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Research on path planning based on new fusion algorithm for autonomous vehicle

Content Provider	SAGE Publishing
Author	Yuan, ChaoChun Wei, Yue Shen, Jie Chen, Long He, Youguo Weng, Shuofeng Wang, Tong
Copyright Year	2020
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.
Related Links	https://journals.sagepub.com/doi/pdf/10.1177/1729881420911235?download=true
ISSN	17298806
Issue Number	3
Volume Number	17
Journal	International Journal of Advanced Robotic Systems (ARX)
e-ISSN	17298814
DOI	10.1177/1729881420911235
Language	English
Publisher	Sage Publications UK
Publisher Date	2020-05-11
Publisher Place	London
Access Restriction	Open
Rights Holder	© The Author(s) 2020
Subject Keyword	path planning Autonomous vehicle pheromone update improved artificial potential field (IAPF)) environment perception fusion algorithm (improved ant colony algorithm (IACA) road adhesion coefficient
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Science Applications Software

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