  Robotic data gathering (RDG) is an emerging paradigm that navigates a robot
to harvest data from remote sensors. However, motion planning in this paradigm
needs to maximize the RDG efficiency instead of the navigation efficiency, for
which the existing motion planning methods become inefficient, as they plan
robot trajectories merely according to motion factors. This paper proposes
radio map guided model predictive communication (MPCOM), which navigates the
robot with both grid and radio maps for shape-aware collision avoidance and
communication-aware trajectory generation in a dynamic environment. The
proposed MPCOM is able to trade off the time spent on reaching goal, avoiding
collision, and improving communication. MPCOM captures high-order signal
propagation characteristics using radio maps and incorporates the map-guided
communication regularizer to the motion planning block. Experiments in IRSIM
and CARLA simulators show that the proposed MPCOM outperforms other benchmarks
in both LOS and NLOS cases. Real-world testing based on car-like robots is also
provided to demonstrate the effectiveness of MPCOM in indoor environments.
