Quadruped Robot Attitude Control Algorithm and its Application in Graduate Education

To improve the control accuracy of the quadruped robot, a method to estimate and control the attitude of the quadruped robot is presented using a nine-axis IMU sensor and kinematics model. An extended Kalman filter (EKF) is designed to filter the real-time data obtained from sensors such as a gyroscope, accelerometer, and magnetometer. After extended Kalman filtering, the nine-axis data of IMU is fused to obtain a more accurate quaternion. The quaternion is converted into the attitude angle to obtain the roll angle, yaw angle, and pitch angle of the quadruped robot. The filtered attitude angle is obtained by inversion to perform attitude compensation so that the quadruped robot can return to the normal attitude. After calculating the attitude compensation matrix, we solve the inverse kinematics of the quadruped robot to obtain the joint angle and understand the control of the quadruped robot's standing posture. The simulation results show that this method can effectively process the IMU sensor data and obtain a high-precision robot attitude angle. We will apply the above research results to the teaching of robot mechanisms for graduate students. The effectiveness of the algorithm is verified by the joint simulation of Matlab and CopperiaSim, and students have a further understanding of the quadruped robot attitude and control.