A hormone arbitration system for energy efficient foraging in robot swarms

James Wilson; Jon Timmis; Andy Tyrrell

doi:10.1007/978-3-319-96728-8_26

A hormone arbitration system for energy efficient foraging in robot swarms

James Wilson^*
, Jon Timmis
, Andy Tyrrell

^*Corresponding author for this work

Department of Computer Science

University of York

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

2 Citations (Scopus)

Abstract

Keeping robots optimized for an environment can be computationally expensive, time consuming, and sometimes requires information unavailable to a robot swarm before it is assigned to a task. This paper proposes a hormone-inspired system to arbitrate the states of a foraging robot swarm. The goal of this system is to increase the energy efficiency of food collection by adapting the swarm to environmental factors during the task. These adaptations modify the amount of time the robots rest in a nest site and how likely they are to return to the nest site when avoiding an obstacle. These are both factors that previous studies have identified as having a significant effect on energy efficiency. This paper proposes that, when compared to an offline optimized system, there are a variety of environments in which the hormone system achieves an increased performance. This work shows that the use of a hormone arbitration system can extrapolate environmental features from stimuli and use these to adapt.

Original language	English
Title of host publication	Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings
Editors	Maria Elena Giannaccini, Manuel Giuliani, Tareq Assaf
Publisher	Springer Nature
Pages	305-316
Number of pages	12
ISBN (Print)	9783319967271
DOIs	https://doi.org/10.1007/978-3-319-96728-8_26
Publication status	Published - 2018
Event	19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018 - Bristol, United Kingdom of Great Britain and Northern Ireland Duration: 25 Jul 2018 → 27 Jul 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10965 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018
Country/Territory	United Kingdom of Great Britain and Northern Ireland
City	Bristol
Period	25 Jul 2018 → 27 Jul 2018

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Energy efficiency
Foraging
Hormone arbitration
Robotics
Swarm

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10.1007/978-3-319-96728-8_26

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Wilson, J., Timmis, J., & Tyrrell, A. (2018). A hormone arbitration system for energy efficient foraging in robot swarms. In M. E. Giannaccini, M. Giuliani, & T. Assaf (Eds.), Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings (pp. 305-316). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10965 LNAI). Springer Nature. https://doi.org/10.1007/978-3-319-96728-8_26

Wilson, James ; Timmis, Jon ; Tyrrell, Andy. / A hormone arbitration system for energy efficient foraging in robot swarms. Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings. editor / Maria Elena Giannaccini ; Manuel Giuliani ; Tareq Assaf. Springer Nature, 2018. pp. 305-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A hormone arbitration system for energy efficient foraging in robot swarms",

abstract = "Keeping robots optimized for an environment can be computationally expensive, time consuming, and sometimes requires information unavailable to a robot swarm before it is assigned to a task. This paper proposes a hormone-inspired system to arbitrate the states of a foraging robot swarm. The goal of this system is to increase the energy efficiency of food collection by adapting the swarm to environmental factors during the task. These adaptations modify the amount of time the robots rest in a nest site and how likely they are to return to the nest site when avoiding an obstacle. These are both factors that previous studies have identified as having a significant effect on energy efficiency. This paper proposes that, when compared to an offline optimized system, there are a variety of environments in which the hormone system achieves an increased performance. This work shows that the use of a hormone arbitration system can extrapolate environmental features from stimuli and use these to adapt.",

keywords = "Energy efficiency, Foraging, Hormone arbitration, Robotics, Swarm",

author = "James Wilson and Jon Timmis and Andy Tyrrell",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG, part of Springer Nature 2018.; 19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018 ; Conference date: 25-07-2018 Through 27-07-2018",

year = "2018",

doi = "10.1007/978-3-319-96728-8\_26",

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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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Wilson, J, Timmis, J & Tyrrell, A 2018, A hormone arbitration system for energy efficient foraging in robot swarms. in ME Giannaccini, M Giuliani & T Assaf (eds), Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10965 LNAI, Springer Nature, pp. 305-316, 19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018, Bristol, United Kingdom of Great Britain and Northern Ireland, 25 Jul 2018. https://doi.org/10.1007/978-3-319-96728-8_26

A hormone arbitration system for energy efficient foraging in robot swarms. / Wilson, James; Timmis, Jon; Tyrrell, Andy.
Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings. ed. / Maria Elena Giannaccini; Manuel Giuliani; Tareq Assaf. Springer Nature, 2018. p. 305-316 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10965 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (ISBN)

TY - GEN

T1 - A hormone arbitration system for energy efficient foraging in robot swarms

AU - Wilson, James

AU - Timmis, Jon

AU - Tyrrell, Andy

N1 - Publisher Copyright: © Springer International Publishing AG, part of Springer Nature 2018.

PY - 2018

Y1 - 2018

N2 - Keeping robots optimized for an environment can be computationally expensive, time consuming, and sometimes requires information unavailable to a robot swarm before it is assigned to a task. This paper proposes a hormone-inspired system to arbitrate the states of a foraging robot swarm. The goal of this system is to increase the energy efficiency of food collection by adapting the swarm to environmental factors during the task. These adaptations modify the amount of time the robots rest in a nest site and how likely they are to return to the nest site when avoiding an obstacle. These are both factors that previous studies have identified as having a significant effect on energy efficiency. This paper proposes that, when compared to an offline optimized system, there are a variety of environments in which the hormone system achieves an increased performance. This work shows that the use of a hormone arbitration system can extrapolate environmental features from stimuli and use these to adapt.

AB - Keeping robots optimized for an environment can be computationally expensive, time consuming, and sometimes requires information unavailable to a robot swarm before it is assigned to a task. This paper proposes a hormone-inspired system to arbitrate the states of a foraging robot swarm. The goal of this system is to increase the energy efficiency of food collection by adapting the swarm to environmental factors during the task. These adaptations modify the amount of time the robots rest in a nest site and how likely they are to return to the nest site when avoiding an obstacle. These are both factors that previous studies have identified as having a significant effect on energy efficiency. This paper proposes that, when compared to an offline optimized system, there are a variety of environments in which the hormone system achieves an increased performance. This work shows that the use of a hormone arbitration system can extrapolate environmental features from stimuli and use these to adapt.

KW - Energy efficiency

KW - Foraging

KW - Hormone arbitration

KW - Robotics

KW - Swarm

UR - https://www.scopus.com/pages/publications/85051108074

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DO - 10.1007/978-3-319-96728-8_26

M3 - Conference Proceeding (ISBN)

AN - SCOPUS:85051108074

SN - 9783319967271

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

SP - 305

EP - 316

BT - Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings

A2 - Giannaccini, Maria Elena

A2 - Giuliani, Manuel

A2 - Assaf, Tareq

PB - Springer Nature

T2 - 19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018

Y2 - 25 July 2018 through 27 July 2018

ER -

Wilson J, Timmis J, Tyrrell A. A hormone arbitration system for energy efficient foraging in robot swarms. In Giannaccini ME, Giuliani M, Assaf T, editors, Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings. Springer Nature. 2018. p. 305-316. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-96728-8_26

A hormone arbitration system for energy efficient foraging in robot swarms

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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