Sensing, Navigation and Payload Directed Flight for Small UAV

The current research represents a first steps towards developing a decentralized network of autonomous, intelligent and inexpensive unmanned aerial vehicles (UAV), which could be used for a variety of scientific missions where measurements from a distributed network of nodes could significantly improve the prediction. First we present a ground robot and a UAV implementation of Payload Directed Flight showing how a camera sensor can be used for guidance and navigation. Secondly we present an adaptive airborne sensor network, which fuses the onboard sensors information acquired from multiple agents to monitor environmental processes over space and time. We use a fleet of small and affordable unmanned aerial vehicles (UAVs) as the carrier platform for the mobile network nodes. The network will be able be continuously reconfigure in a tridimensional space according to the circumstances (e.g., continuously evolving scientific phenomena) to optimize the location of individual nodes. As proof of concept and validation, we will apply the proposed sensing approach to monitoring diffusing volcanic plumes. This proposed work will (i) develop fast converging mathematical algorithms that can predict the volcanic plume in real time using single and multiple autonomous agents. (ii) validate the the a proposed algorithms using hardware in the loop computer based simulations and physical autonomous agents. Lastly we present a sensor fusion algorithm for UAV navigation in GPS degraded environments.