Real‑Time Mixed‑Case Palletising Powered by Photoneo MotionCam‑3D

By Pavel Soral • April 15 2026

Photoneo’s MotionCam‑3D supplied the critical visual intelligence that enabled Jacobi Robotics and Delta Technology to transform a chaotic, injury‑prone palletising line into a fully automated, real‑time system.

At a Glance

• Challenge: Automating a manual palletising line that handled random, unsequenced parcels, creating safety and reliability issues.
• Missing Link: Real‑time, high‑fidelity 3D perception to “see” each incoming case and verify stability of the outgoing stack.
• Vision Solution: Photoneo MotionCam‑3D.
• Partners: Photoneo (vision) + Jacobi Robotics (software) + Delta Technology (integration) + FANUC (motion).

Result: A live production cell delivering 100 % pallet stability and detecting damaged goods before they enter the stack.

The Challenge: Blind Robots Cannot Handle Chaos

In a major defense‑manufacturer facility, the shipping dock was a bottleneck. Operators manually built pallets from a conveyor that delivered completely random cases—varying in weight (up to 27.2 kg/60 lb), size, and condition.

Delta Technology supplied integration expertise and Jacobi Robotics provided the planning engine, yet the system suffered from a fundamental physics problem: robots that cannot “see” cannot handle disorder.

In a brownfield environment with no pre‑sorting, the robot had no knowledge of what was coming next. Conventional automation relies on fixed recipes, but here the recipe changed every second. A solution required eyes that could capture reality in milliseconds.

The Solution: MotionCam‑3D as the Source of Truth

Equipping the cell with Photoneo’s MotionCam‑3D solved the problem. Unlike static scanners that require the conveyor to stop, MotionCam‑3D captures high‑resolution 3D point clouds of objects in motion, feeding precise, real‑time data into Jacobi’s OmniPalletizer without slowing throughput.

The vision system performs two mission‑critical roles:

1. In‑feed Scanner: Accurate Perception in Motion

As cases flow, MotionCam‑3D acts as a gatekeeper.

• Real‑Time Dimensioning: The scanner instantly measures every box’s length, width, height and orientation, providing Jacobi’s path planner with the data needed to solve the “Tetris” puzzle of optimal placement.
• Quality Control: High‑fidelity 3D data detects crushed corners, open flaps or structural damage before the robot picks a case. Damaged boxes are flagged and rejected, preventing unstable stacks and ensuring freight‑carrier compliance.

2. Verification Scanner: Closing the Loop

Placement accuracy is everything in mixed‑case palletising. After the robot places a case, a secondary visual check confirms the reality matches the plan.

• Placement Verification: Confirms the box is at the correct coordinates and orientation.
• Stack Stability Check: Scans building layers to ensure the pallet remains flat and stable, guaranteeing 100 % stability even as the stack rises.

Why the Partnership Works

This deployment demonstrates that successful brownfield automation is an ecosystem play.

Even the most advanced AI planner cannot build a stable pallet if its input data is wrong. Photoneo provides Jacobi’s ‘brain’ with a perfect picture of reality, cycle after cycle.

• Photoneo: Eyes – high‑quality 3D data & quality control.
• Jacobi Robotics: Brain – real‑time path planning & physics‑aware logic.
• FANUC: Muscle – reliable industrial robotics.
• Delta Technology: Body – seamless integration into the warehouse floor.

The Outcome: Validated, Safe, and Scalable

By giving the robot the ability to see, measure, and verify, the manufacturer achieved results that blind automation could never match:

• 100 % Stability: Vision‑verified stacking eliminates product loss during transit.
• Zero Downtime from “Surprises”: Damaged boxes are identified and stopped before causing faults.
• High Cube Utilisation: Precise dimensioning allows Jacobi to pack pallets up to 90 % density, outperforming human stacking.
• Sim‑to‑Real Accuracy: 3D data accuracy enabled simulation to match real‑world cycle times with 0 % error.

This project moves mixed‑case palletising from a “science project” to a robust, industrial reality, powered by the partnership of superior vision and intelligent software.