Active detection of drivable surfaces in support of robotic disaster relief missions

Over the past few decades, the usage of unmanned vehicles has grown exponentially, expanding into applications such as the automation of industrial processes and automobiles. However, their utility has often been limited by operational concerns. Fully controlled unmanned vehicles require multiple human operators, while their fully autonomous counterparts lack the ability to handle the complex maneuvers necessary in natural disaster relief and/or search and rescue situations. Semi-autonomous UAVs offer a feasible compromise between the two extremes. In this scenario, an unmanned aerial vehicle (UAV) sends birds-eye images of the terrain beneath it to a computing cluster, which will identify easily traversable terrain and generate a path of least risk to an unmanned ground vehicle (UGV). If the path's risk is below a certain threshold, then the UGV will be permitted to proceed on its own. Otherwise, a human operator will be notified, so that he or she may control the UGV directly until it exits the most dangerous terrain. This paradigm allows a single operator to manage several UAVs simultaneously.