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Two mathematical programming-based approaches for wireless mobile robot deployment in disaster environments

Citation

Su, X and Zhang, M and Bai, Q, Two mathematical programming-based approaches for wireless mobile robot deployment in disaster environments, The Computer Journal, 62, (6) pp. 905-918. ISSN 0010-4620 (2019) [Refereed Article]

Copyright Statement

Copyright 2019 The British Computer Society

DOI: doi:10.1093/comjnl/bxz010

Abstract

This paper addresses the issue of the wireless mobile robot deployment for the ad hoc network establishment in disaster environments, which aims to maximize the important locations covered by the established ad hoc network so as to improve the performance of task allocation. In many disaster environments, the number of wireless mobile robots usually is much less than the number of important locations in the environment so that maximizing the important locations covered by the established ad hoc network is the primary objective of wireless mobile robot deployment approaches. To maximize the coverage of important locations, most of the current approaches were developed based on greedy algorithms. Due to the myopia of greedy algorithms, these approaches can only maximize the coverage of important locations of each wireless mobile robot rather than the whole network. To this end, two mathematical programming-based wireless mobile robot deployment approaches are proposed for ad hoc network establishment in disaster environments. The proposed approach can create suitable deployment locations for all wireless mobile robots in a disaster environment. The experimental results demonstrate that ad hoc networks established by the proposed approaches can cover more important locations in a disaster environment than those established by greedy algorithm-based approaches.

Item Details

Item Type:Refereed Article
Keywords:multi-agent systems, agent-based modelling, wireless mobile robots deployment, disaster environments, linear programming, quadratic programming
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Health protection and disaster response
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:138240
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2020-03-29
Last Modified:2020-05-27
Downloads:0

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