The evolution and future of robotics in manufacturing

Industrial jobs often involve doing the same physical tasks over and over, like lifting and putting together heavy parts. These types of manual tasks can be risky. In 2023, there were 5,283 fatal work injuries reported in the United States alone.

However, with the growing use of smart industrial robots and technologies like artificial intelligence (AI) and computer vision, many of these high-risk tasks are now being handled by machines. Robots in manufacturing are now able to lift heavy materials, inspect equipment for issues, and work alongside people to improve safety and efficiency on the factory floor.

In this article, we’ll take a look at how industrial robots are changing the way factories work and helping to create safer and more productive workplaces. Let’s get started!

What are industrial robots?

Industrial robots are smart machines designed specifically to assist with manufacturing tasks. In particular, robots in manufacturing are usually built either to lift heavy product components, such as car or airplane parts, or to handle tiny, detailed tasks very quickly, like assembling electronic circuits or packaging products. 

Unlike the humanoid robots we often see in science fiction movies like The Terminator or I, Robot, industrial robots are generally stationary and built with a single robotic arm. Typically, this robotic arm can move in several directions and be programmed for different jobs in manufacturing, such as welding, assembling, or moving materials. 

Industrial robots are especially good at doing repetitive work quickly and accurately without needing breaks, which makes them ideal for use in factories and warehouses. As a result, more than 4 million robots are used in factories globally. 

Types of industrial robots

Robots in factories are becoming more common and are taking on a wide range of tasks. Here are some different types of industrial robots and how they’re used to make factory work more efficient and safe: 

• Cartesian robots: Also known as gantry robots, these robots move along straight lines using three sliding joints along the X, Y, and Z axes. Their simple design allows for high precision, making them ideal for manufacturing and automation tasks.

• Articulated robots: These jointed-arm robots mimic the movement of a human arm using multiple rotary joints. They offer flexibility, a wide range of motion, and are commonly used in assembly, painting, and packaging.

• Delta robots: Featuring three lightweight arms connected to a triangular base, delta robots are built for speed and agility. They are well-suited for high-speed pick-and-place operations, especially in the packaging industry.

• Polar robots: Among the earliest types of industrial robots, polar robots use a combination of rotary and linear joints to provide a spherical range of motion. They are useful for tasks that require a wide, multidirectional reach.

• SCARA robots: Short for Selective Compliance Assembly Robot Arm, SCARA robots use two rotary joints and one linear joint. They are ideal for tasks that require fast and precise horizontal and vertical movements, such as electronics assembly and food processing.

History of robotics in manufacturing

Before we dive into specific examples of how industrial robots are making a difference, let’s take a look at the evolution of robots in manufacturing and gain a better understanding of how industrial robotics has changed over the years:

• Early manufacturing (Pre-robotics era): Before robotics, manufacturing relied entirely on manual labor and basic tools. The Industrial Revolution introduced steam power, machinery, and assembly lines, which boosted productivity but still left many tasks repetitive, hazardous, or labor-intensive.

• Introduction of industrial robots (1950s - 1980s): In 1954, George Devol invented Unimate, the first programmable robot. By 1961, General Motors had deployed it for die casting and welding, and it became the first industrial robot in use. This marked a major shift, automating dangerous and repetitive tasks, especially in car manufacturing.

• Expansion and refinement (1990s - 2000s): Robots became faster, more precise, and more affordable. Their use expanded into industries like electronics, pharmaceuticals, and food processing. Flexible automation made it possible for robots to handle multiple tasks with minimal reprogramming.

• Rise of collaborative robots (2010s - present): Collaborative robots, or cobots, emerged to work safely alongside humans. With built-in sensors, AI, and camera systems, they can adapt to their environment and assist with complex or delicate tasks.

‍

Looking ahead, it's likely that industrial robots will become even smarter and more adaptable. Researchers and engineers are actively working on technologies that enable robots to learn, adjust to new situations, and collaborate more closely with people in supportive and dynamic ways.

Examples of industrial robotics

Next, we'll explore real-world examples of robots in manufacturing and how they’re being used on the factory floor.

Industrial robots in aircraft manufacturing

Aircraft manufacturing involves complex and delicate processes, especially for large aircraft like the Boeing 777. For instance, assembling a single 777 requires more than 60,000 rivets. Traditionally, this task involved two workers: one to operate the rivet gun and another to hold a steel bar behind the panel to secure the fastener. 

These types of tasks can be physically demanding and lead to injuries in the arms, back, and shoulders. In addition to this, precision is critical in aircraft manufacturing, and there is little room for error.

To enhance such workflows, Boeing has adopted industrial robots. At its 777 factory in Everett, Washington, the company introduced the Fuselage Automated Upright Build (FAUB) system, a robotic assembly process designed to automate the drilling and riveting of fuselage sections.

‍

Once programmed, these robots can drill tens of thousands of perfect holes for rivets. Unlike the older setup with fixed rigs, FAUB robots are mobile and can move along assembly lines on guided vehicles. After workers position the fuselage panels, the robots take over the drilling and riveting, increasing both speed and accuracy. This approach aligns with recent developments in the robotics industry, which continues to push for smarter, safer, and more efficient solutions in manufacturing.

Food manufacturing enabled by industrial robots

Robots in manufacturing are also being widely adopted in the food industry. At Nestlé’s factory in Germany, for example, baby food production is managed through a fully automated packaging line. Robots handle tasks such as moving filled and sealed food trays into sterilization crates and, afterward, into packaging for shipping. This makes the entire operation faster, safer, and more reliable.

Nestlé also uses mobile robots like Boston Dynamics' Spot to monitor maintenance issues across its facilities. Unlike traditional fixed sensors that can only detect problems in specific areas, Spot can move freely around the factory. This concept of mobile, flexible automation is a growing trend in the robotics industry.

Spot can climb stairs, navigate tight spaces, and handle uneven floors. It’s equipped with special sensors that help it check factory machines like motors and compressors for heat, noise, or other warning signs. Spot can also easily catch problems early, helping fix issues before they get serious. 

‍

Manufacturing cars with the help of industrial robots

Industrial robots have always been a key part of car manufacturing. In fact, 33% of all industrial robot installations in the US are in the automotive industry. 

An interesting example of this is BMW’s Spartanburg plant in 2013. At this facility, people and robots worked side by side on the door assembly line without safety fences, making it the first BMW facility to use this kind of direct human-robot collaboration in regular production.

Four robots were used to install sound and moisture insulation inside the doors of BMW X3 models. Workers first placed and lightly pressed the adhesive foil into position, and then the robots would take over, using roller heads to complete the job with high precision.

The system was fully automated and could measure the exact pressure applied during the process, allowing for constant monitoring of quality. If the robot’s work was ever interrupted, a human worker could easily step in and finish the task manually, keeping production running without delays.

‍

Benefits of robotics in manufacturing

Next, let’s take a closer look at some of the key benefits of using robots in manufacturing.

• Precision and accuracy: Industrial robots provide high levels of precision and speed. Some are capable of performing tasks within a micron of accuracy. 
  ‍
• Reliability: With lifespans reaching up to 100,000 hours without failure, industrial robots can operate for long spans of time without interruption. 
  ‍
• Enhanced workplace safety: Robots also handle dangerous tasks, such as working in confined spaces or around hazardous materials, helping reduce 35% of workdays lost due to injury.

• Boost productivity: Unlike humans, robots don’t need breaks, days off, or rest. They can work around the clock, which can significantly boost productivity. 

• Cost effectiveness: While purchasing and installing these robots can be expensive at first, they lead to significant savings over time. They lower labor costs, reduce potential errors, and cut down on injury-related expenses.

• Scalability: Once robots are set up, manufacturers can increase production more easily without needing to hire extra workers or make major changes. This means manufacturers can respond quickly to growing demand and stay flexible in a changing market.

Challenges and limitations of robotics in manufacturing

While industrial robots offer many advantages, they also come with a few challenges, especially with respect to expertise and maintenance. These robots in factories require skilled professionals to program, operate, and maintain them. 

Even though many robots in industry use cases today use artificial intelligence and machine learning, they still require regular servicing to prevent breakdowns. If a team of manufacturers doesn’t already have this knowledge, training staff can be both expensive and time-consuming.

Interestingly, the solution to these challenges also comes in the form of Vision AI, more specifically, computer vision, which is a branch of AI that focuses on understanding visual data. For instance, computer vision models like Ultralytics YOLO11 can be trained to detect and track industrial robots. Insights from tracking these robots using YOLO11 can be used to spot problems early (known as predictive maintenance). This cuts down on the need for expert supervision and reduces unexpected breakdowns.

Beyond this, computer vision can also support the creation of real-time digital twins. Digital twins are virtual models of physical machines and robots, built using visual data collected from the manufacturing environment. 

Digital twins let manufacturers monitor equipment in real time, identify issues before they cause disruptions, and test process improvements without interrupting actual production. This technology drives more consistent performance, improves decision-making, and reduces costly downtime.

Artificial intelligence and machine learning technologies

While discussing the challenges of using industrial robots, we saw that many are now powered by AI and machine learning. But how does this actually work, and what is AI’s role in robotics?

Traditional industrial robots are limited to fixed, repetitive tasks. They follow pre-programmed instructions and cannot easily adapt to changes on the production line. This makes them less efficient in environments where flexibility, speed, and accuracy are essential. 

Without AI, robots can’t detect product defects in real time or adjust to slight variations in materials or positioning, often leading to slower processes, more errors, and increased downtime. AI in manufacturing is letting robots go beyond simple, pre-programmed tasks. 

Specifically, with machine learning in manufacturing, robots can analyze data from their environment, recognize patterns, and improve their performance over time. For instance, a vision-enabled robot can identify different objects on an assembly line, adjust its movements based on what it sees, and even detect defects or anomalies in real time. Behind the scenes, computer vision is the driving force behind this innovation.

Typically, a vision-enabled robot is equipped with the necessary hardware infrastructure to run computer vision models like Ultralytics YOLO11. When integrated with cameras and computer vision, a robot gains the capabilities of the underlying model. In the case of YOLO11, this means a robot can perform computer vision tasks such as object detection, tracking, and segmentation.

The impact of the Internet of Things (IoT) 

Another couple of concepts related to industrial robots are IoT in manufacturing and edge computing. IoT refers to a network of connected devices that collect and share data (mainly over the internet). On the other hand, edge computing handles the data right at the source, like a robot or sensor, without needing to send it all the way to a central server first. 

When industrial IoT (IIoT) devices collect large amounts of data, sending it to a central system on the cloud for analysis can cause delays (known as latency) and slow things down. But by using edge computing along with IoT, manufacturers can process the data instantly, making it possible to get real-time responses and empower automation. 

A clear example of AI and IoT working together in manufacturing is predictive maintenance. In smart factories, one of the main goals of Industry 4.0 is to anticipate equipment failures before they happen. 

To achieve this, IIoT devices have to remain fully functional and reliable. By combining edge computing, AI, and computer vision, these devices can continuously monitor their own condition, detect when maintenance or recharging is needed, and automatically trigger the necessary actions. This keeps machines running smoothly, reduces unplanned downtime, and improves overall efficiency.

How automation and robotics improve manufacturing efficiency

Now that we have a better understanding of technologies like AI, computer vision, IoT, and edge computing, let’s explore how these can work together to make manufacturing automation more efficient. 

The main goal of automation is to streamline processes and make them faster, more reliable, and less prone to human error. Take, for example, a factory that assembles consumer electronics such as smartphones. Vision-enabled robotic arms can handle the delicate task of placing tiny components onto circuit boards with precision. 

At the same time, AI-powered vision systems can inspect each step of the assembly, identifying defects like misaligned parts or faulty solder joints in real time. Meanwhile, IoT sensors can monitor environmental factors such as temperature, dust, and vibration, which could impact the quality of sensitive components. 

With edge computing, the system can instantly process this data and make on-the-spot adjustments, like pausing the line or recalibrating a robot, without waiting for cloud-based responses. Together, automated manufacturing can create a production line that is faster, more accurate, and highly adaptive, resulting in higher product quality and lower operational costs.

How are robotics transforming the future of manufacturing

The future of industrial robots is moving quickly, with technologies like Vision AI in manufacturing and the IoT playing a major part. With these tools, robots can see what they’re working on, spot defects, check product quality, and predict issues as they happen. Many manufacturers are already using these systems to make their operations more efficient and consistent.

The industrial robotics market has been growing steadily, and this growth comes from constant improvements in robotics, easier access to skilled engineers, and the use of simulation and virtual testing. These developments make it faster to design and fine-tune robots for real-world use. As more factories adopt digital tools and automation, they’re becoming more flexible, reliable, and ready to handle future challenges.

Join our community and GitHub repository to explore more about AI. Check out our solution pages to read about AI in retail and computer vision in agriculture. Discover our licensing options and start building with computer vision today!