Autonomous things are objects that interact with each other and also with people in interact with an expanded ecosystem. The areas of application are broad here. From autonomous household appliances to driverless transport systems in manufacturing, drones in warehouse management, damage detection in wind turbines and buildings to self-driving vehicles: The new autonomy of things is the result of advances in areas such as artificial intelligence (AI), network technology, and cloud and Edge computing.
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Autonomous mobile robots, drones and vehicles offer companies a way to increase their efficiency and also to counteract the labor shortage in certain business areas. Last but not least, employees can be protected from danger in this way. In the healthcare sector, for example, automated guided vehicles (AGV) are already helping with the transport of materials and with disinfection in hospitals. With increasing autonomy of mobile robots, productivity and the potential for cost reduction also increase. Robots generate and collect huge amounts of data. With the help of computer vision, but also object and pattern recognition, autonomous vehicles find their way and constantly record data such as GPS locations, image and audio signals, temperature or humidity. This is made possible by sensors and cameras, microphones as well as gas and radiation detectors. Wireless connectivity, an intelligent system architecture and cloud edge computing allow the collected data to be used via AI and machine learning algorithms. This data can be analyzed for various purposes, for example for the digital system display in building information management, to detect damage to structures or objects, for intelligent maintenance scenarios or the creation of a digital twin.
Inventory drone records inventory Barcode scanner. (Image: Shutterstock)
AI at the “edge” in the distributed cloud approach
Depending on the depth of the AI level, the requirements for computing power and energy consumption considerable. Autonomous devices can be equipped with the level of intelligence they need to perform well. This includes so-called weak AI on a robot for simple and time-critical tasks such as image and object recognition as well as performance-enhanced versions for more demanding tasks. Here it is particularly important to place the AI in the right place so as not to overload the robot and to maintain the speed of decision-making.
In an intelligent network, the AI is distributed over several levels from a central cloud to an edge cloud to the individual robot. Transferring data to the cloud saves energy and is particularly suitable for tasks that would exceed the storage capacity of the device. In order to accelerate the transfer to a central cloud service for deep processing or cross-system modeling, the collected data can be preprocessed at the edge. Among other things, this drastically reduces the data volume. This approach can also be followed in order to protect sensitive – such as personal – data from being accessed in the best possible way.
Mobile robots in practical use in industry
Autonomous mobile robots can use the automation potential in different scenarios increase significantly. The robot Spot from Boston Dynamics can climb stairs on four legs, take high-resolution photos with machine image processing and collect valuable data for use in preventive maintenance. A specific application is the use in vehicle inspection. Previously, car rental companies recorded the damage after the rental car was returned in a time-consuming and costly manual process. Equipped with computer vision and the necessary computing power, Spot can relieve the burden by moving around the vehicle independently and recording its condition. As an edge device, it analyzes the data before it is sent to the cloud. This reduces the data flow and enables immediate knowledge. The identified damage is then documented. With additional dashboards that can be viewed on a display in an app, employees can see all the details at a glance. Even in building management or system monitoring, the robot can recognize potential dangers to health, safety or the environment with the help of predictive maintenance models and deep learning algorithms. The robot’s AI-controlled sensor technology enables precise visual and acoustic measurements as well as gas detection in inaccessible areas.
The autonomously acting robot dog SPOT. (Image: Reply)
The future of robot technology
The future of autonomous things and the associated transformation of business models was one of the topics of the annual im Reply Xchange that takes place in the summer. This year, the international event for customers and employees took place online to present the latest products and technologies digitally. Reply recognizes the enormous potential that lies in the advances in innovation technologies. The economic effects are analyzed and new fields of application are researched while projects are already being implemented and ready-made solutions are being offered that generate added value.
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