Tracking System Using Artificial Neural Network for FPGA Cart Follower

Abstract

Abstract Wheelchair users may have difficulty carrying their luggage while travelling. A proposed solution is introduced based on the problem stated. A tracking system for cart followers introduced to improve wheelchair mobility while transporting their luggage. The cart will track and follow the wheelchair at an appropriate distance. A Camera acts as input to enable the cart to track and follow the wheelchair. Pixy CMUcam5 is used to detect the predefined colour code (CC) in this project. The visual-based sensor gathered the information of the CC, which situated behind the wheelchair and translated the collected data into relative position information, such as distance and skew angle, which helps the cart in following the wheelchair. This translation is done in the Artificial Neural Network (ANN). The type of ANN used is a Multilayer perceptron (MLP) with the Levenberg-Marquardt training algorithm. Log sigmoid (Logsig) and Pure linear (Purelin) activation functions used in the hidden and output layer, respectively. The errors for distances and angles after computed with ANN are presented accordingly in Simulink. The MSE value obtained is 0.14007. The ANN implemented in the Field Programmable Gate Array (FPGA). The implementation of the ANN on the FPGA done through software and hardware configuration. The time taken for hardware implementation is faster than software due to parallel computations. The error value for output distance is less than 0.8000, and 0.3000 for the output angle during the simulation.