Aiming at the problem that the low-cost Strapdown Inertial Navigation System (SINS) / Global Positioning System (GPS) cannot effectively achieve fast in-flight alignment with short-endurance, an improved alignment method based on backtracking navigation is proposed. Firstly, the existing backtracking alignment scheme is refined by fully considering the low-cost inertial sensor biases and correcting the correlated errors, which can effectively suppress the sensor biases that cannot be ignored while making full use of the limited observation information. Secondly, a cubature Kalman filter (CKF) based on Lie group is designed to correct the sensor biases. The corresponding errors encountered during the construction of the observation vectors for alignment are directly compensated by filtering, thus avoiding the traditional schema of coarse alignment followed by fine alignment, and simplifying the alignment process. The effectiveness of the proposed in-flight alignment method is demonstrated via ground vehicle tests. The test results show that, compared with the existing advanced alignment methods, the proposed method has higher alignment accuracy, faster alignment speed and better stability, and is applicable to low-cost SINS for short-endurance alignment.

The paper presents the research progress on the disturbance rejection of Micro Air Vehicle (MAV) in Atmospheric Boundary Layer (ABL) area. The reaction torque of a bio-inspired reaction wheel mechanism inspired by active flight stabilization of Manduca sexta is applied to attenuate the disturbance resulted from unsteady flow fields. The advantage of using this mechanism is to help to recover the MAV posture quickly by applying reaction torque to attenuate the perturbed angle of the coaxial helicopter MAV flying in unsteady wind environments. Another advantage of adopting reaction wheel in coaxial helicopter is that it will replace traditional stabilizer bar or flyer bar to greatly stabilize flying attitude as well as to reduce the structure complexity and volume size induced by stabilizer bars or flyer bars. In this paper, we start to study the dynamic model and disturbance reject performance of stabilizer bar on the micro helicopter. To compare with stabilizer bars, the wind resistance reaction wheels were designed and a novel bio-inspired coaxial helicopter structure with the reaction wheel stabilization mechanism is presented. In addition, its nonlinear and linear dynamic models are derived by Lagrange method. Based on the established models, the PID controller is designed for reaction wheel stabilization system and dynamic responses of the actively controlled system are then extensively studied through simulations. Preliminary results verify the disturbance rejection and control performance of the bio-inspired reaction wheel stabilization mechanism.

Exact attitude estimation of an unmanned aerial vehicle (UAV) is the critical requirement for an autonomous and stable flight. Under dynamic conditions, the autopilot of a UAV cannot complete self-alignment and calculate its accurate attitude only by relying on an inertial measurement unit (IMU). This paper presents a dynamical alignment and estimation method for the horizontal attitude (the pitch and roll Euler angle) based on vision and inertial measurement units (IMU). Firstly, the horizontal attitude is estimated through image information of a visible light camera (hereinafter referred to as visual pose), and is used as the initial alignment input for IMU. Then the visual pose is corrected according to the normalized accelerometer output when UAV is stationary. Finally, Sage-Husa Adaptive Kalman Filter (SHAKF) is used for the fusion of the visual pose and the inertial attitude (the attitude calculated by the IMU). The simulation results show that the maximum estimation error of the UAV's horizontal attitude is within 3°, and the average estimation absolute error is less than 1°, which verifies the effectiveness of this method.