Universal Logic’s years as practitioners, implementing state-of-art automation, has given us deep insight into the interplay between invention and utility. We have engaged with a spectrum of clients from technocrats to novices, on a range of industrial applications. Through it all, one thing remains clear. Success in deploying a complex, multi-technology solution – blending vision, AI and motion control, resides in the close interplay and analysis between these disparate engineering domains. As magnificent as robots, sensors and algorithms are individually, it is their combination in service to an application that allows cutting edge automation to handle real-time dynamic variability.
Our full-stack software, Neocortex, is comprised of DeepEye (AI), Autonomy (path planning and obstacle avoidance), and Spatial Vision (middleware). It is machine agnostic. The software provides a closed-loop, behavior-based system of sense/act/learn and represents a profound expansion of manufacturing execution systems (MES) in an area of Industry 4.0 know as IoRT (Internet of Robotics Things). Neocortex is embodied, that is, physically in the world, controlling movement through space, and learning from that action. This is a paradigm shift in the way to think about software. Intelligence emerges because it has a physical form. The software/hardware union and the environment constitute two halves of a complex dynamical system. Neocortex senses the environment and reacts, changing the environment which updates the sensing and behavior. They are a closed loop.
Software structures easily allow for highly layered communications and decision making processes. As exposure to new variables increases over time, a rich set of exception handling protocols develop in Neocortex. The data base of retained and applied learning are brought to bear with every installation. There is a direct corollary between robust behavior and the layers of decision making options the software evaluates in milliseconds before executing an action.
The embodied character of Neocortex is reflected in Universal’s IOT profile, in that we are both a software developer and a system integrator. This is a great strength. Our intimate experience coupling the software and hardware, provides the real-world feedback necessary to perfect the craftmanship of Neocortex. We also team with other integrators, who benefit from our advice and sympathy as fellow integrators.
Our time on the line has given us the humility to know where the state-of-art actually is. Give us a call to discuss your application. We are very generous with our knowledge.Read More >>
If your robots could text you, it’s first message would read: “If I only Had a Brain”
Neocortex Leads the industry with a software “Brain” that gives robots human-like flexibility at high speed.
As Ray Bolger rightly exclaimed in the Wizard of Oz in 1939 “I could think of things I’d never Thunk before, And then I’d sit down and think some more.” Welcome to Industry 4.0 and the world of smart robots. Over the last few years, advances in the coupling of AI, 3D vision, and robot control, have spawned the expansion of machines replacing or working closely with laborers, manifesting all the benefits of automation to new horizons.
Universal spent a decade advancing it’s robot software brain, Neocortex. Today the technology is integrated across the supply chain from handling retail products to manufacturing, allowing our customers to reap the benefits of lower operating cost, improved reliability, and mitigation of risk. The product is achieved 6 Sigma reliability with picking speeds up to 1800 cycles per hour, tripling typical manual output.
Upgrade or Retrofit your current robots to Smart Robots and achieve both increased adaptability and productivity out of your Robots!
Read a fascinating article Dumb Robots vs. Smart Robots “If we want machines to think, we need to teach them to see” Fei-Fei Le, Chief Scientist Google, AI/Machine Learning.
From Bloomberg News: Make Dumb Industrial Robots SmartRead More >>
Sarah Mellish of Yaskawa Motoman Robotics explains the role of artificial intelligence for robotic order fulfillment:
“Technical innovations in artificial intelligence and robotics have unlocked massive potential for retailers to automate the handling of diverse Stock Keeping Units (SKUs) in the order fulfillment process with human-like flexibility at high speed….”Read More >>
A Neocortex® G2R (Goods to Robot) Cell is easy to deploy, like a collaborative robot. Yet it is uses an industrial Yaskawa Motoman robot with up to twice the capacity and 150% the speed of a collaborative robot (up to 800 items per hour).
How easy to deploy is the Neocortex G2R Cell? The average time for a Cell to be up and running at a customer site is one day. See our fun time-lapse video here. After it is up and running, our team then optimizes the robot and trains your operators. This contrasts with integrating an industrial robot system that may take weeks before being ready to run. The Cell’s deployment efficiency is comparable a collaborative robot’s average setup time, which is about half a day.Read More >>
Robotic material handling occurs in unstructured environments. In addition, the objects to be moved or manipulated by the robot often have never been seen before, vary constantly, and are in random locations. In the past, there was a tradeoff between throughput and flexibility.
Enjoy this short video blog as I explain on the whiteboard the current status of robotic solutions in the depalletizing application workspace, using throughput and flexibility as the main variables. It represents state of the art, industrial grade solutions for vision-guided robots, advanced vision-guided robots, and machine learning-guided robots.
Enjoy!Read More >>
In a recent article, Boston Consulting Group (BCG) points out that spending on robots worldwide is expected to grow from $15 billion in 2010 to $67 billion in 2025. The $52 billion increase in 15 years is a compounded annual growth rate of 10%. They attribute this growth to a convergence of falling hardware prices, performance improvements, and easier application software combined with increased flexibility and finesse. This results in robots being useful in a much broader set of applications than you might traditionally think of – such as automotive assembly and welding.Read More >>
Note from Bob Ferrari’s Post on Aug 7, 2014 entitled Permanent Shifts in Consumer Shopping Trends Have Supply Chain Implications. He comments on a quote in the article: Online Customer Fulfillment, Retail Supply Chain, Supply Chain Business Process that cites the following from Shopper-Trak: “Online sales have grown more than 15% every quarter for the past two years and are having a big impact on the way many companies are looking at their brick-and-mortar stores…. Rather than networks of distribution centers and fleets supporting individual physical stores, the new emphasis will be on high-volume online fulfillment supported by combinations of fulfillment centers and multi-purpose retail outlets.”Read More >>
Check out this interesting article on advances in deep learning for robots: Robots Helped Inspire Deep Learning and Might Become Its Killer App?
It’s worth comparing/contrasting between deep learning techniques and Neocortex. Neocortex is Universal Robotics’ patented machine learning platform, based on a seven-year development effort between NASA and Vanderbilt University. Even though the technology is currently employed on Robonaut 2 on the International Space Station, Universal’s focus with Neocortex is material handling tasks. By learning to recognize new objects or recognize previously seen objects that have changed, Neocortex machine intelligence brings flexibility to material handling automation.Read More >>
Check out this video showing Universal Robotics Neocortex – a patented next-generation machine intelligence software platform. It is guiding a robot to pick up a wide range of parts, providing flexible automation.
Traditionally, flexibility consisted of manually reconfiguring mechanical systems and sensors, and manually re-engineering algorithms to accommodate new parts. Neocortex’s machine learning can automatically handle a wide range of changing parts, reducing the need for manual changeovers. In the past, when a new part was introduced, even if the robot could pick up the new part, the robotic work cell would still require changes in fixturing, sensors, machine vision algorithms, and machine control.
In this video, see Neocortex guide the robot to handle various parts. Also shown is a demonstration of the simple training method for those occasional times an operator needs to teach Neocortex something new – a process that takes less than two minutes.Read More >>
We’ve recently discussed how the Microsoft Kinect can be used in robotic mobile manipulation. See above a video showing what is, to our knowledge, the first commercial integration of the Kinect with an industrial robot. Signal from standard webcams is also used. A software from Universal Robotics crunches the data to obtain a 3D representation of the scene. The application shown is the palletizing of randomly-placed boxes with Motoman robots.
Seeing consumer priced sensor entering into the industrial arena is very exciting, as long as the integration can prove to be robust enough.Read More >>