Artificial Intelligence for Long-Term Robot Autonomy: A Survey
July 13, 2018 Β· The Cartographer Β· π IEEE Robotics and Automation Letters
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"Title-pattern auto-detect: Artificial Intelligence for Long-Term Robot Autonomy: A Survey"
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Authors
Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, TomΓ‘Ε‘ KrajnΓk
arXiv ID
1807.05196
Category
cs.RO: Robotics
Cross-listed
cs.AI
Citations
184
Venue
IEEE Robotics and Automation Letters
Last Checked
8 days ago
Abstract
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 sub-disciplines of Artificial Intelligence (AI) including navigation & mapping, perception, knowledge representation & reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, 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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