Reinforcement-Learning-Based Task Planning for Self-Reconfiguration of Cellular Satellites

Cellular satellites, which are composed of many standard unit cells, represent a class of novel and promising satellites for future space explorations. Their potentials have been well recognized in the aerospace field. The most attractive feature of cellular satellites is the on-orbit self-reconfiguration capability through cell-by-cell moves. However, it is extremely challenging for a cellular satellite to autonomously achieve the optimal self-reconfiguration with fewest cell moves, because the search space for legal actions may be larger than that of the game of Go if the satellite has a certain number of cells. In this article, we propose a reinforcement learning-based task planning strategy for the self-reconfiguration of cellular satellites. Inspired by the recent progress of AlphaGo and AlphaGo Zero, we calculate the cell move sequence and predict the cell placements in the self-reconfiguration process by combining the Monte Carlo tree search and the neural network. The reinforcement learning-based task planning strategy is validated by comparing with the traditional melt-sort-grow algorithm. The validation results demonstrate that the proposed strategy can significantly reduce the number of cell moves for the self-reconfiguration of cellular satellites.