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Sensors Make Industrial Robots Smarter

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Smart sensors are transforming industrial robots, expanding their capabilities and intelligence. Conventional industrial robots require large amounts of programming, regularly scheduled maintenance, and significant safety measures. But smart sensors for robots can allow them to detect their own maintenance needs, sense humans and objects around them, operate autonomously with minimal programming, and much more.

From predictive maintenance to computer vision, there are many exciting possibilities for industrial robots with smart sensors. What exactly can robots do with advanced sensors, and what are the benefits of adopting them?

Smart Sensors for Robots Improve Performance

Equipping industrial robots with sensors can improve performance through predictive maintenance and efficient operations. Humans recognize when they are ill or injured — robots can do the same when they have suitable smart sensors. This process is known as predictive or preventive maintenance.

Predictive Maintenance

Connected IoT sensors are the best option for enabling predictive maintenance in industrial robots. They can autonomously report live performance data to a robot management system, where it can be analyzed and monitored. IoT sensors use a five-layer architecture that records sensor data, transmits it between devices, processes it through the cloud, and relays it to platforms where people can view and monitor the data in real-time.

When performance metrics stray outside a specified range, the smart sensor will activate an alert indicating the robot needs a maintenance check-up. This process can prevent major part failures, saving time and money an organization would have spent on expensive repairs. Plus, it allows robot technicians to use their time more efficiently, only performing check-ups when a robot needs one.

For example, a pick-and-place robot might have an IoT smart sensor installed to monitor the robot’s pick rate. Ideally, the robot would pick up items and deposit them into packaging at a reasonably consistent pace. If the robot began sorting fewer items per minute, it could indicate the start of a serious mechanical issue.

An IoT sensor gives the robot the intelligence to notice when something seems amiss. Performance issues may be due to simple things like a part that needs oil or a robot that needs a calibration tune-up. If the cause is a severe mechanical failure or a software malfunction, catching it early can significantly affect the cost, work needs, and overall productivity.

In fact, predictive maintenance with smart sensors was a key reason why some manufacturers could bounce back from the pandemic quicker than others. By identifying problems sooner, manufacturers that used predictive maintenance were able to minimize their downtime as much as possible. As a result, they lessened the negative impact of uncontrollable factors like supply shortages or shipping delays.

Power and Fuel Efficiency

In addition to predictive maintenance, smart sensors for robots can also minimize power and fuel consumption. IoT sensors allow industrial businesses to monitor how much power or fuel their robots use. This can highlight which robots are the least energy efficient.

Additionally, smart sensors can allow robots to manage their power consumption intelligently. With the help of an AI algorithm, industrial robots can use smart sensors to distribute power efficiently throughout their systems as needed. By optimizing power consumption, industrial robots can minimize electricity or fuel usage, resulting in lower operating costs.

Sensors Simplify Programming and Deployment

Smart sensors for industrial robots can make programming and deployment more efficient. Some robots are, by nature, easier to implement than others. For example, the greater maneuverability of 7-axis robots allows for much more flexibility in the design and deployment stages compared to robots with fewer axes of motion. Sensors can increase implementation efficiency even further.

Motion mapping is one of the most tedious parts of preparing and deploying a new industrial robot. Programming every motion needed can be time-consuming and results in a robot being limited to a small set of movements. Things get even more complicated when a robot needs to pick something up with an end effector.

Sensors streamline all this programming by allowing the robot to detect things independently. Force or torque sensors are great examples of this. Without a force sensor, a mechanical gripper or other — an end-effector would need exact movement measurements pre-programmed — such as the specific number of millimeters to open and close.

A force sensor acts as a shortcut around this. It allows the robot to simply tell when it has applied enough force to pick up an object without damaging it. Not only does this simplify the robot’s programming needs, but it also allows the robot to handle more delicate objects.

Proximity sensors are also invaluable. Many industrial robots must be in safety enclosures to prevent a passing employee from accidentally being hit. With proximity sensors, industrial robots can detect when something is nearby, whether that be a wall, a piece of equipment, or a person. This improves workplace safety in addition to making robots more efficient to deploy.

Proximity sensors can reduce the amount of programming needed for a robot. Rather than programming how far to travel, proximity sensors tell it to stop or when it’s too close to something.

Sensors Expand the Capabilities of Industrial Robots

Industrial robots were typically limited to performing a single programmed task in the past. With the help of smart sensors, they can do new jobs and operate more flexibly. New technologies like AI computer vision have significantly expanded what industrial robots can do, adding tasks previously only possible for humans.

Autonomous Navigation

Smart sensors for robots aren’t just helpful for pick-and-place applications. They’re also transforming the way mobile industrial robots operate. RFID tags, proximity sensors, and infrared sensors are becoming increasingly common in warehouses, where they help mobile robots autonomously navigate around facilities.

Before, a robot would need to run on a track or have a route carefully programmed to move around. This can be especially dangerous in warehouses and industrial spaces where the robot could accidentally run into a passing employee.

With sensors, the robot has “eyes” that allow it to navigate and avoid running into things and people. RFID tags help the robot orient itself, while infrared and proximity sensors tell it what its surroundings are. IoT sensors can even monitor where autonomous robots are around a facility remotely.

Smart sensors can make industrial robots safer for employees to work around in addition to enabling autonomous navigation. By giving industrial robots sensors that help them work better with humans, they become collaborative robots, or “cobots.” One of the top benefits of collaborative robots is improved safety thanks to their smart sensors.

Computer Vision

Computer vision involves turning a camera into a sensor for a robot using an AI algorithm. The camera becomes the robot’s eyes. Footage from the camera is processed in real-time by an AI algorithm trained to recognize specific images. Depending on what the algorithm detects in the camera footage, the algorithm will give the robot a command. This effectively allows the robot to sense its surroundings as a human would.

A great example of this is the use of an AI camera sensor on an industrial robot that sorts plastic waste at Evergreen, a PET recycling facility. Thanks to the smart camera sensor and AI algorithm, Evergreen was able to increase their plastic bottle pick rates by 200% and remove up to 90% of contamination on the bottles.

Computer vision can also allow industrial robots to perform autonomous quality control. QC is one of the most critical parts of the manufacturing process, but it can be time-consuming. Industrial robots with smart cameras can rapidly detect defects using computer vision, often with greater accuracy than a human quality control specialist.

Innovating With Smart Sensors for Robots

Smart sensors can expand the capabilities of industrial robots, making them smarter, better optimized, and safer for employees. Sensors within the robot can enable a predictive maintenance schedule, which can reduce repair costs and improve robotic equipment’s performance and life span.

External sensors can help industrial robots detect their surroundings and navigate safely and autonomously, opening up countless automation possibilities. AI-powered smart sensors can even mimic human senses, allowing robots to perform complex tasks like quality control. Adopting smart sensors in industrial robots not only makes the robots smarter but also transforms industrial operations for maximum efficiency and productivity.

Featured Image Credit: Natalia Dziubek; Unsplash; Thank you!

Emily Newton

Emily Newton is a technical and industrial journalist. She regularly covers stories about how technology is changing the industrial sector.

Politics

Fintech Kennek raises $12.5M seed round to digitize lending

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London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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