Improved artificial potential field method for mobile robot path planning

Abstract

Path planning has already been used in areas like robots and unmanned vehicles to prevent collisions in certain environments. A Path planning algorithm is needed to achieve such tasks and Artificial Potential Field (APF) method is one of the methods. However, APF has limitations facing various situations like being stuck in a local minimum such as a dead-end or a narrow path. To solve the problem, First, a side force is added to the algorithm along with two types of definitions of the force direction. Then a variable is proposed to prevent the dead-end situation. Finally, the variable step size is used to improve the efficiency of the algorithm. The simulation results demonstrate the effectiveness of the method. In comparison to APF, the improved APF could prevent the local minimum and reach the target position with fewer processing steps and better performance.

Autonomous Agent Navigation Model Based on Artificial Potential Fields Assisted by Heuristics

“…Other methods for handling the local minima problem add forces under specific circumstances without changing the potential field itself [14,15]. In [16] the problem of local minima is addressed by adding a force perpendicular to the vector of movement of the agent. This technique alongside the random forces have been used to avoid a local minimum since its mention in Motor Schema by Arkin [17].…”