Order Picking using Yaskawa and Mech-Mind

This logistics automation solution utilizes Mech-Mind 3D Vision and the Yaskawa GP8 robot to automate the order picking of various unstructured objects.

By leveraging a highly cost-effective 3D vision system that recognizes both the position and shape of products, the system can perform picking tasks without additional manual teaching—even when new products are added or existing ones are changed. The project was specifically designed to automate the picking of irregular items in logistics center picking zones.

Components

Robot	Robot: Yaskawa GP8 6-axis high-speed articulated robot. Payload: 8kg Reach: 727mm Weight: 32kg Repeatability: ±0.02mm Vision System: Mech-Mind 3D Vision (AI-driven recognition of randomly piled items).

Workflow

STEP 1.	Recognition: The 3D vision system identifies the individual shapes and locations of products randomly stacked in a bin/box.
STEP 2.	Transfer: The robot picks the identified product using a vacuum gripper and transfers it to the discharge conveyor.

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Key Features

Rapid and Accurate Sorting Performance

Industrial-Grade Speed: Leverages high-performance industrial robots for swift operational throughput.

Efficient Cycle Times: Sorting an individual item takes approximately 3 to 4 seconds (variable based on performance tuning).

Parallel Processing: Executes vision recognition simultaneously with robot movement to drastically reduce overall cycle times.

Unbeatable Vision Value & Performance

Breakthrough Pricing: A high-performance 3D vision solution at a highly competitive price point in the early 20 million KRW range.

Deep Learning Recognition: Exceptional ability to recognize diverse objects using AI-based 3D vision.

Shape Agnostic: Capable of identifying and picking items regardless of complexity or irregular geometry.

Robust in Difficult Conditions: Maintains high recognition accuracy even for dark-colored products or surfaces with high light reflection, such as packaging tape.

Intelligent Motion Planning: Easily performs collision avoidance and handles dense stacking through effective path planning (compatible with most major robot brands).

Implementation Results

ROI Period	24 months
Key Metrics	Labor Savings: Significant reduction in personnel required for manual order picking. Accuracy: Drastic decrease in picking errors (Human Error) during fulfillment.
Client Feedback	We previously delayed automating our order picking due to the sheer variety of product types and shapes. However, thanks to deep learning, we can now sort items without the need for manual registration, which has exponentially increased our automation efficiency. The decision was easy once we saw the accurate recognition rates and the affordable price of the vision system.

※ The content provided by Marosol is protected under U.S. and Canadian copyright and intellectual property laws. Unauthorized reproduction, distribution, or use of this content is strictly prohibited.

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The content provided by Marosol is protected under U.S. and Canadian copyright and intellectual property laws. Unauthorized reproduction, distribution, or use of this content is strictly prohibited.

※ The content provided by Marosol is protected under U.S. and Canadian copyright and intellectual property laws. Unauthorized reproduction, distribution, or use of this content is strictly prohibited.

Components

Workflow

Key Features

Implementation Results

Order Picking using Yaskawa and Mech-Mind

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