The RAMI competition
RAMI competition aims at addressing Inspection and Maintenance (I&M) tasks achieved by aerial and underwater robots, offering the possibility of increasing the spatial/temporal resolution of the inspection process, improving the operation persistency and the quality of the acquired data. At the same time, these robotic domains have the potential to reduce operational costs and to increase the safety of workers, especially in dangerous areas, such as explosive atmosphere (ATEX) environments, or works at height.
However, in order to tackle the different challenges of the I&M sector, and increase the added value of using robots, it is key to increase their autonomy level. A high degree of autonomy is especially required when a direct link with an operator cannot be guaranteed, or when it is required to perform inspection tasks in a repetitive way. Autonomous decisions can also increase the robot mission performance and guarantee robot survival in hostile or cluttered environments, where it is difficult to teleoperate robots safely.
Moreover, it has been identified that the most promising applications in the I&M sector require the use of aerial and underwater robots due to the risks and costs associated to work at height or underwater inspection performed by human operators. This is why the RAMI competition will focus on these two types of robots, in order to push the state of the art in terms of autonomy navigation and performance. In particular, RAMI will focus on the Oil&Gas and renewable energy sectors, both off-shore and on-shore facilities. In these domains, commercial robots used for I&M are usually teleoperated such as the Remotely Operated Vehicles in these industries, or drones for visual inspection of large infrastructures in refineries. RAMI addresses this need by increasing, assessing and evaluating the robot autonomy in I&M tasks.
The RAMI evaluation plan available below describes the competition and its evaluation and will be continuously updated. Additional information will be posted here as well.