{"title":"基于样本的多机器人系统运动规划","authors":"M. Muradi, R. Wanka","doi":"10.1109/ICCAR49639.2020.9108020","DOIUrl":null,"url":null,"abstract":"We present a methodology for robot path planning and smoothing based on the probabilistic roadmap algorithm and simulated annealing algorithm. Furthermore, a fast and accurate collision detection and a suitable format for the abstract description of robot problems are introduced. The methods are tested on the automotive sealing process by applying them to a practical problem.","PeriodicalId":412255,"journal":{"name":"2020 6th International Conference on Control, Automation and Robotics (ICCAR)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Sample-Based Motion Planning for Multi-Robot Systems\",\"authors\":\"M. Muradi, R. Wanka\",\"doi\":\"10.1109/ICCAR49639.2020.9108020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a methodology for robot path planning and smoothing based on the probabilistic roadmap algorithm and simulated annealing algorithm. Furthermore, a fast and accurate collision detection and a suitable format for the abstract description of robot problems are introduced. The methods are tested on the automotive sealing process by applying them to a practical problem.\",\"PeriodicalId\":412255,\"journal\":{\"name\":\"2020 6th International Conference on Control, Automation and Robotics (ICCAR)\",\"volume\":\"16 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 6th International Conference on Control, Automation and Robotics (ICCAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCAR49639.2020.9108020\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 6th International Conference on Control, Automation and Robotics (ICCAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAR49639.2020.9108020","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sample-Based Motion Planning for Multi-Robot Systems
We present a methodology for robot path planning and smoothing based on the probabilistic roadmap algorithm and simulated annealing algorithm. Furthermore, a fast and accurate collision detection and a suitable format for the abstract description of robot problems are introduced. The methods are tested on the automotive sealing process by applying them to a practical problem.
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