SCOPE: Safety-Constrained Online Preview Enforcement for Efficient Encirclement in Multi-UAV Pursuit-Evasion

摘要

Unmanned aerial vehicle swarms in pursuit-evasion requires encirclement efficiency while maintaining safety constraints, facing a critical safety-efficiency trade-off. Existing safe multi-agent reinforcement learning (MARL) methods often yield either unsafe task policies or conservative policies. This is challenging since forward feasible safety under dense interactions requires online safety preview. To address this issue, we propose Safety-Constrained Online Preview Enforcement (SCOPE), a MARL-based algorithm that balances encirclement efficiency and safety by short-horizon preview and safety enforcement. SCOPE learns an online safety-preview dynamics model that rolls out future trajectories to inform encirclement decisions checking. By proposing preview-fused actor-critic, SCOPE uses short-horizon previews for efficient encirclement and less unsafe behavior. Hierarchical safety enforcement performs safety look-ahead and online action correction to maintain forward feasible safety. Experiments show that SCOPE better balances encirclement efficiency and safety than safe MARL baselines, maintaining average encirclement time while reducing the average agent cost by 65.6%. Code could be found at https://github.com/98177qdn/SCOPE.