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.

In this letter we present an autonomous system intended for aerial monitoring, inspection and assistance in Search and Rescue (SAR) operations within a constrained workspace. The proposed system is designed for deployment in demanding real-world environments with extremely narrow passages only slightly wider than the aerial platform, and with limited visibility due to the absence of illumination and the presence of dust. The focus is on precise localization in an unknown environment, high robustness, safety and fast deployment without any need to install an external infrastructure such as an external computer and localization system. These are the main requirements of the targeted SAR scenarios. The performance of the proposed system was successfully evaluated in the Tunnel Circuit of the DARPA Subterranean Challenge, where the UAV cooperated with ground robots to precisely localize artifacts in a coal mine tunnel system. The challenge was unique due to the intention of the organizers to emulate the unpredictable conditions of a real SAR operation, in which there is no prior knowledge of the obstacles that will be encountered.

Autonomous Micro Aerial Vehicles (MAVs) have the potential to assist in real-life tasks involving grasping and transportation, but not before solving several difficult research challenges. In this work, we address the design, control, estimation, and planning problems for cooperative localization, grasping, and transportation of objects in challenging outdoor scenarios. We demonstrate an autonomous team of MAVs able to plan safe trajectories for manipulation of ferrous objects, while guaranteeing interrobot collision avoidance and automatically creating a map of the objects in the environment. Our solution is predominantly distributed, allowing the team to pick and transport ferrous disks to a final destination without collisions. This result is achieved using a new magnetic gripper with a novel feedback approach, enabling the detection of successful grasping. The gripper design and all the components to build a platform are clearly provided as open-source hardware for reuse by the community. Finally, the proposed solution is validated through experimental results, where difficulties include inconsistent wind, uneven terrain, and sandy conditions.

Mobile manipulators have the potential to revolutionize modern agriculture, logistics and manufacturing. In this work, we present the design of a ground-based mobile manipulator for automated structure assembly. The proposed system is capable of autonomous localization, grasping, transportation and deployment of construction material in a semi-structured environment. Special effort was put into making the system invariant to lighting changes, and not reliant on external positioning systems. Therefore, the presented system is self-contained and capable of operating in outdoor and indoor conditions alike. Finally, we present means to extend the perceptive radius of the vehicle by using it in cooperation with an autonomous drone, which provides aerial reconnaissance. Performance of the proposed system has been evaluated in a series of experiments conducted in real-world conditions.