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Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm; for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.
9848-9854
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4

Bossens, David and Tarapore, Danesh (2021) Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation. In 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 9848-9854 . (doi:10.1109/ICRA48506.2021.9560958).

Record type: Conference or Workshop Item (Paper)

Abstract

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm; for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.

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More information

Published date: 2021

Identifiers

Local EPrints ID: 452619
URI: http://eprints.soton.ac.uk/id/eprint/452619
PURE UUID: 5f964199-09eb-4cd0-8576-126cab73b086
ORCID for David Bossens: ORCID iD orcid.org/0000-0003-1924-5756
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 11 Dec 2021 11:29
Last modified: 17 Mar 2024 03:46

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Contributors

Author: David Bossens ORCID iD
Author: Danesh Tarapore ORCID iD

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