Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference Center
In this letter, we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center. We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some loca...
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Published in | IEEE/CAA journal of automatica sinica Vol. 9; no. 4; pp. 732 - 734 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Piscataway
Chinese Association of Automation (CAA)
01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) School of Mathematics,Southeast University,Nanjing 210096,China%School of Mathematics,Southeast University,Jiangsu Provincial Key Laboratory of Networked Collective Intelligence,Nanjing 210096,China |
Subjects | |
Online Access | Get full text |
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Summary: | In this letter, we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center. We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some local constraints. In the multi-robot system with virtual reference center, each robot has messages on its own constraints and objective function, as well as the message about the formation that interacts with the virtual reference center. At the same time, all the robots collaborate to find the minimum value of the function defined by the formation. To find the optimal formation, we propose an algorithm with fixed step size with better performance. In addition, we use a combination of the Hungarian assignment algorithm and the proposed formation algorithm to get the optimal matching formation of the multi-robot system. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
ISSN: | 2329-9266 2329-9274 |
DOI: | 10.1109/JAS.2022.105473 |