Pioneering AI in UAV-Assisted 6G Networks for the Advancement of Mobile Robotics in Industry 5.0

New methods are needed to keep up with the fast growth of Industry 5.0 when UAVs are added to 6G networks. This study talks about new ways to make tracking, communication, security, resource sharing, and finding problems better in 6G networks that use unmanned aerial vehicles (UAVs). Using exploration-exploitation loops, the Bellman equation, and dynamic adaptation, the Reinforcement Learning for UAV Navigation method makes decisions that are flexible. Machine Learning is used in Dynamic Network Adaptability to make communication methods better in real time. This makes UAV networks more flexible and efficient. Using AI algorithms to design a secure communication protocol gives great security. Important data is sent safely in 6G networks with UAVs using encryption driven by AI. The Scalable Resource Allocation using Genetic Algorithms Algorithm also offers a fresh way of allocating resources, which makes it flexible and scalable for a range of task situations. Anomaly detection in real time is needed for Industry 5.0 apps, and the Real-time Anomaly Detection using Deep Learning Algorithm makes security better. A full test of comparison shows that the suggested options do better in terms of accuracy, dependability, freedom, and openness. The pictures support the idea that the suggested options are better than the current ones. A research paper on Reinforcement Learning for UAV Navigation focuses on the exploration-exploitation, dynamic adaptation, and Bellman equation use of the method. Overall, these options give you all the tools you need to make UAV activities better over 6G networks in Industry 5.0. They make it easier to use UAVs in many situations by encouraging flexible choices, smart use of resources, safe contact, and instant discovery of strange events.