Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning

被引：65
|
作者：
Liu, Xiaohuan [1 ,2 ]
Zhang, Degan [1 ,2 ]
Zhang, Ting [1 ,2 ]
Cui, Yuya [1 ,2 ]
Chen, Lu [1 ,2 ]
Liu, Si [1 ,2 ]
机构：
[1] Tianjin Univ Technol, Minist Educ, Key Lab Comp Vis & Syst, Tianjin 300384, Peoples R China
[2] Tianjin Univ Technol, Tianjin Key Lab Intelligent Comp & Novel Software, Tianjin 300384, Peoples R China
来源：
APPLIED INTELLIGENCE | 2021年 / 51卷 / 12期
基金：
中国国家自然科学基金;
关键词：
A* algorithm; Reinforcement learning; Intelligent driving; Path planning;
D O I：
10.1007/s10489-021-02303-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Path planning of intelligent driving vehicles in emergencies is a hot research issue, this paper proposes a new method of the best path selection for the intelligent driving vehicles to solve this problem. Based on the prior knowledge applied reinforcement learning strategy and the searching- optimized A* algorithm, we designed a hybrid algorithm to help intelligent driving vehicles selecting the best path in the traffic network in emergencies including limited height, width, weight, accident, and traffic jam. Through simulation experiments and scene experiments, it is proved that the proposed algorithm has good stability, high efficiency, and practicability.
引用
收藏
页码：9015 / 9029
页数：15
相关论文
共 50 条
  • [1] Novel best path selection approach based on hybrid improved A* algorithm and reinforcement learning
    Xiaohuan Liu
    Degan Zhang
    Ting Zhang
    Yuya Cui
    Lu Chen
    Si Liu
    Applied Intelligence, 2021, 51 : 9015 - 9029
  • [2] A New Algorithm of the Best Path Selection Based on Machine Learning
    Liu, Xiao-Huan
    Zhang, De-Gan
    Yan, Hao-Ran
    Cui, Yu-Ya
    Chen, Lu
    IEEE ACCESS, 2019, 7 : 126913 - 126928
  • [3] Path planning for mobile robot based on improved reinforcement learning algorithm
    Xu X.
    Yuan J.
    Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology, 2019, 27 (03): : 314 - 320
  • [4] New Method of the Best Path Selection with Length Priority Based on Reinforcement Learning Strategy
    Liu, Xiao-huan
    Zhang, De-gan
    Zhang, Ting
    Cui, Yu-ya
    2019 28TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION AND NETWORKS (ICCCN), 2019,
  • [5] A novel hybrid approach based on relief algorithm and fuzzy reinforcement learning approach for predicting wind speed
    Malik, Hasmat
    Yadav, Amit Kumar
    SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS, 2021, 43
  • [6] A novel path planning approach for AUV based on improved whale optimization algorithm using segment learning and adaptive operator selection
    Huang, Yujie
    Li, Yibing
    Zhang, Zitang
    Sun, Qian
    OCEAN ENGINEERING, 2023, 280
  • [7] Mobile Robot Path Planning Based on Improved DDPG Reinforcement Learning Algorithm
    Dong, Yuansheng
    Zou, Xingjie
    PROCEEDINGS OF 2020 IEEE 11TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2020), 2020, : 52 - 55
  • [8] Genetic algorithm based on reinforcement learning for a novel drilling path optimization problem
    Zhu G.-Y.
    Zhang D.-S.
    Kongzhi yu Juece/Control and Decision, 2024, 39 (02): : 697 - 704
  • [9] Path Planning Based on Improved Hybrid A* Algorithm
    Tang, Bijun
    Hirota, Kaoru
    Wu, Xiangdong
    Dai, Yaping
    Jia, Zhiyang
    JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2021, 25 (01) : 64 - 72
  • [10] A novel path planning approach for unmanned ships based on deep reinforcement learning
    Chen, Chen
    Ma, Feng
    Liu, Jia-Lun
    Yan, Xin-Ping
    Chen, Xian-Qiao
    DATA SCIENCE AND KNOWLEDGE ENGINEERING FOR SENSING DECISION SUPPORT, 2018, 11 : 626 - 633
12345