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When a robot arm picks a part from a bin, the camera does not do the picking. The software does. The camera captures an image or point cloud. That raw data contains everything needed to guide the robot, but only if something processes it correctly: identifying the target object, calculating its position and orientation, selecting a grasp point, transforming the coordinates into the robot's reference frame, and outputting a command the controller can execute. That entire chain

Adding a camera to a robot arm sounds straightforward. Mount a camera, connect it to some software, and the robot can see. In practice, the gap between a robot with a camera and a robot with a camera that works reliably in production is wider than most buyers expect. This post is a buyer's guide, not a technology explainer. It focuses on what people get wrong when they add cameras to robot arms, what decisions actually determine whether a vision-guided robot cell performs con

The most significant constraint on robot automation for most of its history has not been mechanical. Robot arms have been fast, precise, and powerful for decades. The constraint has been perceptual. Robots could not see the world in three dimensions, which meant they could only operate reliably in environments where nothing ever changed position. 3D vision removes that constraint. When a robot has access to depth data about its environment, it can locate objects wherever they

If you have spent any time reading cobot datasheets, you have noticed that repeatability is always listed and accuracy is almost never mentioned. That is not an oversight. It is a deliberate reflection of which specification actually determines how well a robot performs in production. Most buyers assume accuracy is the important number. It sounds more rigorous. In practice, repeatability is the specification that determines whether your automation cell works reliably cycle af

There is a meaningful difference between a robot that can detect an object and a robot that can recognize it. Detection answers the question: is something there? Recognition answers a harder question: what is it, specifically? That distinction matters enormously in production environments where multiple part types share the same workspace, where the correct action depends on identifying which object the robot is looking at, and where the product mix changes frequently enough