A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization

Cheuk Ho Yuen; Kam Tim Woo

doi:10.1007/978-3-319-61833-3_18

A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization

Cheuk Ho Yuen^*, Kam Tim Woo

^*Corresponding author for this work

Department of Electronic and Computer Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

7 Citations (Scopus)

Abstract

This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings
Editors	Ben Niu, Hideyuki Takagi, Yuhui Shi, Ying Tan
Publisher	Springer Verlag
Pages	167-175
Number of pages	9
ISBN (Print)	9783319618326
DOIs	https://doi.org/10.1007/978-3-319-61833-3_18
Publication status	Published - 2017
Event	8th International Conference on Swarm Intelligence, ICSI 2017 - Fukuoka, Japan Duration: 27 Jul 2017 → 1 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10386 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th International Conference on Swarm Intelligence, ICSI 2017
Country/Territory	Japan
City	Fukuoka
Period	27/07/17 → 1/08/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Foraging
Multi-robot searching
Particle swarm optimization
Survivability
Swarm robotics

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10.1007/978-3-319-61833-3_18

Cite this

Yuen, C. H., & Woo, K. T. (2017). A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization. In B. Niu, H. Takagi, Y. Shi, & Y. Tan (Eds.), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings (pp. 167-175). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10386 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-61833-3_18

Yuen, Cheuk Ho ; Woo, Kam Tim. / A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization. Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. editor / Ben Niu ; Hideyuki Takagi ; Yuhui Shi ; Ying Tan. Springer Verlag, 2017. pp. 167-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.",

keywords = "Foraging, Multi-robot searching, Particle swarm optimization, Survivability, Swarm robotics",

author = "Yuen, \{Cheuk Ho\} and Woo, \{Kam Tim\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 8th International Conference on Swarm Intelligence, ICSI 2017 ; Conference date: 27-07-2017 Through 01-08-2017",

year = "2017",

doi = "10.1007/978-3-319-61833-3\_18",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Yuen, CH & Woo, KT 2017, A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization. in B Niu, H Takagi, Y Shi & Y Tan (eds), Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10386 LNCS, Springer Verlag, pp. 167-175, 8th International Conference on Swarm Intelligence, ICSI 2017, Fukuoka, Japan, 27/07/17. https://doi.org/10.1007/978-3-319-61833-3_18

A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization. / Yuen, Cheuk Ho; Woo, Kam Tim.
Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. ed. / Ben Niu; Hideyuki Takagi; Yuhui Shi; Ying Tan. Springer Verlag, 2017. p. 167-175 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10386 LNCS).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization

AU - Yuen, Cheuk Ho

AU - Woo, Kam Tim

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.

AB - This paper aims at outlining an algorithm for groups of swarm robots solely powered by light energy to survive and complete target searching tasks in unknown fields where light energy charging points and targets are scattered. To sustain the searching operation and solve energy consumption conflicts between surviving and searching, this paper introduces a multi-robot algorithm based on Multi-Objective Particle Swarm Optimization (MOPSO) and energy-saving decision rules. A novel mechanism of selecting the best performing particle in PSO is introduced. Several sets of simulation experiments were conducted and results show that a 15-robot swarm system running this algorithm is able to search a single target and stabilize the energy level for the long-term simultaneously. It demonstrates the feasibility of applying this energy-optimized MOPSO as a design framework for a long-term searching swarm robot system.

KW - Foraging

KW - Multi-robot searching

KW - Particle swarm optimization

KW - Survivability

KW - Swarm robotics

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M3 - Conference Paper published in a book

SN - 9783319618326

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings

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A2 - Takagi, Hideyuki

A2 - Shi, Yuhui

A2 - Tan, Ying

PB - Springer Verlag

T2 - 8th International Conference on Swarm Intelligence, ICSI 2017

Y2 - 27 July 2017 through 1 August 2017

ER -

Yuen CH, Woo KT. A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization. In Niu B, Takagi H, Shi Y, Tan Y, editors, Advances in Swarm Intelligence - 8th International Conference, ICSI 2017, Proceedings. Springer Verlag. 2017. p. 167-175. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-61833-3_18

A survivability enhanced swarm robotic searching system using multi-objective particle swarm optimization

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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