Computational Design of a Low-Visibility UAV Using a Human-Aligned Perceptual Metric
This work addresses the need for low-visibility drones for applications like surveillance or wildlife monitoring, but the approach is incremental as it applies known concepts (motion blur, LPIPS) to a new domain.
The paper introduces Phantom Twist, a single-propeller UAV designed for low visibility via high-speed spinning and motion blur, and presents an automated design pipeline that optimizes component placement to minimize a human-aligned perceptual metric (LPIPS) while satisfying flight constraints. Flight tests confirm the optimized UAV is stable, controllable, and significantly less perceptible than conventional quadcopters.
We introduce Phantom Twist, a type of single-propeller UAV designed to achieve low visibility through high-speed spinning and the exploitation of motion blur. We develop a two-stage automated design pipeline that optimizes the placement of functional components including batteries, control PCB, motor-propeller assembly, and counterweights. The pipeline minimizes visibility as measured by a human-aligned perceptual metric (LPIPS) while strictly satisfying inertial and aerodynamic constraints required for stable flight. We validate this approach through fabrication and flight testing of multiple prototypes. These tests confirm that our pipeline produces stable, controllable designs and that the optimized UAV exhibits significantly reduced visual perceptibility compared to conventional quadcopters.