Photoneo
Hardware : Sensor Systems : Machine Vision
Photoneo is a leading provider of robotic vision and intelligence. Based on a patented 3D technology, Photoneo developed the world’s highest-resolution and highest-accuracy 3D camera, thus unlocking the full potential of powerful, reliable, and fast machine learning and also reducing the training and deployment time. By bringing intelligent robots into the field, Photoneo helps companies mainly in the automotive, logistics, e-commerce, food, and medical industries to improve the performance and efficiency of their manufacturing, fulfillment, and assembly processes.
Assembly Line
Automated control and filling of crates
How is 3D machine vision transforming manufacturing processes?
3D machine vision employs 3D cameras that provide robots with data and information pertaining to particular parts. These three-dimensional cameras can be installed at various locations to create 360-degree, multi-angle images for surface and volume inspection.
The topographical map results from reflected laser displacement. Taking images from two distinct angles facilitates you in getting the 3D data of the image. Then, the separation between each perspective in 3D space is computed. There’s some installed software that can do some substantial image processing and analysis. To evaluate an object with machine vision software, a PC-based machine vision system is hardwired to vision cameras and image capture boards.
Photoneo Brightpick Group Raises Additional $19 Million to Complete $40 Million Series B
Photoneo Brightpick Group, the parent company of Photoneo, a leading provider of robotic vision sensors and intelligence software, and Brightpick, a leading provider of warehouse automation solutions for ecommerce and grocery order fulfillment, today announced it has raised an additional $19 million to complete a $40 million Series B. Taiwania Capital led this most recent round, which also included follow-on investments by prior Series B lead investors IPM Group and Alpha Intelligence Capital. Additional investors in this round include H&D Asset Management, Venture to Future Fund and Kolowrat Group. This round brings the company’s total capital raised since inception to $53 million.
Locator studio
Industry 5.0: Adding the Human Edge to Industry 4.0
The arms of pick and place robots are equipped with end effectors similar to human hands that are specifically designed for picking various types of objects. These may include components that are further used in the manufacturing processes of products.
Pick and place robots have a wide range of capabilities. Depending on specific application requirements, they can be equipped with several types of end effectors. The most common ones include vacuum grippers with suction cups, fingered grippers, clawed grippers, magnetic grippers, or custom grippers. To achieve a high level of flexibility, pick and place robots are often equipped with multiple arms and heads. This helps them approach objects from several angles at any given time.
Evaluation Criteria for Trajectories of Robotic Arms
This paper presents a complex trajectory evaluation framework with a high potential for use in many industrial applications. The framework focuses on the evaluation of robotic arm trajectories containing only robot states defined in joint space without any time parametrization (velocities or accelerations). The solution presented in this article consists of multiple criteria, mainly based on well-known trajectory metrics. These were slightly modified to allow their application to this type of trajectory. Our framework provides the methodology on how to accurately compare paths generated by randomized-based path planners, with respect to the numerous industrial optimization criteria. Therefore, the selection of the optimal path planner or its configuration for specific applications is much easier. The designed criteria were thoroughly experimentally evaluated using a real industrial robot. The results of these experiments confirmed the correlation between the predicted robot behavior and the behavior of the robot during the trajectory execution.
3D Vision Technology Advances to Keep Pace With Bin Picking Challenges
When a bin has one type of object with a fixed shape, bin picking is straightforward, as CAD models can easily recognize and localize individual items. But randomly positioned objects can overlap or become entangled, presenting one of the greatest challenges in bin picking. Identifying objects with varying shapes, sizes, colors, and materials poses an even larger challenge, but by deploying deep learning algorithms, it is possible to find and match objects that do not conform to one single geometrical description but belong to a general class defined by examples, according to Andrea Pufflerova, Public Relations Specialist at Photoneo.
“A well-trained convolutional neural network (CNN) can recognize and classify mixed and new types of objects that it has never come across before,”