Vision-Based Dynamic Control of Car-Like Mobile Robots

Abstract

Most existing controllers for Car-Like Mobile Robots (CLMR) are designed to handle dynamic effects by decoupling speed and steering controls, also assume that full states are accessible, which are unrealistic for real-world applications. This paper presents a combined speed and steering control system for CLMR. To provide the essential state for the controller, a newly developed visual algorithm is adopted for estimating the high-update rate longitudinal and lateral velocities of the robot which cannot be accurately measured by wheel encoders due to the skidding and slipping effects. The stability of the proposed system can be guaranteed by Lyapunov method since the velocity estimation error, the speed tracking error and the lateral deviation converging to zero simultaneously. Real-world experiments are conducted on an electric autonomous tractor with online estimation to demonstrate the feasibility of the approach.