Research on path planning based on new fusion algorithm for autonomous vehicle. (8th May 2020)

• Record Type: Journal Article
• Authors: Yuan, ChaoChun; Wei, Yue; Shen, Jie; Chen, Long; He, Youguo; Weng, Shuofeng; Wang, Tong
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 3(2020:May/Jun.)
• Publication Date: 2020-05-08
• Subjects: Autonomous vehicle -- path planning -- fusion algorithm (improved ant colony algorithm (IACA) -- improved artificial potential field (IAPF)) -- road adhesion coefficient -- environment perception -- pheromone update; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420911235
• Languages: English
• ISSNs: 1729-8806

Abstract

Ant colony algorithm or artificial potential field is commonly used for path planning of autonomous vehicle. However, vehicle dynamics and road adhesion coefficient are not taken into consideration. In addition, ant colony algorithm has blindness/randomness due to low pheromone concentration at initial stage of obstacle avoidance path searching progress. In this article, a new fusion algorithm combining ant colony algorithm and improved potential field is introduced making autonomous vehicle avoid obstacle and drive more safely. Controller of path planning is modeled and analyzed based on simulation of CarSim and Simulink. Simulation results show that fusion algorithm reduces blindness at initial stage of obstacle avoidance path searching progress and verifies validity and efficiency of path planning. Moreover, all parameters of vehicle are changed within a reasonable range to meet requirements of steering stability and driving safely during path planning progress.