Automated Guided Vehicles (AGVs) are widely adopted in various industries due to their efficiency and adaptability. However, safely deploying AGVs in dynamic environments remains a significant challenge. This paper introduces an online trajectory optimization framework, the Fast Safe Rectangular Corridor (FSRC), designed for AGVs in obstacle-rich settings. The primary challenge is efficiently planning trajectories that prioritize safety and collision avoidance. To tackle this challenge, the FSRC algorithm constructs convex regions, represented as rectangular corridors, to address obstacle avoidance constraints within an optimal control problem. This conversion from non-convex to box constraints improves the collision avoidance efficiency and quality. Additionally, the Modified Visibility Graph algorithm speeds up path planning, and a boundary discretization strategy expedites FSRC construction. The framework also includes a dynamic obstacle avoidance strategy for real-time adaptability. Our framework's effectiveness and superiority have been demonstrated in experiments, particularly in computational efficiency (see Fig. \ref{fig:case1} and \ref{fig:case23}). Compared to state-of-the-art frameworks, our trajectory planning framework significantly enhances computational efficiency, ranging from 1 to 2 orders of magnitude (see Table \ref{tab:res}). Notably, the FSRC algorithm outperforms other safe convex corridor-based methods, substantially improving computational efficiency by 1 to 2 orders of magnitude (see Table \ref{tab:FRSC}).

翻译：自动导引车（AGV）因其高效性和适应性被广泛应用于各类工业场景。然而，在动态环境中安全部署AGV仍是一项重大挑战。本文提出一种名为快速安全矩形走廊（FSRC）的在线轨迹优化框架，专为在障碍物密集环境中运行的AGV设计。核心难点在于高效规划兼顾安全性与避障能力的轨迹。为应对这一挑战，FSRC算法通过构建凸区域（以矩形走廊形式呈现），将最优控制问题中的避障约束转化为箱型约束。这种从非凸约束到箱型约束的转换显著提升了避障效率与质量。此外，改进的可视图算法加速了路径规划，边界离散化策略则提高了FSRC的构建速度。该框架还集成了动态避障策略以实现实时适应性。实验验证了本框架的有效性与优越性，尤其在计算效率方面（见图\ref{fig:case1}和\ref{fig:case23}）。与当前最先进框架相比，本轨迹规划框架在计算效率上提升了1至2个数量级（见表\ref{tab:res}）。特别地，FSRC算法优于其他基于安全凸走廊的方法，并将计算效率大幅提升1至2个数量级（见表\ref{tab:FRSC}）。