  Fleets of unmanned robots can be beneficial for the long-term monitoring of
large areas, e.g., to monitor wild flocks, detect intruders, search and rescue.
Monitoring numerous dynamic targets in a collaborative and efficient way is a
challenging problem that requires online coordination and information fusion.
The majority of existing works either assume a passive all-to-all observation
model to minimize the summed uncertainties over all targets by all robots, or
optimize over the jointed discrete actions while neglecting the dynamic
constraints of the robots and unknown behaviors of the targets. This work
proposes an online task and motion coordination algorithm that ensures an
explicitly-bounded estimation uncertainty for the target states, while
minimizing the average number of active robots. The robots have a limited-range
perception to actively track a limited number of targets simultaneously, of
which their future control decisions are all unknown. It includes: (i) the
assignment of monitoring tasks, modeled as a flexible size multiple vehicle
routing problem with time windows (m-MVRPTW), given the predicted target
trajectories with uncertainty measure in the road-networks; (ii) the nonlinear
model predictive control (NMPC) for optimizing the robot trajectories under
uncertainty and safety constraints. It is shown that the robots can switch
between active and inactive roles dynamically online as required by the unknown
monitoring task. The proposed methods are validated via large-scale simulations
of up to $100$ robots and targets.
