Error minimization in Bluetooth based indoor localization of a mobile robot using Cuckoo Search algorithm

GPS (Global Positioning System) is a popularly and most widely used tracking system which provides very good positioning estimates for outdoor environments. But this system cannot estimate indoor positions (inside building) because it makes use of satellites. These satellites cannot be used in indoor environments because of signal obstruction and attenuation due to reflections caused by walls, furniture and various other factors present inside a building. Hence, technologies like Wi-Fi, WLAN, RFID, Bluetooth are used for finding out positions for indoor locations. Bluetooth, having low cost, low power consumption and having availability in almost every single device from costly smart phones, laptops to cheap phones, is trending lately for positioning purposes which gives good estimate of position. In this paper, we present the tracking of a mobile robot using Bluetooth. We have used Iterative Trilateration and Cuckoo Search (CS) algorithm implementation of Monte Carlo simulation and obtained mean error of 0.265 ± 0.09m. We have also compared our results with Particle filter implementation of Monte Carlo Simulation. Cuckoo search comes out to be better than Particle filter.