An optimal parameters in path planning issue with artificial potencial field based on evolutions algorithm

Abstract

The traditional artificial potential field method’s parameter values are usually determined empirically would cause unsmooth planned paths, low calculation efficiency, unreachable goal spots. This study suggests optimization parameters in the artificial potential field in finding the shortest route for mobile robots using the differential evolution algorithm (EA). Three parameters of the positive proportional gain coefficient of the gravitational field, the repulsive field gain coefficient, and the influence distance of obstacles to find the shortest path are considered to modify the infeasible ways of search space in optimizing the algorithm in locating the fastest route. A collision-free path model is constructed by using known global environment information for the mobile robot, and the EA algorithm is used to plan the robot’s best path. In the experiment section, the obtained results from the suggestion scheme are compared with the other techniques in the literature. The results show that the suggested approach can produce faster convergence and higher satisfaction quality than the other.