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Crowd robotics: real-time crowdsourcing for crowd controlled robotic agents

Crowd robotics: real-time crowdsourcing for crowd controlled robotic agents
Crowd robotics: real-time crowdsourcing for crowd controlled robotic agents
Major man-made and natural disasters have a significant and long-lasting economic and social impact on countries around the world. The response effort in the first few hours of the aftermath of the disaster is crucial to saving lives and minimising damage to infrastructure. In these conditions, emergency response organisations on the ground face a major challenge in trying to understand what is happening, and where the casualties are. Crowdsourcing is often used in disasters to analyse the masses of data generated, and report areas of importance to the first responders, but the results are to slow to inform immediate decision making. This thesis describes techniques for utilising real-time crowdsourcing to analyse the disaster data in real-time. We utilise this real-time analysis to influence or control robotic search agents, unmanned aerial vehicles, that are increasingly being used in disaster scenarios. We investigate methods for reliably and promptly aggregating real-time crowd input, for two different crowd robotic applications. First, direct control, used for directing a robotic search and rescue agent around a complicated and dynamic environment. Second, real-time locational sensing, used for rapidly mapping disasters and to augment a pilot's video feed, such that they can make more informed decisions on the fly, but could be used to inform a higher artificial intelligence process to direct a robotic agent. We describe two systems, CrowdDrone and CrowdAR, that use state-of-the-art methods for human-intelligent control and sensing for crowd robotics.
University of Southampton
Salisbury, Elliot
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Salisbury, Elliot
3573f86f-8305-4850-b911-41fdf896e946
Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Stein, Sebastian
cb2325e7-5e63-475e-8a69-9db2dfbdb00b

Salisbury, Elliot (2018) Crowd robotics: real-time crowdsourcing for crowd controlled robotic agents. University of Southampton, Doctoral Thesis, 122pp.

Record type: Thesis (Doctoral)

Abstract

Major man-made and natural disasters have a significant and long-lasting economic and social impact on countries around the world. The response effort in the first few hours of the aftermath of the disaster is crucial to saving lives and minimising damage to infrastructure. In these conditions, emergency response organisations on the ground face a major challenge in trying to understand what is happening, and where the casualties are. Crowdsourcing is often used in disasters to analyse the masses of data generated, and report areas of importance to the first responders, but the results are to slow to inform immediate decision making. This thesis describes techniques for utilising real-time crowdsourcing to analyse the disaster data in real-time. We utilise this real-time analysis to influence or control robotic search agents, unmanned aerial vehicles, that are increasingly being used in disaster scenarios. We investigate methods for reliably and promptly aggregating real-time crowd input, for two different crowd robotic applications. First, direct control, used for directing a robotic search and rescue agent around a complicated and dynamic environment. Second, real-time locational sensing, used for rapidly mapping disasters and to augment a pilot's video feed, such that they can make more informed decisions on the fly, but could be used to inform a higher artificial intelligence process to direct a robotic agent. We describe two systems, CrowdDrone and CrowdAR, that use state-of-the-art methods for human-intelligent control and sensing for crowd robotics.

Text
Final Thesis - Version of Record
Available under License University of Southampton Thesis Licence.
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Published date: April 2018

Identifiers

Local EPrints ID: 423477
URI: http://eprints.soton.ac.uk/id/eprint/423477
PURE UUID: 47c40062-60a9-45de-980b-608c31063619
ORCID for Sarvapali Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Sebastian Stein: ORCID iD orcid.org/0000-0003-2858-8857

Catalogue record

Date deposited: 24 Sep 2018 16:30
Last modified: 18 Feb 2021 17:11

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Contributors

Author: Elliot Salisbury
Thesis advisor: Sarvapali Ramchurn ORCID iD
Thesis advisor: Sebastian Stein ORCID iD

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