Trajectory planning method has been proved to be an effective way to suppress robot vibration in multiple experiments, which can be divided into off-line trajectory planning for repetitive task and online trajectory modified for uncertain task. Particle swarm optimization (PSO) is a typical optimization algorithm, which can be used in offline joint trajectory planning, but classical PSO is incapable of optimizing a joint trajectory with variable constraints. Thus, in this paper, a novel compression factor PSO with penalty function named CP-PSO is proposed to modify joint trajectory, thereby achieve minimum residual elastic potential energy in the case of joint angle constraint. However, CP-PSO algorithm still needs plenty of computation time, which is unsuitable for online optimizing uncertain operational task’s trajectory. To solve this issue, trajectory modified based on vibration prediction criteria is put forward to suppress manipulator vibration. Particularly, vibration prediction criteria is based on torque error between theoretical joint torque and actual joint torque, as well as the modified trajectory is named as DKA trajectory by the reason of it is composed of three sections, namely, smooth decelerating process, low velocity keeping process, and accelerating process. When vibration is predicted to occur via vibration prediction criteria, DKA trajectory is switched ON to suppress vibration. Finally, experiments are performed on manipulator to verify the proposed methods.

The calibration of kinematic parameters has been widely used to improve the pose (position and orientation) accuracy of the robot arm. Intelligent measuring equipment with high accuracy is usually provided for the industrial manipulator. Unfortunately, large noise exists in the vision measurement system, which is provided for space manipulators. To overcome the adverse effect of measuring noise and improve the optimality of calibrating time, a calibration method based on extended Kalman filter (EKF) for space manipulators is proposed in this paper. Firstly, the identification model based on the Denavit–Hartenberg (D-H) modeling method is established. Then, the camera which is rigidly attached to the end-effector takes pictures of a calibration board that is settled around the manipulator. The actual pose of the end-effector is calculated based on the pictures of the calibration board. Subsequently, different data between the actual pose and theoretical pose as input, whilst error parameters are estimated by EKF and compensated in the kinematic algorithm. The simulation result shows that the pose accuracy has been improved by approximately 90 percent. Compared with the calibration method of the least squares estimate (LSE), EKF is beneficial to further optimize the calibrating time with a faster computation speed and ensure the stability of the calibration.

Dexterous grasping is a crucial technique in humanoid robot manipulation. However, existing methods still fall short in effectively detecting dexterous grasps in cluttered environments. In this work, we propose DexMGNet, a novel multi-mode dexterous grasping framework designed for such challenging scenarios. We introduce the concept of pre-grasping and redefine dexterous grasping to enhance adaptability. We propose an effective pre-grasp and grasp data sampling strategy and develop a conditional generative model for grasp and pre-grasp generation. Additionally, we integrate pre-grasp collision detection within the hand's workspace, significantly improving grasping performance in cluttered environments. Our method supports multi-mode grasping, including two-finger, three-finger, and four-finger grasps, enabling greater flexibility across diverse grasping tasks. In real-world desktop grasping experiments, our approach achieves a 93.3% success rate in single-object scenes and a 78.3% success rate in multi-object scenes, demonstrating its effectiveness and superiority.

This study investigates the visual servo control of the space station macro/micro manipulator system. The proposed approach is based on the position-based eye-in-hand visual servo (PBVS) and takes advantage of the hardware sensors to overcome the macro manipulator’s base flexibility and joint backlash. First, a vibration suppression approach based on the reaction force feedback control is proposed, the deflection forces are measured by the six-axis force/torque sensor at the base of the micro-manipulator, and damping is injected into the flexible base in the closed-loop control to suppress the base vibration. Second, the small changes of joint backlash are compensated based on the macro manipulator joint angles sensor and converted to the desired motion of the payloads. Finally, PBVS with the lag correction is proposed, which is adequate for the precise positioning of large payloads with significant low-frequency oscillations. Ground micro-gravity experiment implementation is discussed, simulations and experiments are carried out based on the equivalent 3-DOF flexible base manipulator system and the macro/micro manipulator ground facilities, and results demonstrate the effectiveness of the proposed control algorithm.

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.