Abstract:

 Focusing on the on-orbit manipulating environment with uncertain target characters，the route planning strategy of a typical space manipulator is studied. The Sarsa(λ) reinforcement learning algorithm is used to achieve the goal of the autonomous route planning and intelligent decision for the tasks on target tracking and obstacle avoidance. This method considers each arm in a manipulator system as a decision agent, by means of percepting the two dimensional states consisting of the target deviation and the degree of obstacle distance, designing and fitting a reward function corresponding to the artificial experience，and the reinforced training on rotating action by each arm，the final state-action value function table of each agent can be used as a decision basis for the online manipulator route planning. We use this method on route planning task by a space manipulator with multi-degree of freedom, the simulation result shows that this new algorithm can achieve the requirement for stable tracking of a moving target and simultaneous obstacle avoidance within finite training times, meanwhile, the state-action value function table of each agent obtained from the reinforcement learning possesses the strong capacity of subsequent online autonomous adjustment, which validates the robustness and intelligence of this algorithm.

Key words: Reinforcement learning, Sarsa method, Space manipulator, Route planning