{"title":"基于冗余自适应鲁棒卡尔曼滤波的多机器人位置自适应编队控制","authors":"Xiancui Wei, Zhiguo Shi","doi":"10.1109/ROBIO.2014.7090445","DOIUrl":null,"url":null,"abstract":"Leader-follower based formation control is a promising technique in multi-robot motion planning system. When the pose of leader-robot mutated or disturbed by external disturbance, the follower-robot usually cannot react quickly, resulting in loss tracing. A redundant adaptive robust Kalman filter (RAREKF) is adopted to predict the relative movement parameters between the leader and follower, so that the followers can reach the desired position and orientation quickly and accurately. According to the actual situation, the redundancy factor in RAREKF is designed to compensate the timeliness lack of the tracking process. This approach has the advantages of eliminating the system state noises and errors generated by the sudden change of formation, which can ensure rapidity and accuracy of the tracking process and maintain the stability of the formation. The validity of the method mentioned above has been verified by simulation experiments.","PeriodicalId":289829,"journal":{"name":"2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Position adaptive formation control for multi-robot system using a redundant adaptive robust Kalman filter\",\"authors\":\"Xiancui Wei, Zhiguo Shi\",\"doi\":\"10.1109/ROBIO.2014.7090445\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Leader-follower based formation control is a promising technique in multi-robot motion planning system. When the pose of leader-robot mutated or disturbed by external disturbance, the follower-robot usually cannot react quickly, resulting in loss tracing. A redundant adaptive robust Kalman filter (RAREKF) is adopted to predict the relative movement parameters between the leader and follower, so that the followers can reach the desired position and orientation quickly and accurately. According to the actual situation, the redundancy factor in RAREKF is designed to compensate the timeliness lack of the tracking process. This approach has the advantages of eliminating the system state noises and errors generated by the sudden change of formation, which can ensure rapidity and accuracy of the tracking process and maintain the stability of the formation. The validity of the method mentioned above has been verified by simulation experiments.\",\"PeriodicalId\":289829,\"journal\":{\"name\":\"2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ROBIO.2014.7090445\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ROBIO.2014.7090445","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Position adaptive formation control for multi-robot system using a redundant adaptive robust Kalman filter
Leader-follower based formation control is a promising technique in multi-robot motion planning system. When the pose of leader-robot mutated or disturbed by external disturbance, the follower-robot usually cannot react quickly, resulting in loss tracing. A redundant adaptive robust Kalman filter (RAREKF) is adopted to predict the relative movement parameters between the leader and follower, so that the followers can reach the desired position and orientation quickly and accurately. According to the actual situation, the redundancy factor in RAREKF is designed to compensate the timeliness lack of the tracking process. This approach has the advantages of eliminating the system state noises and errors generated by the sudden change of formation, which can ensure rapidity and accuracy of the tracking process and maintain the stability of the formation. The validity of the method mentioned above has been verified by simulation experiments.