Path planning using genetic algorithms (2nd Report, selfish planning and coordinative planning for multiple mobile robot systems)

This paper presents a new strategy for path planning of multiple mobile robots using Genetic Algorithms (GAs). When a mobile robot moves from a point to a target point, it is necessary to plan the optimal or feasible path for itself, avoiding obstructions in its way and minimizing costs in terms of time, energy, and distance. We call this ″selfish planning″. When many robots move around in the same space, it is necessary to select the most reasonable path so as to avoid collisions with other robots and to minimize costs. We call this ″coordinative planning″. The GAs are search algorithms based on the mechanics of natural selection and natural genetics. We apply the GAs to both selfish planning and coordinative planning for multiple mobile robots.