As global maritime transport rapidly advances, the demands for intelligent, safe, and efficient automated container ports have significantly increased. In this evolving landscape, multi-automated guided vehicle (AGV) systems have emerged as a critical element of port automation, playing an essential role. Within automated container terminals, quay cranes, AGVs, and yard cranes are the primary equipment for loading and unloading operations on ships. However, the complexity of simultaneously considering numerous practical factors and the intricate relationships among them has made optimization modeling in this area a challenging task. To tackle this challenge, we have developed a path optimization model for multi-AGV systems in port environments, based on an enhanced artificial potential field (APF) algorithm. This algorithm utilizes the initial states of AGVs, target locations, and obstacle information as inputs. It creates attractive forces near the target locations and repulsive forces around static obstacles. Moreover, a minimum safety distance between AGVs is established; when AGVs approach closer than this threshold, the algorithm introduces repulsive forces between them to prevent collisions. The algorithm dynamically recalculates the repulsive potential field in response to real-time feedback and changes in the environment, enabling continuous adjustment to the AGV paths and action plans. This iterative process continues until all AGVs reach their designated targets. The effectiveness of this algorithm has been validated through port environment simulations, demonstrating clear advantages in enhancing the safety and smoothness of multi-AGV path planning.

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Automated guided vehicles (AGVs),Path planning,Improved APF algorithm,Autonomous port

• Computer and Information Sciences - Natural Sciences
• Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology