07Future of human-machine collaboration
Cross-industry resilience and efficiency, driven by manufacturing
Introduction
Today
Humans are uniquely adaptable to change, and robots have tireless endurance for precise, repetitive tasks. As costs lower and technology becomes more widely available, collaborative robots (co-bots) are beginning to become affordable in healthcare, scientific labs, food & beverage, material manufacturing and agriculture. 99% of all businesses in Europe are small and medium-sized - under the right conditions, we could be at the beginning of a new co-bot driven industrial revolution.
What will change
When co-bots become cheap and widely available, they will move out from the factory floors of automotive giants to revolutionise the work of SMEs in manufacturing, research, healthcare and many other industries. Co-bots will also be key enablers of many of the Bets for the Future - both vertical farming and vehicle refuelling already employ co-bots, and the likes of Samsung and LG envision co-bots as an integral part of future smart home ecosystems.
Emerging technologies
Software technologies such as integrations, computer vision, data analysis and user interfaces that are easy enough for everyone to use will be key components of co-bot roll out. Hardware-wise, the production of cheap, easily programmable units that are inexpensive compared to their “dumb” alternatives will continue to lower the barrier to adoption. Restaurants are already seeing adoption of co-bots for front of house food deliveries as well as running limited operations out back.
Opportunities worth taking
Co-bots are already used in gas stations for fully automated refuelling, a function that can easily be adapted for electric vehicle level-3 charger operation. Software-wise, applications are currently developed by the hardware provider or system integrators in a closed ecosystem manner. Even so, a shortage in robotics engineering skills and growing demand indicate the beginnings of an open app store ecosystem for co-bots - a significant driver of the broader adoption of co-bots in the future.
Opportunities
Healthcare
Recent advancements in the field of collaborative robots have made for additional possibilities to support the healthcare sector across a variety of applications. More than 1500 surgical co-bots working alongside or remotely with humans will be installed by the end of 2020.
Sustenance
Demand for co-bots will accelerate with the increasing deployment of machines in traditional and modern agricultural applications such as soil management, mapping, monitoring and harvesting.
Manufacturing of goods
The applications for co-bots in manufacturing will continue to dominate the market. The most common manufacturing use cases include welding, material handling, quality testing and painting. In the near future, we will see SMEs integrate co-bots into their manufacturing process as costs per unit goes down.
Service business
The adoption of collaborative robots will gain impetus in retail, banking, food & beverage industries. Easily programmable co-bots can both handle mundane tasks like cashier operations and provide opportunities for customisation.
Deep-dive into this bet
- Market outlook
- Things to consider
- Start-ups to watch
- Tech enabling the shift
Total market 2024
Household appliance market $770 bn, robotics hardware and software market size almost $ 100 bn.
Look for these in 2024
Methodology: A high-level view gathered from various openly available data sources on market size & compound annual growth rates. To simplify comparison between analysts sources, we’ve removed differences in market details by calculating an estimated average for total market size 2024 and average CAGR for selected time period. This allows us to gain “correct enough estimates”. Unless linked directly to one source, each of our market estimate has included 2-8 sources e.g. WEF, CB Insights, Mordor Intelligence, Markets & Markets, Research & Markets, Grand View Research, PWC, Allied Market Research, McKinsey, Market Watch
The collaborative robot market will account for 30% of the global Robotics Market by 2027.
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Top 18 unicorn sniffing smart money VCs, 1376 investments 2018-2020
Source: Crunchbase Pro, accessed: 03/20, research of company description terms between 01/2018-02/2020, investment types: early stage investments by top 18 unicorn sniffing smart money VCs, n=1376 investments
Robotics 4%, computer vision 3%, Edge 2%.
Special focus
As the market grows and more units are installed, the demand for software will increase in three areas: - Improving human–machine interface (HMI) and the increased integration of zero UI tech such as voice to imitate human behaviour - Developing tools to help robots make better, safer decisions - Specific applications and skills for co-bots that need to be developed as use cases multiply
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Moravec’s paradox
Although high-level reasoning requires limited computation, low-level sensory or motor skills require enormous computational resources.
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Dexterity & Speed Issues
Robot grip and mechanical capabilities are still limited.
Cost
Co-bot unit costs are approximated to fall between $24000 - $45000 excluding set up. The business case for such an outlay is clear for SMEs in manufacturing, but costs need to significantly reduce before co-bots are widely adopted in small businesses.
Shortage of Robotics Engineering Skills
Competition is stiff for people with robotics engineering skills, and many talents are poached by competing industries like aviation and autonomous driving.
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5G
The 5th generation of wireless technology that operates on a broader set of frequencies than 4G. This enables faster speed and latency in data transfer, as well as uninterrupted connectivity
Wearables
Smart electronic devices that can be incorporated into clothing or worn on the body as implants or accessories as point of interaction or source of data.
Sensor technology
Ultra thin,connected, printable, stretchable sensors have the potential to turn every physical surface into a digital experience by seamless tracking heat, pressure, touch.
AI/ML Artificial intelligence (AI)
Computer systems able to perform tasks normally requiring human intelligence by using a combination of Machine Learning (ML) toolbox of algorithms and learning rules from data.
Edge computing
A microcontroller unit (a small chip) is embedded with the ability to perform AI/ML computing offline based on sensor inputs without needing a cloud connection.
Computer vision
Using video image as a source for identifying, analysing and acting (objects & faces to provide access, personalise, charge, recommend, etc.).
Speech recognition
Using voice as a source for identifying, analysing and acting. (e.g. speech to text, identification, measuring, etc.)
Get in touch
Get in touch
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