A Multirobot Distributed Collaborative Region Coverage Search Algorithm Based on Glasius Bio-Inspired Neural Network

Abstract

There are many constraints for a multirobot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multirobot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). First, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Second, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search subteams. Within each subteam, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multirobot region coverage search task.