Research on Mobile Robot Local Path Planning Based on Improved Artificial Potential Field Method

The traditional Artificial Potential Field (APF) algorithm is improved from three aspects in this paper. First, the improved APF algorithm revises the attraction function in the traditional algorithm, which can avoid the target point exerts excessive attraction on the robot that is far away from the end point. At the same time, the revised attraction function can avoid collision when the robot is traveling on the planned path. Second, a new repulsion function is introduced into the improved algorithm, which can solve the problem that the target point is unreachable because of the obstacles around it in the traditional algorithm. Third, the improved APF algorithm introduces gradient descent by adding a random perturbation. With the addition of random disturbance, the mobile robot can avoid falling into the local optimal solution. In order to test the performance of the improved algorithm, simulation experiments are carried out on the platform of Matlab. The universality of improved APF is verified by the tests that select different types of maps. The obstacles in the maps are of the following types: linear, geometric, pocket, zigzag, and maze. The simulation results show that the improved APF algorithm can plan a collision-free path for the mobile robot in different map environments.