Bin picking, also known as part picking or piece picking, refers to the task of identifying and removing specific items (i.e., screws, pipes, lids, etc.) from a bin, box, or other container. In bin picking, machine vision refers to the use of computer vision techniques to enable a robot to locate and pick up specific objects from a jumbled assortment of objects, or a “bin,” using visual information. Machine vision systems for bin picking typically consist of a camera or other optical sensor, a computer for processing the images captured by the sensor, and algorithms that analyze the images to identify the objects of interest and determine their locations. The robot arm then uses this information to plan a path to pick up the object and move it to the desired location. Machine vision is a key enabling technology for bin picking because it allows the robot to “see” the objects and understand their locations and orientations.
There are several ways that bin picking robots can use machine vision to identify parts. One common approach is to use image processing techniques to analyze the shape, size, and other visual features of the objects in the images captured by the camera. For example, the robot might use feature detection algorithms to find the feature points of the objects, or it might use an object identification algorithm like YOLO to identify specific objects.
Industrial cameras like Bottlenose™ (which uses GigE Vision 2.1) are able to perform front-end tasks of object identification and 3D point cloud generation.
Utilizing bin picking robots in combination with machine vision, improves productivity and reduces errors in the manufacturing process. Industrial cameras like Bottlenose™ are particularly useful for bin picking tasks because they are able to process large amounts of visual data quickly and accurately, even in complex and cluttered environments. They are also able to work in a variety of lighting conditions, making them suitable for use in a wide range of factory settings. Bottlenose utilizes a powerful ISP, on-camera AI, feature point detection, and point cloud generation which can all be used to achieve a powerful bin picking system.