Periodic Reporting for period 1 - REMARO (Reliable AI for Marine Robotics)
Période du rapport: 2020-12-01 au 2022-11-30
To advance autonomy and reliability of underwater robot applications, Europe needs research-level experts in Artificial Intelligence for marine robotics, experts who master reliability methods for functional safety engineering and can communicate with safety certification bodies, end-users, and entrepreneurs. The European Training Network on Reliable AI for Marine Robotics REMARO trains 15 early stage researchers (ESRs) who will push forward the underwater robotics industry in Europe and grow the European blue economy. The overall goal and objectives of REMARO focuses on reliable AI—the robotic brain—for autonomous robots performing inspection, surveying, and maintenance tasks under water. REMARO ESRs work at the intersection of two disciplines: AI and software reliability. For this purpose, the network consists of 11 beneficiaries and 3 partner organizations that excel both in scientific and industrial leadership in these disciplines. The ESRs are exposed to both the robotics AI body of knowledge (e.g. AI methods for vision, localization, knowledge representation, planning, and control), and software reliability methods and tools (e.g. design patterns, testing and estimation methods, fault tolerance and reliability analysis, verification methods for safe control).
The REMARO network is operating under full capacity. The ESRs are studying methods for building and assessing robust AI components using case studies and tasks from marine robotics. The network has organized three PhD schools spanning the fields of AI, Control Theory, Kinematics, Computer Vision, Robotics, Sotware Engineering, Formal Methods and Theoretical Computer Science. These trainings give a remarkably solid and expert foundation for the young researchers to impact the field of marine robotics in Europe.
In the first two years of operation, the ESRs of the network have produced methodological and conceptual results. Working with marine robotics and inspection tasks resulted in the creation of benchmark datasets for underwater computer vision and 3D scene reconstructions. Furthermore, we deployed an underwater drone in the canals of Copenhagen to gather real underwater data while imitating wall inspection missions. These new data sets will complement the closed data of industrial partners and will allow for open dissemination of results. The datasets can be used within and outside the network, to boost research on underwater perception.
Several test beds reflecting underwater inspection scenarios have been created as well, building upon the Robot Operating System (ROS), Gazebo simulator, and the uuv_simulator package for physics simulation. Visualization is being addressed using the engines of gazebo, airsim, Blender, and Unreal.
Besides perception, the REMARO ESRs continued to work on cognitive architectures for underwater robots using technologies like meta-control, ontologies, and planning.
While perception allows the underwater robots to "see", a cognitive architecture allows robots to make decisions, where to move and what actions to perform.
In the first two years of operation, the ESRs of the network have produced methodological and conceptual results. Working with marine robotics and inspection tasks resulted in the creation of benchmark datasets for underwater computer vision and 3D scene reconstructions. Furthermore, we deployed an underwater drone in the canals of Copenhagen to gather real underwater data while imitating wall inspection missions. These new data sets will complement the closed data of industrial partners and will allow for open dissemination of results. The datasets can be used within and outside the network, to boost research on underwater perception.
Several test beds reflecting underwater inspection scenarios have been created as well, building upon the Robot Operating System (ROS), Gazebo simulator, and the uuv_simulator package for physics simulation. Visualization is being addressed using the engines of gazebo, airsim, Blender, and Unreal.
Besides perception, the REMARO ESRs continued to work on cognitive architectures for underwater robots using technologies like meta-control, ontologies, and planning.
While perception allows the underwater robots to "see", a cognitive architecture allows robots to make decisions, where to move and what actions to perform.
A crucial aspect of the REMARO work is developing methods for assessing the reliability of AI components. Here the ESRs are developing test data generation methods using adversarial neural networks, online testing methods based on reachability analysis and model-based testing, as well as verification methods based on technologies like model-checking, ontology reasoners, and SMT solving.An intensive secondment period, involving companies like EIVA, ROSEN Group, Kraken GmBH, OceanScan MST, and DNV-GL enables the ESRs to access industrial datasets, problem specifications, and vehicles.
REMARO boasts three published papers, three accepted papers, and two submitted papers already. What is more important is that most research work exploits the diversity of the ESRs - multiple teams combine skills from robotics, artificial intelligence, and formal methods to achieve research breakthroughs. The network has organized a workshop at IROS 2021, has participated in the EU Robotics meeting, and maintains a healthy social media presence. Its training events are designed to involve industrial participation among the audience and as lecturers.
The expected results of the project involve several testing frameworks for perception components, verification methods for planning and failure recovery components, methods for increase of reliability through censor fusion, and methods for uncertainty assessment for machine learning components. As experience with artificial intelligence does not directly translate between domains, it is crucial that all these methods will be evaluated on submarine robotics applications and scenarios. This will enable a much faster adoption of AI in this sector, which in turn will allow new scientific studies of the oceans, and new safety and maintenance methods for underwater infrastructure.
REMARO boasts three published papers, three accepted papers, and two submitted papers already. What is more important is that most research work exploits the diversity of the ESRs - multiple teams combine skills from robotics, artificial intelligence, and formal methods to achieve research breakthroughs. The network has organized a workshop at IROS 2021, has participated in the EU Robotics meeting, and maintains a healthy social media presence. Its training events are designed to involve industrial participation among the audience and as lecturers.
The expected results of the project involve several testing frameworks for perception components, verification methods for planning and failure recovery components, methods for increase of reliability through censor fusion, and methods for uncertainty assessment for machine learning components. As experience with artificial intelligence does not directly translate between domains, it is crucial that all these methods will be evaluated on submarine robotics applications and scenarios. This will enable a much faster adoption of AI in this sector, which in turn will allow new scientific studies of the oceans, and new safety and maintenance methods for underwater infrastructure.