  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.
