AI-based Real Time Location System (AI-RTLS)

Localization as nature does: The Real Time Location System based on AI object recognition and the W2MO Digital Twin

easy to use and powerful

Health and safety standards at work during Corona-times: W2MO Machine learning can check if you’re social distancing properly

easy to use and powerful

Automatic recognition and recording of goods movements without scanning and manual efforts

easy to use and powerful

Neural networks ensure easy pattern learning, fast processing of images with previously unreached recognition rates, and permanent learning

easy to use and powerful

AI camera-based identification solution represents the cooperation between ceiling recognition units (cameras with GPU) and digital twin for full area coverage, real-time transparency of all processes and easy reorganizing of structure, restrictions, or routes in a few clicks

easy to use and powerful

The digital twin includes an integrated simulation engine for easy generation of scenarios to evaluate changes in workforce, shift times, or transport resources

easy to use and powerful

High accuracy of decentralized object recognition and localization in combination with 100% privacy data protection: all faces are blurred, no streaming of videos, only transfer of position data and properties of objects

Real time location is a key requirement for all kind of industry 4.0 applications. So far real time location systems were based on UWB or RFID or similar technologies.

But there is an easier, much more powerful way! Real time location can be done like humans do: just look around, see what is there, where it is, how it looks like and what it does. Object recognition with Artificial Intelligence allows this straightforward approach now to be done by computers with special, but standard processing units, so called GPUs. Combine this idea with a Digital Twin which represents reality in a 3D computer model. This combination allows an extremely powerful location, thus remaining simple and fast. AI can recognize a forklift and where on the picture the forklift is located. So you can draw a straight line from the camera to the forklift. As we all know: forklifts are not flying. They stand or drive on the floor. The Digital Twin can tell where is the floor in this building – at the point of intersection of the floor and the straight line from the camera must be the forklift. As AI can recognize very good the brand and model of the forklift the dimensions of the forklift are known, this means the location can be very precise. And just one camera with a GPU can do the location in cooperation with the digital twin. No triangulation needed!

If objects may look identical they get an AprilTag attached. This is like a number plate of a car and can be printed with any printer.


Background for a Technology Revolution

  • Neural networks automatically "learn" the patterns required for object identification. In practice, often only a few sample images are sufficient
  • Extensive databases with training data enable previously unreached identification rates (outperforming human decision makers)
  • Once learned, the trained networks can be used for many tasks
  • Very fast processing: less than 0.1 seconds per image
  • Extremely fast development of this technology due to fast enhancement of the underlying hardware

Key Features

  • Object localization with cameras, AI and Digital Twin
  • Real time location, tracking and mirroring to the Digital Twin
  • Supervision of storage areas, lanes or buffers with cameras and AI
  • Recognition of state of transporter: loaded – onloaded, no additional sensor needed
  • Both transporters or assets or goods can be located and tracked
  • Automatic booking in WMS when e.g. pallets are picked or dropped
  • Can be set up to be 100% privacy data protection compliant by blurring the faces of persons
  • High accuracy is achieved by automatic camera calibration – from 1 mm to 10 cm
  • Time-consuming scanning processes and sources of errors are eliminated
  • Optimal routing of the transporters from start to end – full navigation system
  • Highly distributed, decentralized and parallelized software architecture
  • Possibility of integration of all location data to ERP, MES or WMS systems
  • All functions can be selected easily using intuitive interface
  • Seamless switching between indoor and outdoor spaces

How does AI-Based RTLS work

  • The operations area is stored as a true-to-scale digital twin in W2MO
  • 2D and 3D representation in digital twin to create real-time transparency of all processes
  • AI camera-based identification solution consists of two key components: Recognition units (cameras +GPU) mounted in such way that the entire operations area is supervised and a digital twin allowing to create and change structure, restrictions, and routes with a few clicks and save all tracking data.
  • Fast camera installation based on digital twin camera plan to ensure full coverage of area
  • Low cost of recognition units
  • Recognition is decentralized, directly done on the recognition units – no streaming of videos, only transfer of position data and properties of objects.
  • One camera per 100 m2 to 1.500 m2 – precision from 1mm to 10cm
  • Usage of AprilTags (like number plates of cars) to locate and identify similar looking objects.
  • AprilTags can be printed on paper, plastic or metal – low cost of the whole solution

Recognition unit with two cameras, metal housing and cooling element, robust, dust resistant and easy to mount.
Power supply and network connection by PoE

Advantages of an AI-based RTLS

  • Recognizes and locates any trained object, finds its properties and what the object is doing (unlike UWB, FRID, Laser which determine only a tag)
  • Digital twin maps reality with digital model and allows many more applications e.g. fleet management, staff planning, action triggering, security, collision prevention, social distancing and much more.
  • Area supervision included, fill grades of lanes or buffers can be recognized, too
  • Digital Twin allows to “understand” the environment, e. g. if a barrier is between a forklift and a person or the person is in danger
  • Persons can be either blurred in order to protect privacy or they can be located and tracked – in order to measure productivity (e. g. for using a performance-based remuneration of workers)
  • Extremely high reliability and stability
  • Very affordable investment and long-term total cost of ownership

W2MO Machine learning can check if you’re social distancing properly at work during Corona-times

  • Monitoring the minimum distance – e. g. six feet – between people in a production site or in a warehouse
  • If the minimum distance is violated, a real time notification will happen, e.g., siren, headlights, flashlight controlled via IP switch actuators, as well as mail notifications or smartphone warning tones (only if privacy protection is not used)
  • If desired: documentation for health authorities can be provided
  • Option to include or exclude privacy protection (according to GDPR)

W2MO tracks the movements of people and other objects and can calculate the distances between them. As soon as people get too close and violate the minimum social distancing, a warning can be issued: optically, with lamps / headlights, or acoustically. If desired, employees wearing / not wearing a mask can also be identified. This makes it easy to monitor that only employees with a mask are working in certain areas.

Privacy protection can be handled differently depending on the customer's wishes. If the protection of privacy is of great importance, people are automatically blurred on the detection unit. In this case, the system only warns that two people have come too close.

To enable traceability in Corona times, individual people can also be explicitly identified and tracked. If there is too close contact, the system can document who has been in contact with whom and for how long, thus effectively supporting the follow-up of health authorities.

Detection takes place directly on the detection units (cameras with ML-stick) - no streaming of huge videos required.

The customer can install the detection units by themselves - the software can be provided remotely by Logivations. A virtual project without physical contact is easily possible.

Automatic recording of goods movements

  • Automatic recording of storage and retrieval
  • For high-bay storage and floor spaces
  • Customer specific training of the load appearance possible
  • Can be combined with Logivations identification gate or onboard forklift cameras
  • No modifications or additions to the forklift required

In addition to tracking the forklifts, goods movements can also be tracked and automatically posted to the WMS. The storage and retrieval of pallets can be recognized with the help of detection units. The current position of the forklift is known from the recognition functions.

This means that all bookings can be made automatically when moving goods. Storage and retrieval can also be documented in order to have seamless traceability, which means documentation of all goods movements is saved in case of investigations.

No modifications to the forklifts are required for the automatic booking of goods movements. No matter which manufacturer and which year of manufacture - the system can always be trained so that goods movements are recognized.

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