Semi-Structured vs Random Pile Bin-Picking: How to Get Started with Industrial Automation

June 29, 2021 | 12 PM - 1 PM ET

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3D Machine Vision systems can reduce cycle time, increase productivity, and make applications more efficient in your facility. By being able to handle a variety of part types and pile types (semi-structured or even completely random part orientation), 3D vision systems can successfully recognize your parts and greatly improve your process as a whole.

If you’re looking to dive into 3D vision and industrial automation without the complexity of random picking, semi-structured picking is a great place to start as the majority of bin-picking applications are semi-structured and are running extremely successfully.

In this webinar, we will discuss the benefits of all types of bin-picking and what you need to get started quickly and easily, in addition to:

  • Difficulties and resolutions in semi-structured picking
  • Difficulties and resolutions in random picking
  • Benefits of semi-structured over random picking
  • Benefits of random picking over semi-structured
  • High-level overview of 3D vision
  • Benefits of industrial automation

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Exclusive Sponsor

Sponsor Logo The Imaging Solutions Group within Canon USA Inc. incorporates current and future Canon technologies into new products and solutions, including innovative CMOS sensors that overcome major technical barriers in the vision industry. With local facilities in the USA, and collaboration with Canon Inc. in Japan, Canon has extensive capabilities and works with all Canon optics and sensor technologies ranging from lens modifications and custom glass coatings down to the image sensor itself.

Image of Grant Zahorsky, Sales Engineer

Grant Zahorsky, Sales Engineer

Originally, from Kansas City, MO, Grant Zahorsky started working for Canon USA in 2019, focusing on the progression of the Canon RV-Series machine vision system. This system utilizes Canon's high-end consumer and professional cameras to globally assist companies by automating their facilities, thereby creating a more efficient and safer workplace. Grant's background is in Robotics Engineering, in which he earned a Bachelor of Science degree from Worcester Polytechnic Institute in Massachusetts. He has also had experience working at a factory that specializes in robotic welding for Tier 1 companies in the automotive industry. With his involvement in the world of robotics, artificial intelligence, and machine vision systems, Grant has set the stage to leave his mark on the industry.

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