A Novel Cooperative Path Planning for Multirobot Persistent Coverage in Complex Environments
Yuan Yan Tang, Rui Zhou, Guibin Sun, Bin Di, Rongling Xiong
Abstract
In this paper, a new path planning algorithm is developed to address the problem of persistent and cooperative coverage using a group of robots in a complex environment. Previous works have mainly used geometric division to achieve collaborative coverage search, but ignored the environmental complexity and the path length difference of each robot. In contrast, this paper presents a novel strategy for multi-robot persistent coverage, which aims to obtain more equal coverage route for each robot while guaranteeing both the obstacle avoidance and the minimization of the coverage period. The strategy first deploys the sensors in the target region to satisfy coverage requirements. Then it classifies the sensor points by improved k-means clustering which introducing the feedback mechanism to balance the length of each route. Finally, the approach solves the travelling salesman problem to obtain the closed route for each robot. The numerical simulations show that the proposed approach is feasible to implement the cooperative and persistent coverage in consideration of complicated obstacles, equilibrium of the path length, and the minimization of total cost.