Efficient robotic path planning algorithm based on artificial potential field

Path planning is an utmost important requirement for a robot to be able to reach a target point safely and accomplish a given mission. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). In path planning,, three essential criteria are important to be satisfied such as path length, computational complexity, and completeness. However, the above-mentioned methods are not capable of achieving all the three criteria simultaneously, which limit their application in optimal and real-time path planning. This paper proposes a path planning method based on PF called dynamic artificial PF (DAPF) which is capable to eliminate the local minima which frequently occurs in the conventional PF. This feature fulfills the third criterion of path planning i.e. completeness. In terms of path length and computational complexity, DAPF has been compared with VG, which is well-known for its optimal path. From simulation, it has been found that DAPF is consistent in generating paths with relatively low computation time in obstacle rich environments. However, the produced paths by DAPF are near-optimal and slightly longer than the ones generated by VG.