A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites

TL;DR

This paper introduces a goal-oriented reinforcement learning algorithm for path planning in modular satellites, effectively handling multiple configurations and overcoming previous limitations with high success rates.

Contribution

It presents the first RL-based path planning method capable of managing multiple target configurations in modular satellite clusters.

Findings

Achieves 95% success rate with four units.

Achieves 73% success rate with six units.

Incorporates Hindsight Experience Replay and Invalid Action Masking.

Abstract

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.