基于微粒群优化的有限通信多机器人气味寻源

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.

Localizing odor sources using multiple robots based on particle swarm optimization in limited communication environments

Considering the constraint of limited communication among robots, a method of localizing odor sources using
multiple robots based on particle swarm optimization is presented on the condition of abstracting each robot as a particle.
Firstly, a strategy incorporating with a repulsive function is utilized to guide a robot to rapidly search for a plume. Then
the range of communication among robots is estimated based on the log-distance loss model of wireless signal propagation
to form a dynamic topology structure of a particle swarm and to determine the global optimum of particles. Finally, the
sampling/recovery time of a sensor is incorporated to update a particle so as to trace the plume. The proposed method is
applied to localize odor sources in three various scenarios and the experimental results show its effectiveness.