Self-localised swarms of micro aerial vehicles in real-world scenarios
Real-world (outdoor and indoor) deployment of large teams of Micro Aerial Vehicles (MAVs) without possibility precise external localisation, such as GNSS and motion capture systems, is a challenging task. In this talk, the latest results of endeavour towards fully autonomous compact flocks of MAVs with onboard artificial intelligence will be presented. Stabilisation, control, and motion planning techniques for steering swarms of unmanned MAVs will be discussed with the main focus given to biologically inspired techniques that integrate swarming abilities of individual particles with a Model Predictive Control (MPC) methodology respecting the fast dynamics of unmanned quadrotors. The proposed swarming system relies on a unique sensor of relative localisation of group members using UV-based active markers, which provide a high robustness and reliability in real-world conditions. Three application scenarios will be discussed and demonstrated in hardware experiments of cooperating MAVs:
- Compact MAV swarms in high obstacle density areas, such as forest
- Indoor documentation of large historical objects (cathedrals) by cooperating MAVs, where one MAV carries a camera and its neighbors carry light sources
- Fully autonomous flying robot, Eagle.one, hunting for unauthorised drones that are relatively localised by its onboard sensors.