多无人机视觉协同捕获失控无人机，实测成功率64.7%。
Vision-Based Cooperative MAV-Capturing-MAV
- 多架无人机通过视觉系统协同探测并追踪目标
- 采用MPC优化轨迹，实时预测飞网运动实现捕获
- 适合无人机反制、安防监控等场景
MAV捕获无人机（MCM）是应对滥用或恶意无人机的有效方法之一。本文提出一种基于视觉的协同MCM系统，多个配备机载视觉系统的追捕无人机可自主检测、定位并追踪目标无人机。为增强鲁棒性，系统采用分布式状态估计与控制框架，实现追捕无人机的自主协同。追捕轨迹通过模型预测控制（MPC）优化，并由低层SO(3)控制器执行，确保追逐过程平稳稳定。当满足捕获条件时，追捕无人机自动投放飞行网进行拦截，捕获条件基于飞网运动预测确定。为实现实时决策，提出一种轻量级计算方法近似飞网运动，避免求解完整飞网动力学带来的高开销。系统在仿真和真实实验中均验证有效。真实测试中，成功捕获以4米/秒速度、1米/秒²加速度移动的目标，捕获成功率达64.7%。
原文摘要 · Abstract (English)
MAV-capturing-MAV (MCM) is one of the few effective methods for physically countering misused or malicious MAVs.This paper presents a vision-based cooperative MCM system, where multiple pursuer MAVs equipped with onboard vision systems detect, localize, and pursue a target MAV. To enhance robustness, a distributed state estimation and control framework enables the pursuer MAVs to autonomously coordinate their actions. Pursuer trajectories are optimized using Model Predictive Control (MPC) and executed via a low-level SO(3) controller, ensuring smooth and stable pursuit. Once the capture conditions are satisfied, the pursuer MAVs automatically deploy a flying net to intercept the target. These capture conditions are determined based on the predicted motion of the net. To enable real-time decision-making, we propose a lightweight computational method to approximate the net motion, avoiding the prohibitive cost of solving the full net dynamics. The effectiveness of the proposed system is validated through simulations and real-world experiments. In real-world tests, our approach successfully captures a moving target traveling at 4 meters per second with an acceleration of 1 meter per square second, achieving a success rate of 64.7 percent.
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