多无人机协同飞行实现近最优时间控制，靠强化学习与避障机制结合。
Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning
- 用多智能体强化学习设计去中心化飞行策略，训练时集中、执行时分散。
- 实测两架无人机在5.5×5.5×2米空间内达13.65米/秒速度，碰撞率低。
- 适合需要高速自主协同飞行的场景，如竞技表演或应急搜救。
近年来，自主无人机在单机配置中实现了时间最优飞行，并通过最优控制和基于学习的方法提升了多机系统的机动性。然而，少有研究能在高敏捷动作或动态场景下实现多无人机的时间最优路径规划。本文提出一种基于多智能体强化学习的去中心化策略网络，兼顾飞行效率与避碰。通过在中央训练、分散执行（CTDE）框架下定制PPO算法，提升训练效率与稳定性，同时保证轻量部署。大量仿真表明，尽管性能略逊于单机系统，该方法仍保持近似时间最优表现且碰撞率极低。真实实验验证了有效性：两架四旋翼无人机使用相同网络，在5.5米×5.5米×2.0米空间内，沿多种轨迹以最高13.65米/秒速度、13.4弧度/秒机体速率飞行，完全依赖机载计算完成。
原文摘要 · Abstract (English)
Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. However, few studies have achieved time-optimal motion planning for multi-drone systems, particularly during highly agile maneuvers or in dynamic scenarios. This paper presents a decentralized policy network using multi-agent reinforcement learning for time-optimal multi-drone flight. To strike a balance between flight efficiency and collision avoidance, we introduce a soft collision-free mechanism inspired by optimization-based methods. By customizing PPO in a centralized training, decentralized execution (CTDE) fashion, we unlock higher efficiency and stability in training while ensuring lightweight implementation. Extensive simulations show that, despite slight performance trade-offs compared to single-drone systems, our multi-drone approach maintains near-time-optimal performance with a low collision rate. Real-world experiments validate our method, with two quadrotors using the same network as in simulation achieving a maximum speed of 13.65 m/s and a maximum body rate of 13.4 rad/s in a 5.5 m * 5.5 m * 2.0 m space across various tracks, relying entirely on onboard computation.
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