未知动态环境下非完整移动群机器人围捕

针对未知动态障碍物环境下非完整移动群机器人围捕,提出了一种基于简化虚拟受力模型的自组织方法.首先给出了个体机器人的运动方程,然后给出了未知动态环境下目标和动态障碍物的运动模型.通过对复杂环境下围捕行为的分解,抽象出简化虚拟受力模型,基于此受力模型,设计了个体运动控制方法,接着证明了系统的稳定性并给出了参数设置范围.不同情况下的仿真结果表明,本文给出的围捕方法可以使群机器人在未知动态障碍物环境下保持较好的围捕队形,并具有良好的避障性能和灵活性.最后分析了本文与基于松散偏好规则的围捕方法相比的优势.

Nonholonomic mobile swarm robots hunting in unknown dynamic environments

A self-organizing method based on a simplified virtual-force model is proposed for nonholonomic mobile swarm robots hunting in unknown dynamic environment. First, the motion equations of individual robots are given; then the motion models for the hunting target and dynamic obstacles in unknown dynamic environment are presented. Through the decomposition of hunting behavior under complicated environments, a simplified virtual-force model is formed. Based on the virtual-force model, the control method is designed for swarm robots following motions of the hunting target; after that, the stability of the hunting system is analyzed and the control parameter ranges are given. Simulation results for different situations demonstrate that the proposed hunting method can make the group of robots maintain a good hunting formation in unknown dynamic obstacle environments and has good performance of obstacle avoidance and flexibility. Finally, some advantages of this hunting method are presented, compared with the hunting method based on loose-preference rule.