田间作物表型获取无人车平台主体结构设计与优化

doi:10.3785/j.issn.1008-9209.2022.01.241

浙江大学学报（农业与生命科学版）

2023, Vol. 49

Issue (2): 280-292 DOI: 10.3785/j.issn.1008-9209.2022.01.241

农业工程

田间作物表型获取无人车平台主体结构设计与优化

唐政^1,²(

),余越^1,²,刘羽飞^1,²,岑海燕^1,²(

)

^1.浙江大学生物系统工程与食品科学学院，浙江杭州 310058
^2.农业农村部光谱检测重点实验室，浙江杭州 310058

Design and optimization of main structure of unmanned vehicle-based field crop phenotyping platform

Zheng TANG^1,²(

),Yue YU^1,²,Yufei LIU^1,²,Haiyan CEN^1,²(

)

^1.College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, Zhejiang, China
^2.Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, Zhejiang, China

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摘要：

本研究旨在设计和优化一种稳定、轻量化的无人车载田间作物表型获取平台主体结构。为了满足高安全性、高稳定性、轻量化的要求，采用Pro/Engineer Wildfire 5.0软件设计无人车平台主体结构模型，并采用HyperWorks 2020软件进行有限元分析和结构模型优化。同时，在设计过程中对结构进行静力学和动力学分析。以结构整体质量最小化为目标函数，以材料屈服强度和一阶模态为约束条件，采用试验设计法提取多工况下对一阶模态和应力敏感的部件结构参数作为设计变量，大大减少了变量数量。应用自适应响应面法进行迭代计算，优化获取自适应的结构变量。与有限元模型的对应输出响应相比，自适应响应面近似模型在主体结构质量和一阶模态频率的误差分别为3.79%和4.32%，在静止与匀速、启动、停车工况下的最大应力误差分别为4.24%、4.14%和1.26%，表明自适应响应面近似模型具有满足设计要求的精度且误差均低于5%。相比于优化前的主体结构，在保持各工况安全系数在5.0以上的情况下，实现整体质量减少63.61%，得到了安全系数高、稳定性强的田间作物表型获取平台主体结构。

关键词： 田间作物表型获取平台; 无人车; 有限元分析; 结构优化; 自适应响应面法

Abstract:

This study aims to design and optimize the main structure of a stable and lightweight unmanned vehicle-based field crop phenotyping platform. In order to meet the requirement of high safety, high stability, and lightweight, Pro/Engineer Wildfire 5.0 software was used to design the main structure model of the platform, and HyperWorks 2020 software was employed to perform the finite element analysis and optimize the structure model. Meanwhile, the statics and dynamics analysis of the structure was implemented during the design process. Taking the main structural mass as the objective function, with the material yield limit and the first-order mode as the constraints, the design of experiment (DOE) method was applied to extract the structural parameters of parts with the high sensitivity to the first-order mode and stress under multi-working conditions as design variables, which greatly reduced the variable number. Then, the adaptive response surface method (ARSM) was applied for iterative calculation to obtain the optimal variables. Compared with the corresponding output response of the actual finite element model, the ARSM approximate model produced a low error of 3.79% and 4.32% in the main structure mass and the first-order modal frequency, respectively, which also obtained the maximum stress error of 4.24%, 4.14%, and 1.26% under the static and uniform speed conditions, starting conditions, and emergency shutdown conditions, respectively. These results show that the ARSM approximate model has a high accuracy and the error is less than 5%. Compared with the original structure, the final overall mass was reduced by 63.61% at maintaining the safety factor of each working condition above 5.0. As a result, the main structure of field crop phenotyping platform is obtained with high safety factor and meeting usage requirements.

Key words: field crop phenotyping platform unmanned vehicle finite element analysis structural optimization adaptive response surface method

收稿日期: 2022-01-24 出版日期: 2023-04-27

CLC:

S224.9

基金资助: 浙江省重点研发计划项目(2020C02002)

通讯作者: 岑海燕 E-mail: zhengtang@zju.edu.cn;hycen@zju.edu.cn

作者简介: 唐政（https://orcid.org/0000-0002-5118-7931），E-mail：zhengtang@zju.edu.cn

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引用本文:

唐政,余越,刘羽飞,岑海燕. 田间作物表型获取无人车平台主体结构设计与优化[J]. 浙江大学学报（农业与生命科学版）, 2023, 49(2): 280-292.

Zheng TANG,Yue YU,Yufei LIU,Haiyan CEN. Design and optimization of main structure of unmanned vehicle-based field crop phenotyping platform. Journal of Zhejiang University (Agriculture and Life Sciences), 2023, 49(2): 280-292.

链接本文:

https://www.zjujournals.com/agr/CN/10.3785/j.issn.1008-9209.2022.01.241 或 https://www.zjujournals.com/agr/CN/Y2023/V49/I2/280

图1 无人车示意图（可负载150 kg）

图2 无人车平台主体结构示意图

图3 无人车平台主体结构简化图L：悬臂长度；F：载荷；y：最大挠度。

图4 无人车平台主体结构前部伸出长度最小化示意图

图5 载荷设置示意图

图6 信息采集平台田间作业路径规划示意图

图7 各工况下无人车平台主体结构静力分析A.静止与匀速工况下变形云图（最大位移0.40 mm）；B.静止与匀速工况下应力云图（最大冯·米塞斯应力18.43 MPa）；C.停车工况下变形云图（最大位移0.47 mm）；D.停车工况下应力云图（最大冯·米塞斯应力15.53 MPa）；E.启动工况下变形云图（最大位移0.39 mm）；F.启动工况下应力云图（最大冯·米塞斯应力21.22 MPa）。

表1 不同路面类型的道路不平度波长

图8 无人车平台主体结构前10阶模态图

图9 无支撑件的无人车平台主体结构第一阶模态图

图10 各响应主效应曲线A.主体结构质量主效应曲线；B.静止与匀速工况下主体结构应力主效应曲线；C.停车工况下主体结构应力主效应曲线；D.启动工况下主体结构应力主效应曲线；E.主体结构一阶模态主效应曲线。

表2 不同主效应下各组变量灵敏度排序（前10）

图11 最终设计变量分组图

图12 自适应响应面法流程图

图13 主体结构质量的优化迭代过程自适应响应面方法的目标函数迭代曲线。

表3 各部件厚度变化 (mm)

图14 优化后无人车平台主体结构厚度分布示意图

表4 有限元模型与自适应响应面近似模型计算值比较

表5 优化后无人车平台主体结构模态分析结果

图15 信息采集平台田间工作图

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