栅格地图中多机器人协作搜索目标

目标搜索是多机器人领域的一个挑战.本文针对栅格地图中多机器人目标搜索算法进行研究.首先,利用Dempster-Shafer证据理论将声纳传感器获取的环境信息进行融合,构建搜索环境的栅格地图.然后,基于栅格地图建立生物启发神经网络用于表示动态的环境.在生物启发神经网络中,目标通过神经元的活性值全局的吸引机器人.同时,障碍物通过神经元活性值局部的排斥机器人,避免与其相撞.最后,机器人根据梯度递减原则自动的规划出搜索路径.仿真和实验结果显示本文提及的算法能够实现栅格地图中静态目标和动态目标的搜索.与其他搜索算法比较,本文所提及的目标搜索算法有更高的效率和适用性.

Cooperative target search of multi-robot in grid map

Target search is a challenge in multi-robot exploration. This paper focuses on an effective strategy for multi-robot target search in grid map. First, the Dempster-Shafer theory of evidence is applied to extract information of environment from the sonar data to build a grid map of the environments. Then, a topologically organized biologically inspired neural network based on the grid map is constructed to represent the dynamic environment. The target globally attracts the robots through the dynamic neural activity landscape of the model, while the obstacles locally push the robots away to avoid collision. Finally, the robots plan their search path to the targets autonomously by a steepest gradient descent rule. The proposed algorithm deals with various situations such as static targets search, dynamic targets search in the grid map. The results of simulation and experiment show that the proposed algorithm is capable of guiding multi-robot to achieve search task of multiple targets with higher efficiency and adaptability compared with other algorithms.