  This work presents an efficient and implementable solution to the problem of
joint task allocation and path planning in a multi-UAV platform deployed for
biometric data collection in-the-wild. The sensing requirement associated with
the task gives rise to an uncanny variant of the traditional vehicle routing
problem with coverage/sensing constraints. As is the case in several
multi-robot path-planning problems, our problem reduces to an $m$TSP problem.
In order to tame the computational challenges associated with the problem, we
propose a hierarchical solution that decouples the vehicle routing problem from
the target allocation problem. As a tangible solution to the allocation
problem, we use a clustering-based technique that incorporates temporal
uncertainty in the cardinality and position of the robots. Finally, we
implement the proposed techniques on our multi-quadcopter platforms.
