WIP Vehicle Control Method Based on Improved Artificial Potential Field Subject to Multi-obstacle Environment

Abstract

This paper presents a control method for a WIP vehicle in multi-obstacle environment based on improved artificial potential field. Firstly, an improved artificial potential field (IAPF) is developed, where a safe distance is introduced to the existing repulsive potential field to solve the security issue, while the local minima can also be eliminated in the meantime. Next, an obstacle avoidance controller is designed based on the IAPF, where the nonholonomic constraint and underactuated characteristic of the WIP vehicle are fully considered, and the stability condition of the system is analyzed by means of the related control theory. Moreover, to further improve the control performance, a key parameter that play an important role in the controller is adjusted by taking advantage of fuzzy logic, and detailed analyses are given to demonstrate its necessity and effectiveness. Finally, considering a motion environment that contains dense obstacles, narrow corridor and an obstacle near the target, numerical simulations are conducted to validate the proposed method, whose results indicate that the method has a good performance to control the WIP vehicle in multi-obstacle environment.

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

“…Oscar Loyola, John Kern, and Claudio Urrea proposed to generate the robot's path by adding a boredom element without a reward [47]. Yigao Ning et al proposed to add a safe distance to the existing repulsive potential energy field, and an obstacle avoidance controller was designed based on IAPF to study vehicle path planning under a multi-obstacle environment [48]. Novak Zagradjanin et al proposed a cloud-based autonomous multi-robot system to perform highly repetitive tasks.…”

Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field

“…Matoui et al 23 used a non-minimum velocity algorithm to solve the local minimum problem. Ning et al 24 introduced a safety distance to eliminate the problem of local minima caused by obstacles around a target. Subsequently, an obstacle avoidance controller based on the improved APF was designed.…”