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基于數(shù)字孿生的溫室作業(yè)底盤行駛狀態(tài)在線監(jiān)測方法
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國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2019YFD1002401)和國家自然科學(xué)基金項(xiàng)目(31971805)


Online Prediction Method for Greenhouse Operation Chassis Driving Status by Digital Twins-driven
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    摘要:

    數(shù)字孿生技術(shù)通過對(duì)物理實(shí)體全生命周期的數(shù)字化實(shí)現(xiàn)其狀態(tài)的監(jiān)測和控制,為實(shí)現(xiàn)機(jī)器人的遠(yuǎn)程控制和連續(xù)式作業(yè)提供了解決思路。作業(yè)底盤行駛過程的高精度控制是保證機(jī)器人作業(yè)質(zhì)量的關(guān)鍵,本文針對(duì)溫室環(huán)境變化和底盤損耗導(dǎo)致行駛狀態(tài)預(yù)測模型誤差大,以及動(dòng)態(tài)數(shù)據(jù)在線采集困難等問題,提出一種基于數(shù)字孿生的溫室作業(yè)底盤行駛狀態(tài)在線監(jiān)測方法,。首先,開發(fā)了面向底盤行駛狀態(tài)的溫室作業(yè)底盤數(shù)字孿生系統(tǒng),在線感知行駛過程中的動(dòng)態(tài)數(shù)據(jù)并實(shí)時(shí)仿真底盤行駛狀態(tài)變化過程;然后,結(jié)合底盤行駛狀態(tài)時(shí)變偏差量化模型和考慮行駛過程中的各種不確定因素,構(gòu)建了溫室作業(yè)底盤行駛狀態(tài)在線預(yù)測模型;最后,搭建底盤行駛狀態(tài)在線監(jiān)測試驗(yàn)環(huán)境,并進(jìn)行在線監(jiān)測試驗(yàn)和行駛效果驗(yàn)證試驗(yàn),。結(jié)果表明:本文在線預(yù)測方法對(duì)應(yīng)數(shù)據(jù)集M1、M2,、M3,、M4的橫向偏移預(yù)測精度分別為96.32%、95.96%,、95.69%和96.11%,縱向偏移預(yù)測精度分別為96.58%,、96.36%、96.51%和96.13%,對(duì)比基于BP+SVR方法的橫向偏移預(yù)測精度分別提升3.61%,、3.26%,、3.92%和3.98%,縱向偏移預(yù)測精度分別提升2.96%、2.78%,、3.27%和3.06%,證明了本文提出的在線預(yù)測方法能夠有效修正地面波動(dòng)和底盤損耗帶來的偏差影響;實(shí)際底盤行駛橫向偏移和縱向偏移平均值相較于基于固定行駛參數(shù)的行駛方法分別降低48.13%和49.49%,本文方法能夠基于底盤實(shí)時(shí)行駛狀態(tài)進(jìn)行動(dòng)態(tài)調(diào)整,。本文提出的基于數(shù)字孿生的溫室作業(yè)底盤行駛狀態(tài)在線監(jiān)測方法具有強(qiáng)實(shí)時(shí)性和高精度的特點(diǎn),可為設(shè)施農(nóng)業(yè)機(jī)器人的連續(xù)式作業(yè)技術(shù)提供依據(jù)和參考。

    Abstract:

    Digital twin technology digitizes the entire lifecycle of physical entities to achieve monitoring and control of their states, providing a solution for remote control and continuous operation of robots. The high-precision control of the operation chassis during the driving process is the key to ensuring the quality of robot operation. Aiming to address the issues of large error in the driving state prediction model due to changes in greenhouse environment and chassis wear, as well as the difficulty of online dynamic data collection, a digital twin based online monitoring method for the driving state of the greenhouse operation chassis was proposed. Firstly, a digital twin system of the greenhouse operation chassis geared towards the driving state was developed. It dynamically perceived the dynamic data in the driving process online and simulated the change process of the chassis driving state in real time. Then an online prediction model of the driving state of the greenhouse operation chassis was constructed by combining the temporal deviation quantification model of the chassis driving state and considering various uncertain factors during the driving process. Finally, an experimental environment for online monitoring of the chassis driving state was set up, and online monitoring experiments and driving effect verification tests were carried out. The results showed that the online prediction method proposed corresponded to lateral offset prediction accuracies of 96.32% , 95.96% , 95.69% and 96.11% for datasets M1 , M2 , M3 , M4 , respectively. The longitudinal offset prediction accuracies were 96.58% , 96.36% , 96.51% and 96.13% for datasets M1 , M2 , M3 , M4 , respectively. Compared with the BP + SVR method, the prediction accuracy of lateral displacement was increased by 3.61% , 3.26% , 3.92% , and 3.98% , respectively, and the prediction accuracy of longitudinal displacement was increased by 2.96% , 2.78% , 3.27% , and 3.06% , respectively. This proved that the online prediction method proposed can effectively correct for the bias effects caused by ground fluctuations and chassis wear and tear. The average values of the actual lateral and longitudinal deviations of the chassis during driving were reduced by 48.13% and 49.49% , respectively compared with the chassis driving method based on fixed driving parameters. This method can dynamically adjust based on the real-time driving status of the chassis. The online monitoring method for greenhouse operation chassis driving status based on digital twins had the characteristics of strong real-time and high accuracy, which can provide a basis and reference for the continuous operation method and technology of robots in facility agriculture.

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王明輝,徐健,周政東,王玉龍,崔永杰.基于數(shù)字孿生的溫室作業(yè)底盤行駛狀態(tài)在線監(jiān)測方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2025,56(2):92-104. WANG Minghui, XU Jian, ZHOU Zhengdong, WANG Yulong, CUI Yongjie. Online Prediction Method for Greenhouse Operation Chassis Driving Status by Digital Twins-driven[J]. Transactions of the Chinese Society for Agricultural Machinery,2025,56(2):92-104.

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  • 收稿日期:2024-10-02
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  • 在線發(fā)布日期: 2025-02-10
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