How Does Computer Vision Improve Accuracy in Robotic Pick and Place Systems?

All answers to this question.

Transitioning to "Vision-Guided Robotics" (VGR) is the gold standard for modern logistics. While image processing does add a few milliseconds of latency, modern GPUs and edge computing have reduced this to the point where it's negligible. The real benefit is the reduction in "dry picks" and damaged goods. 3D vision allows the robot to calculate the depth and orientation (6DoF), meaning it can pick parts from a messy bin or a moving belt with 99% accuracy. If you use an "eye-in-hand" configuration, the robot can even adjust its path mid-flight to compensate for moving targets.

   Answered 2025-01-16 by Sandra Higgins

Are you looking at structured light sensors or Time-of-Flight cameras for your 3D mapping, and what kind of lighting environment is the conveyor in?

   Answered 2025-01-19 by Jeffrey Collins

• Jeffrey, we are leaning toward structured light because our warehouse lighting is quite inconsistent. We’ve found that standard 2D cameras struggle with the glare from plastic packaging. Structured light seems to handle those reflections much better, providing a cleaner point cloud for the AI to analyze. The goal is to eliminate the need for custom jigs that hold every part in a "perfect" position before the robot reaches for it.

     Commented 2025-01-21 by Kevin Parker

We added vision to our FANUC arms last year. The cycle time increased by 0.2 seconds, but our error rate dropped from 12% to nearly zero.

   Answered 2025-01-23 by Pamela Reed

• Pamela's data is consistent with what we see. That 0.2-second trade-off is nothing compared to the cost of a human having to stop the line to fix a jammed robot.

     Commented 2025-01-24 by Mark Fletcher