Space Manipulator Assembly Operation Technique based on Deep Residual Reinforcement Learning

Abstract

In recent years, there are more and more space complex operational tasks such as the maintenance and assembly of on-orbit aircraft. Traditional robot planning and control methods require precise dynamic models, which are difficult to accommodate to on-orbit assembly operations in extreme space environments. Typical space operation tasks, such as plug and pull operation, whose control strategy can be artificially designed. Being artificially designed by combining the output control strategy and deep reinforcement learning algorithm, which can simplify the training difficulty of deep reinforcement learning, making the learning process more efficient and training results better. In this paper, a deep Residual reinforcement learning algorithm combined with a heuristic control strategy is constructed to complete the space mechanical arm assembly operation training in a highly realistic simulation environment. Based on the experimental data, the Residual deep reinforcement-learning algorithm designed in this paper shows the performance of rapid convergence and can complete the on-orbit assembly operation task with a high probability.