In the future, mobile work machines will collaborate, potentially multiplying productivity. Collaboration is propelling Finland to the forefront of the latest tech revolution.
Large mobile machinery is on the brink of a massive technological leap. According to Mehdi Rasti, an assistant professor of wireless communications at the University of Oulu, this will fundamentally change the operational models of the industry.
History is full of groundbreaking technologies that have flipped society upside down. Rasti believes the next major technological leap is just around the corner, and it is tied to artificial intelligence. Rasti notes that the last IT revolution we experienced was 4G technology at the turn of the 2000s and 2010s. It enabled faster data transfer, leading to a rapid growth in applications and e-commerce.
Shortly after 4G, we witnessed the rise of Uber, Instagram, Tinder, and TikTok, all of which have had a tremendous economic, cultural, and political impact. Rasti believes that the upcoming breakthroughs in machine learning will be equally significant. The shift could be particularly big in the mobile work machines industry due to the nature of its operations and growing demand.
The continuous growth of the Earth's population increases the demand for construction, freight transport, and waste sorting, all of which require large mobile machinery. This boosts the demand for Class A equipment, as technological advancements enhance the productivity and energy efficiency of the machines.
According to Rasti, the demand for modern machinery is also driven by the tightening legislation related to emissions. In Europe, the allowable net greenhouse gas emissions are determined by the European Climate Law. Through this legislation, the European Union aims to cut emissions to below half of 1990 levels by 2030.
Machines learn from each other's slip-ups
At the heart of the digital green transition is data. Before a modern work machine begins its work, it assesses the experiences of other machines. Using this data, machines evaluate the most efficient and safest work methods. Throughout the operation, new data continues to accumulate.
According to Rasti, a rock drilling machine can gather information about the soil, optimal drilling angles, and part wear. Meanwhile, a forest machine can measure the type of wood material available in different environments. This way, machines enhance each other and learn from each other’s mistakes.
Thanks to intelligent processes, machine efficiency improves and components have a longer lifespan. For instance, a mobile work machine can automatically adjust its power to mitigate the risk of battery overheating, reducing component wear, minimizing downtime, and lowering the risk of accidents.
The more data accumulates, the smoother the processes become. Autonomous work machines can also perform multiple tasks simultaneously and share the information they collect for researchers to use, Rasti explains.
AI lights a Fire Under Everyone
The intelligent work process also has a downside: many job tasks will disappear. However, Rasti reminds that the history of humanity is filled with transformative technologies, and people have never been left idle. Progress creates new needs, sectors, jobs, and employers.
The transformation of the working environment is essential. The EU's climate targets for 2040 require up to a 90% reduction in emissions compared to 1990 levels. According to Rasti, achieving this goal is impossible without extensive structural changes affecting businesses, employees, and researchers.
According to Rasti, artificial intelligence will increasingly shift mechanical tasks to machines, enabling humans to focus more on managing the bigger picture. He also believes that researchers will dedicate more time to understanding what questions to ask AI and why.
From Finland's perspective, the development looks positive, with our expertise being top-notch at the European level, Rasti says. This has been supported by funding models that promote research and development, such as the Academic Fellows program, kicked off by Tampere University. It is able to attract international talents like Rasti.
Despite these successes, Rasti reminds us that additional funding is still needed.
This text is part of a series on the Mobile Machines Platform of Excellence (PoE) network, exploring six themes. The other five themes are autonomy and robotics, the new value of data, intelligent control systems, sustainable energy solutions, and humans in the loop.
The themes are based on a roadmap developed by the SIX Mobile Work Machines cluster. The cluster is coordinated by Tamlink and includes Ponsse, Epec, Sandvik, Valmet Automotive, Valtra, Kalmar, Normet, Tana, Nokia, Danfoss, Junttan, Hevtec, Cargotec, VTT, and Tampere University.
The texts are part of an EAKR-funded project called the Twin transition of mobile work machines (SIX-PoE).
Mehdi Rasti, who moved to Finland three years ago, specializes in machine learning as well as 5G and 6G technologies. At the University of Oulu, he researches how to integrate technologies used by smart machinery.
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