{"title":"A framework for coordination and navigation of multi-robot systems","authors":"Anmin Zhu, Simon X. Yang","doi":"10.1109/ICAL.2010.5585308","DOIUrl":null,"url":null,"abstract":"In this paper, a novel framework is proposed to incorporate task assignment, path planning, and tracking control of a multi-robot system. The dynamic task assignment of multi-robots is achieved using a self-organizing map based feature. The real-time collision-free robot path is generated from a neuro-dynamics network through sensor measurement and responding immediately to dynamic elements in the environment including the robot, the target, and obstacles. The tracking control is accomplished by a neuro-dynamics and back-stepping based model. This type of control is able to generate smooth, bounded acceleration control signals for a non-holonomic mobile robot to track the reference path generated by the path planner. Experiments under various situations demonstrated the effectiveness of this integrated system.","PeriodicalId":393739,"journal":{"name":"2010 IEEE International Conference on Automation and Logistics","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Conference on Automation and Logistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAL.2010.5585308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, a novel framework is proposed to incorporate task assignment, path planning, and tracking control of a multi-robot system. The dynamic task assignment of multi-robots is achieved using a self-organizing map based feature. The real-time collision-free robot path is generated from a neuro-dynamics network through sensor measurement and responding immediately to dynamic elements in the environment including the robot, the target, and obstacles. The tracking control is accomplished by a neuro-dynamics and back-stepping based model. This type of control is able to generate smooth, bounded acceleration control signals for a non-holonomic mobile robot to track the reference path generated by the path planner. Experiments under various situations demonstrated the effectiveness of this integrated system.