多节点探测器软着陆的路径规划方法

The deep space probe with flexible-connected multiple nodes is probably a solution to the possible overturn or rebound in single-node probe landing on an asteroid. Therefore, we construct a probe with flexible-connected three nodes, model the soft landing process, and propose a multi-task deep reinforcement learning method with self-attention mechanism. Each node's state is described referring to the probe base. Furthermore, joint learning among nodes is used to improve their adaptability. At the same time, the self-attention is applied to make the nodes focus on their own tasks and learn better strategies to obtain higher rewards for feature extraction of the probe and obstacles. Experimental results show that the method proposed in this paper is more beneficial to the stable landing of the probe compared with other methods.