Distributed control of multirobot sweep coverage over a region with unknown workload distribution
In this article, we consider the problem of using a multirobot system to conduct sweep coverage over a region with uneven and unknown workload distribution. Uneven workload distribution means that a robot has to spend different amounts of time covering a unit area at different locations in the regio...
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sg-ntu-dr.10356-1718012023-11-08T04:06:53Z Distributed control of multirobot sweep coverage over a region with unknown workload distribution Cao, Muqing Cao, Kun Li, Xiuxian Xie, Lihua School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Control Systems Cooperative Systems In this article, we consider the problem of using a multirobot system to conduct sweep coverage over a region with uneven and unknown workload distribution. Uneven workload distribution means that a robot has to spend different amounts of time covering a unit area at different locations in the region. Unknown workload distribution means that the amount of workload at any location is unknown prior to the operation, hence online sensing and allocation of workload is needed for better efficiency. In this work, we adopt the formulation in which the entire region is separated into multiple stripes, and a discrete-time distributed workload allocation algorithm is used to allocate workload on a stripe to each robot. Previous works that adopt similar formulations do not provide rigorous stability analysis and experimental verification and lack consideration of practical aspects, such as limited sensor range. This work addresses these weaknesses and bridges the gap between theory and practice. First, compared with the existing works, the convergence of the distributed workload allocation algorithm to the optimal workload assignment is established under a more realistic assumption, and less conservative error bounds are derived, which serve as a better indicator of the effectiveness of the algorithm. Second, we propose a new algorithm that addresses the limited sensor range of robots, which is an important constraint in applications, such as agricultural spraying and building inspection. The stability analysis and error bound of the proposed algorithm are also provided. Third, realistic simulations and actual flight experiments using unmanned aerial vehicles are carried out to demonstrate the practicality and validate the theoretical results. Nanyang Technological University This work was supported by the Wallenbery-NTU Presidential Postdoctoral Fellowship in Nanyang Technological University, Singapore. 2023-11-08T04:06:53Z 2023-11-08T04:06:53Z 2023 Journal Article Cao, M., Cao, K., Li, X. & Xie, L. (2023). Distributed control of multirobot sweep coverage over a region with unknown workload distribution. IEEE Transactions On Systems, Man, and Cybernetics: Systems, 53(10), 6503-6515. https://dx.doi.org/10.1109/TSMC.2023.3285264 2168-2216 https://hdl.handle.net/10356/171801 10.1109/TSMC.2023.3285264 2-s2.0-85163789070 10 53 6503 6515 en IEEE Transactions on Systems, Man, and Cybernetics: Systems © 2023 IEEE. All rights reserved. |
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Engineering::Electrical and electronic engineering Control Systems Cooperative Systems Cao, Muqing Cao, Kun Li, Xiuxian Xie, Lihua Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
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In this article, we consider the problem of using a multirobot system to conduct sweep coverage over a region with uneven and unknown workload distribution. Uneven workload distribution means that a robot has to spend different amounts of time covering a unit area at different locations in the region. Unknown workload distribution means that the amount of workload at any location is unknown prior to the operation, hence online sensing and allocation of workload is needed for better efficiency. In this work, we adopt the formulation in which the entire region is separated into multiple stripes, and a discrete-time distributed workload allocation algorithm is used to allocate workload on a stripe to each robot. Previous works that adopt similar formulations do not provide rigorous stability analysis and experimental verification and lack consideration of practical aspects, such as limited sensor range. This work addresses these weaknesses and bridges the gap between theory and practice. First, compared with the existing works, the convergence of the distributed workload allocation algorithm to the optimal workload assignment is established under a more realistic assumption, and less conservative error bounds are derived, which serve as a better indicator of the effectiveness of the algorithm. Second, we propose a new algorithm that addresses the limited sensor range of robots, which is an important constraint in applications, such as agricultural spraying and building inspection. The stability analysis and error bound of the proposed algorithm are also provided. Third, realistic simulations and actual flight experiments using unmanned aerial vehicles are carried out to demonstrate the practicality and validate the theoretical results. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Cao, Muqing Cao, Kun Li, Xiuxian Xie, Lihua |
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Article |
author |
Cao, Muqing Cao, Kun Li, Xiuxian Xie, Lihua |
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Cao, Muqing |
title |
Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
title_short |
Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
title_full |
Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
title_fullStr |
Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
title_full_unstemmed |
Distributed control of multirobot sweep coverage over a region with unknown workload distribution |
title_sort |
distributed control of multirobot sweep coverage over a region with unknown workload distribution |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171801 |
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1783955589663031296 |