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Robotics today and tomorrow

Robotics and automation systems are found in a wide range of industries, performing repetitive, dangerous, and unpleasant tasks. In many cases, they assist rather than replace human workers, enabling people to complete jobs more efficiently and accurately, or work for longer periods of time. Some automation is used to perform physical tasks, while other solutions may be purely software based, used to analyze and process data to solve problems and help us make decisions.

March 14, 2024

Angus Muirhead

A robot that puts the package on the production line.

Key takeaways

While robotics and automation systems are in use in a number of industries, their role is limited largely to repetitive and predictable tasks that do not require any dynamic interaction with their surroundings.

Machine learning and deep neural networks can enable much greater autonomy, opening a wide range of new possibilities for automation systems and robotics. 

Powered by AI, robotic systems are likely to become significantly more useful to people, businesses, and governments around the world.

As technologies, such as artificial intelligence (AI), continue to advance, robotics and automation systems are likely to become smarter, cheaper and easier to use, and able to operate with greater autonomy. As a result, they may become economically viable and useful in many more roles and industries.

What are major applications of robotic automation systems today?

Automation systems are often hidden from view, operating in the background. We often do not notice their presence, except for when something goes wrong, and the system does not work. Although there are a wide variety of use-cases for robotic systems, here are five of the most common:

1. Automotive industry

Car manufacturing employs more robots than any other industry in the world; welding and gluing parts of the chassis together with great speed and high precision, and typically running almost non-stop for ten years or more. The International Federation of Robotics estimates that car production employs approximately 1 million robots, and Korea leads the way in robot density in the auto industry at approx. 2.9 robots for every 10 factory workers.1 Advances in technology are making new automation solutions available to car makers. A good example are autonomous mobile robots (AMRs). While in a traditional production line vehicles wait in queues, in a modular production system AMRs – mobile platforms – carry entire vehicles to different workstations and sophisticated sensors allow robots to operate safely without physical safety cages. The possibilities in this industry are growing fast.

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2. Semiconductor industry

Robotics and automation are well suited to manufacturing cars, but also to making chips. That’s because cars and chips typically offer ideal production characteristics for automation: large volumes of product, with little variation or “mix”. As the world becomes more digital, semiconductors are being designed into more products than ever before. The process of making semiconductors is one of the most technically sophisticated in the world, typically requiring 11 specific steps2, many of which are repeated multiple times to produce the chip. The equipment used in all the critical steps is highly automated and specialized, and one of the most sophisticated tools is “EUV lithography”. Dutch manufacturer ASML spent more than EUR 6 billion in research & development (R&D) to develop their extreme ultraviolet (EUV) tool3. It sells with a price tag of more than USD 300 million4. As the world adopts more AI technology, more automated tools will be needed to produce the chips.

3. Logistics

We don’t give a second thought to ordering items online and receiving them a few days later, or even the same day. However, the logistical acrobatics required to make this happen is remarkable. Traditional commerce is much simpler: taking goods from the factory to a distribution hub and then to a regional distribution center and on to a retail outlet. Fixed routes, regular quantities, repeated and predictable. However, e-commerce allows anyone with an internet connection to order from a vast choice of goods, for delivery to any address in the world. Automation and robotics are critical in enabling this to be done fast and cost effectively. Software helps automate inventory management all the way down to individual retail outlets, route planning for shipments, preparation of custom forms and payment of duties, and various robotic solutions facilitate packing, sorting, and handling of the items for shipment. Since e-commerce is expected to grow at 9% a year5, the industry needs to invest more in automation to meet this demand.

4. Simulation

Not so long ago, industrial designers and engineers would make physical prototypes of new products in development. This was done to uncover unforeseen issues in design and for manufacture, and to test them in real-world scenarios such as submersion in water, extreme temperature and pressure, repeated use, and crash or collision testing. Nowadays, software can automate many of these processes, simulating thousands of real-world scenarios in a virtual world, with great accuracy and faster and more efficiently than could be done with physical prototyping. Since so many simulations can be run, the resulting products are typically more reliable, safer, and long-lasting than in the past. And this area is now evolving fast thanks to some smart AI technologies.

5. Food processing

Much of our food, fish, meat, vegetables, and fruit, as well as processed food such as pasta, cookies, cakes, and chocolate, are produced with varying degrees of automation. While some types of food present technical challenges to automation due to the wide variety of shapes, sizes, and consistency (there are over 200 varieties of edible fish for example), others are far more uniform and relatively simple to process automatically. The global market for pasta is worth approximately USD 66 billion6 (revenues) and most commercial pasta production is automated. Handmade or “artisanal” pasta may taste nicer, but it is unlikely to be price competitive against the vast scale of the commercial giants. In Parma, Italy, the flagship factory of the world’s largest pasta maker, Barilla Group, is a fully automated “lights-out” operation which runs 24 hours a day 365 days of the year. With 120 laser-guided vehicles and 37 robotics systems, it produces 320’000 tons of pasta every year.7 This type of automation at scale helps feed the world at affordable prices, but it can also minimize waste and reduce the risk of human injuries and food contamination, ensuring food safety standards are met.

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Robots are becoming smarter

AI has come a long way since the first digital computers of the 1940s. In the early days, AI were rule-based systems used to make simple predictions and calculations. More recently, machine learning and deep neural networks can enable more creative or “generative” tasks, opening a wide range of new possibilities for automation systems and robotics. At the same time, as technology becomes more widespread tech companies enjoy greater economies of scale, and this enables robotics and automation makers to build systems more cheaply. In turn, lower priced automation solutions attract new customers and find new use cases.

By contrast, human labor is becoming more expensive and people are less inclined to perform certain tasks. In many countries there is a severe shortage of skilled workers in factories, logistics centers, on farms and in hospitals and nursing homes. Businesses are also under increasing pressure to stay competitive and to meet strict regulations to ensure the health and safety of their workforce, and high quality and safe products for their customers. As technology advances, smarter and cheaper automation systems may provide an answer to these challenges.

Opportunities for smarter automation

As robots become smarter and safer to use, they are likely to make their way to a broader range of industries, equipped with skills and capabilities that far surpass those of earlier generations. Here are five of the most significant opportunities for smarter automation in our view:

1. Agriculture

Approximately 40% of the world’s land is used for farming, one third crops and two thirds’ livestock.8 As the global population grows and people consume more food, traditional modes of agriculture are leading to unsustainable land degradation. Precision agriculture uses satellite imagery and ground sensors to reduce the use of water, fertilizers, and weed killers. Some solutions claim to cut more than 90% of chemical run-off.9 Smart robotic solutions are also making their way onto farms, autonomously navigating fields to destroy weeds and identify bugs and crop disease. Others are tasked with pruning the rows of crops, harvesting, and sorting and grading fruit and vegetables.10

2. Diagnostics

Recognizing a disease early can in some cases increase the chance of curing it. Medical diagnosis stands to take a huge step forward with the use of AI technologies to find patterns in enormous volumes of medical data (often unstructured data) ranging from human DNA to medical records, family history of disease, blood types, and environmental factors. This promises to greatly facilitate the early prediction of disease, allowing patients time to change their lifestyles and allowing doctors to deliver preventative medicines before the symptoms appear.

3. Traffic management

Singapore has one of the most intelligent traffic systems in the world, with sensors and cameras gathering data in real-time across their network of 160 km of motorways to improve traffic flow and road safety. The government’s “2023 Smart Mobility” plan11 describes a smart, sustainable, and interactive transport system using GNSS (a.k.a. GPS) data in mobile phones and cars, together with sensors and cameras in the road and traffic light infrastructure, to dynamically change road signs and control traffic lights to improve the flow of traffic and avoid congestion. Over the next decade, as vehicles become more autonomous and intelligently connected, the possibility to improve convenience, traffic flow, and energy efficiency, should increase dramatically.

Picture 1: Smart city

Intelligent traffic management system based on a long-range wide network

For illustrative purposes only. 
Sources: Credit Suisse, IEEE Future Directions. Based on IEEE Future Directions (2018), link, derived on 01.03.2024

4. Generative design

Generative AI technologies are taking product design to new levels of sophistication. Generative design uses AI technologies to produce and evaluate multiple design alternatives, which the engineer can then assess, adapt, and take forward into development. Effectively, it reverses the “simulation” process. The engineer inputs the desired outcome, parameters such as strength, weight, flexibility, physical dimensions, etc, and the system analyzes a vast library of physical properties to create the optimal design. Interesting to note that many of the designs resemble trees, plants, and other elements of our natural world.

5. Research & Development

Saving possibly the biggest and the best for last, the potential for AI to revolutionize every field of scientific research is huge. So much innovation today is discovered by chance, and the more we understand about specific fields, the more difficult it is for people to attain a polymathic oversight of knowledge across a range of subjects. One example of the great potential opportunity was demonstrated by DeepMind, a UK company acquired by Google in 2014. After beating the world champions at the game “Go”, a type of Chinese checkers, DeepMind turned their attention to predicting the 3D structure of proteins.12 This represents one of the fundamental challenges in biology. DeepMind’s “AlphaFold” team trained AI algorithms on the 170,000 proteins which were publicly known and available at the time, and only a few years later in 2021 published a database of more than 200 million 3D protein structures.13 DeepMind made this vast library freely available to help accelerate scientific research. With giant steps forward in fundamental knowledge of science and our natural world such as these, it is likely that the pace of innovation will experience a period of exponential acceleration.

AI – the driving force behind robotics

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The individual company mentioned on this page is meant for illustration purposes only and is not intended as a solicitation or an offer to buy or sell any interest or any investment.

To the extent that these materials contain statements about the future, such statements are forward looking and are subject to a number of risks and uncertainties and are not a guarantee of future results/performance.

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1 International Federation of Robotics, IFR, (2023). One Million Robots Work in Car Industry Worldwide – New Record. Press Release on 22.03.2023; Link; accessed on 19.02.2024.
2 Semiconductor manufacturing process steps: wafer cleaning, film deposition, post-deposition cleaning, resist coating, exposure, development, etching, implantation and activation, resist stripping and packaging/assembly.
3 ASML (n.d.). The road to EUV. EUV technology took more than two decades to develop. Link. Accessed on 14.02.2024.
4 Reuters (2024). ASML's next chip challenge: rollout of its new $350 mln 'High NA EUV' machine. Published on 09.02.2024. Link. Accessed on 06.03.2024.
5 Boston Consulting Group (2023). Winning formulas for e-commerce growth. Published on 31.10.2023; Link; “9% CAGR through 2027.”; accessed on 19.02.2024.
Fortune Business Insights (2023). Market research report: Food processing & processed food. Pasta Market. Published 09.2023. Link; accessed on 20.02.2024.
7 Robotics Tomorrow (2022). World’s largest pasta production plant a showcase for integrated robotics and sustainable distribution. Published on 28.12.2022; Link; accessed on 19.02.2024.
8 Our World in Data (22024). Half of the world’s habitable land is used for agriculture. Published on 16.02.2024; Link; accessed on 06.03.2024.
9 John Deere (2023). [John Deere acquires Smart Apply] Smart Apply technology virtually eliminates over-spraying. Published on 14.07.2023; Link; accessed on 15.02.2024.
10 World Economic Forum in collaboration with McKinsey & Company (2023). The rise of autonomous farms: How technology is revolutionizing agriculture. Published on 04.07.2023; Link; accessed on 19.02.2024.
11 Land Transport Authority, a Singapore Government Agency website (n.d.). Intelligent Transport Systems. Smart Mobility 2030; Link; accessed on 19.02.2024.
12 OECD (2020). Deepmind’s Alphafold: a solution to the 50-year-old challenge of protein folding. Published on 14.12.2020;Link; Accessed on 20.02.2024.
13 Google DeepMind (2022). AlphaFold reveals the structure of the protein universe. Published on 28.07.2022;Link; Accessed on 20.02.2024.

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