/Using Information Invariants to Compare Swarm Algorithms and General Multi-Robot Algorithms

Using Information Invariants to Compare Swarm Algorithms and General Multi-Robot Algorithms

Gabriel Arpino, Kyle Morris, Sasanka Nagavalli and Katia Sycara
Conference Paper, 2018 IEEE International Conference on Robotics and Automation (ICRA), May, 2018

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Robotic swarms are decentralized multi-robot systems whose members use local information from proximal neighbors to execute simple reactive control laws that result in emergent collective behaviors. In contrast, members of a general multi-robot system may have access to global information, all-to-all communication or sophisticated deliberative collaboration. Some algorithms in the literature are applicable to robotic swarms. Others require the extra complexity of general multi-robot systems. Given an application domain, a system designer or supervisory operator must choose an appropriate system or algorithm respectively that will enable them to achieve their goals while satisfying mission constraints (e.g. bandwidth, energy, time limits). In this paper, we compare representative swarm and general multi-robot algorithms in two application domains — navigation and dynamic area coverage — with respect to several metrics (e.g. completion time, distance travelled). Our objective is to characterize each class of algorithms to inform offline system design decisions by engineers or online algorithm selection decisions by supervisory operators. Our contributions are (a) an empirical performance comparison of representative swarm and general multi-robot algorithms in two application domains, (b) a comparative analysis of the algorithms based on the theory of information invariants, which provides a theoretical characterization supported by our empirical results.

BibTeX Reference
@conference{Arpino-2018-104713,
author = {Gabriel Arpino and Kyle Morris and Sasanka Nagavalli and Katia Sycara},
title = {Using Information Invariants to Compare Swarm Algorithms and General Multi-Robot Algorithms},
booktitle = {2018 IEEE International Conference on Robotics and Automation (ICRA)},
year = {2018},
month = {May},
}
2018-02-27T12:57:39+00:00