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Multi-Feature Collective Decision Making in Robot Swarms

J. Ebert,Melvin Gauci,R. Nagpal

Published 2018 in Adaptive Agents and Multi-Agent Systems

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 single-feature 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.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    Adaptive Agents and Multi-Agent Systems

  • Publication date

    2018-07-09

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.65109/woeg6231
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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