{"title":"Machine learning applications in off-road vehicles interaction with terrain: An overview","authors":"","doi":"10.1016/j.jterra.2024.101003","DOIUrl":null,"url":null,"abstract":"<div><p>With the advent of artificial intelligence, the analysis of systems related to complex processes has become possible or easier. The interaction of the traction factor of off-road vehicles with soil or other uncommon surfaces is one of the complex mechanical problems, which has been very difficult to model and analyze in conventional and previous methods due to numerous and variable parameters. This review article delves into the imperative and progression of integrating AI algorithms within the realm of modeling and predicting target parameters in Terramechanics engineering. Such endeavors are especially pertinent to predicting various soil properties, including soil compaction, traction, energy consumption, deformation, and associated factors. The application of AI encompasses various facets, including modeling and predicting traction, soil sinkage, rut depth, contact area, soil stress, density, and energy wasted on the traction device’s movement on the soil. The present study evaluates the solutions and benefits offered by AI-based methodologies in addressing soil-machine interaction challenges. Furthermore, the study investigates the constraints inherent in utilizing these methodologies.</p></div>","PeriodicalId":50023,"journal":{"name":"Journal of Terramechanics","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0022489824000454/pdfft?md5=cdd07c6b8be6c20390df080d09d807d6&pid=1-s2.0-S0022489824000454-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Terramechanics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022489824000454","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
With the advent of artificial intelligence, the analysis of systems related to complex processes has become possible or easier. The interaction of the traction factor of off-road vehicles with soil or other uncommon surfaces is one of the complex mechanical problems, which has been very difficult to model and analyze in conventional and previous methods due to numerous and variable parameters. This review article delves into the imperative and progression of integrating AI algorithms within the realm of modeling and predicting target parameters in Terramechanics engineering. Such endeavors are especially pertinent to predicting various soil properties, including soil compaction, traction, energy consumption, deformation, and associated factors. The application of AI encompasses various facets, including modeling and predicting traction, soil sinkage, rut depth, contact area, soil stress, density, and energy wasted on the traction device’s movement on the soil. The present study evaluates the solutions and benefits offered by AI-based methodologies in addressing soil-machine interaction challenges. Furthermore, the study investigates the constraints inherent in utilizing these methodologies.
期刊介绍:
The Journal of Terramechanics is primarily devoted to scientific articles concerned with research, design, and equipment utilization in the field of terramechanics.
The Journal of Terramechanics is the leading international journal serving the multidisciplinary global off-road vehicle and soil working machinery industries, and related user community, governmental agencies and universities.
The Journal of Terramechanics provides a forum for those involved in research, development, design, innovation, testing, application and utilization of off-road vehicles and soil working machinery, and their sub-systems and components. The Journal presents a cross-section of technical papers, reviews, comments and discussions, and serves as a medium for recording recent progress in the field.