Self‐organizing cooperative hunting for unmanned surface vehicles with constrained kinematics

Qun Deng, Yan Peng, Tingke Mo, Jinduo Wang, Dong Qu, Yangmin Xie
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Abstract

SummaryThe article aims at solving a cooperative hunting problem for multiple unmanned surface vehicles (USVs) subject to constrained kinematics. In order to cooperatively trap the evader into the hunting domain, a velocity model with control variable for the pursuers is firstly proposed according to the Apollonius circle. Then, a flexible self‐organizing control strategy is developed, which enables the pursuers to approach the evader while forming an encirclement. The pursuers can dynamically adapt their strategies in real‐time by choosing the optimal control variable. Additionally, take into account the limitation imposed on the vessel's motion, the optimal control variable with constraint can be obtained by using the particle swarm optimization with log‐barrier method. The simulation results ultimately demonstrate the validity and superiority of the proposed cooperative hunting algorithm.
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具有约束运动学的无人水面飞行器的自组织合作狩猎
摘要 本文旨在解决多个无人水面飞行器(USV)在运动学约束下的合作狩猎问题。为了将逃逸者协同捕获到狩猎域中,首先根据阿波罗圆提出了带有控制变量的追逐者速度模型。然后,开发出一种灵活的自组织控制策略,使追逐者能够在形成包围圈的同时接近逃避者。追逐者可以通过选择最佳控制变量来实时动态调整策略。此外,考虑到对船只运动的限制,利用粒子群优化与对数屏障法可以获得带约束的最优控制变量。仿真结果最终证明了所提出的合作狩猎算法的有效性和优越性。
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