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Over the past few years, smart manufacturing initiatives such as digital twins and the internet of things (IoT) has caused Industry 4.0 the trend toward digital transformation in manufacturing and industrial sectors to explode. However, robots and drones tasked with visually inspecting machines havent yet seen the same growth.That is set to change in a big way, Bill Ray, vice president and analyst, emerging technologies and trends at Gartner, told VentureBeat.

The robots, drones and cameras that inspect machines to perform predictive maintenance and relay analog information to operations staff can now function autonomously. Even better the longer theyre at their jobs, the better they do.

AI and machine learning tools bestow moving inspection systems that is, robots and drones with the capability to read the analog output from industrial machines that havent been digitized. In this way, they act as a bridge between Industry 4.0 and the analog age it will replace.

With cognitive intelligence, Ray said, robots and drones can make proactive decisions. And, he added, this is still early days. We expect it to be standard for Industry 4.0 in the next five years, Ray said.

In fact, Gartner expects asset inspection and human augmentation (helping out human inspectors) to be the fastest growing use of robotics. The inspection robots market size reached $940 million in 2020, according to a report from Allied Market Research. The report predicts the industry will grow at 31 percent to reach $1.4 billion by 2030.

Rays words underscore todays announcement from Florida-based Levatas that it has raised $5.5 million in a seed round led by Castellan Group. The company provides cognitive intelligence for automating industrial inspection equipment. It does not make robots, drones or vision systems. Instead, it partners with those makers to instill equipment with cognitive intelligence, said Chris Nielsen, CEO of Levatas.

The Levatas software guides these autonomous systems to learn how to do the everyday, mundane operational tasks that keep manufacturers running. For example, they learn to read gauges and to inspect and report abnormal temperature changes. Over time, through the use of machine learning, cognitive machines get better and better at their jobs.

The company competes with other makers of robotic cognitive intelligence systems for a piece of the growing industry. Levatas boasts annual revenues at around $21 million. Competitors include makers of robotics inspection systems for smaller businesses, including Stradigi AI, ReadSense, MotionCloud and Visenze.

Levatas also competes with companies that have a much larger share of the smart robotics inspection sector, said Ray, including Karsh Hagan, with annual revenues of about $19 million and industry giant Profero, which returns annual revenues around $132 million.

This summer, the company plans to roll out a new feature: change detection. Inspection systems will be able to report negative changes in the plant environment, such as a missing fire extinguisher, Nielsen said.

The Levatas system results in higher efficiencies, more uptime, safer workplaces and measurable ROI, Nielsen added: We sit at the heart of Industry 4.0, at the intersection of advanced robotics and inspection systems.

Levatas-trained robots, drones and vision systems are already in place at automakers, oil refineries, energy producers and a large U.S. brewery.

That is precisely the kind of growth Ray expects to see in the future for cognitive intelligence. It has the potential to take inspection and other plant operations to a different level.

You can imagine a robot inspecting the outside of a building and it sees a wet patch, he explained. Maybe AI will highlight the patch, but the drone knows it needs to fly in closer and maybe change the angle to see the patch. These proactive decisions, he explained, totally changes the inspection mission.

This kind of feedback can be folded into Industry 4.0, which will digitize the entire factory floor, using data returned by everything from sensors to conveyor belts to optimize plant performance. But many factories havent digitized every last machine. Upgrades like that would be way too costly especially when analog machines still work fine and would mean significant downtime.

That was true for Global Foundries, one of the worlds leading semiconductor manufacturers, which uses Boston Dynamics Spot robots to improve manufacturing efficiency with help from Levatas.

Global Foundries has around 2000 filter pumps that would cost millions to upgrade to digital sensors. The company put a roving robot on the job with mobile sensors to spot pump malfunctions that, if left unattended, could cost hundreds of thousands of dollars in repairs and downtimes.

As Industry 4.0 advances, these analog-reading robots like Spot wont be pushed out of their jobs. Organizations will simply find other ways to put their cognitive intelligence to work, Ray said.

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How AI-driven robots and drones bring cognitive intelligence to Industry 4.0 - VentureBeat

Astronaut Kayla Barron, posing with astronauts (from left) Samantha Cristoforetti, and Jessica Watkins, is pictured in her SpaceX flight suit before departing for Earth on May 5.

Robotics and human research were the dominant research themes aboard the International Space Station on Monday. The Expedition 67 crew also began the workweek maintaining a variety of exercise equipment and life support gear aboard the orbiting lab.

NASA Flight Engineer Kjell Lindgren turned on the Astrobee robotic free-flyers and let them roam around inside the Kibo laboratory module on Monday. The cube-shaped devices, powered by fans and programmed using algorithms, photographed imagery of Kibos racks and systems to demonstrate their ability to autonomously monitor and maintain spacecraft systems. Lindgren also tended to the XROOTS space botany study before tightening gas connections on the Combustion Integrated Rack.

NASA Flight Engineer Jessica Watkins wore the Bio-Monitors vest and headband today testing its ability to monitor crew health without interfering with crew activities. Watkins also assisted NASA astronaut Bob Hines who began the six-month maintenance and inspection tasks on the COLBERT treadmill located in the Tranquility module.

ESA (European Space Agency) astronaut Samantha Cristoforetti spent Monday testing the rHEALTH ONE medical device demonstrating its ability to identify cells, microorganisms, and proteins in microgravity. She also shared photographs of the lunar eclipse from the station as it orbited above the Pacific Ocean northeast of New Zealand.

Station Commander Oleg Artemyev joined Flight Engineer Denis Matveev on Monday on ventilation systems and radiation detection tasks in the orbiting labs Russian segment. Roscosmos Flight Engineer Sergey Korsakov started his day exploring future planetary piloting techniques before servicing a Russian oxygen generator.

The space station is orbiting higher after the ISS Progress 79 cargo craft fired its engines on Saturday afternoon. The orbital reboost places the station at the correct altitude for Russias next cargo craft, the ISS Progress 81, slated to launches on June 3 and docks to the Zvezda service modules rear port about three-and-a-half hours later.

NASA and Boeing are still proceeding toward the launch of the uncrewed Orbital Flight Test-2 mission at 6:54 p.m. EDT on Thursday. Boeings Starliner crew ship is targeted to dock to the Harmony modules forward port about 24 hours later where it will stay for up to 10 days of cargo and test operations.

Learn more about station activities by following thespace station blog,@space_stationand@ISS_Researchon Twitter, as well as theISS FacebookandISS Instagramaccounts.

Get weekly video highlights at:http://jscfeatures.jsc.nasa.gov/videoupdate/

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Advanced Robotics, Life Science Today; Station Boosts Orbit for New Missions - NASA (.gov)

In the second half of 2021 the number of deals relating to robotics decreased by 8.7% from the same period in 2020.

This marks an acceleration in growth from the 25% decrease in deals that occurred in H1 2021 relative to the same period a year earlier.

GlobalDatas deals database looks at mergers, acquisitions and venture capital and private equity investments taking place daily between thousands of companies across the world.

During second half of 2021, deals relating to robotics accounted for 2.9% of all deals taking place in the sector. This represents a decrease from the figure of 3.3% in second half of 2020.

GlobalData's thematic approach to sector activity seeks to group key company information on investments to see which industries are best placed to deal with any issues they may encounter.

These themes, of which robotics is one, are best thought of as "any issue that keeps a CEO awake at night", and by tracking them, it becomes possible to ascertain which companies are leading the way on specific issues and which ones have some work to do.

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Deals relating to robotics decreased in the tech industry in H2 2021 - Verdict

Dublin, May 17, 2022 (GLOBE NEWSWIRE) -- The "Global Medical Rehabilitation Robotics Market 2021-2031" report has been added to ResearchAndMarkets.com's offering.

A recent market study on the medical rehabilitation robotics market offers a global industry analysis for 2016-2020 and an opportunity assessment for 2021-2031.

The report consists of a comprehensive assessment of the most important market dynamics. On conducting thorough research on the historical as well as current growth parameters of the medical rehabilitation robotics market, growth prospects of the market are obtained with maximum precision.

The report features unique and salient factors that may make a huge impact on the development of the medical rehabilitation robotics market during the forecast period. It can help market players modify their manufacturing and marketing strategies to envisage maximum growth in this market in the coming years.

The report provides detailed information about the current and future growth prospects of medical rehabilitation robotics market in the most comprehensive way for better understanding of readers.

Key Segments of Medical Rehabilitation Robotics Market

This study on the medical rehabilitation robotics market offers information divided into seven important segments - product, therapy type, extremity, patient type, application, end user and region. This report offers comprehensive data and information about the important market dynamics and growth parameters associated with these categories.

Product

Application

End User

Region

Key Topics Covered:

1. Executive Summary

2. Market Overview2.1. Market Coverage / Taxonomy2.2. Market Definition / Scope / Limitations

3. Key Market Trends3.1. Key Trends Impacting the Market 3.2. Product Innovation / Development Trends

4. Key Success Factors4.1. Technology Assessment4.2. Strategic Promotional Strategies4.3. Recent product Launches4.4. Regulatory Landscape 4.5. Reimbursement Outlook4.6. Supply Chain Analysis

5. Market Background and Foundation Data points5.1. Macro-Economic Factors5.1.1. Key Facts and Prevalence5.1.2. Global GDP Growth Outlook5.1.3. Global Healthcare Expenditure Outlook5.1.4. Per-Capita Healthcare Expenditure5.1.5. Medical Robot Market- Parent Analysis5.2. Forecast Factors - Relevance & Impact5.2.1. Rising Prevalence of Neurological Disorders and Traumatic Brain Injuries 5.2.2. Growing Technological Advancements and Increasing demand for Rehabilitation Robots to support market growth5.2.3. Aging Population and increase in cases of stroke to drive market growth5.2.4. Integration into Developmentally Informative Research Designs to Increase Ease of Use in Neurodevelopmental Populations5.3. Market Dynamics5.3.1. Drivers5.3.2. Restraints5.3.3. Opportunity

6. COVID19 Crisis Analysis6.1. Current COVID19 Statistics and Probable Future Impact6.2. Current GDP Projection and Probable Impact6.3. Current Economic Projection as compared to 2008 financial analysis6.4. COVID19 and Impact Analysis6.5. 2020 Market Scenario

7. Global Medical Rehabilitation Robotics Market Demand (in Value or Size in US$ Mn) Analysis 2016 - 2020 and Forecast, 2021 - 20317.1. Historical Market Value (US$ Mn) Analysis, 2016 - 20207.2. Current and Future Market Value (US$ Mn) Projections, 2021 - 20317.2.1. Y-o-Y Growth Trend Analysis7.2.2. Absolute $ Opportunity Analysis

8. Global Medical Rehabilitation Robotics Market - Pricing Analysis8.1. Regional Pricing Analysis By Product Type8.2. Global Average Pricing Analysis Benchmark

9. Global Medical Rehabilitation Robotics Market Demand (in Volume or in Units) Analysis 2016 - 2020 and Forecast, 2021 - 20319.1. Historical Market Volume (in Units) Analysis, 2016 - 20209.2. Current and Future Market Volume (in Units) Projections, 2021 - 20319.2.1. Y-o-Y Growth Trend Analysis

10. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Product Type 10.1. Introduction / Key Findings10.2. Historical Market Size (US$ Mn) & Volume Analysis By Product Type, 2016 - 202010.3. Current and Future Market Size (US$ Mn) and Volume Forecast By Product Type, 2021 - 203110.3.1. Exoskeleton10.3.2. Therapeutic/ Assistive10.4. Market Attractiveness Analysis By Product Type

11. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Extremity 11.1. Introduction / Key Findings11.2. Historical Market Size (US$ Mn) Analysis by Extremity, 2016 - 202011.3. Current and Future Market Size (US$ Mn) Analysis and Forecast By Extremity, 2021 - 203111.3.1. Upper extremity11.3.1.1. Arm11.3.1.2. Wrist11.3.1.3. Shoulder11.3.1.4. Elbow11.3.2. Lower extremity 11.4. Market Attractiveness Analysis By Extremity

12. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Therapy Type 12.1. Introduction / Key Findings12.2. Historical Market Size (US$ Mn) Analysis by Therapy Type, 2016 - 202012.3. Current and Future Market Size (US$ Mn) Analysis and Forecast By Therapy Type, 2021 - 203112.3.1. Limb mobility12.3.2. Gait12.3.3. Sensory12.4. Market Attractiveness Analysis By Therapy Type

13. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Patient Type 13.1. Introduction / Key Findings13.2. Historical Market Size (US$ Mn) Analysis by Patient Type, 2016 - 202013.3. Current and Future Market Size (US$ Mn) Analysis and Forecast By Patient Type, 2021 - 203113.3.1. Adult13.3.2. Paediatric 13.4. Market Attractiveness Analysis By Patient Type

14. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Application 14.1. Introduction / Key Findings14.2. Historical Market Size (US$ Mn) Analysis by Application, 2016 - 202014.3. Current and Future Market Size (US$ Mn) Analysis and Forecast By Application, 2021 - 203114.3.1. Neurorehabilitation14.3.2. Physical Rehabilitation14.3.3. Others 14.4. Market Attractiveness Analysis By Application

15. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by End User 15.1. Introduction / Key Findings15.2. Historical Market Size (US$ Mn) Analysis by End User, 2016 - 202015.3. Current and Future Market Size (US$ Mn) Analysis and Forecast By End User, 2021 - 203115.3.1. Hospitals15.3.2. Wellness Centers15.3.3. Rehabilitation centers15.3.4. Home care15.4. Market Attractiveness Analysis By End User

16. Global Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031, by Region16.1. Introduction16.2. Historical Market Size (US$ Mn) & Volume Analysis by Region, 2016 - 202016.3. Current Market Size (US$ Mn) Analysis and Volume Forecast by Region, 2021 - 203116.3.1. North America16.3.2. Latin America16.3.3. Europe16.3.4. South Asia16.3.5. East Asia16.3.6. Oceania16.3.7. Middle East and Africa (MEA)16.4. Market Attractiveness Analysis by Region

17. North America Medical Rehabilitation Robotics Market Analysis 2016 - 2020 and Forecast 2021 - 2031

18. Latin America Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

19. Europe Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

20. South Asia Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

21. East Asia Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

22. Oceania Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

23. Middle East and Africa Medical Rehabilitation Robotics Market 2016 - 2020 and Forecast 2021 - 2031

24. Emerging & Key Countries Medical Rehabilitation Robotics Market Analysis

25. Market Structure Analysis25.1. Market Analysis by Tier of Companies 25.2. Market Share Analysis25.3. Market Presence Analysis25.3.1. Regional footprint of players25.3.2. Product footprint of players25.3.3. Channel footprint of players

26. Competition Analysis26.1. Competition Dashboard26.2. Competition Benchmarking26.3. Competition Deep Dive 26.3.1. Ekso Bionics Holdings Inc.26.3.1.1. Overview & Key Financials26.3.1.2. SWOT Analysis26.3.1.3. Sales Footprint26.3.1.4. Analyst commentary26.3.1.5. Strategy Overview26.3.1.5.1. Marketing Strategy26.3.1.5.2. Product Type Strategy26.3.1.5.3. Channel Strategy26.3.2. AlterG, Inc.26.3.3. Bionik Laboratories Corp.26.3.4. Cyberdyne Inc.26.3.5. ReWalk Robotics26.3.6. Instead Technologies Ltd.26.3.7. Hocoma AG26.3.8. Fourier Intelligence26.3.9. Tyromotion26.3.10. Toyota Motor Corporation26.3.11. BeatBots LLC26.3.12. LEADERS REHAB ROBOT Co. Ltd.

27. Assumptions and Acronyms Used

28. Research Methodology

For more information about this report visit https://www.researchandmarkets.com/r/txqu4o

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Worldwide Medical Rehabilitation Robotics Industry to 2031 - by Product, Application, End-user and Region - GlobeNewswire

FIRST Lego League, an international robotics competition, where students build robots to perform tasks, and develop an innovation project to help tackle world issues held a competition at Rothesay Netherwood School. Image submitted.

Students are using LEGO and robotics to solve real-world problems.

FIRST LEGO League teamed with Amazon to tackle challenges involving cargo with a competition held at Rothesay Netherwood School in April.

Julie Letkeman is a homeschool teacher in the Kennebecasis Valley and entered a team in the virtual component of the competition.

Teams were challenged to come up with an idea on how to change something about how cargo is moved.

The kids then come up with a problem, research it, and then develop and design a solution.

Letkeman said their team decided to tackle the issues that delivery drivers face like falls, dog bites, and porch pirates.

The students did research, chatted with airspace and drone specialists, and designed a prototype drone, that the driver could launch safely from their truck, to deliver packages.

In one aspect of the competition, the students had to design, build, and code a robot to interact with a plane.

[They] have it travel to the airplane, lift its arms so it can pull down a lever so that it can unload the plane cargo, back up and go back to base and then go out and do another mission, Letkeman said.

She said interest is growing in the program.

A couple of years ago there were only a couple of teams in New Brunswick. This year it seems like there were five or six first-year teams from school throughout New Brunswick, Letkeman said.

Letkeman would love to see robotics get more attention since STEM learning science, technology, engineering and mathematics has become so important in todays world.

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FIRST LEGO League Teams With Amazon On Robotics Competition - 91.9 The Bend

A team of researchers led by University of Toronto ProfessorTim Barfootis using anew strategy that allows robots toavoid collidingwith people by predicting the future locations of dynamic obstacles in their path.

The project, which is supported byApple Machine Learning, will be presented at the International Conference on Robotics and Automation in Philadelphia at the end of May.

The results from a simulation, which are not yet peer-reviewed,are available on the arXiv preprint service.

The principle of our work is to have a robot predict what people are going to do in the immediate future, saysHugues Thomas, a post-doctoral researcher in Barfoots lab at the U of TInstitute for Aerospace Studies in Faculty of Applied Science & Engineering. This allows the robot to anticipate the movement of people it encounters rather than react once confronted with those obstacles.

To decide where to move, the robot makes use of Spatiotemporal Occupancy Grid Maps (SOGM). These are 3D grid maps maintained in the robots processor, with each 2D grid cell containing predicted information about the activity in that space at a specific time.The robot choses its future actions by processing these maps through existing trajectory-planning algorithms.

Another key tool used by the team is light detection and ranging (lidar), a remote sensing technology similar to radarexcept that it uses light instead of sound. Each pingof the lidar creates a point stored in the robots memory.Previous work by the team has focused on labeling these points based on their dynamic properties. This helps the robot recognize different types of objects within its surroundings.

The teams SOGM network is currently able to recognize four lidar point categories:the ground; permanent fixtures, such as walls; things that are moveable but motionless, such as chairs and tables; and dynamic obstacles, such as people. No human labelling of the data is needed.

With this work, we hope to enable robots to navigate through crowded indoor spaces in a more socially aware manner, says Barfoot. By predicting where people and other objects will go, we can plan paths that anticipate what dynamic elements will do.

In the paper, the team reports successful results from the algorithm carried out in simulation. The next challenge is to show similar performancein real-world settings, wherehuman actions can be difficult to predict. As part of this effort, the team has tested their design on the first floor of U of Ts Myhal Centre for Engineering Innovation & Entrepreneurship, where the robot was able to move past busy students.

When we do experiment in simulation, we have agents that are encoded to a certain behaviourand they will go to a certain point by following the best trajectory to get there, says Thomas. But thats not what people do in real life.

When people move through spaces, they may hurry or stop abruptly to talk to someone else or turn in a completely different direction. To deal with this kind of behaviour,the network employs a machine learning technique known as self-supervised learning.

Self-supervised learning contrasts with other machine-learning techniques, such as reinforced learning, where the algorithm learns to perform a task by maximizing a notion of reward in a trial-and-error manner. While this approach works well for some tasks for example, a computer learning to play a gamesuch as chess or Go it is not ideal for this type of navigation.

With reinforcement learning, you create a black box that makes it difficult to understand the connection between the input what the robot sees and the output, or the robot does, says Thomas. It would also require the robot to fail many times before it learns the right calls, and we didnt want our robot to learn by crashing into people.

By contrast, self-supervised learning is simple and comprehensible, meaning that its easier to see how the robot is making its decisions. This approach is also point-centric rather than object-centric, which means the network has a closer interpretation of the raw sensor data, allowing for multimodal predictions.

Many traditional methods detect people as individual objects and create trajectories for them.But since our model is point-centric, our algorithm does not quantify people as individual objects, but recognizes areas where people should be. And if you have a larger group of people, the area gets bigger, says Thomas.

This research offers a promising direction thatcould have positive implications in areas such as autonomous driving and robot delivery, where an environment is not entirely predictable.

In the future, the team wants to see if they can scale up their network to learn more subtle cues from dynamic elements in a scene.

This will take a lot more training data, says Barfoot. But it should be possible because weve set ourselves up to generate the data in more automatic way: where the robot can gather more data itself while navigating, train better predictive models when not in operationand then use these the next time it navigates a space.

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Researchers design 'socially aware' robots that can anticipate and safely avoid people on the move - University of Toronto