Ai Based Sense And Avoid System For Autonomous Flight

Abstract

This paper describes a robust, multi-layered method for implementing an automated obstacle detection and avoidance system. Our approach leverages the power of artificial intelligence (AI) and advanced data/sensor fusion to create a comprehensive and resilient safety net around the aircraft. By integrating inputs from a suite of complementary sensors, such as radar, LiDAR, and electro-optical/infrared cameras, the system generates a high-fidelity, 360-degree model of the operational environment. This fused sensor data feeds into sophisticated AI algorithms capable of real-time threat assessment, distinguishing between static obstacles (e.g., terrain, buildings, wires) and dynamic traffic. Crucially, this includes an integrated anti-collision function designed for multi-asset scenarios, ensuring safe deconfliction with other friendly aircraft. The system is engineered to align with the European Union Aviation Safety Agency (EASA) roadmap for autonomy, supporting a scalable implementation from initial automation with a human in the loop-providing critical alerts and decision support-to eventual full autonomy where the aircraft can independently execute safe-haven maneuvers. Ultimately, this approach will not only serve as a powerful flight safety enhancer by drastically reducing the probability of collisions but will also significantly advance flight automation by bestowing a higher degree of intelligent autonomy upon the helicopter platform.