The MURAB project has the ambition to revolutionise the way cancer screening and muscle diseases are researched for patients and has the potential to save lives by early detection and treatment. The project intends to create a new paradigm in which, the precision of great medical imaging modalities like MRI and Ultrasound are combined with the precision of robotics in order to target the right place in the body. This will be achieved by identifying a target using Magnetic Resonance Imaging (MRI) and then use a robot with an ultrasound (US) probe to match the images and navigate to the right location. This will be done thanks to a new innovative technique, which will be developed in the project and called Tissue Active Slam (TAS) which will use different techniques and modalities, like elastography, in order to cope with the deformation of the tissues. Such a procedure has the potential to drastically improve the clinical workflow and save lives by ensuring an exact targeting of (small) lesions, which are visible under MRI and not under US. Technologies developed within MURAB also have the potential to improve other clinical procedures. Clinically, two applications will be targeted and validated in the project: breast cancer diagnostics (MUW and ZGT) and muscle disease diagnostics (UMCN). Considering the potential for the market, industrial partners are involved with expertise in the delivery of safe robotics components and applications (KUKA), as well as with great knowledge and ambition in pushing innovation to the medical market (SIEMENS).

While online grocery stores are expanding, supermarkets continue to provide customers with the sensory experience of choosing goods while walking between display shelves. Therefore, retail and logistics companies are concerned with making the shopping experience more comfortable and exciting while, at the same time, using technology to reduce costs and improve efficiency. The REFILLS project aims at developing robotic systems able to address the in-store logistics needs of the retail market. Three scenarios building on top of each other are considered. In the 1st scenario, mobile robots inspect shelves and generate semantic environment maps for layout identification and store monitoring. The 2nd scenario employs robot arms for autonomous sorting of articles in the backroom and for assisting human clerks with shelf refilling in the shop. In the 3rd scenario, the autonomy of the robot is strengthened, resulting in a robotic clerk capable of manipulating articles varying in shape, surface, fragility, stiffness and weight, and refill shelves without human intervention. These scenarios trigger a number of research and technology challenges that are tackled within REFILLS. Information on the supermarket articles is exploited to create powerful knowledge bases, which are used by the robots to identify shelves, recognize missing or misplaced articles, handling them and navigate the shop. Reasoning allows robots to cope with changing task requirements and contexts, and perception-guided reactive control makes them robust to execution errors and uncertainty. A modular approach is adopted for the design of cost-efficient robotic units. The work plan will generate exploitable results through three integration and evaluation phases. A final demonstration will take place at a real retail store. In sum, REFILLS is committed to generating wide impact in the retail market domain and beyond through the development of efficient logistics solutions for professional use in supermarkets.