Showing posts tagged with: AI

Neocortex AI links cyber and physical systems for Industry 4.0

Artificial Intelligence, eCommerce, Machine Learning, Neocortex, Neocortex G2R Cell, Robotic Order Fulfillment, Robotics / 26.10.2017

Neocortex artificial intelligence links cyber/digital systems and physical systems of the Industry 4.0 revolution – where software algorithms control machines that are integrated with each other and users.

Neocortex uses a closed loop system, known as cybernetics, to control robots.  This embedded system uses sensor data coupled with physical movement, further integrating the Internet of Things.

In the supply chain, the above smart system provides a highly flexible robot cell that operates at maximum speed – matching the utility of semi-skilled labor.  The Neocortex-based robotic cell (Neocortex G2R Cell) continues to get smarter with time – adjusting to parts, processes, containers, and orders. Thus, its value increases over time and with use (just like an employee), leading to a SaaS (software as a service) delivery model.

Neocortex SaaS also spans cloud-computing servers.

This broadens the scope and significance of patterns discovered and monitored across the cyber-physical barrier, leading to valuable breakthroughs in advanced analytics of big data: supply chain effectiveness, group learning of robots, introduction of SKUs, quality or vendor problems, damage trends, and customer order satisfaction. This both lowers costs and increases sales.

Watch a great overview of Industry 4.0 implications here.

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Robotic Throughput vs Flexibility

3D Vision Guidance, Artificial Intelligence, Machine Learning, Neocortex, Robot Vision Guidance, Robotics / 19.09.2014

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!

Robotic Depalletizing Throughput vs Flexibility Video Blog

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BCG: Growth of Robotics Through 2025

3D Vision Guidance, Artificial Intelligence, Machine Learning, Robot Vision Guidance, Robotic Order Fulfillment, Robotics, Robotics Business / 09.09.2014

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.

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Online Orders Push Need for Accurate, Automated Order Fulfillment

3D Vision Guidance, Artificial Intelligence, eCommerce, Machine Learning, Neocortex, Random Bin Picking, Robot Vision Guidance, Robotic Order Fulfillment, Robotics, Robotics Business / 14.08.2014

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.”

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Siri has competition… Viv; Consumer-Friendly AI that Teaches Itself

Artificial Intelligence / 12.08.2014

Artificial intelligence is taking yet another step forward. Here’s an update on a new effort by those who helped create Apple’s Siri. It’s consumer friendly, and teaches itself as it goes. Sounds familiar… 🙂

Siri’s Inventors Are Building a Radical New AI That Does Anything You Ask

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Deep Learning for Robots

3D Vision Guidance, Artificial Intelligence, Cybernetics, Neocortex, Robotics, Robotics Business / 08.08.2014

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.

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Universal Robotics Neocortex Shifts Material Handling Flexibility to Software

3D Vision Guidance, Artificial Intelligence, Machine Learning, Neocortex, Neocortex G2R Cell, Random Bin Picking, Robot Vision Guidance, Robotics / 05.08.2014

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.

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