A Self-Rotating Tri-Rotor UAV for Field-Of-View Expansion and Autonomous Flight
Xiaobin Zhou, Zihao Zheng, Aoxu Jin, Lei Qiang, Bo Zhu
AI summary
Problem
UAV perception is constrained by narrow sensor fields of view, while existing expansion methods add weight, cost, or power. This paper addresses how to effectively widen sensor coverage while maintaining stable, autonomous flight on a lightweight platform.
Approach
The design harnesses motor counter-torque to induce controlled body rotation, naturally sweeping sensors across a wider area. A nonlinear control framework combining model predictive control and dynamic inversion stabilizes flight and compensates for aerodynamic disturbances.
Key results
- Expands LiDAR vertical field of view from 59° to 89° through controlled self-rotation
- Achieves high-precision trajectory tracking at speeds up to 2.0 m/s using nonlinear MPC and INDI
- Maintains stable hover and flight under wind gusts up to 4.8 m/s
- Demonstrates fully autonomous waypoint navigation in GNSS-denied forests and parking garages
Why it matters
Provides a lightweight, energy-efficient alternative to multi-sensor fusion for enhancing UAV perception and navigation in complex, GPS-denied environments.
Abstract
Unmanned Aerial Vehicles (UAVs) per- ception relies on onboard sensors like cameras and LiDAR, which are limited by the narrow field of view (FoV). We present Self-Perception INertial Naviga- tion Enabled Rotorcraft (SPINNER), a self-rotating tri-rotor UAV for the FoV expansion and autonomous flight. Without adding extra sensors or energy con- sumption, SPINNER significantly expands the FoV of onboard camera and LiDAR sensors through con- tinuous spin motion, thereby enhancing environmen- tal perception efficiency. SPINNER achieves full 3- dimensional position and roll–pitch attitude control using only three brushless motors, while adjusting the rotation speed via anti-torque plates design. To address the strong coupling, severe nonlinear- ity, and complex disturbances induced by spinning flight, we develop a disturbance compensation control framework that combines nonlinear model predictive control (MPC) with incremental nonlinear dynamic inversion. Experimental results demonstrate that SPINNER maintains robust flight under wind dis- turbances up to 4.8 m/s and achieves high-precision trajectory tracking at a maximum speed of 2.0 m/s. Moreover, tests in parking garages and forests show that the rotational perception mechanism substan- tially improves FoV coverage and enhances perception capability of SPINNER.