Recyclable Plastic Sorting using Delta Robots and Deep Learning

Watch "Recyclable Plastic Sorting using Delta Robots and Deep Learning" on YouTube

Recyclable Plastic Sorting using Delta Robots and Deep Learning

Solution Provider: Please log in to view

Application Field: Industry > Metal·Plastics, Application > Classification, Sector > Manufacturing

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.

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

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.

Results

Client Feedback

Please log in to view

Recommended Use Cases

Line-Side Product Transport using OMRON LD-60/90

This solution implements in-line product transport using OMRON's LD-60/90. OMRON's LD-60/90 mobile robots are Autonomous Mobile Robots (AMRs) that create their own maps and navigate autonomously along optimal paths according to real-time changes on-site. By mounting a conveyor on top of the LD-60/90, it can perform automated transport tasks, such as loading or unloading goods from existing conveyor systems. A total of nine LD mobile robots were used, and their flow is managed by an advanced fleet management system (FLOW, Fleet Operations Workspace). This solution, utilizing roller conveyors, enables more flexible production within the line.

Shape Recognition and Sorting Automation using Neuromeka Indy7 and IndyEye

This process is a demo for shape recognition and product sorting automation utilizing 2D vision and a deep learning algorithm (Instance Segmentation). Because it performs tasks by recognizing product positions and types in real-time using 2D vision, it can automatically recognize and work even if product positions or types change.

Doosan Robotics H-Series Collaborative Robot Palletizing System

This solution is a robot-integrated automatic stacking system utilizing the Doosan Robotics H-Series collaborative robots (H2017, H2515). As a modularized palletizing solution optimized by Doosan Robotics, it allows for easy and convenient implementation through an intuitive dedicated software interface. Project Background & Objectives Automation of palletizing tasks. Mitigation of safety accident risks associated with handling heavy loads. Space-constrained environments where traditional automation is difficult to install. Reducing the initial investment burden for companies adopting automation.