Information exchange design patterns for robot swarm foraging and their application in robot control algorithms
- 1Department of Computer Science, University of Bristol, United Kingdom
- 2Department of Electronics and Computer Science, University of Southampton, United Kingdom
In swarm robotics, a design pattern provides high-level guidelines for the implementation of a particular robot behaviour and describes its impact on swarm performance. In this paper, we explore information exchange design patterns for robot swarm foraging. First, a method for the specification of design patterns for robot swarms is proposed that builds on previous work in this field and emphasises modular behaviour design, as well as information-centric micro-macro link analysis. Next, design pattern application rules that can facilitate the pattern usage in robot control algorithms are given. A catalogue of six design patterns is then presented. The patterns are derived from an extensive list of experiments reported in the swarm robotics literature, demonstrating the capability of the proposed method to identify distinguishing features of robot behaviour and their impact on swarm performance in a wide range of swarm implementations and experimental scenarios. Each pattern features a detailed description of robot behaviour and its associated parameters, facilitated by the usage of a multi-agent modeling language, BDRML, and an account of feedback loops and forces that affect the pattern's applicability. Scenarios in which the pattern has been used are described. The consequences of each design pattern on overall swarm performance are characterised within the Information-Cost-Reward framework, that makes it possible to formally relate the way in which robots acquire, share and utilise information. Finally, the patterns are validated by demonstrating how they improved the performance of foraging e-puck swarms and how they could guide algorithm design in other scenarios.
Keywords: swarm robotics, Design patterns, foraging, Communication, information, Control algorithm, Bee-inspired, Ant-inspired
Received: 14 Mar 2018;
Accepted: 11 Apr 2018.
Edited by:Giovanni Beltrame, École Polytechnique de Montréal, Canada
Reviewed by:Sebastian Götz, Technische Universität Dresden, Germany
Richard Hanten, Universität Tübingen, Germany
Copyright: © 2018 Pitonakova, Crowder and Bullock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Lenka Pitonakova, University of Bristol, Department of Computer Science, Bristol, United Kingdom, email@example.com