  We propose a novel probabilistically robust controller for the guidance of an
unmanned aerial vehicle (UAV) in coverage planning missions, which can
simultaneously optimize both the UAV's motion, and camera control inputs for
the 3D coverage of a given object of interest. Specifically, the coverage
planning problem is formulated in this work as an optimal control problem with
logical constraints to enable the UAV agent to jointly: a) select a series of
discrete camera field-of-view states which satisfy a set of coverage
constraints, and b) optimize its motion control inputs according to a specified
mission objective. We show how this hybrid optimal control problem can be
solved with standard optimization tools by converting the logical expressions
in the constraints into equality/inequality constraints involving only
continuous variables. Finally, probabilistic robustness is achieved by
integrating the unscented transformation to the proposed controller, thus
enabling the design of robust open-loop coverage plans which take into account
the future posterior distribution of the UAV's state inside the planning
horizon.
