Cooperative Robotics Visible Light Positioning: An Intelligent Compressed Sensing and GAN-Enabled Framework
Sicong Liu, Xianyao Wang, Jian Song, Zhu Han
Abstract
This paper presents a compressed sensing (CS) based framework for visible light positioning (VLP), designed to achieve simultaneous and precise localization of multiple intelligent robots within an indoor factory. The framework leverages light-emitting diodes (LEDs) originally intended for illumination purposes as anchors, repurposing them for the localization of robots equipped with photodetectors. By predividing the plane encompassing the robot positions into a grid, with the number of robots being notably fewer than the grid points, the inherent sparsity of the arrangement is harnessed. To construct an effective sparse measurement model, a sequence of aggregation, autocorrelation, and cross-correlation operations are employed to the signals. Consequently, the complex task of localizing multiple targets is reformulated into a sparse recovery problem, amenable to resolution through CS-based algorithms. Notably, the localization precision is augmented by inter-target cooperation among the robots, and inter-anchor cooperation among distinct LEDs. Furthermore, to fortify the robustness of localization, a generative adversarial network (GAN) is introduced into the proposed localization framework. The simulation results affirm that the proposed framework can successfully achieve centimeter-level accuracy for simultaneous localization of multiple targets.