Collaborative Robot Market size was over USD 1.53 billion in 2023 and is anticipated to reach USD 66.1 billion by 2036, growing at around 33.6% CAGR during the forecast period i.e., between 2024-2036. In the year 2024, the industry size of collaborative robot is estimated at USD 1.94 billion. Further, the market registered a revenue of ~USD 1 Billion in the year 2021. Increasing affordability and ease of programming offered by collaborative robots are primarily expected to drive market growth in the forthcoming years. Furthermore, the world is witnessing a surge in demand for performing tasks requiring higher payload capacity efficiently. Cobots are well-equipped to perform effectively with employees in an uncaged environment, which in turn is projected to skyrocket the demand for these machines in the near future.
The growth of the market can also be attributed to the high return on investment as compared to traditional industrial robotic system, and rising adoption of industrial robots across various industry verticals such as chemicals, automotive, electronics and others. It is calculated that almost 34 percent of industrial robots will be sold by the end of 2025. Also, about 40,000 cobots entered the robotics industry in 2020. Moreover, as per the International Federation of Robotics, automotive industry accounted for an installation of 105 industrial robots in the year 2019, bagging the highest number of installations in all industrial verticals, followed by electronics industry with 88 installations.
Growth Drivers
Challenges
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Base Year |
2023 |
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Forecast Year |
2024-2036 |
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CAGR |
33.6% |
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Base Year Market Size (2023) |
USD 1.53 billion |
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Forecast Year Market Size (2036) |
USD 66.1 billion |
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Regional Scope |
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The market is segmented by end user into electronics, automotive, manufacturing, food and beverage, chemicals, and pharmaceutical, out of which, the automotive segment is anticipated to hold the largest share in the global collaborative robot market. This can be accounted to the growing robot innovations taking place in the automotive industry with augmented human capacity and capability. In addition, the significance of robots in industrial automation, including their ability to manage complicated and repetitive tasks with accuracy, even in hazardous environments, is also predicted to drive growth to the market segment in the future. Moreover, on the basis of payload, the up to 5 kg segment is assessed to grab the largest share over the forecast period owing to the lightweight, flexibility, high reliability and ability of collaborating heavier-weight processes.
Our in-depth analysis of the global market includes the following segments
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By Payload |
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By End User |
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By Application
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On the basis of geographical analysis, the global market is segmented into five major regions including North America, Europe, Asia Pacific, Latin America and the Middle East & Africa region. The market in Asia Pacific is estimated to witness noteworthy growth over the forecast period on the back of the increasing emphasis on the quality and precision of cobots, and rising adoption of automation and artificial intelligence technologies in the region. For instance, it is evaluated that AI will contribute to more than 25 percent of China’s GDP share in the year 2030, followed by North America with 14 percent and United Arab Emirates with 13 percent. Moreover, the market in North America is projected to acquire the largest share over the forecast period ascribing to the early adoption of robotics, and growing usage of collaborative robots for medical surgeries in the region. Apart from these, increase in investments by the government to support development of modern technologies in the industrial robotics market is also anticipated to boost the region’s market growth.
April 2020- ABB Ltd. launched a block-based programming interface, ‘Wizard Easy Programming’ with the aim to make programming its single arm YuMi cobot more intuitive.
September 2019- Universal Robots launched a brand-new collaborative robot model UR16e, which is designed for heavy payload application for tackling heavy duty tasks effectively.
Author Credits: Shweta Patidar
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