  Drones can provide a minimally-constrained adapting camera view to support
robot telemanipulation. Furthermore, the drone view can be automated to reduce
the burden on the operator during teleoperation. However, existing approaches
do not focus on two important aspects of using a drone as an automated view
provider. The first is how the drone should select from a range of quality
viewpoints within the workspace (e.g., opposite sides of an object). The second
is how to compensate for unavoidable drone pose uncertainty in determining the
viewpoint. In this paper, we provide a nonlinear optimization method that
yields effective and adaptive drone viewpoints for telemanipulation with an
articulated manipulator. Our first key idea is to use sparse human-in-the-loop
input to toggle between multiple automatically-generated drone viewpoints. Our
second key idea is to introduce optimization objectives that maintain a view of
the manipulator while considering drone uncertainty and the impact on viewpoint
occlusion and environment collisions. We provide an instantiation of our drone
viewpoint method within a drone-manipulator remote teleoperation system.
Finally, we provide an initial validation of our method in tasks where we
complete common household and industrial manipulations.
