A pattern-based genetic algorithm for multi-robot coverage path planning minimizing completion time

Sensor-based multi-robot coverage path planning problem is one of the challenging problems in managing flexible, computer-integrated, intelligent manufacturing systems. A novel pattern-based genetic algorithm is proposed for this problem. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a sequence of the disks for each robot to minimize the coverage completion time determined by the maximum time traveled by a robot in a mobile robot group. So the environment needs to be partitioned among robots considering their travel times. Robot turns cause the robot to slow down, turn and accelerate inevitably. Therefore, the actual travel time of a mobile robot is calculated based on the traveled distance and the number of turns. The algorithm is designed to handle routing and partitioning concurrently. Experiments are conducted using P3-DX mobile robots in the laboratory and simulation environment to validate the results.