The Association for Advancing Automation (A3) and ITR Economics has delivered a Global Economic and Automation Outlook, providing year-to-date updates and forecasts through 2021 based on recent market data reflecting the impact of COVID-19. A3 statistics showed robotics, machine vision, and motion control markets all contracted in the first half of 2020, compared to the same period in 2019.

Robotics

“It’s clear that our industry is feeling the effects of COVID-19, its strain on supply chains, and the overall economic uncertainty due to our current circumstances,” said Alex Shikany, A3 Vice President, Membership & Business Intelligence. “Despite the numbers reflecting these recent challenges, our latest market surveys tell us that there is optimism for what the next six months will bring.”

With 13,524 units ordered, the North American robotics market decreased 18% from the first half of 2019. Order revenue totaled $716 million, which is also down 18% compared to the first half of 2019.

“Despite the overall contractions, there were two notable industry bright spots in life sciences (+97%) and plastics & rubber (+49%),” Shikany said.

A3’s recent survey of its robotics industry members showed optimism about what’s to come. When respondents were asked what they believe will happen to their sales in the next six months, 36% believe they will increase moderately (between 1% and 10%), while 22% believe they will increase significantly (more than 10%). Only 19% of respondents believe there will be further decreases, while 23% believe their sales will remain flat.

Machine Vision

The North American machine vision market is also below one year ago levels, decreasing 8% in total to $1.3 billion.

Machine vision components, consisting of cameras, lighting, optics, imaging boards, and software, fell 9% in total to $174 million. Machine vision systems, including application specific machine vision (ASMV) and smart cameras, fell 8% to $1.1 billion.

When asked about the next six months, A3 vision and imaging members were similarly optimistic, with 42% believing sales will increase moderately (between 1% and 10%), and 17% believing sales will increase significantly (more than 10%). A greater share of vision respondents (27%) believe there will be further decreases, while 15% expect sales to remain flat.

Motion Control

Order revenue in motion control and motor markets totaled $1.669 billion in the first half of 2020, marking a 6% decrease from last year. All product categories (including motion controllers, AC drives, motors, AC motors, actuators, sensors, and support) experienced year-over-year decreases in revenue.

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Who doesn’t love shiny things? Well… robots for one. The same goes for transparent objects.

At least, that’s long been the case. Machine vision has stumbled when it comes to shiny or reflective surfaces, and that’s limited use cases for automation even as advances in the field push robots into more and more new spaces.

Now, researchers at robotics powerhouse Carnegie Mellon report success with a new technique to identify and grasp objects with troublesome surfaces. Rather than relying on expensive new sensor technology or intensive modeling and training via AI, the system instead goes back to basics, relying on a simple color camera.

To understand why it’s necessary to understand how robots currently sense objects prior to grasping. Cutting edge computer vision systems for pick-and-place applications often rely on infrared cameras, which are great for sensing and precisely measuring the depth of an object — useful data for a robot devising a grasping strategy — but fall short when it comes to visual quirks like transparency. Infrared light passes right through clear objects and is reflected and scattered by reflective surfaces.

Color cameras, however, can detect both. Just look at any color photo and you’ll clearly discern a glass on a table or a shiny metal railing, each with lots of rich detail. That was the vital clue. The CMU researchers built on this observation and developed a color camera system capable of recognizing shapes using color and, crucially, sensing transparent or reflective surfaces. 

“We do sometimes miss,” David Held, an assistant professor in CMU’s Robotics Institute, acknowledged, “but for the most part it did a pretty good job, much better than any previous system for grasping transparent or reflective objects.”

That the solution is low-cost and the sensors battle-tested give it a tremendous leg up when it comes to the potential for adoption. The researchers point out that other attempts at robotic grasping of transparent objects have relied on training systems based on trial and error or on expensive human labeling of objects.

In the end, it’s the end, it’s not new sensors, but new strategies to use them that may give robots the powers they need to function in everyday life. 

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Machine vision, natural language processing, data analytics and other deep learning applications will propel global AI software revenues over the next five years via a growing list of industry segments spanning automotive and health care to financial services and retail.

Market tracker Omdia forecasts AI software revenues will surge through 2025 to $126 billion, a 12-fold increase over a $10.1 billion industry in 2018. “The narrative is shifting from asking whether AI is viable to declaring that AI is now a requirement for most enterprises that are trying to compete on a global level,” said Keith Kirkpatrick, principal analyst with Omdia.

“AI is likely to trigger major transformations in industries where there is a clear case for incorporating AI, rather than in pie-in-the-sky use cases that may not generate a return on investment for many years,” Kirkpatrick added.

Omdia estimates that more than half of AI revenues will be generated by machine vision and language applications, with deep learning deployments driving the AI market. Deep learning models are proving more capable for perception applications since they can operate without expensive training and are able to tap very large data sets to evolve. Omedia sees those attributes as attractive for applications like cybersecurity, health care and investment trading.

Hence, the market tracker predicts deep learning will account for an estimated $74.5 billion in AI software sales by 2025, or 59 percent of total AI revenues.

The consumer sector has seeded the AI software market via early applications such as digital assistants, smart speakers and automotive applications. Voice and speech recognition apps have so far generated the most AI software revenue.

Those consumer applications tapped into large data sets that resulted in improved AI algorithms and processing engines. Omdia expects other sectors to apply these early uses cases to more ambitious, data-driven applications centered around Internet of Things deployments. Meanwhile, the shift to edge computing and the efforts of infrastructure vendors to move computing and storage resources closer to where data resides are expected to spur development of specialized deep learning algorithms and improved processing capabilities at the network edge.

The market tracker also foresees hybrid AI deployments in which deep learning models are combined with machine vision, natural language processing and “machine reasoning.”

That combination is seen overtaking the role of machine learning for data analytics applications. Hence, deep learning applications will help drive “AI in the long run due to the wide range of use cases that will be enabled now and well into the future,” Omdia said.

That bullish AI software forecast squares with other prognostications. For example, Fortune Business Insights predicted earlier this year that the global market for all AI technologies would grow at a 33-percent clip through 2026, reaching more than $202 billion.

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