QoS Aware Robot Trajectory Optimization with IRS-Assisted Millimeter-Wave Communications

TL;DR

This paper proposes an IRS-assisted mm-wave communication framework for robot trajectory optimization that minimizes energy consumption while satisfying QoS constraints, using a novel successive-convex algorithm and channel decoupling techniques.

Contribution

It introduces a decoupled optimization approach for robot trajectory and beamforming in IRS-assisted mm-wave systems, with a new algorithm handling LOS/NLOS transitions.

Findings

Significant reduction in robot motion energy consumption.

Fast convergence of the proposed optimization algorithm.

Passive IRSs improve radio coverage and energy efficiency.

Abstract

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.