评估基于学习的任务规划在主动清除任务中的鲁棒性和适应性
Evaluating Robustness and Adaptability in Learning-Based Mission Planning for Active Debris Removal
AI总结：
本文评估了基于学习的任务规划在主动清除任务中的鲁棒性和适应性，比较了三种规划器在不同约束条件下的性能，发现领域随机化PPO在适应性上表现更优，而MCTS在处理约束变化时更稳定但计算成本高。
摘要：
自主的主动清除（ADR）任务规划必须在效率、适应性和燃料及任务持续时间的严格可行性约束之间取得平衡。本文比较了三种规划器用于低地球轨道上的受限多碎片对接问题：一种在固定任务参数下训练的名义性屏蔽近端策略优化（PPO）策略，一种在变化的任务约束下训练的领域随机化屏蔽PPO策略以提高鲁棒性，以及一种普通的蒙特卡洛树搜索（MCTS）基线。评估在高保真的轨道模拟中进行，包括加油、真实的转移动力学和随机化的碎片场，共进行了300个测试案例，在名义性、减少燃料和减少任务时间的场景中。结果表明，名义性PPO在条件匹配训练时表现最佳，但在分布偏移时急剧下降；领域随机化PPO表现出改进的适应性，仅在名义性性能上略有损失。MCTS始终能最好地处理约束变化，但计算时间高出了数量级。这些发现强调了学习策略的速度与基于搜索的方法的适应性之间的权衡，并建议结合训练时的多样性与在线规划可能是未来稳健ADR任务规划器的有前途的路径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
