Smart Palletizing Using the Hyundai Robotics HH050 and Mech-Mind 3D Vision 🦾🤖

#Hyundai#HH050#MechMind#MechEye#Palletizing#Boxhandling#3DVision#LaborReduction#CostSaving

Process Overview

This palletizing automation solution uses the Hyundai Robotics HH050 and Mech-Mind 3D vision to detect mixed-size boxes, align them by size, and automatically stack them onto pallets. By leveraging a cost-effective 3D vision system, the cell can reliably palletize even when cartons of varying dimensions are mixed in the flow, ensuring stable operation.

Components

Robot	Mech-Mind Mech-Eye Laser L (Industrial 3D camera) FOV: 1250 × 1460 @ 1.5 m 2500 × 2890 @ 3 m Resolution: 2048 × 1536 (3 MP) Accuracy: 1.0 mm @ 2 m Scan time: 0.9–1.3 s Recommended scanning distance: 1.5–3 m Mech-Viz Intelligent Programming Environment: robot programming tool Hyundai Robotics HDR50-22 (HH050) Repeatability: ±0.06 mm
Peripherals	Tooling Vacuum gripper 3D Vision Mech-Eye Laser L (FOV: 1250 × 1460 @ 1.5 m 2500 × 2890 @ 3 m, Resolution: 2048 × 1536 (3 MP), Accuracy: 1.0 mm @ 2 m, Scan time: 0.9–1.3 s, Recommended scanning distance: 1.5–3 m, Mech-Viz Intelligent Programming Environment: robot programming tool) Peripheral Equipment Robot base Vision mounting stand Box infeed stand Pallet fixing guide

Workflow

STEP 1.	Scan available pallet stacking space using 3D vision
STEP 2.	Pick each box and place it into the remaining pallet space based on its size
STEP 3.	Repeat until palletizing is complete

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Key Features

(Hyundai Robotics) Holds the #1 domestic market share with a strong sales and service network in Korea.
Since entering the domestic robot business in 1984, has secured a broad portfolio of solutions and product lineups.
Can deliver customer-tailored solutions through end-to-end execution across robot manufacturing, engineering, and installation.
Handles high payloads and a wide working envelope.
Payload: 50 kg; Reach: 2,239 mm
A single robot can handle most logistics box-handling tasks.
A reasonably priced 3D vision solution starting in the low KRW 20 million range.
Deep-learning-based 3D object recognition provides strong detection performance across a wide variety of objects.
Maintains reliable recognition even with strong reflections (e.g., from tape) or dark-colored products.
High capability to distinguish individual boxes even when cartons are tightly packed together.
Easy motion planning for collision avoidance and dense stacking.
Wide-area recognition supports palletizing tasks with stacking heights above 2 m.

Implementation Results