VISIO NERF
Overview
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HQ Location
France
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Year Founded
1990
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Company Type
Private
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Revenue
< $10m
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Employees
11 - 50
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Website
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Company Description
Visio Nerf is engaged in the production of industrial vision solutions. The Company designs and builds modular image processing systems dedicated to camera controls, including automatons and associated software. It designs its products for the automobile, food processing, electronics, aerospace and armament, as well as for packaging sectors.
IoT Snapshot
VISIO NERF is a provider of Industrial IoT analytics and modeling technologies, and also active in the automotive industries.
Technologies
Use Cases
Functional Areas
Industries
Services
Technology Stack
VISIO NERF’s Technology Stack maps VISIO NERF’s participation in the analytics and modeling IoT Technology stack.
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Devices Layer
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Edge Layer
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Cloud Layer
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Application Layer
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Supporting Technologies
Technological Capability:
None
Minor
Moderate
Strong
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Case Studies.
Case Study
Robotic Guidance: Automatic Wheel Mounting
There are many challenges to address when designing applications that can improve performance, flow, and quality. These challenges involve rotors and the process of installing tires:Over 60 different edges are used for different types of surfaces (dark, matte, glossy), which makes 2D camera systems difficult to capture due to the influence of lighting solutions on different types of surfaces.The bolts are pre-installed on every router. Each bolt cap has a small surface area, which means a 3D, high-resolution vision system is necessary to accurately locate the point cloud for each bolt cap. During installation, the rotor has random rotation, which means that the bolts are in different positions for each installation, and a solution is needed that will identify the bolt positions.The solution also needed to be capable of 3D matching or large point clouds, as the rotor could rotate 15 degrees in both directions along with the vehicle.In addition to these technical factors, the part is heavy and has a limited cycle time of only 3.5 seconds for full point cloud grabbing and processing.
