An Infrastructure-Free Automated Guided Vehicle Based on Computer Vision

Abstract

Automated guided vehicles (AGVs) have been operating effectively in factories for decades. These vehicles have successfully used strategies of deliberately structuring the environment and adapting the process to the automation. The potential of computer vision technology to increase the intelligence and adaptability of AGVs is largely unexploited in contemporary commercially available vehicles. We developed an infrastructure-free AGV that uses four distinct vision systems. Three of them exploit naturally occurring visual cues instead of relying on infrastructure. When coupled with a highly capable trajectory generation algorithm, the system produces four visual servo controllers that guide the vehicle continuously in several contexts. These contexts range from gross motion in the facility to precision operations for lifting and mating parts racks and removing them from semi-trailers. To our knowledge, this is the first instance of an AGV that has operated successfully in a relevant environment for an extended period of time without relying on any infrastructure.