Disruptive innovation is a term used to describe a process by which a new technology or product enters a market and disrupts the existing industry. This disruptive innovation typically starts with a small foothold in an underserved or overlooked segment of the market and then grows to replace the established technology or product.

In the case of AI (artificial intelligence), AR (augmented reality), and industrial robotics, disruptive innovation could occur if a new technology or product emerges that fundamentally changes the way industrial robots are designed, developed, and used. For example, new technology could make it easier and less expensive to create robots that are more flexible and adaptable to changing needs. Or a new product could be developed that combines the capabilities of AI and AR in a way that significantly improves the productivity and efficiency of industrial robots.

Disruptive innovation could also occur in the way industrial robots are used and deployed. For example, a new service could emerge that provides access to industrial robots on a pay-per-use basis, making it easier and more affordable for smaller businesses to take advantage of these technologies. This could disrupt the existing business models of industrial robotics manufacturers, who currently sell robots directly to businesses for a high upfront cost.

AI (artificial intelligence) and AR (augmented reality) are two distinct technologies that are increasingly being used together to create more powerful and innovative applications. While they have different functions, they can complement each other and work together to create new and exciting possibilities.

AR is a technology that overlays digital content in the real world, creating a mixed-reality experience. It has the potential to enhance how we interact with the world around us and transform industries such as education, gaming, and retail. However, AR is limited by its ability to recognize and interact with the physical environment, making it difficult to accurately identify objects or translate languages in real time.

This is where AI comes in. AI has the ability to process and analyze large amounts of data in real time, making it possible to recognize objects and translate languages accurately. By incorporating AI into AR applications, users can benefit from real-time translation, improved object recognition, and more natural interactions. For example, AI-powered AR applications can recognize and identify products in real-time, providing users with detailed information about the product.

Furthermore, AI can also help to overcome some of the technical challenges associated with AR, such as the need for high processing power and complex algorithms. By using AI algorithms to optimize the performance of AR applications, it’s possible to improve their efficiency, speed, and accuracy.

One way AI can disrupt AR is by creating more intelligent and personalized AR experiences. With AI, AR applications can use machine learning algorithms to analyze user data and behavior, allowing the AR system to personalize the experience for each individual user. For example, AI can use data from sensors to track how a user interacts with a virtual object, and adjust the object’s behavior accordingly.

Another way AI can disrupt AR is by improving the accuracy and efficiency of AR tracking and image recognition. With the help of AI algorithms, AR applications can recognize and track objects in real time, and accurately superimpose virtual objects on top of the real world. This can create a more seamless and immersive AR experience for the user.

AI can also disrupt AR by enabling more natural interactions with virtual objects. With the help of AI, AR applications can interpret and respond to natural language and gestures, making it easier for users to interact with virtual objects. This can make AR more accessible to a wider audience, and lead to the development of new AR applications in areas such as workers’ training.

AI and AR can work together to create even more powerful applications. For example, AI can be used to enhance AR by providing real-time translation, improving object recognition, and enabling more natural interactions.

At the limit, also AR can help AI. One potential area where AR could potentially impact AI is in the realm of data collection. One challenge with AI is that it requires vast amounts of data in order to be trained and work effectively. AR has the potential to provide a rich new source of data, by enabling users to interact with the world in new ways and providing real-time visual feedback. For example, an AR application could allow users to annotate and tag real-world objects, creating a massive new dataset that could be used to train machine learning models.

Additionally, AR could also be used to improve the accuracy of computer vision systems, which are a core component of many AI applications. By using AR to provide additional contextual information, such as depth and scale, computer vision systems could be better equipped to understand the real world and make more accurate predictions and decisions.

AI and AR can work together in the field of industrial robotics to create more efficient and effective manufacturing processes. For example, an industrial robot equipped with AR technology could use computer vision to detect and recognize objects in the environment, while AI could be used to optimize the robot’s movements and decision-making based on the data collected. By using computer vision, the robot can identify and recognize objects in its environment, allowing it to better understand its surroundings and perform tasks more accurately. By incorporating AI, the robot can optimize its movements and decision-making processes based on the data it collects, allowing it to work faster and more intelligently. The result is a more advanced and capable industrial robot that can increase productivity, reduce errors, and save time and money. Additionally, the use of AR technology can make the robot easier to operate and more intuitive for human operators, further enhancing its value.

AR can also be used to enhance the training of industrial robots. By overlaying digital instructions and information onto the physical environment, AR can provide more intuitive and immersive training experiences for workers. AI can be used to analyze the training data and provide feedback to improve the training process. In the context of industrial robotics, AR can provide a visual interface for human operators to interact with the robot and the production environment. For example, an AR headset could display real-time information about the robot’s operation and the status of the production line, allowing operators to monitor and control the robot more effectively. AR can also be used to provide guidance and instructions to the operator, such as visual overlays that highlight the location of the next part to be picked up or the correct position for assembly.

Additionally, AR can be used to enhance the robot’s perception and awareness of the environment. By overlaying computer-generated images in the real-world environment, AR can help the robot to better understand its surroundings and identify objects that may be difficult to detect with sensors alone. This can be especially useful in environments with complex and dynamic objects, such as manufacturing facilities with many machines and moving parts.

Another application of AI and AR in industrial robotics is quality control. By using computer vision and machine learning, AI can identify defects in manufactured products, while AR can provide real-time visual feedback to workers on how to fix the issues. The operator can see without AR, but the AR technology can provide additional information to the operator that they may not be able to see with their naked eye or other traditional means of visualization. This additional information can help the operator make more informed decisions, improve efficiency, and reduce errors.

For example, an industrial robot equipped with AR technology can use computer vision to detect and recognize objects in the environment, and the AR display can show the operator additional information about the object, such as its weight, dimensions, or the best way to pick it up. The AR display can also show the operator the robot’s planned path and movements, helping the operator monitor and control the robot’s actions. AR can also provide the operator with real-time feedback on their actions, allowing them to make adjustments and correct mistakes in real time. This can improve the efficiency of the overall process, reduce errors, and increase safety.

At the end of this post, let us imagine an example use case for how AI and AR can work together in the field of industrial robotics: A manufacturing company is looking to improve the efficiency of its production line, which involves assembling complex machinery. The company has a team of skilled operators who manually assemble the machinery, but the process is time-consuming and prone to errors. To address this, the company decides to invest in an industrial robot equipped with AR technology and AI. The robot is programmed to perform certain tasks on the production line, such as picking up and placing components. The AR technology on the robot’s camera enables it to recognize the different components and their correct orientation. It also provides a real-time overlay of instructions and guidance for the robot’s movements, displayed on a monitor for the operator to see. AR technology can be used to provide the robot’s camera with additional visual information that can assist in object recognition and identification. For example, a system could be created where AR markers or tags are placed on the different components, which can be detected by the robot’s camera. The AR markers can contain information about the component’s identity, orientation, and other relevant details. Using this information, the robot’s software can make more informed decisions about how to pick up and manipulate the different components, ensuring that they are properly oriented and aligned. This can help to improve the accuracy and efficiency of the robotic assembly process. Additionally, AR technology can also be used to provide real-time feedback to the operator, allowing them to monitor the robot’s progress and intervene if necessary. For example, an AR overlay could be displayed on the operator’s screen, showing them the current status of the assembly process, highlighting any errors or issues, and providing suggestions for corrective actions. The AI on the robot is constantly analyzing data from the production line, such as the speed of the conveyor belt and the position of other components. Based on this data, the AI optimizes the robot’s movements and decision-making to perform the task more efficiently and with fewer errors. The operator oversees the process and can intervene if necessary, but AR technology and AI help to reduce the need for manual intervention and improve the overall productivity of the production line.

What do you think? Isn’t nice to see how innovation works?


credits: Stelian Brad