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3D vision enables smart factories, autonomous warehouses and robot automation in Industry 4.0

Øyvind Borgan
2018-07-31

The fourth industrial revolution is happening, and it has the potential to disrupt how we work in global and regional industries. It's clear that human-machine and human-robot collaboration will generate new business outcomes. One of the critical enablers for Industry 4.0 is for machines and robots to work seamlessly in our world. For this to happen, they need to see "our" world.

Zivid is at the forefront of a new generation of 3D machine and robot vision companies.

Our products, Zivid One 3D cameras, are an essential step forward for the machine vision industry. They have a unique combination of high-end features in an industry ready, compact and cost-effective package.

For end-customers, this translates to radically increased robustness and performance of existing automation systems. It will enable robots to take on new and challenging tasks, previously difficult or impossible to automate. It will also make it possible to handle dangerous, dirty, and dull tasks better.

The result for both existing and new markets, and in local and sustainable small-scale facilities include

  • improved manufacturing and fulfillment
  • higher production and order processing rates
  • higher throughput
  • more stable quality
  • improved better profitability

Zivid-One-Industry-4.0-3D-vision

Many applications are primed and ready for support by robots and 3D vision. 

  • manufacturing, machine feeding, order fulfillment in a warehouse or palletizing/de-palletizing for postal and logistics. Randomly arranged objects in e.g. a container can now be detected, identified, picked and retrieved. Additional handling includes placing, sorting, packing, and storing. 
  • kitting and assembly operations for automated building and construction. 3d vision is used to mount and connect multiple components to finalize goods.
  • Automated positioning in «jig-less» setups.  large fixtures are replaced with 3D vision guiding.
  • High-accuracy guiding, and control of robotized gluing, welding, deburring, painting, etc.
  • Fast inspection and quality control. Taking precise 3D images of components and revealing small defects or anomalies in shape or color. 
  • Lightning fast 3D digitization of objects. Useful in 3D printing, 3D visualization, and digitization in addition to a broad set of other automation applications.

The different industries and markets affected are many, some examples can be:

  • Manufacturing industries
  • Logistics, postal, retail, warehouse and e-fulfillment
  • Agriculture
  • Consumer (VR/AR/Gaming/Entertainment)
  • Food industries (handling & processing)
  • Medical & healthcare

In high-cost countries, smart automation will help businesses to produce more goods locally. Local production means less transportation, less pollution and enables nations to retain technological knowledge and know-how of its home-grown companies.

Similarly, for logistics and warehousing as consumers buy more, robotics is needed to pick and package boxes of mixed items. Flexible solutions with lower cost in local distribution centers can operate on a smaller scale, locally closer to the customers for faster order fulfillment in an efficient, user-friendly and sustainable way.

Small-scale production and rapid production changes are factors that require new, smart and flexible yet reliable robot and automation solutions. An essential factor for successful automation outcomes is to go from traditional 2D camera systems to 3D imaging.

Being the first 3D color camera designed explicitly for Industry 4.0, Zivid One provides the highest degree of Quality of Data;

  • Provides high-accuracy data output and absolute accuracy across the complete imaging sensor
  • accurately represents shapes and forms, surface details and colors
  • Optimal baseline reduces shadowing between projector and camera, providing minimal occlusion
  • Provides data on every type of surface, including shiny, reflective, translucent, dark, and absorptive
  • Reduces noise-per-pixel by controlling optical challenges with reflections and scattering
  • Pixel-wise processing compared to obsolete methods such as analysis of spatial neighborhoods and region-based averaging

 

 

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