Robot Path Planning Using Artificial Potential Field With Multiple Regression Search

Mobile robots are a hot research area for most researchers as they attempt to solve a myriad of challenges that go with mobile robots. Among the challenges to being solved is the navigation problem. Many methods have come into existence and many are still being improved up. The thesis focuses on the navigation problem and proposes a way to solve the navigation problem by using the potential fields method (PFM); which uses the idea of imaginary forces called potentials acting on a robot. In these approaches obstacles exert repulsive forces onto the robot, while the target applies an attractive force to the robot. The sum of all forces, the resultant force R, determines the subsequent direction and speed of travel.There are several ways PFM’s are implemented to meet the need of mobile robot navigation. The traditional method considers a known environment where fixed potentials can be assigned to the goal and the obstacles. When the obstacles are unknown the potential fields have to be adapted as the robot advances, and detects new obstacles. The implementation of the potential fields method with one attraction potential assigned to the goal point and fixed repulsion points assigned to the obstacles, has the important limitation that for some obstacle configurations it may not be possible to produce appropriate resultant forces to avoid the obstacles.Due to these limitations, the potential functions are redefined to eliminate non-reachable and local minima problems, and utilize virtual local target for robot to escape oscillations and then a multiple regression search (MRS) method is introduced to optimize the planned path is introduced. The optimization path is calculated by connecting multiple sequential points, which are produced by the improved APF...