  In this paper, we consider the motion energy minimization problem for a robot
that uses millimeter-wave (mm-wave) communications assisted by an intelligent
reflective surface (IRS). The robot must perform tasks within given deadlines
and it is subject to uplink quality of service (QoS) constraints. This problem
is crucial for fully automated factories that are governed by the binomial of
autonomous robots and new generations of mobile communications, i.e., 5G and
6G. In this new context, robot energy efficiency and communication reliability
remain fundamental problems that couple in optimizing robot trajectory and
communication QoS. More precisely, to account for the mutual dependency between
robot position and communication QoS, robot trajectory and beamforming at the
IRS and access point all need to be optimized. We present a solution that can
decouple the two problems by exploiting mm-wave channel characteristics. Then,
a closed-form solution is obtained for the beamforming optimization problem,
whereas the trajectory is optimized by a novel successive-convex
optimization-based algorithm that can deal with abrupt line-of-sight (LOS) to
non-line-of-sight (NLOS) transitions. Specifically, the algorithm uses a radio
map to avoid collisions with obstacles and poorly covered areas. We prove that
the algorithm can converge to a solution satisfying the Karush-Kuhn-Tucker
conditions. The simulation results show a fast convergence rate of the
algorithm and a dramatic reduction of the motion energy consumption with
respect to methods that aim to find maximum-rate trajectories. Moreover, we
show that the use of passive IRSs represents a powerful solution to improve the
radio coverage and motion energy efficiency of robots.
