用贝塞尔曲线设计无奇异性的路径引导场，提升轮式机器人路径跟踪稳定性。
Singularity-Free Guiding Vector Field over Bézier's Curves Applied to Rovers Path Planning and Path Following
- 在高维空间构建无奇异的引导向量场，确保全局收敛
- 结合贝塞尔曲线定义路径，支持曲率变化的速度设定
- 适用于有转向半径限制的移动机器人，实测验证有效
本文提出一种用于解决参数化路径跟踪问题的引导算法，并为陆基轮式移动机器人（WMRs）设计了曲率可变的速度设定。该算法基于无奇异引导向量场（SF-GVF），将期望路径和引导向量场扩展至高维空间，引入角度控制函数以实现对目标路径的全局渐近收敛，同时避免场奇异。在SF-GVF中，路径需采用参数化定义，因此使用贝塞尔曲线来构造期望路径具有优势。结合曲率自适应速度设定，该算法在存在物理约束（如最小转弯半径或最大侧向加速度）时仍能有效加速收敛。论文提供了理论分析、仿真及基于现成组件搭建的室外实验平台的实测结果。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。