PhoenixAI

We specialize in the development of advanced route optimization, A.K.A. Autopilot for UAV/Drone operations within Cellular, Satellite, or WiFi networks. Our approach hinges on the utilization of reinforcement learning algorithms, a process of continuous learning and path adaptation, to ensure our "always on" connectivity. The algorithm is designed to encompass a wide array of factors often causing issues with BVLOS drones, including signal quality, throughput, latency, environmental conditions, and even weather patterns. By leveraging these inputs, we meticulously train the reinforcement learning model offline, creating a path that's near-optimal from the drone's first flight. This comprehensive training approach ensures our software accounts for the pivotal elements that influence wireless communication. Our roadmap includes external factors such as environment and weather conditions (i.e., wind, rain, fog, snow, etc.), clutter over terrain, and physical movements (roll, pitch, yaw) to influence the route optimization decision-making process. Our AI/ML stack can be used for other Autonomous connectivity programs such as V2X and IoT.