Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous

TL;DR

This paper applies a deep reinforcement learning approach using masked PPO and Lambert solver to optimize space debris rendezvous sequences, significantly reducing mission time compared to traditional heuristics.

Contribution

It introduces a novel RL-based method for space debris mission planning that outperforms existing heuristics in efficiency and speed.

Findings

Reduced total mission time by ~11-14% compared to heuristics

Neural network efficiently predicts near-optimal debris visitation sequences

RL approach offers faster computation and better planning efficiency

Abstract

This research introduces a novel application of a masked Proximal Policy Optimization (PPO) algorithm from the field of deep reinforcement learning (RL), for determining the most efficient sequence of space debris visitation, utilizing the Lambert solver as per Izzo's adaptation for individual rendezvous. The aim is to optimize the sequence in which all the given debris should be visited to get the least total time for rendezvous for the entire mission. A neural network (NN) policy is developed, trained on simulated space missions with varying debris fields. After training, the neural network calculates approximately optimal paths using Izzo's adaptation of Lambert maneuvers. Performance is evaluated against standard heuristics in mission planning. The reinforcement learning approach demonstrates a significant improvement in planning efficiency by optimizing the sequence for debris rendezvous, reducing the total mission time by an average of approximately {10.96\%} and {13.66\%} compared to the Genetic and Greedy algorithms, respectively. The model on average identifies the most time-efficient sequence for debris visitation across various simulated scenarios with the fastest computational speed. This approach signifies a step forward in enhancing mission planning strategies for space debris clearance.