Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Open accessAbstract
Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty, and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e., weeks, months, or years) poses many challenges. Some of these have been investigated by subdisciplines of Artificial Intelligence (AI) including navigation and mapping, perception, knowledge representation and reasoning, planning, interaction, and learning. The different subdisciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this letter, we survey and discuss AI techniques as “enablers” for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy.
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Most studies surveying AI with respect to space applications have either been reviews concerning closely related aspects of space missions or detailed topical reviews on specific AI techniques or algorithms to a specific field.
“Summarily, most studies surveying AI with respect to space applications have either been reviews concerning closely related aspects of space missions (([5], [7]) or detailed topical reviews with respect to the development and application of specific AI techniques or algorithms ([15], [11] and [16]) to a specific field ([17]).”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [7]
For autonomous agents operating in an unstructured environment, the operational environment could change over the lifetime of the agent.
“Similarly, for autonomous agents operating in an unstructured environment, the operational environment could change over the lifetime of the agent [7].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [7]
Cites
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age
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Visual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079 · see citing works
Social Robots for Long-Term Interaction: A Survey
Leite, I., Martinho, C., & Paiva, A. (2013) · International Journal of Social Robotics · doi:10.1007/s12369-013-0178-y · Cited by 871 · see citing works
A two-month field trial in an elementary school for long-term human–robot interaction
Kanda, T., Sato, R., Saiwaki, N., & Ishiguro, H. (2007) · IEEE Transactions on Robotics · doi:10.1109/tro.2007.904904 · Cited by 397 · see citing works
Experience-based navigation for long-term localisation
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Hybrid collective intelligence in a human–AI society
Peeters, M. M. M., van Diggelen, J., van den Bosch, K., Bronkhorst, A., Neerincx, M. A., Schraagen, J. M., & Raaijmakers, S. (2021) · AI & SOCIETY · doi:10.1007/s00146-020-01005-y · Cited by 229 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“…most studies surveying AI with respect to space applications have either been reviews concerning closely related aspects of space missions (([5], [7]) or detailed topical reviews with respect to the development and application of specific AI techniques or algorithms ([15], [11] and [16]) to a…”
BackgroundInfluential2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
- Authors
- Kunze, L., Hawes, N., Duckett, T., Hanheide, M., & Krajnik, T.
- Affiliations
- University of Oxford (GB)
- University of Lincoln (GB)
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2018-10
- Volume
- 3
- Issue
- 4
- Pages
- 4023-4030
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2884628842
- Cited by
- 191
- References
- 5
- Citations by year
- 2026: 14 · 2025: 18 · 2024: 27 · 2023: 28 · 2022: 38 · 2021: 29 · 2020: 21 · 2019: 15 · 2018: 1
- FWCI
- 9.29
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Robotics and Automation Letters (publishedVersion)
- Lincoln Repository (University of Lincoln) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Figshare (submittedVersion, cc-by-nc)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/M019918/1, EP/R02572X/1)
- EU (732737)
- CZ projects (17-27006Y, CZ.02.1.01/0.0/0.0/16_019/0000765)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Keywords
- autonomous systems, robot perception, knowledge representation and reasoning, planning, robot learning, robot interaction, service robotics, field robotics
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