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 >>
Would you like to hear some good news? Here’s a great example of people making a difference – bringing high touch to high tech, while backing it up with their wallet.
Today, a new International Council on Artificial Intelligence and Robotics (iCAIR) was formed by the UAE Prime Minister’s Office, inspired by the World Economic Forum’s Global Agenda Council on Artificial Intelligence and Robotics on the occasion of the 7th Global Agenda Summit in Dubai, UAE. The Council will advise on the best way to use robotics and artificial intelligence to improve people’s lives, including the creation of educational materials and a global action plan.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 >>
Clint Reiser points out in the linked article that a new revolution in supply chain analytics is occurring. There is new level of data detail, it’s coming at us faster, providing bigger patterns and better insights.
The detail isn’t just point-of-sale information, customer buying patterns, or fleet telematics. From Universal’s perspective, the combining of machine learning and machine vision into robust automated solutions for material handling processes previously thought as random is a unique source of this data.Read More >>
As noted by ImpactLab.net, Pew Research questioned 1,896 experts about whether artificial intelligence (AI) applications and robots will have displaced more jobs than they have created by 2025. Fifty-two percent think more jobs will be created, 48% think more jobs will be displaced. So, its up for grabs as to who is right.
Note that if history repeats itself, technology has always been a job creator – not a job destroyer. However, the type of jobs, the skills required, and even entire industries will certainly go through massive change as part of the transition. As Vint Cerf, vice president for Google stated, “Historically, technology has created more jobs than it destroys and there is no reason to think otherwise in this case. Someone has to make and service all these advanced devices.”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 >>
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… 🙂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 >>