Energy-Saving Planning Method for Autonomous Driving Mining Trucks Based on Composite Dynamic Sampling

被引：0

作者：

Ting C. ^{[1
]}

Wang Y. ^{[2
]}

Zhang Y. ^{[1
]}

Wu M. ^{[1
]}

Wang Y. ^{[2
]}

机构：

[1] The School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai

[2] Shanghai Jiao Tong University, National Engineering Research Center for Automotive Power and Intelligent Control, Shanghai

来源：

Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 04期

关键词：

autonomous driving; fuel economy; intelligent vehicle; local path planning;

D O I：

10.19562/j.chinasae.qcgc.2024.04.004

中图分类号：

学科分类号：

摘要：

In recent years，path planning methods of autonomous driving mining truck that consider safety and efficiency have been gradually maturing and have been implemented in various mining scenarios. Simultaneous⁃ ly，the utilization of path planning methods to improve the fuel efficiency of mining trucks is paid more and more at⁃ tention to by both the industry and academia. In response to this requirement，a method for energy-efficient path planning of autonomous driving trucks within mining environments is proposed in this paper. Its primary features en⁃ compass the utilization of composite dynamic sampling for S-L（Station - Lateral deviation）and S-T（Station - Time），based on speed，road gradient，and obstacles. A fuel consumption index for typical terrain scenarios in min⁃ ing environments is established. Additionally，a comprehensive path evaluation model of safety，efficiency and ener⁃ gy consumption is introduced. To prevent the entrapment of the evaluation model′s weights in local optima，an adap⁃ tive optimization method based on the particle swarm algorithm with simulated annealing strategy is designed. Through testing in real mining scenarios，the method proposed in this paper has exhibited an average improvement of 11.28% in fuel economy metrics compared to existing methods. © 2024 SAE-China. All rights reserved.

引用

页码：588 / 595

页数：7

共 15 条

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