A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites
This addresses path planning for satellite clusters, enabling diverse mission tasks, but it is incremental as it builds on reinforcement learning with specific enhancements.
The paper tackled path planning for modular self-reconfigurable satellites, which face issues like high computational complexity and poor generalization, by proposing a goal-oriented reinforcement learning algorithm that achieved success rates of 95% for four-unit clusters and 73% for six-unit clusters in reaching arbitrary target configurations.
Modular self-reconfigurable satellites refer to satellite clusters composed of individual modular units capable of altering their configurations. The configuration changes enable the execution of diverse tasks and mission objectives. Existing path planning algorithms for reconfiguration often suffer from high computational complexity, poor generalization capability, and limited support for diverse target configurations. To address these challenges, this paper proposes a goal-oriented reinforcement learning-based path planning algorithm. This algorithm is the first to address the challenge that previous reinforcement learning methods failed to overcome, namely handling multiple target configurations. Moreover, techniques such as Hindsight Experience Replay and Invalid Action Masking are incorporated to overcome the significant obstacles posed by sparse rewards and invalid actions. Based on these designs, our model achieves a 95% and 73% success rate in reaching arbitrary target configurations in a modular satellite cluster composed of four and six units, respectively.