The VORTEX project proposes a new approach for exploring unknown indoor environments using a fleet of autonomous drones (UAVs). We propose to define a strategy based on swarm intelligence exploiting only vision-based behaviors. The fleet will deploy as a dynamic graph self reconfiguring according to events and discovered areas. Without requiring any mapping or wireless communication, the drones will coordinate by mutual perception and communicate by visual signs. This approach will be developed with RGB and event cameras to achieve fast and low-energy navigation.Performance, swarm properties, and robustness will be evaluated by building a demonstrator extending a quadrotor prototype developed  in the consortium.

This project aims at increasing the navigation autonomy of Search-And-Rescue drones while preserving their energy autonomy. This requires improving existing Simultaneous Localization And Mapping (SLAM) and obstacle-avoidance algorithms already employed on drones. Towards this goal, we advocate the enhancement of sensing and processing tasks through low-energy hardware such as event cameras and Field-Programmable Gate Arrays (FPGAs) and to design SLAM and obstacle-avoidance algorithms in a way that capitalize deep neural network (DNNs) architectures that are adapted to this new hardware. This project will prototype such an integrated system that will be made available to the scientific community to allow further investigations on the opportunities brought by this novel concept of drone architecture.

This project aims at increasing the navigation autonomy of Search-And-Rescue drones while preserving their energy autonomy. This requires improving existing Simultaneous Localization And Mapping (SLAM) and obstacle-avoidance algorithms already employed on drones. Towards this goal, we advocate the enhancement of sensing and processing tasks through low-energy hardware such as event cameras and Field-Programmable Gate Arrays (FPGAs) and to design SLAM and obstacle-avoidance algorithms in a way that capitalize deep neural network (DNNs) architectures that are adapted to this new hardware. This project will prototype such an integrated system that will be made available to the scientific community to allow further investigations on the opportunities brought by this novel concept of drone architecture.

The project aims to develop a computer vision-based approach for functional capacity evaluation (FCE), namely, the assessment of a person’s ability to perform activities of daily living and work tasks. Such a system can be generalized to automatically evaluate physical rehabilitation exercises, notably in the context of supervision of patients who are recovering from surgeries, or for treating various musculoskeletal disorders.