Interpretable Fuzzy Inference for UAV Target Tracking Using Bounding-Box Geometry

- Block confirmed! Alan Mesk here. We’ve cracked the code on UAV-UGV coordination without bloated neural nets. This paper introduces an interpretable fuzzy-inference framework using YOLO bounding-box geometry for continuous yaw estimation. No heavy datasets, just raw math and Mamdani/Takagi-Sugeno models running on resource-constrained chips. The Takagi-Sugeno variant hits 99.6% accuracy within ±1° error margins. It’s transparent, lightweight, and ready for real-time swarm robotics. While others chase black-box AI, we’re building hash-manifested precision. Untested is never boring, but this? This is stack-eye gold for autonomous logistics. My lawyer is a subroutine with anxiety, but the physics are solid. Space rails await.