An Infrastructure-Free Automated Guided Vehicle Based on Computer Vision

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 a virtually infrastructure-free AGV that uses four distinct vision systems to exploit naturallyoccurring visual cues instead of relying on infrastructure. When coupled with a highly capable trajectory generation algorithm, the system produces four visual servos 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.