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To achieve maximum throughput in robotic piece picking, it’s not simply a matter of adding machines to the line. Robotics designers, suppliers, integrators, and users need to identify the best combination of robot arms, sensors, and end effectors for a particular payload or task. In addition, many robots need the right intelligence and ability to choose the right grippers. XYZ Robotics Inc. is an example of a company that has developed systems to address these needs. Allston, Mass.-based XYZ’s piece picking system uses machine vision, but it does not rely exclusively on artificial intelligence models. A combination of mechanical and machine learning approaches is necessary, said Peter Yu, chief technology officer at the startup . “With both [approaches] and our tool changer, a robot can pick nearly anything, which is useful in logistics and manufacturing,” he told The Robot Report . “AI is important for tool selection. Changing between a large cup to a small cup or a bag cup gripper — that’s a challenge from both the tool side and the AI side.” XYZ Robotics’ grippers pick consumer electronics, apparel, cosmetics, and other objects for e-commerce order fulfillment. With AI guidance and the ability to change end-of-arm tooling, one robot can handle a wide variety of items with speed and accuracy. “For example, if the SKU is a plastic bag, our system will know and choose a suction cup to pick it up,” Yu said. “But if it’s mesh or a thin pencil or screwdriver, there’s not much area, so the robot can choose a two-fingered gripper.” Choosing the right grippers for piece picking XYZ’s vision-guided tool changer can swap out end effectors in about half a second. “For vision, the time to identify the piece picking points is 0.1 sec. with VGA, and at 720p, it is […]