Hawes, N, Burbridge, C, Jovan, F et al. (30 more authors) (2017) The STRANDS Project: Long-Term Autonomy in Everyday Environments. IEEE Robotics and Automation Magazine, 24 (3). pp. 146-156. ISSN 1070-9932
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. This is an author produced version of a paper published in IEEE Robotics and Automation Magazine. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Autonomous systems, Service robots, Spatiotemporal phenomena, Artificial intelligence, Cognition, Real-time systems, Mobile robots, Performance evaluation, Machine learning, Navigation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EU - European Union FP7-ICT-600623 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Dec 2016 11:40 |
Last Modified: | 05 Apr 2018 14:07 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/MRA.2016.2636359 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:109414 |