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Resilient robot teams: a review integrating decentralised control, change-detection, and learning

Resilient robot teams: a review integrating decentralised control, change-detection, and learning
Resilient robot teams: a review integrating decentralised control, change-detection, and learning
Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.

Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.

Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4
Bossens, David
633a4d28-2e59-4343-98fe-283082ba1873
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Tarapore, Danesh
fe8ec8ae-1fad-4726-abef-84b538542ee4

Bossens, David, Ramchurn, Sarvapali and Tarapore, Danesh (2022) Resilient robot teams: a review integrating decentralised control, change-detection, and learning. Current Robotics Reports. (doi:10.1007/s43154-022-00079-4).

Record type: Review

Abstract

Purpose of review: This paper reviews opportunities and challenges for decentralised control, change-detection, and learning in the context of resilient robot teams.

Recent findings: Exogenous fault detection methods can provide a generic detection or a specific diagnosis with a recovery solution. Robot teams can perform active and distributed sensing for detecting changes in the environment, including identifying and tracking dynamic anomalies, as well as collaboratively mapping dynamic environments. Resilient methods for decentralised control have been developed in learning perception-action-communication loops, multi-agent reinforcement learning, embodied evolution, offline evolution with online adaptation, explicit task allocation, and stigmergy in swarm robotics.

Summary: Remaining challenges for resilient robot teams are integrating change-detection and trial-and-error learning methods, obtaining reliable performance evaluations under constrained evaluation time, improving the safety of resilient robot teams, theoretical results demonstrating rapid adaptation to given environmental perturbations, and designing realistic and compelling case studies.

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More information

Accepted/In Press date: 2022
e-pub ahead of print date: 13 June 2022
Published date: 13 June 2022

Identifiers

Local EPrints ID: 457101
URI: http://eprints.soton.ac.uk/id/eprint/457101
PURE UUID: f3b320b2-9c50-4b9e-9e4f-63ffff3442e7
ORCID for David Bossens: ORCID iD orcid.org/0000-0003-1924-5756
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Danesh Tarapore: ORCID iD orcid.org/0000-0002-3226-6861

Catalogue record

Date deposited: 24 May 2022 16:36
Last modified: 22 Jul 2022 01:56

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Contributors

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

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