Recyclable Plastic Sorting using Delta Robots and Deep Learning

Industry > Metal·PlasticsApplication > ClassificationSector > Manufacturing

This solution utilizes 2D Vision, Deep Learning (Instance Segmentation), Reinforcement Learning (RL), and Motion Planning (OMPL) algorithms to rapidly recognize and sort recyclable plastics on a moving conveyor belt.

The system features high-performance vision capable of quickly identifying objects even with highly irregular shapes. Through advanced motion planning, it executes sorting tasks with exceptional speed and precision.

Components

Robot	Robot: 3-Axis Delta Robot High-speed parallel robot optimized for rapid pick-and-place operations.

Workflow

STEP 1.	Input of mixed waste piles.
STEP 2.	Recognition of targeted plastic objects.
STEP 3.	Picking and discharging the identified plastic products.

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Key Features

Effortless Robot Teaching

Teaching Automation: Shortens setup time by automating vision calibration and robot teaching through deep learning.
Operational Flexibility: Eliminates the need for manual recalibration or re-teaching even when working conditions change.
Superior Recognition: High-performance detection of various plastic shapes using deep learning-based object recognition.
Self-Learning Optimization: Automatically learns the optimal picking position tailored to the specific shape of each product.

Rapid Return on Investment (ROI)

High-Speed Performance: Capable of ultra-fast operations at a rate of approximately 1 pick per second.
Operational Optimization: Reduces maintenance costs and optimizes paths through continuous learning, further shortening cycle times.
Automatic Compensation: Maintains accuracy by automatically correcting movements even if the layout shifts or the robot's mechanical precision deviates due to aging.
Minimized Downtime: Ensures stable productivity by reducing system failures and manual interventions.

Implementation Results

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