Artificial Intelligence for Long-Term Robot Autonomy: A Survey

Abstract

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.

Lindsey the Tour Guide Robot - Usage Patterns in a Museum Long-Term Deployment

“…A survey on long-term interaction between users and robots [5] raises the issue that memory and adaptation remains nearly unexplored in the field. Similarly, [6] explores the state of the art on Artificial Intelligence (AI) techniques for LTA asserting that a major future challenge is that of integrating human interactions in the robot system to allow improving its knowledge in unforeseen situations. With the software architecture described in this work, we lay the foundations for a robot framework that allows exploiting the human feedback during interaction in order to optimize its social abilities.…”

Time-varying Pedestrian Flow Models for Service Robots

“…In the mapping and localisation community, the effects of the environment dynamics were studied mainly from the perspective of localisation reliability, which gradually deteriorates if the environment changes are neglected [18]. To deal with the changes, some approaches proposed to gradually adapt the maps by incrementally replacing their elements [4], by remapping the areas which changed [3], or by allowing multiple representations of the same location [2], by identifying the invariant characteristics of the world [19] or by general schemes to incrementally update continuous maps [20] using Bayesian techniques.…”

Adaptive Fuzzy Mediation for Multimodal Control of Mobile Robots in Navigation-Based Tasks

“…Autonomous mobile robots and vehicles are getting out of structured and laboratory-type environments into more dynamic environments [1][2][3][4][5]. Due to the uncertain nature of these environments, a robot's behavior needs to be adaptive in order to work properly without human intervention or with minimal human intervention.…”

“…Due to the uncertain nature of these environments, a robot's behavior needs to be adaptive in order to work properly without human intervention or with minimal human intervention. These environments range from air-, water-and ground-based ones to space-based ones [1,4]. The paper focuses on ground-based autonomous mobile robots, but some of its conclusions could be extended to other domains (like air and water).…”

“…When considering the application of autonomous mobile robots, usually two main concerns are of interest: long-term autonomy and safety [1], especially in light of human-robot cooperation. This has been highlighted by the fact that several recent EU-, USA-, and Japan-based projects consider safety as the main challenge in human-robot interaction [2].…”