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考虑机器人间的通信受限约束,将机器人抽象为微粒,提出基于微粒群优化的多机器人气味寻源方法.首先,采用结合斥力函数的策略,引导机器人快速搜索烟羽;然后,基于无线信号对数距离损耗模型,估计机器人间的通讯范围,据此形成微粒群的动态拓扑结构,并确定微粒的全局极值;最后,将传感器的采样/恢复时间融入微粒更新公式,以跟踪烟羽.将所提出方法应用于3个不同场景的气味寻源,实验结果验证了该方法的有效性.
Considering the constraints of communication between robots, the robots are abstracted as particles, and a multi-robot scent search method based on particle swarm optimization (PSO) is proposed. First, a strategy based on repulsion function is used to guide the robot to search smoke plume quickly. Secondly, The distance between the robot and the communication range is estimated to form the dynamic topology of the particle swarm and to determine the global extremum of the particle. Finally, the sensor sampling / recovery time is incorporated into the particle update formula to track the plume. The proposed method is applied to the sour search of three different scenarios, and the experimental results verify the effectiveness of the proposed method.