Artificial Intelligence for Long-Term Robot Autonomy: A Survey
Lars Kunze, Nick Hawes, Tom Duckett, Marc Hanheide, Tomás Krajník
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 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.
BibTeX
@inproceedings{ral2018_artificialintell,
title = {Artificial Intelligence for Long-Term Robot Autonomy: A Survey},
author = {Lars Kunze and Nick Hawes and Tom Duckett and Marc Hanheide and Tomás Krajník},
booktitle = {RA-L 2018},
year = {2018}
}