Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal

TL;DR

This paper compares learned policies and search-based planning for autonomous debris removal in orbit, highlighting trade-offs between performance, robustness, and computational efficiency in dynamic, constrained environments.

Contribution

It introduces a domain-randomized training approach for reinforcement learning policies to improve robustness in orbital debris removal tasks.

Findings

Domain-randomized PPO improves adaptability over nominal PPO.

MCTS handles constraint changes best but is computationally expensive.

Trade-off identified between policy speed and search-based adaptability.

Abstract

Autonomous mission planning for Active Debris Removal (ADR) must balance efficiency, adaptability, and strict feasibility constraints on fuel and mission duration. This work compares three planners for the constrained multi-debris rendezvous problem in Low Earth Orbit: a nominal Masked Proximal Policy Optimization (PPO) policy trained under fixed mission parameters, a domain-randomized Masked PPO policy trained across varying mission constraints for improved robustness, and a plain Monte Carlo Tree Search (MCTS) baseline. Evaluations are conducted in a high-fidelity orbital simulation with refueling, realistic transfer dynamics, and randomized debris fields across 300 test cases in nominal, reduced fuel, and reduced mission time scenarios. Results show that nominal PPO achieves top performance when conditions match training but degrades sharply under distributional shift, while domain-randomized PPO exhibits improved adaptability with only moderate loss in nominal performance. MCTS consistently handles constraint changes best due to online replanning but incurs orders-of-magnitude higher computation time. The findings underline a trade-off between the speed of learned policies and the adaptability of search-based methods, and suggest that combining training-time diversity with online planning could be a promising path for future resilient ADR mission planners.