Foundation of Self-Learning AI for Drones
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.66Keywords:
Autonomous Drones, Deep Reinforcement Learning, Intelligent Navigation, Sensor Fusion, edge AI, Explainable AI, UAV perceptionAbstract
Full autonomy and the end of manual drone operation require a powerful self-training AI spine. Drones piloted with Static Commands would break in unpredictable environments (e.g., windy weather, moving obstacles). Classical self-learning systems establish these principles with strict rules; instead, a drone acquires its abilities through on-policy experience. The drone is a digital pupil that learns from experience. It’s mathematically rewarded for getting somewhere and penalised for mistakes, like moving too close to a wall. With thousands of rounds of training under its belt, the AI learns an intricate picture of how to handle flight physics that could not have been hand-coded by a person. Rather than relaying the information to a remote server, the drone performs image processing on the cameras and sensors themselves in real time, with the help of powerful internal chips. These offline computations are complemented by online “Sim-to-Real” training, where the AI is first taught to fly in a safe virtual environment before transfer to physical hardware. That means the drone is already an ace at flying before it ever took off. Also, the foundation must enable lifelong learning. The drone can now adapt to changing environments or mechanical ageing over time, without forgetting what it was taught initially. Developing drones on such intelligent platforms makes them reliable partners for challenging missions, including emergency response and autonomous delivery. This innovation ensures the sky’s future will be safe, fluid, and self-determining.
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