Solving Pick and Place Automation Challenges with 3D Machine Vision

NARI SHIN
2021-02-09
  1. Introduction 
  2. Industrial Automation Challenges and Solutions
    Objects that are hard to separate
    Densely stacked
    Colors
    Shiny and reflective
    Occlusion
    Trueness
  3. Application Examples
    Bin-picking 
    Piece picking 
  4. Considerations when choosing a machine vision camera
  5. Conclusion 

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eBook-cover-Solving-Pick-and-Place-Automation-Challenges-with-industrial-3D-Machine-Vision

Introduction 

Across logistics and e-commerce companies, we see a need for more automation to meet the increase in online shopping and demand for international shipping. Traditional picking, handling, and order-fulfillment tasks are highly repetitive, physically strenuous, and prone to human error.

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Automation of high-volume processes gives vendors increased throughput, accuracy, and trustworthiness. It also enables logistics firms to assign human operators to safer or complex tasks, with additional value added.

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However, performing a simple task like automated picking and placing an object is still challenging for robots in industrial environments. According to The Robot Report, the adoption rate of bin-picking stations is low at large manufacturers, and the number is still close to zero at SMEs. For example, Amazon hired additional 400,000 workers to keep pace with e-commerce demand that is not fully automated. It indicates that fundamental challenges in robotic solutions are not solved with existing systems.

This eBook covers typical challenges in industrial automation applications and how we can solve them with new machine vision technologies. Like humans, machines need high-performance vision sensors to detect, pick, and place objects accurately. You will also learn about key considerations for choosing the right machine vision for your industrial robots.

Throughout the e-Book, you will see examples of 3D point clouds used in bin-picking and piece picking applications, captured with industrial Zivid 3D color cameras as well as other depth-sensing cameras. This will help you understand real-world problems in automation scenes and compare results between different machine vision sensors.

Download the eBook to read more here.

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