Handling unknown objects in logistics with 3D vision

At a glance 

Challenge

Logistics is a high-paced real-time operation with any number of boxes, parcels and packages moving, at speed through logistics facilities. It is a very unpredictable situation with no prior knowledge of what will arrive and need to be sorted and sent on to its destination. These demands necessitate a highly adaptable and intelligent solution that can make decisions and learn as it goes, over time a system understands more of what it sees and becomes even faster at differentiating between items.

Solution

Fizyr are experts in pick and place solutions for the logistics industry. With a solid understanding of the challenges involved. Together with Zivid 3D camera technology they have developed, and deployed systems that are operating at some of the biggest logistics facilities on the planet. This is all about recognition, making a decision, acting correctly on that decision. Fizyr excel at software that can perform this task. But the data into any system will determine its efficacity. That is why they use Zivid 3D vision for robots. With their unparalleled point cloud fidelity in color. They are able to depend on what the robot is seeing and make informed decisions about automated picking and sorting.

Result

Fizyr solutions are being used extensively in some of the largest, fastest-moving picking and sorting operations on the globe like the ROSI cell from AWL. Fizyr detection and picking software, together with Zivid's machine vision cameras are proving themselves each and everyday making rapid decisions with a first-time-right solution essential for such a demanding application.

Fizyr’s software product enables the automated picking and 3D stacking of unknown items and parcels in real-life logistics environments. Accurate point clouds are necessary in order for picking robots to move robustly and efficiently. This is why we rely on Zivid’s high-quality robot vision cameras, that can reliably deliver the accurate and detailed point clouds that our deep-learning algorithms require."

Herbert ten Have, CEO of Fizyr