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针对小型两轮自平衡机器人姿态检测所用低成本加速度计(MMA7361)数据不够准确及陀螺仪(ENC03)信号的漂移问题,分别采用互补滤波器、kalamn滤波器和自适应kalman滤波器进行数据融合处理方法研究,通过对比实验确定采用自适应kalman滤波器实现加速度计和陀螺仪检测数据的融合,从而确定小型两轮自平衡机器人的姿态,进而实现其运动平衡控制.物理实验结果表明基于自适应kalman滤波器的加速度计和陀螺仪检测数据融合姿态检测方法不仅适用于小型两轮自平衡机器人的运动平衡控制,而且其参数较先前普遍采用的kalamn滤波器更易调整,姿态检测结果更加可靠.
In order to solve the problem that the data of low cost accelerometer (MMA7361) used in small two-wheeled self-balancing robot pose detection is not accurate and the drifts of gyro (ENC03) signals are used, the data fusion is performed by using complementary filter, kalamn filter and adaptive kalman filter By comparing the experiments, the adaptive kalman filter is used to determine the convergence of the accelerometer and the gyroscope to determine the attitude of the small two-wheeled self-balancing robot and to achieve the balance control of the robot.Experimental results show that the adaptive Kalman filter based on the adaptive kalman Filter accelerometer and gyroscope data fusion attitude detection method is not only suitable for the balance control of small two-wheel self-balancing robot, but also its parameters are easier to adjust than the kalamn filter which is commonly used before, and the attitude detection result is more reliable.