At the Institute for Intelligent Process Automation and Robotics of the Karlsruhe Institute of Technology (KIT), the Robot Learning Group (ROLE) focuses on various aspects of machine learning. The scientists are investigating how robots can learn to solve tasks by trying them out independently.
An Ensenso N35 camera is used to capture the beam position and geometry. As soon as the timber is in the printing position, the robot automatically places the flange-mounted camera so that it can detect the surfaces of the beam.
Deep learning opens up new fields of application for industrial image processing, which previously could only be solved with great effort or not at all. The new, fundamentally different approach to classical image processing causes new challenges for users.
How can industrial robots gain new abilities that can increase their operational value while remaining safe and secure in a factory collaborating with humans?
Our FactorySmart® 3D sensors are used to improve production across the gamut of industrial applications, from factory automation to quality inspection and material/process optimization. Booth #4811
Researchers used deep learning to create a new laser-based system that can image around corners in real time. The systems might one day let self-driving cars "look" around parked cars or busy intersections.
Machine vision is used in a variety of industrial processes, such as material inspection, object recognition, pattern recognition, electronic component analysis, along with the recognition of signatures, optical characters, and currency.
Digital image sensors work based on the Photoelectric effect. When the image sensor is exposed to light (photons), an equivalent charge is produced by the image sensor. It is then converted to image data.
Robotic sight and machine vision systems can help our automata pick up on visual cues that we humans would probably miss or ignore, and they can deliver considerable value to companies that adopt them. Here's a look at where the technology has been and where it's headed.
The conditions placed on original equipment manufacturers and suppliers are immense in order to ensure high quality and continuous improvement, waste reduction and error minimization.
The traditional vision architecture is changing, with an evolution from cameras and sensors to networked and smart-enabled, compact embedded devices with the processing power required for real-time analysis.
For manufacturers, the reliability of the quality control of industrial parts of all kinds is crucial, because defective parts due to non-conformity have serious effects on production performance.
High-speed CXP 2.0 is ideal for AV and ADAS (Advanced Driver Assist Systems) connectivity applications. Camera images from multiple sources around the vehicle, along with data from sensors capturing object shape, speed and distance.
Autonomous Machine Vision (AMV) offers a revolutionary approach to QA, allowing manufacturers to have access to less expensive and user-friendly technology that does not require the intervention of any external expert.
Acquisition of Proven Leader in Mission-Critical Robotics Systems for $385M Will Provide FLIR Entry Into Attractive Unmanned Ground Vehicles Market for Military, Public Safety, and Critical Infrastructure
Records 1 to 15 of 94
The Factory 4.0 ready Agile1500 AGV is equipped with a long lasting lithium battery and can adapt to accommodate diverse manufacturing needs. The two laser scanners on the front and rear guarantee efficient and safe navigation. Comau's AGV has the best in class payload to size ratio, capable of transporting up to 1.5 tons; it can be reconfigured with specific automatic and flexible equipment and is suitable for a wide range of industrial and logistical sectors.