Distributed coverage control of mobile sensors: generalized approach using distance functions

Rihab Abdul Razak; Srikant Sukumar; Hoam Chung

doi:10.1109/CDC.2018.8619224

Distributed coverage control of mobile sensors: generalized approach using distance functions

Rihab Abdul Razak, Srikant Sukumar, Hoam Chung

Mechanical & Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

2 Citations (Scopus)

Abstract

This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the {L}^{2}-distance as a possible metric to design distributed coverage control laws.

Original language	English
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	3323-3328
Number of pages	6
Volume	2018-December
ISBN (Electronic)	9781538613955
DOIs	https://doi.org/10.1109/CDC.2018.8619224
Publication status	Published - 18 Jan 2019
Event	IEEE Conference on Decision and Control 2018 - Miami, United States of America Duration: 17 Dec 2018 → 19 Dec 2018 Conference number: 57th https://cdc2018.ieeecss.org/ https://ieeexplore.ieee.org/xpl/conhome/8592870/proceeding (Proceedings)

Conference

Conference	IEEE Conference on Decision and Control 2018
Abbreviated title	CDC 2018
Country/Territory	United States of America
City	Miami
Period	17/12/18 → 19/12/18
Internet address	https://cdc2018.ieeecss.org/ https://ieeexplore.ieee.org/xpl/conhome/8592870/proceeding (Proceedings)

Access to Document

10.1109/CDC.2018.8619224

Cite this

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title = "Distributed coverage control of mobile sensors: generalized approach using distance functions",

abstract = "This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the {L}^{2}-distance as a possible metric to design distributed coverage control laws.",

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Razak, RA, Sukumar, S & Chung, H 2019, Distributed coverage control of mobile sensors: generalized approach using distance functions. in 2018 IEEE Conference on Decision and Control, CDC 2018. vol. 2018-December, 8619224, IEEE, Institute of Electrical and Electronics Engineers, pp. 3323-3328, IEEE Conference on Decision and Control 2018, Miami, Florida, United States of America, 17/12/18. https://doi.org/10.1109/CDC.2018.8619224

Distributed coverage control of mobile sensors: generalized approach using distance functions. / Razak, Rihab Abdul; Sukumar, Srikant; Chung, Hoam.
2018 IEEE Conference on Decision and Control, CDC 2018. Vol. 2018-December IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 3323-3328 8619224.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

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AU - Sukumar, Srikant

AU - Chung, Hoam

N1 - Conference code: 57th

PY - 2019/1/18

Y1 - 2019/1/18

N2 - This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the {L}^{2}-distance as a possible metric to design distributed coverage control laws.

AB - This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the {L}^{2}-distance as a possible metric to design distributed coverage control laws.

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AN - SCOPUS:85062181231

VL - 2018-December

SP - 3323

EP - 3328

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - IEEE, Institute of Electrical and Electronics Engineers

Y2 - 17 December 2018 through 19 December 2018

ER -

Distributed coverage control of mobile sensors: generalized approach using distance functions

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