ROI

Inspection of industrial facilities is essential in order to maintain them in proper condition, maximize their lifespan, improve production quality, boost profits and reduce their negative impact on the environment. These tasks are still primarily undertaken by humans, occasionally assisted by remotely-controlled robotic systems, as an inspection mission involves complex navigation, scene reasoning, and interaction tasks, alongside correlation with prior data and expert knowledge. This, however, entails that such missions are often very time-consuming, thus leading to significant downtime for the industry and reduced production, while human inspectors face hazardous working conditions. Robotics have for years tried to assist and partially have done so: it is now not uncommon to see a quadrotor - for example - being piloted by an operator on-site and used to capture images. Yet, this merely means that human inspectors have better tools for their job, while the bulk of the activities remains extremely manual. A viable and appealing alternative is to automate this process in an end-to-end manner, a task only possible if robots act fully autonomously in undertaking all the complexities of inspection missions. This includes traversing and accessing extremely challenging environments, reasoning about the objects in their environment, representing the environment in a manner that allows comparison to previous inspections, and performing large-scale missions in a short amount of time thus reducing downtime. Responding to this task, this project aims to research and develop the breakthroughs necessary towards collaborative robotic teams of walking, roving and flying systems-of-systems that go beyond mapping the environment and facilitate semantic reasoning and characterization, and semantically-driven inspection path planning, with full autonomy. To exploit the technologies within a sector of priority, we focus on the energy and oil & gas industries in Norway and the world.