  A framework is introduced for planning unmanned aerial vehicle flight paths
for visual surveillance of ground targets, each having particular viewing
requirements. Specifically, each target is associated with a set of imaging
parameters, including a desired (i) tilt angle, (ii) azimuth, with the option
of a 360-degree view, and (iii) dwell-time. Tours are sought to image the
targets, while minimizing both the total mission time and the time required to
reach the initial target. An epsilon-constraint scalarization is used to pose
the multi-objective problem as a constrained optimization, which, through
careful discretization, can be approximated as a discrete graph-search. It is
shown that, in many cases, this approximation is equivalent to a generalized
traveling salesperson problem. A heuristic procedure for solving the discrete
approximation and recovering solutions to the full routing problem is
presented, and is shown to have resolution completeness properties. Algorithms
are illustrated through numerical studies.
