Autonomous Lane Tracking for an Ackermann-steering Robot

Abstract

This paper presents the implementation of an Ackermann-steeringconfiguration autonomous vehicle using a PID control system for lane tracking through digital image processing. The Ackermann-steering robot modifies its front rotation angle based on camera data, on which HSV and contour filters are applied to define the limits of the track and its center. APID controller is applied to ensure the vehicle's incorporation into the lane at every point on the road, using the control function to calculate the error response and modify the current orientation accordingly, with the error being the deviation from the lane's center. CoppeliaSim is used to create the virtual environment of the vehicle, providing a realistic and efficient simulation platform for testing, and evaluating the performance of the proposed lane tracking system. Additionally, Python programming is used to implement all the functions in the simulation, including image processing, control system calculations, and communication with the virtual environment in CoppeliaSim.Experimental results are presented to evaluate the feasibility of the proposed implementation.