  In many unmanned aerial vehicle (UAV) applications for surveillance and data
collection, it is not possible to reach all requested locations due to the
given maximum flight time. Hence, the requested locations must be prioritized
and the problem of selecting the most important locations is modeled as an
Orienteering Problem (OP). To fully exploit the kinematic properties of the UAV
in such scenarios, we combine the OP with the generation of time-optimal
trajectories with bounds on velocity and acceleration. We define the resulting
problem as the Kinematic Orienteering Problem (KOP) and propose an exact
mixed-integer formulation together with a Large Neighborhood Search (LNS) as a
heuristic solution method. We demonstrate the effectiveness of our approach
based on Orienteering instances from the literature and benchmark against
optimal solutions of the Dubins Orienteering Problem (DOP) as the
state-of-the-art. Additionally, we show by simulation \color{black} that the
resulting solutions can be tracked precisely by a modern MPC-based flight
controller. Since we demonstrate that the state-of-the-art in generating
time-optimal trajectories in multiple dimensions is not generally correct, we
further present an improved analytical method for time-optimal trajectory
generation.
