@inproceedings{9be02719161349dc99485e24a0595872,
title = "A Hormone-Inspired Arbitration System for Self Identifying Abilities Amongst A Heterogeneous Robot Swarm",
abstract = "Current exploration of adaptation in robot swarms requires the swarm or individuals within that swarm to have knowledge of their own capabilities. Across long term use a swarms understanding of its capabilities may become inaccurate due to wear or faults in the system. In addition to this, systems capable of self designing morphologies are becoming increasingly feasible. In these self designing examples it would be impossible to have accurate knowledge of capability before executing a task for the first time. We propose an arbitration system that requires no explicit knowledge of capability but instead uses hormone-inspired values to decide on an environmental preference. The robots in the swarm differ by wheel type and thus how quickly they are able to move across terrain. The goal of this system is to allow robots to identify their strengths within a swarm and allocate themselves to areas of an environment with a floor type that suits their ability. This work shows that the use of a hormone-inspired arbitration system can extrapolate robot capability and adapt the systems preference of terrain to suit said capability.",
author = "James Wilson and Jon Timmis and Andy Tyrrell",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 ; Conference date: 18-11-2018 Through 21-11-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/SSCI.2018.8628754",
language = "English",
series = "Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018",
publisher = "IEEE Press",
pages = "843--850",
editor = "Suresh Sundaram",
booktitle = "Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018",
address = "United States of America",
}