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.Read More >>
Frank Tobe’s article in Robohub regarding what’s hot in robotics reads like a who’s who of companies providing state-of-the-art applications in robotics. He breaks down the trends into four buckets, one of which I’d like to address – Advances in Visual Perception.
Here’s what he says:
“Vision-enhanced robotic systems are becoming the top reason for upgrading and deploying vision-enabled robots and a core reason for the steady upward growth of the robotics industry…. Artificial intelligence and various AI learning systems have been improving regarding visual perception, and many new companies (such as Universal Robotics and their Neocortex system) are now either offering vision systems that can supplement existing fixed systems or offering mobile manipulators that can find and determine how best to pick and handle all sorts of objects from plastic-wrapped toys to boxes, cases and skids of materials.”
Frank’s points are correct. Let’s unpack the reasons for this burgeoning market opportunity.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 >>
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 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 >>