Dynamic path finding method and obstacle avoidance for automated guided vehicle navigation in Industry 4.0

Abstract

Within the scope of Industry 4.0, Automated Guided Vehicles (AGVs) are used to streamline logistics through the usage of efficient path finding methods. The current path finding methods in the industry rely on excessive usage of guidance in the shape of magnets, tapes or QR codes on the floor that the AGVs follow to reach their destinations. However, the current methods lack operational flexibility and are costly to scale in the cases of job-shop floor expansions. In this paper, a dynamic path finding method with obstacle avoidance is presented which utilizes distance measuring sensors to avoid obstacles and reach the goal destination using the most direct path as possible. Tests for functionality and multi-agent scaling have been conducted to evaluate the performance of the dynamic method in a multi-agent setting. The results show that the dynamic method scales properly and is capable of navigating multiple agents through a simulated warehouse environment autonomously and without relying on external guidance. The dynamic method is able to avoid most collisions using distance measuring sensors and multi-agent negotiation to resolve conflicts among the agents that could have resulted in potential collisions. The proposed dynamic method provides a flexible and scalable path finding method for use in Industry 4.0.