The trajectory planning of space robot for capturing space tumbling targets still faces challenging technical problems due to the moving targets, which requires the robot to precisely pinpoint the target in a reliable, safe way. Hard-capture strategy that only cares about the precise pinpoint of the final capturing states cannot ensure a reliable capture due to its lack of a thoughtful approaching strategy. To this end, this paper proposes a soft-capture planning method based on the imitation learning theory for the space robot, considering the robotic approaching strategy by imitating the target’s motion. The most frequently used imitation learning algorithm, named dynamical movement primitives (DMPs), are employed to plan both the positions and orientations of the robot end-effectors in Cartesian space to capture grapple fixtures fixed on the target, in which the pose DMPs are specially formulated to capture such a moving target. To imitate the target’s motion, the soft-capture approaching strategy directly generalizes the motion skill from the target to the robot by learning the weight vectors of the pose DMPs for the robot from the demonstrated target’s pose trajectories. Moreover, the final capturing time, poses, and velocities/angular velocities are considerately optimized to ensure good robot kinematic manipulability. Numerical simulations implemented on a dual-arm space robot validate the effectiveness and generalization of the proposed soft-capture planning method, which shows a longer imitated tracking process for about 4 sec before capturing than that of the hard-capture strategy for less than 1 s The longer imitated tracking process leads to more time for the robot capturing adaptations, which is more reliable for the space capturing tasks.

空间机器人捕获空间翻滚目标的轨迹规划由于目标的移动性仍面临着具有挑战性的技术问题，这就要求机器人以可靠、安全的方式精确定位目标。由于缺乏周到的接近策略，只关心最终捕获状态的精确定位的硬捕获策略无法确保可靠捕获。为此，本文提出了一种基于模仿学习理论的空间机器人软捕获规划方法，通过模仿目标运动来考虑机器人的接近策略。最常用的模仿学习算法，称为动态运动原语（DMP），被用来规划笛卡尔空间中机器人末端执行器的位置和方向，以捕获固定在目标上的抓斗夹具，其中位姿 DMP 是专门为捕获这种移动目标而制定的。为了模仿目标的运动，软捕获接近策略通过从演示目标的姿态轨迹中学习机器人的姿态 DMP 的权重向量，直接将目标的运动技能推广到机器人。此外，最终捕获时间、姿势和速度/角速度都经过精心优化，以确保良好的机器人运动学可操作性。在双臂空间机器人上进行的数值模拟验证了所提出的软捕获规划方法的有效性和泛化性，