New improvement of "dynamic repulsive potential factor" on artificial potential field path planning algorithm

Path-planning as one of the most fundamental tasks of robots should cause less burden on calculation capacity and be efficient when the robots’ major tasks are not only about motion. Artificial Potential Field (APF) algorithm as an effective and simple path-planning method could meet such a requirement. However, the local minimum problem limits its application on robot’s path-planning tasks. To deal with this problem, this paper proposed a method named “Dynamic Repulsive Potential Factor” (DRPF). Compared to other existed improvements such as the sub-target strategy and combination algorithms with Rapidly exploring Random Tree (RRT), DRPF has the advantages of simple and effective. And the produced path of DRPF is shorter than the one produced by APF combing RRT, which is demonstrated in the experiment result. Meanwhile, the robot is continuing to move in the DRPF method while it is stopped when searching path in RRT, demonstrating the effectiveness on time aspect. The application of DRPF could meet the requirements of efficient and less calculation burden for path-planning function of robots whose major tasks are not only about navigation.