Vision-Based Control for Nonholonomic Vehicles

This chapter continues the study of methods for vision-based stabilization of mobile robots to desired locations in an environment, focusing on an aspect that is critical for successful real-world implementation, but often tends to be overlooked in the literature: the control inputs employed must take into account the specific motion constraints of commercial robots, and should conform with feasibility, safety, and efficiency requirements. With this motivation, the chapter proposes a visual control approach based on sinusoidal inputs designed to stabilize the pose of a robot with nonholonomic motion constraints. All the information used in the control scheme is obtained from omnidirectional vision, in a robust manner, by means of the 1D trifocal tensor. The method is developed considering particularly a unicycle kinematic robot model, and its contribution is that sinusoids are used in such a way that the generated vehicle trajectories are feasible, smooth, and versatile, improving over previous sinusoidal-based control works in terms of efficiency and flexibility. Furthermore, the analytical expressions for the evolution of the robot’s state are provided and used to propose a novel state-feedback control law. The stability of the proposed approach is analyzed in the chapter, which also reports on results from simulations and experiments with a real robot, carried out to validate the methodology.