A hormone arbitration system for energy efficient foraging in robot swarms

James Wilson*, Jon Timmis, Andy Tyrrell

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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 languageEnglish
Title of host publicationTowards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings
EditorsMaria Elena Giannaccini, Manuel Giuliani, Tareq Assaf
Number of pages12
ISBN (Print)9783319967271
Publication statusPublished - 21 Jul 2018
Event19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018 - Bristol, United Kingdom
Duration: 25 Jul 201827 Jul 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10965 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2018
Country/TerritoryUnited Kingdom


  • Energy efficiency
  • Foraging
  • Hormone arbitration
  • Robotics
  • Swarm

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