Skip to Main Content
Search and exploration using multiple autonomous sensing platforms has been extensively studied in the fields of controls and artificial intelligence. The task of persistent surveillance is different from a coverage or exploration problem, in that the target area needs to be continuously searched, minimizing the time between visitations to the same region. This difference does not allow a straightforward application of most exploration techniques to the problem, although ideas from these methods can still be used. In this research we investigate techniques that are scalable, reliable, efficient, and robust to problem dynamics. These are tested in a multiple unmanned air vehicle (UAV) simulation environment, developed for this program. A semi-heuristic control policy for a single UAV is extended to the case of multiple UAVs using two methods. One is an extension of a reactive policy for a single UAV and the other involves allocation of sub-regions to individual UAVs for parallel exploration. An optimal assignment procedure (based on auction algorithms) has also been developed for this purpose. A comparison is made between the two approaches and a simplified optimal result. The reactive policy is found to exhibit an interesting emergent behavior as the number of UAVs becomes large. The control policy derived for a single UAV is modified to account for actual aircraft dynamics (a 3 degree-of-freedom nonlinear dynamics simulation is used for this purpose) and improvements in performance are observed. Finally, we draw conclusions about the utility and efficiency of these techniques.