Industry 4.0 Use Cases & Living Lab #1

The first Industry 4.0 Living lab will validate the IoT-NGIN federation framework in real-life applications implemented at BOSCH’s facilities (Trial#4) in Aranjuez, Madrid.

In Aranjuez facilities, BOSCH manufactures a range of car electromechanical brake boosters. Automated Guided Vehicles (AGVs) are widely used to transport materials from the Preparation Zone to the Production Zone (Machining, Boosters, Assembly). In the IoT-NGIN living lab, Toyota AGVs and various Mobile Industrial Robots which differ in shape, size, and operation will be utilized. The AGVs are equipped with safety systems, such as reversing sensors and front safety sensors, which are able to perform emergency stops. However, they cannot be effective if obstacles are permanently in motion, such as human workers, while the sensors have limitations in detecting other types of AGVs, in particular when there is no “visual” contact with the object. To ensure human workers safety and driven Forklifts (from AGVs and the surrounding factory equipment), and to avoid collisions, a user-aware and semi-autonomous IoT system is required, which acts in real-time and solves performance challenges prioritizing human-centered safety.

Use case Applications

Human-centred safety in a self-aware indoor factory environment.IoT-NGIN will provide a high-precision IoT localization layer merging real-time localizations obtained from Ultra-Wide Band (UWB) sensors and a solution providing Visible Light Positioning (VLP). In addition, safety cameras will be deployed to monitor areas with reduced visibility. A high-speed and ultra-low latency wireless access network, which supports quick notifications and massive data uploads, will also be deployed based on a combination of 5GNR technology, using private spectrum at 3.5 GHz, and high-speed Wi-Fi using unlicensed spectrum at 5 GHz. The data from the sensors provide a full description of the environment and how it is changing. It allows to model moving objects and skeletonize human bodies to detect the position of each body part and build a “safety shell” around it to ensure human-centred safety.
In BOSCH facilities, the combination of UWB and VLP will provide cm-level accuracy to accurately track the real-time positions of the AGVs, which will be equipped with inexpensive UWB and CMOS tracking sensors. The factory will feature edge computing resources that will be used to support a set of virtual AI functions that will process the real-time location of the AGVs, the real-time stream coming from the safety cameras. AI applications will predict potential collisions between AGVs, or between and workers, issuing early warnings.
Human-centred Augmented Reality assisted build-to-order assembly.Based on contextual-IoT intelligence, high-precision IoT localization, RFID sensors and camera analysis, IoT-NGIN will be able to recognize the components and the stage of the assembly process using local ML trained models and provide assistance and guided instructions, displaying the procedure and next stage of the customizable manufacturing, either using AR glasses, mobile devices or small industrial screens. In BOSCH’ facilities, the system will provide employees with guided information on the way that they should assemble various customized sub-products in a build-to-order concept.

IoT-NGIN as Differentiator

AGVs-AGVs and AGVs-humans collision prediction, detection and avoidance through real-time cm-level positioning and federated ML functions hosted locally on the IoT nodes and in 5G edge
Combine edge (remote) and semi-autonomous (local) federated ML decision on constrained resources, enabling safe operation and build-to-order assembly even in case of network error, loss of connectivity or weak coverage
“Contextual IoT” through human-centric sensing to notify the worker of potentially dangerous situations and to send a warning signal or a stop command to AGVs
Monitoring employees’ body positions and stress levels to ensure workplace safety through federated ML
High-Accuracy Indoor Positioning (HAIP) deployment enables production optimization