Optimizing Path Planning for Automated Guided Vehicles in Constrained Warehouse Environments: Addressing the Challenges of Non-Rotary Platforms and Irregular Layouts
Pavlo Pikulin, Vitalii Lishunov, Konrad Kułakowski · Robotics · 2025
Efficient path planning for Automated Guided Vehicles (AGVs) in warehouse automation is crucial yet challenging, particularly in environments with irregular structures and constrained spaces. This study addresses these challenges by focusing on AGVs without rotary platforms, which require the rotation of the entire robot-rack assembly for directional changes, demanding additional space and complex path planning. We have developed dedicated algorithms that integrate robotics and optimization principles to tackle these issues. Our methods take into account the spatial requirements for rack rotation, navigating through limited inter-rack clearance, and adapting to irregular warehouse layouts. Extensive simulations and real-world applications validate the proposed solutions, demonstrating significant improvements in traversal efficiency and collision risk mitigation. The results indicate that our algorithms effectively enhance the operational efficiency and reliability of AGV systems in complex warehouse environments. This research adapts AGV path planning by providing robust strategies to optimize navigation in challenging settings, ultimately improving warehouse productivity.