複雑環境におけるマルチUAV協調航法：系統的マルチエージェント深層強化学習によるアプローチ
Cooperative Multi-UAV Navigation in Complex Environments via Systematic Multi-Agent Deep Reinforcement Learning
複雑環境での複数UAVの協調航法における局所最適、疎な報酬、学習不均衡、汎化不足を解決するマルチエージェント深層強化学習フレームワークを提案した。
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著者: Yu Su, Nabil Aouf
分類: cs.RO
原文アブストラクト
Cooperative navigation of multi-agent UAVs in complex environments faces key challenges including local optima traps, sparse rewards, learning imbalance among agents, and insufficient cross-scenario generalisation. This paper proposes a multi-agent deep reinforcement learning framework that addresses these issues through coordinated exploration, demonstration exploitation, safe curriculum scheduling, and structure-aware generalisation. First, a perception mechanism combining memory of visited states, directional novelty estimates, and penalty backpropagation enables agents to proactively detect and escape local optima. Second, a hierarchical collaborative demonstration buffer with tiered behaviour cloning manages trajectories by degree of team collaboration and applies differential supervision to the actor network, improving demonstration utilisation under sparse collaborative signals. Third, a safety-aware dual-condition curriculum scheduling mechanism reviews mastered scenarios through back-testing and experience pre-filling during training, suppressing catastrophic forgetting while ensuring both task performance and flight safety. For generalisation, local geometric features computed from sensor readings are abstracted into a domain parameter, through which a structure-aware gating network and mixture-of-experts mechanism condition the policy on local structural patterns rather than scenario-specific coordinates, enabling cross-scenario transfer without exposure to the target environment. The framework is further validated under mixed static-dynamic obstacle settings, showing robust adaptability to dynamic disturbances. Simulation results confirm strong performance in collaboration success rate, navigation robustness, zero-shot cross-scenario generalisation, and dynamic environment adaptability.
