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Aggregation in robot swarms using odometry

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Abstract

Aggregation is a useful building block behaviour that can allow a swarm of robots to interact with each other or a user more easily. Previous work on swarm robot aggregation has assumed that the capabilities of individual robots are quite limited. We test whether incorporating odometry as an additional capability is helpful and make the argument that odometry is both realizable and biologically plausible. We propose an algorithm called ODOCLUST which takes inspiration from the BEECLUST algorithm but uses a continuously active odometry-based homing process to achieve more tightly packed robot aggregates more quickly than BEECLUST. Initial results in simulation suggest that high-fidelity odometry is not required in order to see these gains.

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Authors and Affiliations

  1. Department of Computer Science, Memorial University of Newfoundland, St. John’s, Canada

    Andrew Vardy

  2. Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, Canada

    Andrew Vardy

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  1. Andrew Vardy
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Correspondence to Andrew Vardy.

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Cite this article

Vardy, A. Aggregation in robot swarms using odometry. Artif Life Robotics 21, 443–450 (2016). https://doi.org/10.1007/s10015-016-0333-2

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  • DOI: https://doi.org/10.1007/s10015-016-0333-2

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