Detection, localization and picking up of coil springs from a pile

Picking of parts loaded in bulk is an industrial need. Thus bin-picking systems for various objects have ever been studied by various ways. However, it is difficult to recognize coil springs randomly placed in a pile by conventional machine vision techniques because of their shape characteristics. In this paper, we propose a method of recognition and pose estimation of coil springs. This method uses their highlights made by illumination for their recognition and pose estimation with stereo vision. We implemented this method as a bin-picking system with an industrial robot. Bin-picking of coil springs was successfully demonstrated on the system. Position errors were less than 2 mm. The average success rate for a coil spring in the part box was 94% when multiple retrials of picking were allowed. This rate could be improved by implementation of collision avoidance.