A Comparative Study of Mobile Robot Positioning Using 5G NR

Panagiotis T. Karfakis, Micael Santos Couceiro, David Portugal, Carlos Henggeler Antunes

Keywords: 5G NR, Mobile Robotics, Position Estimation, Meta-Heuristics, Agriculture, HCP, PSO, GA

Abstract: In this work we study the use of the 5G New Radio (NR) communication model for position tracking of a mobile robotic system. We have deployed the 5G NR in three different configurations in a simulated agricultural environment. We evaluate the impact of using different number of gNodeB (gNB) base stations and the increased topological complexity on the position estimation, using three different heuristic approaches. The setups consist of 5, 10 and 15 gNBs that communicate with the user equipment (UE) carried by the robot. The ground truth trajectory of the system is recorded and estimated by three meta-heuristics, namely Hyperbola Crossing points (HCP), Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). We measure the performance according to statistical metrics such as the average prediction time, the average Euclidean Distance (ED) and their standard deviations. We provide and discuss the qualitative results derived experimentally to assess the positioning capability of 5G NR for a simulated field robotics application.