Shoveling trajectory planning method for wheel loader based on kriging and particle swarm optimization

被引：0
|
作者：
Yu X.-J. [1 ]
Huai Y.-H. [2 ]
Li X.-F. [3 ]
Wang D.-W. [1 ]
Yu A. [1 ]
机构：
[1] School of Mechanical and Electrical Engineering, Kunming University, Kunming
[2] Kunming Motor Vehicle Inspection and Supervision Service Center, Kunming
[3] School of Mechanical and Aerospace Engineering, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 02期
关键词：
Co-simulation; Kriging proxy model; Loading machine; Particle swarm optimization; Shoveling trajectory;
D O I：
10.13229/j.cnki.jdxbgxb20190766
中图分类号：
学科分类号：
摘要：
This paper took a 50-type wheel loader as the research object. Considering the driving, working and structural characteristics of the wheel loader, a loader simulation model containing the loaded material information was established using RecurDyn and EDEM. Based on the driver's experience, the process of shoveling bulk materials was analyzed, and the shovel performance indexes reflecting the comprehensive performance of the bucket filling rate and fuel consumption were constructed. On the above basis, the shovel performance under different working conditions and different trajectory parameters was analyzed. The approximate response relationship between the shovel performance and the trajectory parameters under different working conditions was established using Kriging method. The theoretical trajectory parameters of the shovel under different working conditions were obtained by using Particle Swarm Optimization (PSO). The results show that the shovel performance is significantly improved with the optimized trajectory parameters. © 2020, Jilin University Press. All right reserved.
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收藏
页码：437 / 444
页数：7
相关论文
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