视觉感知与稳定控制融合，实现无高精地图的实时自主导航
Integrated YOLOP Perception and Lyapunov-based Control for Autonomous Mobile Robot Navigation on Track
- 用2D-3D投影+多项式拟合重建车道中心线
- 基于李雅普诺夫理论设计控制器，确保轨迹稳定收敛
- 无需高精地图或卫星定位，适合真实复杂道路场景
本文提出一种面向非完整差速移动机器人的真实世界自主轨道导航框架，通过联合多任务视觉感知与可证明稳定的跟踪控制器实现。感知模块利用2D到3D相机投影、基于弧长的均匀点重采样及鲁棒QR最小二乘优化的三次多项式拟合，重构车道中心线。控制器基于李雅普诺夫稳定性设计，调节机器人线速度与角速度，确保在动态和部分感知车道场景下位置与航向偏差的有界误差动态及渐近收敛，无需依赖高精地图或全局卫星定位。嵌入式平台的真实世界实验验证了系统的保真度、实时性、轨迹平滑性及闭环稳定性，支持可靠自主导航。
原文摘要 · Abstract (English)
This work presents a real-time autonomous track navigation framework for nonholonomic differential-drive mobile robots by jointly integrating multi-task visual perception and a provably stable tracking controller. The perception pipeline reconstructs lane centerlines using 2D-to-3D camera projection, arc-length based uniform point resampling, and cubic polynomial fitting solved via robust QR least-squares optimization. The controller regulates robot linear and angular velocities through a Lyapunov-stability grounded design, ensuring bounded error dynamics and asymptotic convergence of position and heading deviations even in dynamic and partially perceived lane scenarios, without relying on HD prior maps or global satellite localization. Real-world experiments on embedded platforms verify system fidelity, real-time execution, trajectory smoothness, and closed-loop stability for reliable autonomous navigation.
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