Rotorcraft Flight Dynamics and Control in Wind for Autonomous Sampling of Spatiotemporal Processes
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In recent years, there has been significant effort put into the design and use small, autonomous, multi-agent, aerial teams for a variety of military and commercial applications. In particular, small multi-rotor systems have been shown to be especially useful for carrying sensors as they have the ability to rapidly transit between locations as well as hover in place. This dissertation seeks to use multi-agent teams of autonomous rotorcraft to sample spatiotemporal fields in windy conditions. For many sampling objectives, there is the problem of how to accomplish the sampling objective in the presence of strong wind fields caused by external means or by other rotorcraft flying in close proximity. This dissertation develops several flight control strategies for both wind compensation, using nonlinear control techniques, and wind avoidance, using artificial potential-based control. To showcase the utility of teams of unmanned rotorcraft for spatiotemporal sampling, optimal algorithms are developed for two sampling objectives: (1) sampling continuous spatiotemporal fields modeled as Gaussian processes, and (2) optimal motion planning for coordinated target detection, which is an example of a discrete spatiotemporal field. All algorithms are tested in simulation and several are tested in a motion capture based experimental testbed.