Singularity-Free Guiding Vector Field over B\'ezier's Curves Applied to Rovers Path Planning and Path Following

TL;DR

This paper introduces a singularity-free guiding vector field method for path following of wheeled robots, utilizing Bezier curves for smooth, reliable navigation with theoretical guarantees and practical experiments.

Contribution

The paper develops a novel SF-GVF approach that guarantees global convergence and avoids singularities, specifically tailored for Bezier curve paths in mobile robot navigation.

Findings

The SF-GVF method ensures global asymptotic convergence.

The approach effectively handles curvature-varying speed setpoints.

Experimental results validate the method's robustness in real-world scenarios.

Abstract

This paper presents a guidance algorithm for solving the problem of following parametric paths, as well as a curvature-varying speed setpoint for land-based car-type wheeled mobile robots (WMRs). The guidance algorithm relies on Singularity-Free Guiding Vector Fields SF-GVF. This novel GVF approach expands the desired robot path and the Guiding vector field to a higher dimensional space, in which an angular control function can be found to ensure global asymptotic convergence to the desired parametric path while avoiding field singularities. In SF-GVF, paths should follow a parametric definition. This feature makes using Bezier's curves attractive to define the robot's desired patch. The curvature-varying speed setpoint, combined with the guidance algorithm, eases the convergence to the path when physical restrictions exist, such as minimal turning radius or maximal lateral acceleration. We provide theoretical results, simulations, and outdoor experiments using a WMR platform assembled with off-the-shelf components.