A neural network approach to monitoring robot malfunction in multirobot formation control tasks

Can Wang; Wen Shang; Dong Sun

doi:10.1109/ICMA.2009.5244944

A neural network approach to monitoring robot malfunction in multirobot formation control tasks

Can Wang, Wen Shang, Dong Sun

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

3 Citations (Scopus)

Abstract

This paper presents a design of a high-level monitoring system for formation controls of swarms of mobile robots. With necessary information of each robot provided in the centralized high-level planner, the proposed monitoring system can evaluate the robots' working performance and check which robot malfunctions. The malfunction detector, developed with back-propagation neural networks technology, helps make decision whether the robots fails to meet the task requirement and should be abandoned from the team. The neural network is trained using both positive and negative examples. Case studies are performed to demonstrate the effectiveness of the proposed approach. ©2009 IEEE.

Original language	English
Title of host publication	2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009
Pages	2689-2694
DOIs	https://doi.org/10.1109/ICMA.2009.5244944
Publication status	Published - 2009
Event	2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009 - Changchun, China Duration: 9 Aug 2009 → 12 Aug 2009

Conference

Conference	2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009
Place	China
City	Changchun
Period	9/08/09 → 12/08/09

Research Keywords

Formation control
Multirobot formation
Neural network

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10.1109/ICMA.2009.5244944

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title = "A neural network approach to monitoring robot malfunction in multirobot formation control tasks",

abstract = "This paper presents a design of a high-level monitoring system for formation controls of swarms of mobile robots. With necessary information of each robot provided in the centralized high-level planner, the proposed monitoring system can evaluate the robots' working performance and check which robot malfunctions. The malfunction detector, developed with back-propagation neural networks technology, helps make decision whether the robots fails to meet the task requirement and should be abandoned from the team. The neural network is trained using both positive and negative examples. Case studies are performed to demonstrate the effectiveness of the proposed approach. {\textcopyright}2009 IEEE.",

keywords = "Formation control, Multirobot formation, Neural network",

author = "Can Wang and Wen Shang and Dong Sun",

year = "2009",

doi = "10.1109/ICMA.2009.5244944",

language = "English",

isbn = "9781424426935",

pages = "2689--2694",

booktitle = "2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009",

note = "2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009 ; Conference date: 09-08-2009 Through 12-08-2009",

}

Wang, C, Shang, W & Sun, D 2009, A neural network approach to monitoring robot malfunction in multirobot formation control tasks. in 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009., 5244944, pp. 2689-2694, 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009, Changchun, China, 9/08/09. https://doi.org/10.1109/ICMA.2009.5244944

A neural network approach to monitoring robot malfunction in multirobot formation control tasks. / Wang, Can; Shang, Wen; Sun, Dong.
2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009. 2009. p. 2689-2694 5244944.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A neural network approach to monitoring robot malfunction in multirobot formation control tasks

AU - Wang, Can

AU - Shang, Wen

AU - Sun, Dong

PY - 2009

Y1 - 2009

N2 - This paper presents a design of a high-level monitoring system for formation controls of swarms of mobile robots. With necessary information of each robot provided in the centralized high-level planner, the proposed monitoring system can evaluate the robots' working performance and check which robot malfunctions. The malfunction detector, developed with back-propagation neural networks technology, helps make decision whether the robots fails to meet the task requirement and should be abandoned from the team. The neural network is trained using both positive and negative examples. Case studies are performed to demonstrate the effectiveness of the proposed approach. ©2009 IEEE.

AB - This paper presents a design of a high-level monitoring system for formation controls of swarms of mobile robots. With necessary information of each robot provided in the centralized high-level planner, the proposed monitoring system can evaluate the robots' working performance and check which robot malfunctions. The malfunction detector, developed with back-propagation neural networks technology, helps make decision whether the robots fails to meet the task requirement and should be abandoned from the team. The neural network is trained using both positive and negative examples. Case studies are performed to demonstrate the effectiveness of the proposed approach. ©2009 IEEE.

KW - Formation control

KW - Multirobot formation

KW - Neural network

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DO - 10.1109/ICMA.2009.5244944

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781424426935

SP - 2689

EP - 2694

BT - 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009

T2 - 2009 IEEE International Conference on Mechatronics and Automation, ICMA 2009

Y2 - 9 August 2009 through 12 August 2009

ER -

A neural network approach to monitoring robot malfunction in multirobot formation control tasks

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