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.

翻译：本文提出一种用于解决参数化路径跟踪问题的引导算法，以及适用于陆基轮式移动机器人的曲率变化速度设定点。该引导算法基于无奇异性引导向量场。这种新颖的GVF方法将期望机器人路径和引导向量场扩展至高维空间，在该空间中可构建角度控制函数，从而在避免场奇异性的同时确保对期望参数化路径的全局渐近收敛。在SF-GVF中，路径需遵循参数化定义。该特性使得采用贝塞尔曲线定义机器人期望路径具有显著优势。曲率变化速度设定点与引导算法相结合，可在存在最小转弯半径或最大横向加速度等物理约束时，有效促进路径收敛。我们通过理论分析、仿真实验以及采用商用组件搭建的WMR平台进行了户外实验验证。