Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map-Based Approach

Abstract

Integrating robots into cellular networks creating connected robotic users has emerged as a promising technology for future smart cities and smart factories due to their low cost and high maneuverability. However, the requirement of establishing stable and high-quality communication links to the robotic users greatly restricts their applicability, especially in indoor environments where obstacles may block the wireless link. To tackle this challenge, in this paper, an indoor robot navigation system is investigated, where an intelligent reflecting surface (IRS) is employed to enhance the connectivity between the access point (AP) and robotic users. Both single-user and multiple-user scenarios are considered. In the single-user scenario, one mobile robotic user (MRU) communicates with the AP. In the multiple-user scenario, the AP serves one MRU and one static robotic user (SRU) employing either non-orthogonal multiple access (NOMA) or orthogonal multiple access (OMA) transmission. The considered system is optimized for minimization of the travelling time/distance of the MRU from a given starting point to a predefined final location, while satisfying constraints on the communication quality of the robotic users. To this end, a radio map based approach is proposed to exploit location-dependent channel propagation knowledge. For the single-user scenario, a channel power gain map is constructed, which characterizes the spatial distribution of the maximum expected effective channel power gain of the MRU for the optimal IRS phase shifts. Based on the obtained channel power gain map, the communication-aware robot path planing problem is solved by exploiting graph theory. For the multiple-user scenario, a communication rate map is constructed, which characterizes the spatial distribution of the maximum expected rate of the MRU for the optimal power allocation at the AP and the optimal IRS phase shifts subject to a minimum rate requirement for the SRU. The joint optimization problem is efficiently solved by invoking bisection search and successive convex approximation methods. Then, a graph theory based solution for the robot path planning problem is derived by exploiting the obtained communication rate map. Our numerical results show that: 1) the required travelling distance of the MRU can be significantly reduced by deploying an IRS; 2) NOMA yields a higher communication rate for the MRU than OMA; 3) the IRS performance gain is significantly more pronounced for NOMA than for OMA.