Improved Artificial Potential Field Method for Robotic Path Planning

Abstract

Determination of a collision free path for a robot between start and goal positions through obstacles cluttered in a workspace is central to the design of an autonomous robot path planning. This thesis presents an improved artificial potential field-based navigation algorithm for mobile robots that produce an optimal path for a robot to navigate in an environment. To complete the navigation task, the algorithm will read the map of the environment or workspace and subsequently create a potential map for the robot to traverse in the workspace without colliding with objects and obstacles. This method overcomes the issue of deadlock which was a major bottleneck in the case of artificial potential field method. The simulation results infer the ability of the proposed method to overcome the deadlock issue and navigate successfully from an initial position to the goal without colliding into obstacles in both static or dynamic environment.