The STRANDS Project: Long-Term Autonomy in Everyday Environments

Hawes, Nick, Burbridge, Christopher, Jovan, Ferdian, Kunze, Lars, Lacerda, Bruno, Mudrova, Lenka, Young, Jay, Wyatt, Jeremy, Hebesberger, Denise, Kortner, Tobias, Ambrus, Rares, Bore, Nils, Folkesson, John, Jensfelt, Patric, Beyer, Lucas, Hermans, Alexander, Leibe, Bastian, Aldoma, Aitor, Faulhammer, Thomas, Zillich, Michael, Vincze, Markus, Chinellato, Eris, Al-Omari, Muhannad, Duckworth, Paul, Gatsoulis, Yiannis, Hogg, David C., Cohn, Anthony G., Dondrup, Christian, Pulido Fentanes, Jaime, Krajnik, Tomas, Santos, Joao M., Duckett, Tom and Hanheide, Marc (2017) The STRANDS Project: Long-Term Autonomy in Everyday Environments. IEEE Robotics & Automation Magazine, 24 (3). pp. 146-156. ISSN 1070-9932

Full content URL: https://doi.org/10.1109/MRA.2016.2636359

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The STRANDS Project: Long-Term Autonomy in Everyday Environments
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Abstract

Thanks to the efforts of the robotics and autonomous systems community, the myriad applications and capacities of robots are ever increasing. There is increasing demand from end users for autonomous service robots that can operate in real environments for extended periods. In the Spatiotemporal Representations and Activities for Cognitive Control in Long-Term Scenarios (STRANDS) project (http://strandsproject.eu), we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots and deploying these systems for long-term installations in security and care environments. Our robots have been operational for a combined duration of 104 days over four deployments, autonomously performing end-user-defined tasks and traversing 116 km in the process. In this article, we describe the approach we used to enable long-term autonomous operation in everyday environments and how our robots are able to use their long run times to improve their own performance.

Keywords:Autonomous systems, Service robots, Spatiotemporal phenomena, Artificial intelligence, Cognition, Real-time systems, Mobile robots, Performance evaluation, Machine learning, Navigation
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
ID Code:40526
Deposited On:07 Apr 2020 14:58

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