{"title":"Model Predictive Control for Formation Placement and Recovery of Traffic Cone Robots","authors":"Zhiyong Li, Siyuan Chang, Min Ye, Shengjie Jiao","doi":"10.3390/machines12080543","DOIUrl":null,"url":null,"abstract":"The challenge of effectively managing the formation and recovery of traffic cone robots (TCRs) is addressed by proposing a linear time-varying model predictive control (MPC) strategy. This problem involves coordinating multiple TCR formations within a work area to reach a target location, which is a huge challenge due to the complexity of dynamic coordination. Unlike conventional approaches, our method decomposes the formation control problem into two main components: leader TCR motion planning and follower formation tracking control. The motion planning component involves path and velocity planning to achieve leader trajectory control, which serves as a reference trajectory for the follower. The formation tracking task extends to formation control among multiple robots to achieve the traffic cone robot formation placement and recovery task. To address the TCR input limitation problem, input constraints are considered during the design process of the MPC controllers. The effectiveness and practicality of the proposed control strategy are validated through a series of numerical simulations and physical experiments with TCRs.","PeriodicalId":48519,"journal":{"name":"Machines","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machines","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/machines12080543","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The challenge of effectively managing the formation and recovery of traffic cone robots (TCRs) is addressed by proposing a linear time-varying model predictive control (MPC) strategy. This problem involves coordinating multiple TCR formations within a work area to reach a target location, which is a huge challenge due to the complexity of dynamic coordination. Unlike conventional approaches, our method decomposes the formation control problem into two main components: leader TCR motion planning and follower formation tracking control. The motion planning component involves path and velocity planning to achieve leader trajectory control, which serves as a reference trajectory for the follower. The formation tracking task extends to formation control among multiple robots to achieve the traffic cone robot formation placement and recovery task. To address the TCR input limitation problem, input constraints are considered during the design process of the MPC controllers. The effectiveness and practicality of the proposed control strategy are validated through a series of numerical simulations and physical experiments with TCRs.
期刊介绍:
Machines (ISSN 2075-1702) is an international, peer-reviewed journal on machinery and engineering. It publishes research articles, reviews, short communications and letters. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. Full experimental and/or methodical details must be provided. There are, in addition, unique features of this journal: *manuscripts regarding research proposals and research ideas will be particularly welcomed *electronic files or software regarding the full details of the calculation and experimental procedure - if unable to be published in a normal way - can be deposited as supplementary material Subject Areas: applications of automation, systems and control engineering, electronic engineering, mechanical engineering, computer engineering, mechatronics, robotics, industrial design, human-machine-interfaces, mechanical systems, machines and related components, machine vision, history of technology and industrial revolution, turbo machinery, machine diagnostics and prognostics (condition monitoring), machine design.