Automatically evolving a general controller for robot swarms

John Ericksen, Melanie Moses, Stephanie Forrest

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

3 Citations (Scopus)

Abstract

Controller design is an important problem for swarm robotics. Although many successful controllers have been proposed, most are hand-coded, sometimes using adaptive mechanisms to tune parameters of a manually designed algorithm. These solutions are generally tailored to specific environments, or problem instances, and often fail to scale well as swarm size is increased. This paper focuses on the problem of swarm foraging, proposing an automated method for designing scalable controllers that can perform effectively in multiple foraging environments. We use Neuroevolution of Augmented Topologies (NEAT) to design a neural network controller for a swarm of homogeneous robots. Our system, called NeatFA (NEAT Foraging Algorithm), is compared to existing swarm foraging algorithms, the Central Place Foraging Algorithm (CPFA), and the Distributed Deterministic Spiral Algorithm (DDSA). We find that NEAT produces controllers with performance that is comparable to both the CPFA and the DDSA. This is significant because the controller design was evolved automatically without preprogramming high-level behaviors or movements. The evolved neural network controller responds to sensed inputs and produces movements and actions that lead to effective collective foraging by the swarm. We find that the NeatFA controller performs comparably or outperforms the DDSA and CPFA for large swarm sizes. Finally, we show that a NeatFA general controller, when evolved for multiple environments but smaller swarm sizes, scales successfully to larger swarm sizes.

Original languageEnglish (US)
Title of host publication2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-8
Number of pages8
Volume2018-January
ISBN (Electronic)9781538627259
DOIs
StatePublished - Feb 2 2018
Event2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Honolulu, United States
Duration: Nov 27 2017Dec 1 2017

Other

Other2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
CountryUnited States
CityHonolulu
Period11/27/1712/1/17

Fingerprint

Foraging
Swarm
Robot
Robots
Controller
Controllers
Neuroevolution
Topology
Controller Design
Neural Networks
Swarm Robotics
Neural networks
Robotics

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization

Cite this

Ericksen, J., Moses, M., & Forrest, S. (2018). Automatically evolving a general controller for robot swarms. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings (Vol. 2018-January, pp. 1-8). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2017.8285399

Automatically evolving a general controller for robot swarms. / Ericksen, John; Moses, Melanie; Forrest, Stephanie.

2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-8.

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

Ericksen, J, Moses, M & Forrest, S 2018, Automatically evolving a general controller for robot swarms. in 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 1-8, 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017, Honolulu, United States, 11/27/17. https://doi.org/10.1109/SSCI.2017.8285399
Ericksen J, Moses M, Forrest S. Automatically evolving a general controller for robot swarms. In 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-8 https://doi.org/10.1109/SSCI.2017.8285399
Ericksen, John ; Moses, Melanie ; Forrest, Stephanie. / Automatically evolving a general controller for robot swarms. 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-8
@inproceedings{868673d0244b41859ffbd480e7cde6da,
title = "Automatically evolving a general controller for robot swarms",
abstract = "Controller design is an important problem for swarm robotics. Although many successful controllers have been proposed, most are hand-coded, sometimes using adaptive mechanisms to tune parameters of a manually designed algorithm. These solutions are generally tailored to specific environments, or problem instances, and often fail to scale well as swarm size is increased. This paper focuses on the problem of swarm foraging, proposing an automated method for designing scalable controllers that can perform effectively in multiple foraging environments. We use Neuroevolution of Augmented Topologies (NEAT) to design a neural network controller for a swarm of homogeneous robots. Our system, called NeatFA (NEAT Foraging Algorithm), is compared to existing swarm foraging algorithms, the Central Place Foraging Algorithm (CPFA), and the Distributed Deterministic Spiral Algorithm (DDSA). We find that NEAT produces controllers with performance that is comparable to both the CPFA and the DDSA. This is significant because the controller design was evolved automatically without preprogramming high-level behaviors or movements. The evolved neural network controller responds to sensed inputs and produces movements and actions that lead to effective collective foraging by the swarm. We find that the NeatFA controller performs comparably or outperforms the DDSA and CPFA for large swarm sizes. Finally, we show that a NeatFA general controller, when evolved for multiple environments but smaller swarm sizes, scales successfully to larger swarm sizes.",
author = "John Ericksen and Melanie Moses and Stephanie Forrest",
year = "2018",
month = "2",
day = "2",
doi = "10.1109/SSCI.2017.8285399",
language = "English (US)",
volume = "2018-January",
pages = "1--8",
booktitle = "2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Automatically evolving a general controller for robot swarms

AU - Ericksen, John

AU - Moses, Melanie

AU - Forrest, Stephanie

PY - 2018/2/2

Y1 - 2018/2/2

N2 - Controller design is an important problem for swarm robotics. Although many successful controllers have been proposed, most are hand-coded, sometimes using adaptive mechanisms to tune parameters of a manually designed algorithm. These solutions are generally tailored to specific environments, or problem instances, and often fail to scale well as swarm size is increased. This paper focuses on the problem of swarm foraging, proposing an automated method for designing scalable controllers that can perform effectively in multiple foraging environments. We use Neuroevolution of Augmented Topologies (NEAT) to design a neural network controller for a swarm of homogeneous robots. Our system, called NeatFA (NEAT Foraging Algorithm), is compared to existing swarm foraging algorithms, the Central Place Foraging Algorithm (CPFA), and the Distributed Deterministic Spiral Algorithm (DDSA). We find that NEAT produces controllers with performance that is comparable to both the CPFA and the DDSA. This is significant because the controller design was evolved automatically without preprogramming high-level behaviors or movements. The evolved neural network controller responds to sensed inputs and produces movements and actions that lead to effective collective foraging by the swarm. We find that the NeatFA controller performs comparably or outperforms the DDSA and CPFA for large swarm sizes. Finally, we show that a NeatFA general controller, when evolved for multiple environments but smaller swarm sizes, scales successfully to larger swarm sizes.

AB - Controller design is an important problem for swarm robotics. Although many successful controllers have been proposed, most are hand-coded, sometimes using adaptive mechanisms to tune parameters of a manually designed algorithm. These solutions are generally tailored to specific environments, or problem instances, and often fail to scale well as swarm size is increased. This paper focuses on the problem of swarm foraging, proposing an automated method for designing scalable controllers that can perform effectively in multiple foraging environments. We use Neuroevolution of Augmented Topologies (NEAT) to design a neural network controller for a swarm of homogeneous robots. Our system, called NeatFA (NEAT Foraging Algorithm), is compared to existing swarm foraging algorithms, the Central Place Foraging Algorithm (CPFA), and the Distributed Deterministic Spiral Algorithm (DDSA). We find that NEAT produces controllers with performance that is comparable to both the CPFA and the DDSA. This is significant because the controller design was evolved automatically without preprogramming high-level behaviors or movements. The evolved neural network controller responds to sensed inputs and produces movements and actions that lead to effective collective foraging by the swarm. We find that the NeatFA controller performs comparably or outperforms the DDSA and CPFA for large swarm sizes. Finally, we show that a NeatFA general controller, when evolved for multiple environments but smaller swarm sizes, scales successfully to larger swarm sizes.

UR - http://www.scopus.com/inward/record.url?scp=85046157142&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85046157142&partnerID=8YFLogxK

U2 - 10.1109/SSCI.2017.8285399

DO - 10.1109/SSCI.2017.8285399

M3 - Conference contribution

VL - 2018-January

SP - 1

EP - 8

BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -