Path planning for robot navigation based on Cooperative Genetic Optimization

Path planning investigates issues including the shortest path, obstacle avoidance, and computation efficiency, which can be regarded as an optimal problem. Taking advantage of the genetic algorithms to solve various optimal problems, this paper first proposes a Cooperative Genetic Optimization (CGO) Algorithm, including the establishment of an elite policy and larger selection region to minimize the occurrence of local optima so as to increase the speed of convergence. Based on the proposed CGO, a global path planning approach for robots is then presented. As a result, the proposed method of this paper leads to a better performance in comparison with the traditional Genetic algorithm to achieve the goal of obtaining a safer and shorter path.