Robot Integrated 3D Object Recognition and Fetching System for Factory Automation

One of the bottlenecks for manufacturing automation is machine vision. Robots are not able to recognize randomly oriented components coming from the assembly line quickly and accurately just like human operators do. Once this very first step fails, any other subsequent operations such as picking up the component, assembling, welding, painting, etc, are impossible. Currently, manufacturers solve this problem by fixing the component. The robot arm then performs the task and manipulates the component based on this precondition. This approach totally omits the fragile object recognition step and relies solely on the precision and repeatability of the robot arm. Once there are pose error setting up the component, a disastrous consequence may occur and the whole manufacturing process might shutdown simply because of this minor fault. The research objective is to integrate 3D model-based object recognition into the system for the capability of the robot arm to recognize the component in the scene. Furthermore, teaching by touching is integrated to let human operators teach the robot how to pick up the components stably. Two of the most important modules for the success of this integrated system are 3D object recognition system and the manipulator itself. In this research, we successfully implement an integrated system for recognizing and fetching the randomly oriented objects. We also evaluate the system extensively and identify the bottleneck of this system, hoping that this could open up a road for robot-integrated manufacturing automation and become the basis for future research.