Automated Guided Vehicles (AGVs) are essential in various industries for their efficiency and adaptability. However, planning trajectories for AGVs in obstacle-dense, unstructured environments presents significant challenges due to the nonholonomic kinematics, abundant obstacles, and the scenario's nonconvex and constrained nature. To address this, we propose an efficient trajectory planning framework for AGVs by formulating the problem as an optimal control problem. Our framework utilizes the fast safe rectangular corridor (FSRC) algorithm to construct rectangular convex corridors, representing avoidance constraints as box constraints. This eliminates redundant obstacle influences and accelerates the solution speed. Additionally, we employ the Modified Visibility Graph algorithm to speed up path planning and a boundary discretization strategy to expedite FSRC construction. Experimental results demonstrate the effectiveness and superiority of our framework, particularly in computational efficiency. Compared to advanced frameworks, our framework achieves computational efficiency gains of 1 to 2 orders of magnitude. Notably, FSRC significantly outperforms other safe convex corridor-based methods regarding computational efficiency.

翻译：自动导引车（AGV）因其高效性和适应性在各行业中至关重要。然而，在障碍物密集的非结构化环境中规划AGV轨迹面临重大挑战，这源于非完整运动学约束、大量障碍物以及场景的非凸性和约束特性。为此，我们提出一种高效的AGV轨迹规划框架，将问题建模为最优控制问题。该框架采用快速安全矩形走廊（FSRC）算法构建矩形凸走廊，将避障约束表示为箱式约束，从而消除冗余障碍物影响并加速求解速度。同时，我们采用改进的可视图算法加速路径规划，并引入边界离散化策略加快FSRC构建。实验结果表明，该框架在计算效率方面具有有效性和优越性。与先进框架相比，我们的框架实现了1-2个数量级的计算效率提升。值得注意的是，FSRC在计算效率上显著优于其他基于安全凸走廊的方法。