Multi-feature collective decision making in robot swarms

Julia T. Ebert, Melvin Gauci, Radhika Nagpal

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

21 Scopus citations

Abstract

Collective decision making has been studied extensively in the fields of multi-agent systems and swarm robotics, inspired by its pervasiveness in biological systems such as honeybee and ant colonies. However, most previous research has focused on collective decision making on a single feature. In this work, we introduce and investigate the multi-feature collective decision making problem, where a collective must decide on multiple binary features simultaneously, given no a priori information about their relative difficulties. Each agent may only estimate one feature at any given time, but the agents can locally communicate their noisy estimates to arrive at a decision. We demonstrate a decentralized algorithm for singlefeature decision making and a dynamic task allocation strategy that allows the agents to lock in decisions on multiple features in finite time. We validate our approach using simulated and physical Kilobot robots. Our results show that a collective can correctly classify a multi-feature environment, even if presented with pathological initial agent-to-feature allocations.

Original languageEnglish (US)
Title of host publication17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1711-1719
Number of pages9
ISBN (Print)9781510868083
StatePublished - 2018
Externally publishedYes
Event17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 - Stockholm, Sweden
Duration: Jul 10 2018Jul 15 2018

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018
Country/TerritorySweden
CityStockholm
Period7/10/187/15/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

Keywords

  • Collective decision making
  • Heterogeneous collectives
  • Kilobots
  • Task switching

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