{"title":"Flexible Affine Formation Control Based on Dynamic Hierarchical Reorganization","authors":"Yuzhu Li;Wei Dong","doi":"10.1109/LRA.2024.3490407","DOIUrl":null,"url":null,"abstract":"Current formations commonly rely on invariant hierarchical structures, such as predetermined leaders or enumerated formation shapes. These structures could be unidirectional and sluggish, constraining their flexibility and agility when encountering cluttered environments. To surmount these constraints, this work proposes a dynamic hierarchical reorganization approach with affine formation. Central to our approach is the fluid leadership and authority redistribution, for which we develop a minimum time-driven leadership evaluation algorithm and a power transition control algorithm. These algorithms facilitate autonomous leader selection and ensure smooth power transitions, enabling the swarm to adapt hierarchically in alignment with the external environment. Extensive simulations and real-world experiments validate the effectiveness of the proposed method. The formation of five aerial robots successfully performs dynamic hierarchical reorganizations, enabling the execution of complex tasks such as swerving maneuvers and navigating through hoops at velocities of up to 1.05m/s. Comparative experimental results further demonstrate the significant advantages of hierarchical reorganization in enhancing formation flexibility and agility, particularly during complex maneuvers such as U-turns. Notably, in the aforementioned real-world experiments, the proposed method reduces the flight path length by at least 33.8% compared to formations without hierarchical reorganization.","PeriodicalId":13241,"journal":{"name":"IEEE Robotics and Automation Letters","volume":"9 12","pages":"11290-11297"},"PeriodicalIF":4.6000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Robotics and Automation Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10741046/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Current formations commonly rely on invariant hierarchical structures, such as predetermined leaders or enumerated formation shapes. These structures could be unidirectional and sluggish, constraining their flexibility and agility when encountering cluttered environments. To surmount these constraints, this work proposes a dynamic hierarchical reorganization approach with affine formation. Central to our approach is the fluid leadership and authority redistribution, for which we develop a minimum time-driven leadership evaluation algorithm and a power transition control algorithm. These algorithms facilitate autonomous leader selection and ensure smooth power transitions, enabling the swarm to adapt hierarchically in alignment with the external environment. Extensive simulations and real-world experiments validate the effectiveness of the proposed method. The formation of five aerial robots successfully performs dynamic hierarchical reorganizations, enabling the execution of complex tasks such as swerving maneuvers and navigating through hoops at velocities of up to 1.05m/s. Comparative experimental results further demonstrate the significant advantages of hierarchical reorganization in enhancing formation flexibility and agility, particularly during complex maneuvers such as U-turns. Notably, in the aforementioned real-world experiments, the proposed method reduces the flight path length by at least 33.8% compared to formations without hierarchical reorganization.
期刊介绍:
The scope of this journal is to publish peer-reviewed articles that provide a timely and concise account of innovative research ideas and application results, reporting significant theoretical findings and application case studies in areas of robotics and automation.