用强化学习与最优规划实现高机动无人机的快速捕获。
Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning
- 设计小型捕获无人机，结合自定义发射装置保持高机动性。
- 最优规划轨迹更短更灵活，强化学习实时适应性强且稳定。
- 在真实不稳定环境下成功捕获目标，适合复杂空域任务。
飞行微型无人机（MAV）的捕获因挑战性强且应用前景广阔而受到越来越多关注。尽管已有进展，但现有方法多受限于平台性能，策略较为简单。本文针对高机动目标的捕获问题，从大型MAV平台转向一种专为捕获设计的紧凑型捕获MAV，配备定制发射装置，同时保持高机动性。研究对比了时间最优规划（TOP）与强化学习（RL）两种控制策略。仿真结果表明，TOP生成更短、更灵活的轨迹；而RL在实时适应性和稳定性方面表现更优。此外，该方法已在真实场景中验证，成功在不稳定状态下完成目标捕获。
原文摘要 · Abstract (English)
The capture of flying MAVs (micro aerial vehicles) has garnered increasing research attention due to its intriguing challenges and promising applications. Despite recent advancements, a key limitation of existing work is that capture strategies are often relatively simple and constrained by platform performance. This paper addresses control strategies capable of capturing high-maneuverability targets. The unique challenge of achieving target capture under unstable conditions distinguishes this task from traditional pursuit-evasion and guidance problems. In this study, we transition from larger MAV platforms to a specially designed, compact capture MAV equipped with a custom launching device while maintaining high maneuverability. We explore both time-optimal planning (TOP) and reinforcement learning (RL) methods. Simulations demonstrate that TOP offers highly maneuverable and shorter trajectories, while RL excels in real-time adaptability and stability. Moreover, the RL method has been tested in real-world scenarios, successfully achieving target capture even in unstable states.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。