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.

翻译：对飞行中的微型飞行器（MAV）进行捕获，因其极具挑战性且应用前景广阔，日益受到研究关注。尽管近期取得了进展，现有工作的一个关键局限在于捕获策略通常相对简单，且受限于平台性能。本文研究能够捕获高机动性目标的控制策略。在不稳定条件下实现目标捕获的独特挑战，使该任务有别于传统的追逃与制导问题。在本研究中，我们从较大的MAV平台转向一种专门设计的、配备定制发射装置且保持高机动性的紧凑型捕获MAV。我们探索了时间最优规划（TOP）和强化学习（RL）两种方法。仿真结果表明，TOP提供高机动性和更短的轨迹，而RL在实时适应性和稳定性方面表现卓越。此外，RL方法已在真实场景中经过测试，即使在非稳定状态下也成功实现了目标捕获。