Quadrotor Unmanned Aerial Vehicle Autonomous Flight Control System Design And Development

Quadrotor Unmanned Aerial Vehicle, QUAV, is one of the most common unmanned aerial vehicle rotorcraft, which is widely used in the field of military reconnaissance, real-time monitoring, disaster relief. In order to further improve the ability of QUAV to adapting to the complex environment and execute various tasks, the key is having the independent control ability. QUAV is an under-actuated system for its four input and six output, it has multivariable、 nonlinear and strong coupling characteristic. Which make it difficult to design and develop autonomous control system. Aiming at this problem, focused on studying the issue of QUAV flight attitude and the trajectory of its autonomous control, this paper decide to utilizing the STM32microprocessor to design and develop the QUAV’s autonomous control system, achieving its Autonomous flight indoors or without satellite positioning.Specific research findings as follow:(1) Aiming at the problem of spatial localization of QUAV’s flight indoor, this paper presents a position estimation algorithm based on vision navigation to realize the identification and tracking of the track under the given trajectory identification. The algorithm, recording the logo image information given by airborne camera acquisition, completes feature extraction of the left and right edge control line for a given identification through the video feature extraction algorithm, and calculates position of the identification’s center line, then by judging the location of the center line in the image estimates QUAV’s position relative to a given identification in the actual environment, thus completing the spatial localization of QUAV’s trajectory.(2) Aiming at the control problem of QUAV’s flight attitude stabilization, this paper obtains QUAV’s real time attitude data from multiple sensors (including:gyroscope, accelerometer and magnetometer etc), then fuses the attitude data obtained by multisensory through the complementary filtering fusion algorithm aim to reduce the error of steady state control and improve the ability of QUAV’s attitude stability and control.(3) Designing and developing the software and hardware of QUAV’s independent control systems to achieve QUAV’s autonomous tracing flight.