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

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.

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Identifiers

ORCID for Sarvapali Ramchurn: orcid.org/0000-0001-9686-4302

ORCID for Sebastian Stein: orcid.org/0000-0003-2858-8857

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