  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.
