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• A reinforcement learning framework using masked Proximal Policy Optimization (PPO) can optimize multi-debris rendezvous missions for active debris removal.method 90%
• The RL framework reduces collision risk compared to traditional heuristic approaches in ADR missions.result 85%
• The RL framework improves mission efficiency (fuel usage and mission time) compared to traditional heuristic approaches.result 85%
• Small satellites are well suited for active debris removal missions due to their flexibility, cost effectiveness, and maneuverability.market 90%
• The framework integrates refueling strategies, efficient mission planning, and adaptive collision avoidance for spacecraft rendezvous optimization.technical 90%
• The RL agent learns to determine efficient sequences for rendezvousing with multiple debris targets while incorporating necessary refueling stops.technical 85%
• The framework was evaluated using simulated ADR scenarios derived from the Iridium 33 debris dataset.method 90%
• The approach provides a scalable solution for planning complex multi-debris ADR missions applicable to other multi-target rendezvous problems.technical 80%

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