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This research advances micro aerial vehicle (MAV) capture by combining time-optimal planning with reinforcement learning to handle high-maneuverability targets in unstable conditions. While simulations show time-optimal planning yields shorter trajectories, the study validates reinforcement learning's superior real-time adaptability and stability through real-world testing. The work represents a significant step forward in autonomous pursuit-evasion control, particularly for compact platforms with custom launching mechanisms.
Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning
arXiv:2503.06578v2 Announce Type: replace Abstract: 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.
Abstract: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.
Robotics (cs.RO); Systems and Control (eess.SY)
arXiv:2503.06578 [cs.RO]
(or arXiv:2503.06578v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2503.06578 Focus to learn more arXiv-issued DOI via DataCite
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