Process Overview

This solution automates product sorting, assembly, and packing by utilizing 3D vision, deep learning algorithms (instance segmentation), reinforcement learning, and motion planning (OMPL) algorithms.

Since it performs tasks by recognizing the real-time product position and type as well as the box location using 3D vision, it can automatically compensate and operate even when the product position or box position changes.

Even if vision/robot/work jigs become worn or conditions change, the system is corrected through learning, making operation and maintenance easier and minimizing downtime.

Smooth robot teaching

• Vision calibration and teaching time reduced through deep learning

• No need for recalibration or re-teaching even when work conditions change

• Excellent recognition capability for various objects based on deep learning–based 3D object recognition

• Learns optimal pick positions according to product shape

• Reduced training time by leveraging simulation data

• Performs picking by sweeping or moving the product when it is not suitable for gripping

• Fast product pose estimation within 0.2 seconds (6D pose estimation)

Compact and simple installation

• No need to fix the input box in place, eliminating the need for a separate custom feeder

• Enables automation by adding a robot to the operator’s existing workspace

Fast ROI – optimized operation and reduced maintenance cost

• Shorter cycle time through continuous learning and optimization of picking pose and path

• Automatic compensation and task execution even if the layout shifts or the robot becomes worn over time

• Stable production by minimizing downtime