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A wireless mobile robots deployment approach for maximising the coverage of important locations in disaster rescues

Citation

Su, X and Zhang, M and Bai, Q, A wireless mobile robots deployment approach for maximising the coverage of important locations in disaster rescues, Proceedings of the 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 6-9 December 2015, Singapore, pp. 17-20. ISBN 978-1-4673-9618-9 (2015) [Refereed Conference Paper]

Copyright Statement

Copyright 2015 IEEE

DOI: doi:10.1109/WI-IAT.2015.31

Abstract

This paper addresses the wireless mobile robot deployment problem in disaster rescues, which aims to maximise the coverage of important locations of an ad hoc network through deploying wireless mobile robots of the network at suitable locations. Nowadays, most of current approaches deploy wireless mobile robots to cover important locations in disaster environments based on greedy algorithms. Due to the myopia of greedy algorithms, these approaches can only maximise the coverage of important locations of each wireless mobile robot rather than all of them in an ad hoc network. To this end, two mathematical programming-based wireless mobile robot deployment approaches are proposed in this paper. The proposed approaches can create the optimal deployment locations for all wireless mobile robots in an ad hoc network. The experimental results demonstrate that wireless mobile robots in the proposed approaches can cover more important locations than that of in greedy algorithm-based approaches in most disaster environments.

Item Details

Item Type:Refereed Conference Paper
Keywords:multi-agent systems, agent-based modelling, complex systems
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:140710
Year Published:2015
Web of Science® Times Cited:7
Deposited By:Information and Communication Technology
Deposited On:2020-09-02
Last Modified:2020-12-10
Downloads:0

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