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.