Autonomous reconfiguration of homogeneous pivoting cube modular satellite by deep reinforcement learning

Abstract

Modular satellite, which has the ability of self-repairing and accomplishing different tasks, draws more and more satellite designers’ attention recently. One of the trending topics is to design the algorithm of self-reconfigurable path planning, since searching a near-optimal path is an effective way to reduce electrical energy consumption and mechanical loss of satellites. A major thrust of this article is to examine a series of algorithms based on graph theory and deep reinforcement learning. We creatively propose the concept of link module and find the link module by calculating articulation points in the undirected connected graph of configuration. We propose a compressed algorithm of state transition and the deep reinforcement learning algorithms in the domain of self-reconfigurable modular satellites. The simulation results show the feasibility and effectiveness of the proposed planning algorithms.