed and implemented. The experimental results demonstrate that the corrected approximate analytical model agrees well with the empirical data, verifying its validity and accuracy under the given conditions and providing a reliable theoretical basis for the design of future space debris de-tumbling controllers.

Trajectory planning for redundant space manipulators in complex spatial environments is critical for ensuring successful on-orbit task execution, especially when the manipulator’s link lengths are unknown. Traditional algorithms struggle to establish the transformation between task space and joint space under such conditions, posing significant challenges to autonomous trajectory planning. This study proposes a novel trajectory planning method for a seven-degree-of-freedom redundant space manipulator with a shoulder-wrist offset. The method utilizes a trained pose control decision system to generate continuous, smooth, and singularity-free trajectories in real time, satisfying positional and orientational accuracy requirements for target capture. A neural network with strong nonlinear fitting capabilities is employed as the position control decision module for joints 1-4, trained using a "cautious exploration, greedy exploitation" strategy for efficient and safe trajectory optimization. For joints 5-7, an analytical orientation control decision module adjusts the end-effector’s attitude. Together, these modules form a pose control decision system that adheres to constraints on joint angular velocity, acceleration, and singularity avoidance. The proposed approach is validated through simulations in both static and dynamic scenarios, with simulation results confirming its effectiveness.

Purpose The aim of this paper is to enhance the control performance of dexterous hands, enabling them to handle the high data flow from multiple sensors and to meet the deployment requirements of deep learning methods on dexterous hands. Design/methodology/approach A distributed control architecture was designed, comprising embedded motion control subsystems and a host control subsystem built on ROS. The design of embedded controller state machines and clock synchronization algorithms ensured the stable operation of the entire distributed control system. Findings Experiments demonstrate that the entire system can operate stably at 1KHz. Additionally, the host can accomplish learning-based estimates of contact position and force. Originality/value This distributed architecture provides foundational support for the large-scale application of machine learning algorithms on dexterous hands. Dexterity hands utilizing this architecture can be easily integrated with robotic arms.

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.

Purpose This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method. Design/methodology/approach This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. Findings This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations. Originality/value The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.

In this paper, a visual servoing approach is developed to capture the docking rings of tumbling non-cooperative satellites with a space manipulator. The primary challenge addressed is the potential for the docking ring to leave the monocular camera’s field-of-view as the manipulator approaches the target, due to the ring’s large size. To solve this issue, a two-phase visual servoing scheme combining a monocular camera and a three-line structured light vision system is proposed. In an effort to augment the success rate and safety of capture operations, several constraints are formulated, encompassing manipulator’s kinematics, monocular camera’s field-of-view, obstacle avoidance, structured light’s breakpoints and smooth capture. Subsequently, a nonlinear model predictive controller is proposed to manage these constraints in real-time and regulate the system. System models are established based on image moments and pose for each phase, selecting these features as visual feedback to simplify the formulation of servo constraints and avoid the complex circle-based pose measurement. Furthermore, to ensure unbiased predictions, the model disturbances arising from the imprecise estimation of target motion parameter are observed using an extended Kalman filter, which are then incorporated into the predictive control framework. The simulation results demonstrate the effectiveness of this scheme.

This paper presents a collision-free transferring strategy for space manipulator in the static environment. The entire trajectory is planned based on acceleration to improve the smoothness. Firstly, the improved bidirectional alternating search strategy and the variable-step search strategy are applied to A* algorithm to find a collision-free linear acceleration trajectory of the captured target in Cartesian space effectively and rapidly. Then, the collision between the accessories carried on the target and the space robot is avoided by adjusting the attitude of the target in the aforementioned trajectory. Tracking-differentiator (TD) is adopted to achieve the smooth transition between the adjacent expected attitudes of the target. In addition, considering the posture of the manipulator during the task, an acceleration potential field is established to guide the arm-angle within the safe range all the time. Finally, the performance of the trajectory planning strategy is verified by simulation.

Force measurements become important once contact has occurred and the object is held or explored by the hand. The uncertainty associated with the fingertip contact position and the soft finger contact torque can lead to inaccurate force measurement when using a model-based method. To address this issue, we utilized a learning-based method that leverages neural networks to concatenate data from tactile, position, and torque sensors to improve force measurement accuracy in soft finger contacts. Due to the requirement for a large volume of data to train the network, we propose a data collection method that is independent of the fingers. This method utilizes the physical model of the finger to rapidly generate a large dataset. The effectiveness of this data collection method is validated on real-world datasets. Comparing the model-based method and the data-driven method using solely tactile input, our proposed force measurement method achieves the lowest force errors in all three axes and magnitude. Particularly, in the x - axis and force magnitude, the proposed method reduces the error by more than 50% compared to the model-based method. Furthermore, our method demonstrates effectiveness and robustness on unseen objects. This method is not only suitable for dexterous hands but also applicable to underactuated hands equipped with torque sensors and high-dimensional tactile.

Malfunctioned satellites have seriously threatened orbital safety, and the capture of these satellites is of great significance. The pose measurement and the motion estimation of the tumbling satellite is the premise of capture. In this paper, the docking ring of the satellite is identified, which is equivalent to a spatial circle. Combined with the nozzle feature, the pose duality of the spatial circle can be eliminated. And the measurement accuracy is improved by minimizing the reprojection error of the docking ring and the nozzle. Due to the symmetry of the docking ring, the measured pose has only five degrees of freedom, losing the degree of freedom of rotation around the normal vector. In the motion estimation algorithm, the observability of the tumbling motion is firstly analyzed, then an error-state Kalman filter with inertia ratio constraints is designed. To improve the convergence speed and stability of the filter, a rough estimation algorithm of filter initial value based on linear term extraction and particle swarm optimization is proposed. The effectiveness of the pose measurement and motion estimation method is verified by simulations.

Space debris is growing dramatically, which poses a serious threat to space exploration activities. Especially the large non-cooperative target, such as malfunctioning satellites. This paper proposes a capture mechanism for the launch adapter ring that is usually available on satellites as the capture object, which used for in-orbit capture of malfunctioning satellites. Firstly, introduce the design conditions, the overall design plan, carry out the mechanical mechanism design, sensor system configuration, electrical system design, and explain the capture process. Secondly, analyze the capture tolerance. Thirdly, by establishing the kinematics model of the captured finger, use D-H parameter method for kinematic analysis, and analyze the dynamic in the capturing process. In addition, the control strategy is proposed, and the clamping force model, friction identification model, and servo control strategy are established. Then, the prototype is manufactured, and the clamping force, stiffness, capture loads, and capture tolerance are tested. Finally, the air-floating platform is used to verify the capture test of the launch adapter ring in a microgravity environment. The experimental results show that the developed capture mechanism meets the design conditions and has the ability to capture launch adapter ring of satellites in orbit.

This paper proposes a detumbling motion planning algorithm for free-flying space manipulator with a grasped tumbling target in the post-capturing phase. This algorithm can not only collision-freely guide the space manipulator and the target to terminal stationary states but also suppress the residual vibration of the flexible appendage on the space manipulator. First, considering the avoidances of self-collisions and motion singularities, a smooth detumbling path is planned for the space manipulator by a proposed smoothing rapid random tree star algorithm (SM-RRT*). Second, a quintic polynomial function is implemented to generate a continuous detumbling trajectory along the detumbling path. Then, with the object of minimizing the residual flexible vibrations and the constrains of joint acceleration limits, an optimization model is established to refine the detumbling trajectory. Finally, the optimization model is solved by an improved particle swarm optimization algorithm (PSO), where a potential field term is included in the generation of the particle velocity to enhance the computational efficiency. Simulation results validate the effectiveness of the proposed detumbling motion planning algorithm.

This paper proposes a collision avoidance path planning approach for redundant robotic arms based on the RRT algorithm. The study focuses on a 7-DOF robotic arm with an SSRMS configuration. The method involves improved RRT-based path planning in configuration space (C-space) and collision detection in Cartesian space. The results of collision detection are employed to guide the path planning process. Emulating the SSRMS configuration robotic arm, a kinematic model is established. By enhancing the RRT algorithm with the following points: probabilistic sampling towards the target configuration, guided exploration near existing tree nodes, parent node reassignment for new nodes, and the adoption of a bidirectional searching tree, the randomness of sampling is reduced and the planning process is accelerated. By continuously mapping points on the surface of the arm to both the base coordinate system and the self-collision coordinate system, collision detection and self-collision detection are performed in the Cartesian space using a combination of surface equations and projection methods. Finally, a series of simulation experiments were conducted to analyze the impact of various parameters, different obstacles and different methods on the planning process. This validation confirmed the feasibility and effectiveness of the approach.

This paper proposes a state estimation method of non-cooperative target, which can be used to identify the target satellite motion. Firstly, tumbling motion of the target is analyzed, while dynamic of non-cooperative target is built. Secondly, an estimate method based on least squares method is proposed to identify the kinematic and dynamic parameters. Both of them are based on least squares methods. Thirdly, with parameters estimated, error state Kalman filter is used to estimate the angular velocity and filter attitude of the target at the same time. Finally, a simulation experiment is carried out to verify the effectiveness of the method. Simulation results reveal the method proposed by this paper can identify parameter and estimate motion state accurately, which is meaningful for non-cooperative target capture.

Space robots are widely used for on-orbit capturing. After capturing a rotational non-cooperative target, its momentums produce unpredictable movements for the resultant space robot-target compound system. In order to address this problem, a collision-free detumbling strategy (CFDS) is proposed for space robots with a single 7-degree-of-freedom (DOF) manipulator. First, an acceleration potential field (AccPF) is presented for planning detumbling velocity trajectories, which guide the compound system to terminal rest while guaranteeing collision avoidance in dynamic environments. Energy conservation and acceleration limitations are considered in the trajectory generation. Afterwards, with a radial basis function neural network (RBFNN) approximating the target's uncertain dynamics, an adaptive control scheme is established to precisely track the planned detumbling velocity trajectory. Furthermore, system stability is certified by a Lyapunov function. The effectiveness of the CFDS is eventually validated by simulations.