用强化学习让捕获无人机精准追捕敏捷目标
Agile Interception of a Flying Target using Competitive Reinforcement Learning
- 双智能体竞争强化学习，用PPO训练捕获与目标无人机
- 捕获率显著高于传统规则算法，崩溃率更低
- 真实室内场景验证，适合高动态无人系统研究
本文提出一种通过携带捕网的敏捷无人机拦截另一架敏捷无人机的解决方案。将拦截问题建模为竞争式强化学习任务，拦截者与目标无人机分别由独立策略控制，使用近端策略优化（PPO）进行训练。设计了一个高保真仿真环境，集成真实的四旋翼动力学模型和基于JAX实现的低层控制架构，支持在GPU上快速并行执行。训练中使用低层控制信号，包括总推力和机体角速率，以实现两者均具备敏捷飞行能力。在捕获率、捕获时间与碰撞率等方面对比常见启发式基线方法，结果表明所提方案在拦截敏捷目标时表现更优。最后，在室内飞行场中使用真实敏捷无人机对训练策略进行了缩放验证。
原文摘要 · Abstract (English)
This article presents a solution to intercept an agile drone by another agile drone carrying a catching net. We formulate the interception as a Competitive Reinforcement Learning problem, where the interceptor and the target drone are controlled by separate policies trained with Proximal Policy Optimization (PPO). We introduce a high-fidelity simulation environment that integrates a realistic quadrotor dynamics model and a low-level control architecture implemented in JAX, which allows for fast parallelized execution on GPUs. We train the agents using low-level control, collective thrust and body rates, to achieve agile flights both for the interceptor and the target. We compare the performance of the trained policies in terms of catch rate, time to catch, and crash rate, against common heuristic baselines and show that our solution outperforms these baselines for interception of agile targets. Finally, we demonstrate the performance of the trained policies in a scaled real-world scenario using agile drones inside an indoor flight arena.
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