{"title":"Speed control of an autonomous electric vehicle for orchard spraying","authors":"Yoshitomo Yamasaki, Kazunobu Ishii, Noboru Noguchi","doi":"10.1016/j.compag.2025.110419","DOIUrl":null,"url":null,"abstract":"<div><div>We developed an autonomous electric vehicle for orchard spraying, termed a spraying robot. Traveling resistance varies depending on vehicle weight, the front sideslip angle, and surface slope. The vehicle weight must change while traveling, especially for the spraying robot. To adapt to changes in those resistances, it is necessary to develop a speed controller. This research focused on rolling and slope resistance as a traveling resistance, which depends on the vehicle weight. We modeled the resistance and developed a feedforward controller with a proportional-integral-derivative (PID) feedback controller. The developed controller (FF-PID) was compared with a simple PID controller in simulation. The FF-PID was verified to be more rapid and stable response than the PID. Moreover, the FF-PID responded adaptively when the vehicle weight changed. Compared to the PID, the FF-PID reduced the error to the target speed by 50 % during sideslip angle changes and by 48 % during slope angle changes. Finally, we simulated a spraying task based on actual traveling data in a vineyard, factoring in the vehicle weight, steering angle, and slope angle change. The results showed that the FF-PID reduced error by 32 %. This research improved the performance of the spraying robot’s speed controller by modeling traveling resistance in an orchard environment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"236 ","pages":"Article 110419"},"PeriodicalIF":7.7000,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers and Electronics in Agriculture","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0168169925005253","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
We developed an autonomous electric vehicle for orchard spraying, termed a spraying robot. Traveling resistance varies depending on vehicle weight, the front sideslip angle, and surface slope. The vehicle weight must change while traveling, especially for the spraying robot. To adapt to changes in those resistances, it is necessary to develop a speed controller. This research focused on rolling and slope resistance as a traveling resistance, which depends on the vehicle weight. We modeled the resistance and developed a feedforward controller with a proportional-integral-derivative (PID) feedback controller. The developed controller (FF-PID) was compared with a simple PID controller in simulation. The FF-PID was verified to be more rapid and stable response than the PID. Moreover, the FF-PID responded adaptively when the vehicle weight changed. Compared to the PID, the FF-PID reduced the error to the target speed by 50 % during sideslip angle changes and by 48 % during slope angle changes. Finally, we simulated a spraying task based on actual traveling data in a vineyard, factoring in the vehicle weight, steering angle, and slope angle change. The results showed that the FF-PID reduced error by 32 %. This research improved the performance of the spraying robot’s speed controller by modeling traveling resistance in an orchard environment.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.