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在多机器人系统的追捕问题中,预设队形的固定性以及追捕者之间构成刚性结构的稳定性,使系统存在追捕死角,追捕者无法准确高效地完成追捕。因此本文提出一种自适应刚性结构编队算法,追捕者可根据目标所在位置结合改进的刚性结构法,利用边界等环境条件,自适应的选择合适队形来完成围捕。本文研究构造编队中心控制器和队形控制器,使编队中心无限趋近目标,达到合围目的;队形控制器可根据目标所处位置动态调整队形。本文结合提出的自适应刚性编队算法,基于Netlogo研发多机器人追逃仿真平台并进行实验验证,将自适应编队与刚性结构编队在有边界的追捕环境中进行对比。仿真结果显示自适应编队策略可成功避免目标的追捕死角,其时间指标和能量指标优于传统刚性结构法,证明了自适应编队策略是有效的。
In the multi-robot system hunt, the stability of the preset formation and the stability of the rigid structure formed between the huntsmen make the system have a hunt for dead ends, and the huntman can not complete the hunt accurately and efficiently. Therefore, an adaptive rigid structure formation algorithm is proposed in this paper. The huntman can select the appropriate formation adaptively according to the location of the target and the improved rigid structure method, using the boundary condition and other environmental conditions. This paper studies the construction of formation center controller and formation controller, making the formation center infinitely close to the goal, to achieve the purpose of encirclement. The formation controller can dynamically adjust the formation according to the target position. In this paper, the proposed adaptive rigid formation algorithm, based on Netlogo R & D multi-robot chase flight simulation platform and experimental verification, adaptive formation and rigid structure formation in a border hunt environment for comparison. The simulation results show that the adaptive formation strategy can successfully avoid the target hunting angle, and the time index and energy index are superior to the traditional rigid structure method. It proves that the adaptive formation strategy is effective.