Search papers, labs, and topics across Lattice.
This paper introduces an interpretable fuzzy-inference framework that generates continuous yaw commands for UAVs using low-dimensional features derived from YOLO bounding boxes, such as centroid location, area, and aspect ratio. By employing a Mamdani fuzzy system followed by a first-order Takagi-Sugeno model, the approach achieves impressive accuracy with a mean absolute error of just $0.140^\circ$ and a maximum absolute error of $1.254^\circ$. The framework's data efficiency and computational lightweight nature make it particularly suitable for real-time applications in resource-constrained environments, addressing the challenges of reliable UAV guidance toward UGVs.
Achieving a yaw estimation accuracy of $0.140^\circ$ without complex modeling or large datasets could revolutionize UAV guidance for real-time applications.
Vision-based guidance of unmanned aerial vehicles (UAVs) toward unmanned ground vehicles (UGVs) supports cooperative aerial--ground robotics, but reliable continuous yaw estimation from onboard vision remains challenging because of sensing uncertainty, limited computation, and the need for interpretable control. Existing deep-learning and geometric-reconstruction approaches often require large datasets, external localization, or complex modeling assumptions, reducing transparency and deployment suitability on resource-constrained platforms. We present an interpretable fuzzy-inference framework that generates continuous yaw commands from low-dimensional features extracted from YOLO boxes: target centroid location, area, and aspect ratio. No explicit geometric modeling is required. A Mamdani fuzzy system serves as an interpretable baseline using a shoulder--triangle--shoulder input partition. It is followed by a first-order Takagi--Sugeno model with three antecedent membership terms per input, whose parameters are derived from training-set quantiles, yielding a compact 27-rule structure. Evaluation uses 6{,}169 labeled samples from a VICON motion-capture environment. Across five randomized train--test splits, the Takagi--Sugeno model achieves a test-set mean absolute error of $0.140^\circ \pm 0.003^\circ$, a root mean squared error of $0.200^\circ \pm 0.008^\circ$, and a maximum absolute error of $1.254^\circ \pm 0.121^\circ$. Within-threshold accuracies are $99.676% \pm 0.270%$ for $\pm1^\circ$ and $100.000% \pm 0.000%$ for both $\pm3^\circ$ and $\pm5^\circ$. Directional consistency between image-plane horizontal displacement and predicted yaw sign reaches $90.254% \pm 0.612%$. These results show that the framework is transparent, data-efficient, computationally lightweight, and suitable for real-time vision-based UAV guidance toward mobile ground targets.
