PalletTool 3 - Determining the UTOOL Values for Custom Grippers
Learn how to set accurate UTOOL offsets for custom grippers in PalletTool 3, including X, Y, Z, and R values for multi-zone setups.
In this video, we will go over the AI Box Locator vision tool and how it can transform your depalletizing applications. This tool is designed to tackle one of the most challenging tasks in automation: accurately identifying and locating boxes in complex environments. Whether you’re dealing with high-contrast packaging, tightly packed pallets, or boxes with irregular patterns and open flaps, AI Box Locator provides a reliable solution.
The AI Box Locator uses an advanced AI segmentation model to identify boxes and deliver precise Cartesian pick locations along with length and width measurements. This makes it easier to integrate with your robot’s tooling and ensure smooth, efficient operations. We’ll walk through the setup process, from creating a vision process in iRVision to configuring the AI Box Locator tool for optimal performance.
Requirements for Implementation:
What You Will Learn:
By the end of this overview, you’ll understand how AI Box Locator can streamline your operations and reduce errors in depalletizing workflows. If you’re ready to enhance your automation capabilities, this video is the perfect starting point.
Learn how to set accurate UTOOL offsets for custom grippers in PalletTool 3, including X, Y, Z, and R values for multi-zone setups.
Explore the new tools in FANUC PalletTool 3 that make palletizing faster, easier, and more adaptable for modern automation.
Learn how to set up a bin picking application with iRVision, from vision process configuration to TP programming for efficient automation.
Learn how to set up FANUC iRCalibration Vision MultiCal with tips on hardware, software, and verification for accurate robot calibration.
Learn how to set accurate UTOOL values for standard grippers in PalletTool 3, including Z and R offsets, for precise palletizing performance.
How to properly set the Part Z Height in an iRVision 2D Vision Process