Rapid advance of various technologies associated with unmanned aerial vehicles (UAVs) has enabled many complex tasks to be carried out autonomously with no human intervention. UAVs can be deployed for numerous applications such as reconnaissance and surveillance, traffic monitoring, detection and containment of hazardous leakages in industries and so on. Both hovering capability and agility of quad-copter UAVs (popularly known as “drones”) comes very handy in many of these applications. However, for harnessing the true potential, it is quite obvious that drones should have a good built-in mechanism and associated guidance logic to autonomously land successfully and on the designated target with high precision.
This talk will essentially present a bio-inspired Tau-guidance approach for smooth and precision landing of drones. The approach is essentially based on the Tau theory, which has been established by studying the landing behaviour of birds, thereby generating a desired reference trajectory. Next, the differential geometric (dynamic inversion) philosophy is used to generate the necessary guidance commands to the drone. Experimental studies were performed using commercially available low-cost AR-Drone. To overcome the navigational error associated with the sensors, built-in monocular SLAM (Simultaneous Localization And Mapping) and INS (Inertial Navigation System) data were fused using a Kalman Filter, which improved the navigation accuracy remarkably. To improve the accuracy even further in the final terminal area, the landing site was supported by a range-sensing Kinect camera. With this low cost navigation solution, hardware experimental results show that autonomous landing of drones is possible with high precision using the bio-inspired Tau-theory. Details of the Tau-guidance philosophy and the results obtained will be presented in this keynote.
Professor, Dept. of Aerospace Engineering
Indian Institute of Science, Bangalore