两架无人机在无GPS环境下，通过距离感知实现对多目标的动态包围。
Multiple noncooperative targets encirclement by relative distance-based positioning and neural antisynchronization control
- 用模糊小波神经网络+最小二乘法估计目标中心位置。
- 设计分布式反同步控制器，使两机保持相对位置并动态调整包围半径。
- 适用于无人机编队、无人侦察等需避障包围的场景。
从远古围猎到现代卫星定位，目标包围在现实中有广泛应用。然而，在无GPS环境下包围多个非合作目标仍具挑战性。本文研究最少两架任务无人机，利用机载传感器获取与目标的相对距离，直接通过模糊小波神经网络（FWNN）和最小二乘法估计所有目标的中心位置。随后设计一种新的分布式反同步控制器（DASC），使两架无人机能同时包围所有目标且彼此相对。特别地，包围轨迹半径可动态调整，避免无人机与目标之间发生碰撞。基于李雅普诺夫稳定性分析，分别证明了神经网络预测误差、目标中心估计误差及控制器误差的收敛性。最后，通过数值仿真和无人机飞行实验验证算法有效性。飞行测试视频及其他仿真结果见 https://youtu.be/B8uTorBNrl4。
原文摘要 · Abstract (English)
From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two tasking agents, is considered where the relative distance measurements between the agents and the targets can be obtained by using onboard sensors. Based on the measurements, the center of all the targets is estimated directly by a fuzzy wavelet neural network (FWNN) and the least squares fit method. Then, a new distributed anti-synchronization controller (DASC) is designed so that the two tasking agents are able to encircle all targets while staying opposite to each other. In particular, the radius of the desired encirclement trajectory can be dynamically determined to avoid potential collisions between the two agents and all targets. Based on the Lyapunov stability analysis method, the convergence proofs of the neural network prediction error, the target-center position estimation error, and the controller error are addressed respectively. Finally, both numerical simulations and UAV flight experiments are conducted to demonstrate the validity of the encirclement algorithms. The flight tests recorded video and other simulation results can be found in https://youtu.be/B8uTorBNrl4.
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