Flying State Sensing and Estimation Method of Large-Scale Bionic Flapping Wing Flying Robot

A large bionic flapping wing robot has unique advantages in flight efficiency. However, the fluctuation of fuselage centroid during flight makes it difficult for traditional state sensing and estimation methods to provide stable and accurate data. In order to provide stable and accurate positioning...

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Bibliographic Details
Published in:Actuators 2022-08, Vol.11 (8), p.213
Main Authors: Liu, Guangze, Wang, Song, Xu, Wenfu
Format: Article
Language:English
Subjects:
Aircraft
Airframes
Algorithms
Analysis
Attitudes
bionic flapping wing robot
Bionics
Calibration
Centroids
complementary filtering
Flapping wings
Flight
Fuselages
Ground stations
Inertial navigation
Inertial navigation (Aeronautics)
Inertial sensing devices
Kalman filter
Kalman filters
Laboratories
Methods
multiple sensors
posture
Robots
Satellite navigation systems
Sensors
state sensing and estimation
Unmanned aerial vehicles
Visual flight
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Summary:A large bionic flapping wing robot has unique advantages in flight efficiency. However, the fluctuation of fuselage centroid during flight makes it difficult for traditional state sensing and estimation methods to provide stable and accurate data. In order to provide stable and accurate positioning and attitude information for a flapping wing robot, this paper proposes a flight state sensing and estimation method integrating multiple sensors. Combined with the motion characteristics of a large flapping wing robot, the autonomous flight, including the whole process of takeoff, cruise and landing, is realized. An explicit complementary filtering algorithm is designed to fuse the data of inertial sensor and magnetometer, which solves the problem of attitude divergence. The Kalman filter algorithm is designed to estimate the spatial position and speed of a flapping wing robot by integrating inertial navigation with GPS (global positioning system) and barometer measurement data. The state sensing and estimation accuracy of the flapping wing robot are improved. Finally, the flying state sensing and estimation method is integrated with the flapping wing robot, and the flight experiments are carried out. The results verify the effectiveness of the proposed method, which can provide a guarantee for the flapping wing robot to achieve autonomous flight beyond the visual range.
ISSN:2076-0825
2076-0825
DOI:10.3390/act11080213