{"title":"Research on Path Tracking Control Based on Optimal Look-Ahead Points","authors":"Yong Guan, Ning Li, Pengzhan Chen, Yongchao Zhang","doi":"10.1007/s12239-024-00117-4","DOIUrl":null,"url":null,"abstract":"<p>Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions.</p>","PeriodicalId":50338,"journal":{"name":"International Journal of Automotive Technology","volume":"88 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Automotive Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12239-024-00117-4","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions.
期刊介绍：
The International Journal of Automotive Technology has as its objective the publication and dissemination of original research in all fields of AUTOMOTIVE TECHNOLOGY, SCIENCE and ENGINEERING. It fosters thus the exchange of ideas among researchers in different parts of the world and also among researchers who emphasize different aspects of the foundations and applications of the field.
Standing as it does at the cross-roads of Physics, Chemistry, Mechanics, Engineering Design and Materials Sciences, AUTOMOTIVE TECHNOLOGY is experiencing considerable growth as a result of recent technological advances. The Journal, by providing an international medium of communication, is encouraging this growth and is encompassing all aspects of the field from thermal engineering, flow analysis, structural analysis, modal analysis, control, vehicular electronics, mechatronis, electro-mechanical engineering, optimum design methods, ITS, and recycling. Interest extends from the basic science to technology applications with analytical, experimental and numerical studies.
The emphasis is placed on contributions that appear to be of permanent interest to research workers and engineers in the field. If furthering knowledge in the area of principal concern of the Journal, papers of primary interest to the innovative disciplines of AUTOMOTIVE TECHNOLOGY, SCIENCE and ENGINEERING may be published. Papers that are merely illustrations of established principles and procedures, even though possibly containing new numerical or experimental data, will generally not be published.
When outstanding advances are made in existing areas or when new areas have been developed to a definitive stage, special review articles will be considered by the editors.
No length limitations for contributions are set, but only concisely written papers are published. Brief articles are considered on the basis of technical merit.
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