Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results


Interest in control of multiple autonomous vehicles continues to grow for applications like weather monitoring, geographical mapping fauna surveys, and further-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area wants to be continuously surveyed, minimizing the time between visitations to the identical region. This distinction from one-time coverage will not enable a straightforward application of most exploration techniques to the matter, though concepts from these methods will still be used. The aerial vehicle dynamic and endurance constraints add further complexity to the autonomous management problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for prime-level management, that are scalable, reliable, efficient, and strong to problem dynamics. Next, we tend to suggest a modification to the control policy to account for aircraft dynamic constraints. We conjointly devise a health monitoring policy and a control policy modification to improve performance beneath endurance constraints. The Vehicle Swarm Technology Laboratory—a hardware testbed developed at Boeing Analysis and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles—is then described, and these management policies are tested in a very realistic scenario.

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