Bin picking is one of the most requested robotic automation tasks and historically one of the hardest to get right. The concept is simple: a robot reaches into a bin and picks out a part. The execution is complex: the parts are randomly stacked, oriented in every direction, and look different every cycle. No two picks are the same. For decades, reliable automated bin picking required expensive custom systems built around proprietary hardware and months of integration work. Th

The question most manufacturers ask when they start thinking about automating material handling is the wrong one. They ask: "What is the best robot for material handling?" The better question is: "Which material handling task in our operation would benefit most from automation right now?" Automation of material handling is not a single project. It is a series of decisions, each building on the last. Operations that automate one task well, measure the result, and expand from t

Manual bin picking is one of the most persistent bottlenecks in manufacturing and logistics. A worker reaches into a bin, locates a part, orients it correctly, and presents it to the next process. They do this hundreds of times per shift. The task is repetitive, physically tiring, and difficult to staff consistently at the pace modern production demands. Automated bin picking replaces that manual step with a robot arm and a 3D vision system that locates parts wherever they la

"3D vision" is used as if it describes a single thing. It does not. There are at least four distinct technologies that produce 3D spatial data, each using different physics, different hardware, and suited to different industrial applications. Choosing between them without understanding those differences leads to cells that underperform or fail entirely on the parts they were supposed to handle. This post explains the four core 3D vision technologies used in industrial robotic