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.

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.

In this article, we present and evaluate a system, which allows a mobile robot to autonomously detect, model, and re-recognize objects in everyday environments. While other systems have demonstrated one of these elements, to our knowledge, we present the first system, which is capable of doing all of these things, all without human interaction, in normal indoor scenes. Our system detects objects to learn by modeling the static part of the environment and extracting dynamic elements. It then creates and executes a view plan around a dynamic element to gather additional views for learning. Finally, these views are fused to create an object model. The performance of the system is evaluated on publicly available datasets as well as on data collected by the robot in both controlled and uncontrolled scenarios.

We present a method to specify tasks and synthesise cost-optimal policies for Markov decision processes using co-safe linear temporal logic. Our approach incorporates a dynamic task handling procedure which allows for the addition of new tasks during execution and provides the ability to re-plan an optimal policy on-the-fly. This new policy minimises the cost to satisfy the conjunction of the current tasks and the new one, taking into account how much of the current tasks has already been executed. We illustrate our approach by applying it to motion planning for a mobile service robot.

In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.