Path planning method of automatic driving for directional navigation based on particle swarm optimisation

被引：0

作者：

Luo X. ^{[1
]}

Liao R. ^{[2
]}

Hu H. ^{[3
]}

Ye Y. ^{[1
]}

机构：

[1] Information & Communication Branch of Hubei EPC, Wuhan University, Hubei, Wuhan

[2] Information & Communication Branch of Hubei EPC, Huazhong University of Science and Technology, Hubei, Wuhan

[3] Information & Communication Branch of Hubei EPC, Central China Normal University, Hubei, Wuhan

来源：

International Journal of Vehicle Information and Communication Systems | 2022年 / 7卷 / 03期

关键词：

automatic driving; directional navigation; particle swarm optimisation; path planning;

D O I：

10.1504/ijvics.2022.127413

中图分类号：

学科分类号：

摘要：

In order to overcome the poor planning efficiency of the automatic driving trajectory planning method for directional navigation, a Particle Swarm Optimisation (PSO) based trajectory planning method is proposed. The kinematic characteristics of the vehicle are analysed and the vehicle dynamic equation is constructed. The position coordinates, speed and other motion parameters of the directional navigation vehicle are transformed into a Frenet coordinate system. The trajectory quality evaluation model of automatic driving vehicle for directional navigation is constructed. The trajectory quality evaluation index is taken as the constraint, and each variable is iteratively optimised by particle swarm optimisation algorithm, so as to effectively realise the trajectory planning of automatic driving of directional navigation. Simulation results show that the proposed method can effectively improve the efficiency of autopilot trajectory planning and enhance the safety of the whole method. © 2022 Inderscience Enterprises Ltd.

引用

页码：259 / 276

页数：17

共 50 条

[1] A hybrid particle swarm based method for process planning optimisation
Wang, Y. F.
Zhang, Y. F.
Fuh, J. Y. H.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2012, 50 (01) : 277 - 292
[2] Online Path Planning for UAV Navigation Based on Quantum Particle Swarm Optimization
Guo, Jinchao
Wang, Junjie
Cui, Guangzhao
ADVANCED TECHNOLOGY IN TEACHING - PROCEEDINGS OF THE 2009 3RD INTERNATIONAL CONFERENCE ON TEACHING AND COMPUTATIONAL SCIENCE (WTCS 2009), VOL 1: INTELLIGENT UBIQUITIOUS COMPUTING AND EDUCATION, 2012, 116 : 291 - 302
[3] Vehicle path planning method based on particle swarm optimization algorithm
Yu, Sheng-long
Bo, Yu-ming
Chen, Zhi-min
Zhu, Kai
AUTOMATION EQUIPMENT AND SYSTEMS, PTS 1-4, 2012, 468-471 : 2745 - 2748
[4] Study on plant landscape planning method based on discrete particle swarm optimisation
Li, Fang-Lian
Xu, Yue-Guang
INTERNATIONAL JOURNAL OF ENVIRONMENTAL TECHNOLOGY AND MANAGEMENT, 2021, 24 (3-4) : 184 - 199
[5] A new intelligent method for travel path recommendation based on improved particle swarm optimisation
Han, Si
INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS, 2020, 12 (01) : 36 - 50
[6] Robotic path planning using hybrid genetic algorithm particle swarm optimisation
Kala, R. (rahulkalaiiitm@yahoo.co.in), 1600, Inderscience Enterprises Ltd. (04): : 2 - 4
[7] Grid-based construction site layout planning with Particle Swarm Optimisation and Travel Path Distance
Benjaoran, Vacharapoom
Peansupap, Vachara
CONSTRUCTION MANAGEMENT AND ECONOMICS, 2020, 38 (08) : 673 - 688
[8] Curvature Flight Path for Particle Swarm Optimisation
Kheng, Cheng Wai
Ku, Day Chyi
Ng, Hui Fuang
Khattab, Mahmoud
Chong, Siang Yew
GECCO'16: PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, 2016, : 29 - 36
[9] Vector map drawing method for path planning in automatic driving
Zhang Yongxiang
Yu, Feng
Xu, Li
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2023, : 3131 - 3135
[10] Path planning method for vehicles requiring emergency evacuation based on particle swarm optimization
You Z.H.
Zhou T.
Advances in Transportation Studies, 2021, 2021 (Special issue 2): : 111 - 120

← 1 2 3 4 5 →