A unique approach for the mid-air autonomous aerial interception of non-cooperating UAV by a flying robot equipped with a net is presented in this paper. A novel interception guidance method dubbed EPN is proposed, designed to catch agile maneuvering targets while relying on onboard state estimation and tracking. The proposed method is compared with state-of-the-art approaches in simulations using 100 different trajectories of the target with varying complexity comprising almost 14 hours of flight data, and EPN demonstrates the shortest response time and the highest number of interceptions, which are key parameters of agile interception. To enable robust transfer from theory and simulation to a real-world implementation, we aim to avoid overfitting to specific assumptions about the target, and to tackle interception of a target following an unknown general trajectory. Furthermore, we identify several often overlooked problems related to tracking and estimation of the target's state that can have a significant influence on the overall performance of the system. We propose the use of a novel state estimation filter based on the IMM filter and a new measurement model. Simulated experiments show that the proposed solution provides significant improvements in estimation accuracy over the commonly employed KF approaches when considering general trajectories. Based on these results, we employ the proposed filtering and guidance methods to implement a complete autonomous interception system, which is thoroughly evaluated in realistic simulations and tested in real-world experiments with a maneuvering target going far beyond the performance of any state-of-the-art solution.

翻译：本文提出了一种独特的空中自主拦截方法，通过配备拦截网的飞行机器人对非合作无人机进行捕获。我们提出了一种名为EPN的新型拦截制导方法，该方法依赖机载状态估计与追踪技术，旨在捕获高机动性目标。通过在仿真中使用包含近14小时飞行数据的100条不同复杂度目标轨迹，将所提方法与现有先进方法进行比较，EPN展现出最短的响应时间和最高的拦截成功率——这两者正是敏捷拦截的关键性能指标。为实现从理论仿真到实际应用的稳健迁移，我们致力于避免对目标特定假设的过拟合，并着力解决沿未知通用轨迹运动目标的拦截问题。此外，我们指出了若干常被忽视的目标状态追踪与估计问题，这些问题可能对系统整体性能产生重大影响。为此，我们提出采用基于交互多模型(IMM)滤波器的新型状态估计器及创新的测量模型。仿真实验表明，在处理通用轨迹时，所提方案相比常用的卡尔曼滤波(KF)方法在估计精度上取得显著提升。基于这些成果，我们整合提出的滤波与制导方法构建了完整的自主拦截系统，该系统在逼真仿真中得到全面验证，并在真实场景实验中成功拦截远超现有技术性能极限的机动目标。