Realizing the Benefits of 3D Bin Picking
September 19, 2019
Noon - 1 PM ET
ABOUT THIS WEBINAR
Moderated discussion about the production related issues confronting the application of 3D Bin Picking. The Canon RV Series solution enables customers to realize fast, accurate and reliable results.
Attendees of this webinar will learn a basic understanding of how 3D bin picking technology works, including parts ranges, limitation and set-up requirement key features and options that enable practical production solutions.
- Adam Boike, Sales Engineer at Behco
- Walter LaPlante, Systems Engineer, Advanced Manufacturing Engineering at Ford Motor Company
Topic Areas: Vision-Guided Random Bin Picking
The Canon U.S.A. Inc. Industrial Products Division supplies advanced 3D Machine Vision Systems for random bin picking, optomechanical components, and optical test measurement tools for industrial automation. Canon's cutting-edge technologies lead the manufacturing industry in the field of image recognition and processing, and optics while maintaining the utmost quality in product precision and accuracy.
The 3D Machine Vision System has been specifically designed for random bin picking in manufacturing industries such as automotive, electronic, and plastic. Utilizing Canon's leading optical and processing technologies, the system automatically recognizes the parts, determines the best pick, and calculates the optimal path so that a robot can surely pick and place it to its destination.
Other products we offer include Digital Galvano Scanner, Super High Resolution Optical Laser Rotary Encoder, non-contact Laser Doppler Velocimeter, and Surface Reflectance Analyzer.Click Here for More
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.