Multi-robot path planning using co-evolutionary genetic programming

Motion planning for multiple mobile robots must ensure the optimality of the path of each and every robot, as well as overall path optimality, which requires cooperation amongst robots. The paper proposes a solution to the problem, considering different source and goal of each robot. Each robot uses a grammar based genetic programming for figuring the optimal path in a maze-like map, while a master evolutionary algorithm caters to the needs of overall path optimality. Co-operation amongst the individual robots’ evolutionary algorithms ensures generation of overall optimal paths. The other feature of the algorithm includes local optimization using memory based lookup where optimal paths between various crosses in map are stored and regularly updated. Feature called wait for robot is used in place of conventionally used priority based techniques. Experiments are carried out with a number of maps, scenarios, and different robotic speeds. Experimental results confirm the usefulness of the algorithm in a variety of scenarios.