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Automated Guided Vehicles (AGVs) are widely adopted in various industries dueto their efficiency and adaptability. However, safely deploying AGVs in dynamicenvironments remains a significant challenge. This paper introduces an onlinetrajectory optimization framework, the Fast Safe Rectangular Corridor (FSRC),designed for AGVs in obstacle-rich settings. The primary challenge isefficiently planning trajectories that prioritize safety and collisionavoidance. To tackle this challenge, the FSRC algorithm constructs convexregions, represented as rectangular corridors, to address obstacle avoidanceconstraints within an optimal control problem. This conversion from non-convexto 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. Theframework also includes a dynamic obstacle avoidance strategy for real-timeadaptability. Our framework's effectiveness and superiority have beendemonstrated 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 computationalefficiency, ranging from 1 to 2 orders of magnitude (see Table \ref{tab:res}).Notably, the FSRC algorithm outperforms other safe convex corridor-basedmethods, substantially improving computational efficiency by 1 to 2 orders ofmagnitude (see Table \ref{tab:FRSC}).

Fast Safe Rectangular Corridor-based Online AGV Trajectory Optimization with Obstacle Avoidance