Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm

Abstract

For the path planning and obstacle avoidance problem of mobile robots in unknown surroundings, a novel improved artificial potential field (IAPF) model was proposed in this study. In order to overcome the shortages of low efficiency, local optimization trap, and unreachable target in the classical artificial potential field (APF) method, the new adaptive step length adjustment strategy was proposed in IAPF, which improved the path planning and obstacle avoidance efficiency. A new triangular navigation method was designed to solve the local optimization trap in joint force zero condition for a variety of path planning. In order to solve the target unreachable problem, a new target attraction model was established based on the distance of obstacle to improve convergence rate, and the new method was designed such as adding the aim factor to optimize the rejection force function and so on. The two methods of IAPF and APF are compared using MATLAB simulation, the average path planning efficiency of IAPF is increased by 42.8% compared with APF, the average path length is reduced by 8.6%, and the average target convergence rate is increased by 26.1%. Finally, the physical test of the mobile robot verified the effectiveness and accuracy of IAPF.