Proof of Concept 2
Machine Inspection Using Autonomous Robots and VR
Ensuring high-quality video streams in critical operations like machine inspection is essential for effective anomaly detection. When video quality degrades, operators struggle to identify issues promptly, which can lead to significant risks. The unpredictable nature of wireless conditions—affected by interference, obstacles, and varying network traffic—complicates this challenge. To address these issues, an intelligent, adaptive system is necessary. By utilising ZSM and AI/ML-driven Decision Engines, a proactive solution can be developed that detects and predicts video quality problems while automatically adjusting radio resources to maintain optimal performance. This innovative approach enhances operational efficiency and minimises the risk of oversight, ensuring safer and more reliable outcomes in high-stakes environments.
Proof of Concept Aim
This PoC aims to showcase the usage of RAN NDT within a ZSM closed-control loop to guarantee constant quality for the video streams collected from the robots that do machine inspections. In this PoC, we will demonstrate FLECON-6G’s approach to resolving conflicts in a Native AI-driven closed-loop ZSM system to guarantee that robots’ video streams are not degraded, even in bad conditions (channel degradation or competition with other flows), by adapting the radio resources allocated to the robots’ video stream in real-time.