Innovative Collaboration for Unmanned Aerial and Dissimilar Systems

In service to the Navys stated goal to enable more complex missions with dissimilar collaborative systems, Near Earth Autonomy and its partner Carnegie Mellon University propose to leverage recent advances in miniaturization and sensing, autonomous landing and collision avoidance, and semantic mapping to increase the overall capability of unmanned systems. We will focus our attention on two operational scenariosfirst, a ground vehicle autonomously maps a landing zone, annotating it for geometric and semantic hazards such that an approaching air vehicle that is informed of the hazards can descend quickly without loitering to map the site itself. Second, we will consider the case of an air vehicle that helps a ground vehicle navigate without the use of GPS by providing a global aerial map as a reference. We will focus on designing a collaborative system, show a proof of principle demonstration, and produce and quantitative evaluation of our solution.