APF-BUG-BASED INTELLIGENT PATH PLANNING FOR AUTONOMOUS VEHICLE WITH HIGH PRECISION IN COMPLEX ENVIRONMENT

作     者：Sun, Lingyu Fu, Zhumu Tao, Fazhan Si, Pengju Song, Shuzhong Sun, Chang 

作者机构：Henan Univ Sci & Technol Coll Informat Engn Luoyang Henan Peoples R China Henan Univ Sci & Technol Henan Key Lab Robot & Intelligent Syst Luoyang Henan Peoples R China Longmen Lab Luoyang Henan Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION》 (Int J Rob Autom)

年 卷 期：2023年第38卷第4期

页      面：277-283页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China Program for Science and Technology Innovation Talents in the University of Henan Province [23HASTIT021] Key Scientific Research Projects of Universities in Henan Province [22A413002] Scientific and Technological Project of Henan Province [212102210153, 222102240009] Postdoctoral research grant in Henan Province Science and Technology Development Plan of Joint Research Program of Henan Postgraduate Education Reform and Quality Improvement Project of Henan Province [YJS2021AL035] Academic Degrees amp Graduate Education Reform Project of Henan Province [2021SJGLX141Y] 

主　　题：Autonomous vehicle artificial potential field Bug algorithm path planning obstacle avoidance 

摘      要：With the increasing number of vehicles, driving environment is becoming increasingly complex and dynamic, which makes the driving of autonomous vehicle more difficult. In order to improve the safety of vehicle autonomous driving, in this paper, an improved artificial potential field (APF) path-planning algorithm for complex traffic environment with various obstacles is proposed. Considering the influence of vehicle physical characteristics on obstacle avoidance path, the repulsive potential field function of APF is improved. Then, aiming at the problem that traditional APF algorithm is easily fell into local extremum, a bug algorithm is introduced to ensure global performance of the proposed algorithm. Finally, feasibility and robustness of the proposed hybrid planning algorithm are validated by conducting several simulation investigations in a typical scenario.