Mixed-Case Palletizing: Vision and Path Planning

Mixed-case palletizing — stacking boxes of varying sizes and weights — challenges even advanced robots. Modern systems rely on 3D vision, AI path planning, and adaptive gripping to deliver perfect stacks every time.

Core Technologies

• 3D cameras (stereo or structured light) for object recognition and pose estimation.
• Path optimization algorithms to plan collision-free stacking order.
• Dynamic gripping systems with force sensors and vacuum feedback.

Design Tips

• Prioritize weight-down, size-up logic for stability.
• Use pallet pattern generators (e.g., PackML or PalletPRO templates).
• Validate with digital twins before deployment.

Example

An e-commerce 3PL deployed AI-based palletizing using a Zivid 3D camera and custom path planner. Stack accuracy improved by 40%, and unbalanced load errors dropped by 75%.

Related Articles

• Cobot Palletizing in Tight Spaces: Stacked ROI
• Slip Sheets, Stretch Wrap, and End-Effectors
• Throughput vs Stability: How to Tune the Cell

Conclusion

Vision turns mixed-case chaos into structured automation. With AI-driven planning and flexible grippers, cobots can handle anything the warehouse throws at them.