iRVision Multiple Parts in One Picture

In this session, we will go over how to efficiently locate and interact with multiple parts using a single camera shot in FANUC’s iRVision system. This approach can significantly streamline your automation process by reducing the need for repeated imaging, provided that interacting with one part does not disturb its neighbors. We’ll explore the core concepts behind this functionality and demonstrate how it works in FANUC’s ROBOGUIDE simulation environment, which mirrors real-world applications.

The video begins by introducing the foundational requirements for implementing this technique, including a basic understanding of iRVision principles such as camera calibration, vision process setup, and teach pendant programming. From there, we dive into the teach pendant program structure that makes this possible. You’ll see how a single VISION RUN_FIND command can identify multiple parts, followed by looping through VISION GET_OFFSET instructions to interact with each part individually—all without taking another image.

We’ll also cover practical tips for sorting results based on criteria like GPM score or positional data, and how to use VISION GET_NFOUND to determine the total number of parts detected. Finally, the demonstration in ROBOGUIDE will show the entire workflow in action, from snapping the initial image to picking and placing each part.

Requirements Before You Begin

• Basic knowledge of iRVision setup and calibration
• Familiarity with teach pendant programming
• Access to FANUC ROBOGUIDE or a real robot environment

What You Will Learn

• How to find multiple parts with one vision shot
• How to structure a teach pendant program for looping through offsets
• Techniques for sorting detected parts by user-defined criteria
• How to use VISION GET_NFOUND for process optimization
• Practical demonstration in ROBOGUIDE for real-world application

This video is ideal for anyone looking to optimize robotic vision processes and reduce cycle times in part handling. By the end, you’ll have a clear understanding of how to implement this feature and apply it to your own automation projects.