Constrained Motion Planning of 7-DOF Space Manipulator via Deep Reinforcement Learning Combined with Artificial Potential Field
Yinkang Li, Danyi Li, Wenshan Zhu, Jun Sun, Xiaolong Zhang, Shuang Li
Abstract
During the on-orbit operation task of the space manipulator, some specific scenarios require strict constraints on both the position and orientation of the end-effector, such as refueling and auxiliary docking. To this end, a novel motion planning approach for a space manipulator is proposed in this paper. Firstly, a kinematic model of the 7-DOF free-floating space manipulator is established by introducing the generalized Jacobian matrix. On this basis, a planning approach is proposed to realize the motion planning of the 7-DOF free-floating space manipulator. Considering that the on-orbit environment is dynamical, the robustness of the motion planning approach is required, thus the deep reinforcement learning algorithm is introduced to design the motion planning approach. Meanwhile, the deep reinforcement learning algorithm is combined with artificial potential field to improve the convergence. Besides, the self-collision avoidance constraint is considered during planning to ensure the operational security. Finally, comparative simulations are conducted to demonstrate the performance of the proposed planning method.