Zivid-two-piece-picking-warehousing

Piece Picking 

Robotic piece picking is the process of automated order fulfillment, picking various individual items (SKUs) from an inventory bin and place them in an order container for shipping to customers. The choice of the vision system and, in particular, the 3D camera impacts the robot’s ability to successfully detect, pick, and place all types of pieces. Learn how to increase SKU coverage using your Zivid Two 3D camera - without compromising on speed.

Zivid-Detect

Detect

Zivid Two delivers high resolution and extreme precision, native color 3D point clouds with minimal occlusion and excellent artifact suppression. Significantly improving object recognition and increasing the number of detectable SKUs.

Zivid-Pick

Pick

Zivid Two enables more reliable detection of object boundaries for grasp planning, and a true to reality representation of object size, rotation, and absolute position in relation to the robot coordinate system. Essential for accurate picking, avoiding mispicks and crashes.

Zivid-Place

Place

Zivid Two with its groundbreaking trueness, enables more demanding place operations. Dimensioning of objects, and placing for packaging with known position and orientation, with minimal gaps and tight fit, and without colliding or damaging the objects.

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Work faster. Zivid Two delivers significantly higher quality at much faster speeds than typical stereo 3D cameras used in piece picking today. Capture speeds <100ms for low to medium dynamic range scenes of some typical piece picking items.

Challenges & Solutions

Piece picking is still a challenge for robots, the detection and picking of unknown SKUs of various shapes, sizes and materials is still one of the biggest problems in logistics. Unpickable objects goes under many terms, such as “uglies” or “pathologicals”, and the difficulty stems from the inherent uncertainty in physics, perception, and control.

See tiny and detailed objects, densely stacked, or randomly arranged.

Zivid Two provides high resolution and precision point clouds with minimal occlusion, capturing shapes and sizes with all the fine details.

  • Distinguish features smaller than 5mm for reliable object recognition and separation of boundaries for picking.
  • Very small, thin, porous, deformable, or highly irregular objects. Densely stacked with little gap or randomly arranged and piled on top of each other.
  • Low occlusion with almost no shadowing contrary to bigger baseline cameras that can potentially miss smaller objects hidden by shadows in the bin, e.g. close to corners and edges.
Random

Point cloud example with Zivid Two - Randomly arranged, densely packed and thin objects. View in 3D here

See shiny, reflective, glossy, and plastic wrapped objects.

3D HDR and Artifact Reduction Technology (ART) ensures excellent suppression of imaging artifacts from reflections, interreflections, specular highlights and high contrast transitions.

  • Shiny and reflective plastic, aluminum, ceramics, cosmetics, glossy packaging, translucent, semitransparent, and plastic wrapped items.
  • Avoiding artifacts from the highly reflective plastic bins often used in logistics.
  • Capture speeds < 300 ms for high dynamic range scenes such as shiny and reflective
Plastic wrapped objects

Point cloud example with Zivid Two - Plastic wrapped objects. View in 3D here

See a wide variety of objects.

The unique combination of native color and high dynamic range enables imaging of a broad range of SKUs

  • Plastic, ceramic, metal, cardboard, wood, colored, textured, light, dark and absorptive. Single or mixed SKU bin scenarios.
  • Non-laser based white light for broad material coverage and native color capabilities.
  • 2D and 3D data from the same sensor.
  • Capture speeds < 300 ms for high dynamic range scenes, including shiny and dark absorptive.
Wide variety of objects

Point cloud example with Zivid Two - Wide variety of objects. View in 3D here. 

What more would you like to see? Please reach out and we'll do our best to help out!  

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