Photoneo
Canvas Category Machinery : 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
Brightpick Raises $12 Million to Accelerate Rollout in the US
Brightpick, a leading provider of warehouse automation solutions for order fulfillment, announced it has raised an additional $12 million in a mix of equity and debt. The round saw participation from new investor EBRD Venture Capital, alongside existing investors Pavel Baudiš and Eduard Kučera (founders of Avast), Miroslav Trnka (founder of ESET), Maximilian Kolowrat-Krakowsky, and Juraj Duriš.
Founded in 2021 as a spin-off of Photoneo, a leader in 3D robotic vision sensors and intelligence software, Brightpick now has over 200 employees and 300 AI robots deployed across the US and Europe. Its customers include leading companies such as Rohlik Group, Dr. Max, and The Feed. To date, Photoneo has invested over $35 million in Brightpick, with this latest round bringing total funding to $47 million. The new funding will primarily support the deployment of Brightpick’s AI robots in the US, which is expected to generate 50% of the company’s revenue in 2024.
Photoneo’s Bin Picking Studio & Locator Studio Join Kawasaki Robotics’ K-AddOn Ecosystem
Photoneo’s Bin Picking Studio and Locator Studio are now officially part of Kawasaki Robot’s K-AddOn program. This collaboration marks a significant milestone in our mission to make advanced automation accessible to everyone. What can we look forward to? We’re ahead of the streamlined process of connecting our advanced 3D vision systems with Kawasaki robots, making automation solutions more accessible and versatile.
K-AddOn is a platform developed by Kawasaki Robotics that facilitates the integration of their robots with third-party products and accessories. It serves as a bridge between Kawasaki’s robotic systems and compatible external components. With K-AddOn, incorporating Photoneo’s vision systems into your Kawasaki robot workflows is truly effortless. This seamless integration allows you to focus on what matters most – optimizing your automation processes and boosting productivity.
Photoneo and Yaskawa Partnership: Pioneering the Future of Logistics Automation
At Photoneo, we are proud to announce our collaboration with Yaskawa to deliver groundbreaking solutions, revolutionizing the palletizing and depalletizing landscape. By merging Photoneo’s state-of-the-art 3D vision technology with Yaskawa’s robust robotic systems, we are setting new standards for automation in warehouses and distribution centers. But how did the entire journey materialize? It all starts with PackMaster.
In addition to palletizing, our collaboration with Yaskawa has yielded an AI-driven depalletization solution that transforms warehouse unloading processes. By leveraging Photoneo’s advanced 3D Vision sensors and an AI Neural Network capable of processing over 1,000 boxes per hour with an astounding 99.7% accuracy, we are redefining operational efficiency.
Photoneo 3D sensors integrated into TM Robot Ecosystem
As of November 2023, you can seamlessly use Techman Robot with Photoneo best-in-class 3D sensors for various 3D vision-guided robotic applications such as bin picking or depalletization. Techman Robot developed a Depalletization solution that relies on high-quality 3D data from Photoneo cutting-edge 3D camera MotionCam-3D Color. This flexible Depalletization solution includes an agile Techman collaborative robot and Photoneo’s flagship 3D vision device.
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,”