Multi-robot Cooperate Region Coverage Search Algorithm Based on Distributed Control

Aiming at the situation that multi-robot system cannot establish global communication when performing region coverage task in unknown environment, a multi-robot cooperative region coverage search algorithm based on distributed control is proposed. Bio-inspired neural network and raster map are combined to represent dynamic search environment. Weighted average method (WAM) is used to fuse environmental information collected by different robots. In addition, several dynamic search alliances are formed among robots under the framework of distributed model prediction (DMPC). Within the alliance, each member makes iterative collaborative decision in turn, and the genetic algorithm (GA) is used to optimize the solution to obtain the next movement path of each robot. Simulation results show the effectiveness and superiority of the proposed algorithm.