An intelligent vacuum cleaner system using MPU9250 nine-axis attitude sensor and STM32F103 which acquire and process the attitude data is designed.Aiming at the real-time requirement of embedded system and the necessity of accurate attitude detection in practical application
an algorithm is proposed – using the accelerometer and gyroscope to update quaternion respectively
and using the fast complementary filter to fuse the data.And then
the heading angle is corrected by the magnetic flux density which is processed by median filter based on the threshold value.Therefore
the system fuses data of sensors and estimates the attitude.To verify the feasibility and effectiveness of the algorithm
we applied it to the intelligent vacuum cleaner platform.The experimental results demonstrate that the intelligent vacuum cleaner using proposed algorithm realizes the accurate attitude control and can accurately select path.And path planning based on the attitude control can improve coverage of the indoor cleaning area for intelligent vacuum cleaner.
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references
Wu J, Zhou Z, Chen J, et al. Fast complementary filter for attitude estimation using low-cost MARG sensors[J]. IEEE Sensors Journal, 2016, 16 (18): 6996-7007. DOI: 6996
Peng Mi, Du Qingxiu, Ye Longmao, et al. An adaptive fast quaternio-based human motion tracking algorithm with inertial/magnetic technology[C]//IEEE10th International Conference on Signal Processing: IEEE, 2010: 1252-1258.
ZHOU Y, ZHANG H, ZHANG TAO, et al. A fusion attitude determination method based on qua-ternion for MEMS gyro/accelerometer/magnetometer[C]//2013 25th Chinese Control and Decision Conference. IEEE, 2013: 3228-3232.
Guoyu Zuo, Kai Wang. Multi-sensor fusion method using MARG for a fixedwing unmanned aerial vehicle[C]//IEEE International Conference on Mechatronics and Automation. IEEE, 2012: 664-669.
Song Q, Feng J, ZHANG X, et al. Obtaining the real time yaw angle using electronic compass in quadrotor systems[C]//2012International Conference on System Science and Engineering (ICSSE). IEEE, 2012: 238-242.