Highly-scalable traffic management of autonomous industrial transportation systems

Abstract In this paper, we present a novel method for highly-scalable coordination of free-ranging automated guided vehicles in industrial logistics and manufacturing scenarios. The primary aim of this method is to enhance the current industrial state-of-the-art multi-vehicle transportation systems, which, despite their long presence on the factory floor and significant advances over the last decades, still rely on a centralized controller and predetermined network of paths. In order to eliminate the major drawbacks of such systems, including poor scalability, low flexibility, and the presence of a single point of failure, in the proposed control approach vehicles autonomously execute their assigned pick-up and delivery operations by running a fully decentralized control algorithm. The algorithm integrates path planning and motion coordination capabilities and relies on a two-layer control architecture with topological workspace representation on the top layer and state-lattice representation on the bottom layer. Each vehicle plans its own shortest feasible path toward the assigned goal location and resolves conflict situations with other vehicles as they arise along the way. The motion coordination strategy relies on the private-zone mechanism ensuring reliable collision avoidance, and local negotiations within the limited communication radius ensuring high scalability as the number of vehicles in the fleet increases. We present experimental validation results obtained on a system comprising six Pioneer 3DX robots in four different scenarios and simulation results with up to fifty vehicles. We also analyze the overall quality of the proposed traffic management method and compare its performance to other state-of-the-art multi-vehicle coordination approaches.