@inproceedings{5a6db4fe569c4416a5bb464d8110bde7,
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",
language = "English",
isbn = "9783319967271",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "305--316",
editor = "Giannaccini, {Maria Elena} and Manuel Giuliani and Tareq Assaf",
booktitle = "Towards Autonomous Robotic Systems - 19th Annual Conference, TAROS 2018, Proceedings",
address = "Switzerland",
}
