The Architecture of Autonomous Flight

Traditional autopilots rely on rigid state machines. They work well when conditions are predictable, but fail catastrophically in edge cases.

At Kingly Agency, we took a different approach for an aviation client. We built a hybrid neural-symbolic architecture that combines the robustness of formal logic with the adaptability of deep learning.

The Core Loop#

Our control loop runs at 100Hz. At every step, a vision model (YOLOv8-based) processes the visual field, while a symbolic planner validates the proposed action against safety constraints (ACAS-Xu rules).

The result is an agent that can "see" and "react" like a human pilot, but follows safety procedures with machine precision. We successfully demonstrated this in live flight tests, performing autonomous takeoffs with zero human intervention.

Related Work#

• Teaching AI to Fly — Reinforcement learning approaches to autonomous flight
• AI-Native Architecture — The infrastructure patterns behind real-time AI systems
• AI Dictionary — Key terminology for autonomous systems