# Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information
## 4 Numerical Results and Analysis  
### Simulation Experiment  
To test the performance of the control algorithm designed in this paper in realizing the robust control of the bionic robot and the correction of the pose and orientation, the simulation experiment was analyzed, and the simulation platform for the experiment was Matlab, version 7. The measurement accuracy range of the binocular vision navigation system was 0.025 mm, the dynamic tracking frequency of the robot pose parameters was 30 Hz, the measurement range of the digital accelerometer was \(\pm 2/\pm 4/\pm 8\) g, and the sensitivity of the digital magnetometer was 8 mGauss 12. The sampling period of the robot pose was 0.02 s, the Kalman filtering period was 0.25 s, and the initial values of the pitch angle and yaw angle of the robot action were \(\Delta 1=5^{\circ}\) and \(\Delta 2=8^{\circ}\), respectively. According to the above simulation environment and parameter settings, the simulation analysis of robot pose control was performed. First, the optical CCD binocular vision dynamic tracking system was used to measure the terminal pose of the bionic robot, and the sampling data were obtained, as shown in Figure 6.  
Taking the end position and pose parameter data of the bionic robot shown in Figure 6 as the research model, we address the pose determination control of the bionic robot and obtain the geometric parameters of the pose tracking control of each measuring point, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Times** & **Left Arm/mm** & **Right Arm/mm** & **Left Leg/mm** & **Right Leg/mm** \\ \hline
1 & 136.650 & 276.320 & 148.543 & 450.687 \\ \hline
2 & 136.743 & 276.335 & 148.665 & 450.688 \\ \hline
3 & 160.021 & 276.236 & 148.578 & 450.676 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Geometric parameters of robot pose determination and tracking. Figure 6: The end pose measurement of the bionic robot. According to the results in Table 2, the average error of the positioning accuracy of the left arm pose transition is 3.453 mm, and the standard deviation is 0.132 when the robot pose positioning and tracking are conducted using the method in this paper. The average error of the positioning accuracy of the right arm pose transition is 2.332 mm, and the standard deviation is 0.145. The average error of the positioning accuracy of the left leg pose transition is 2.432 mm, and the standard deviation is 0.212. The average error of the positioning accuracy of the right leg pose transition is 1.543 mm, and the standard deviation is 0.132. The average positioning error of the robot's pose is increased 2.5 times, and the average error is reduced by 12% when the error correction method is adopted in this paper. Different methods to accurately measure the end pose of bionic robots include mechanical measurement, visual measurement, and inertial measurement [16]. Mechanical measurement has high accuracy and stability, but its adaptability to environments and pose changes is poor. Visual measurement has high flexibility and adaptability, but it has great influence on factors such as illumination and occlusion. Inertial measurement has fast response speed and good adaptability, but there are problems, such as sensor drift and error accumulation. Therefore, when selecting measurement methods, comprehensive consideration and selection should be applied according to specific application scenarios and requirements. To compare the algorithm's performance, the improved EKF fusion filter and the traditional accelerometer [17] and magnetometer direct calculation method [18] are used to compare the control accuracy of the robot's various attitude angles. The results are shown in Figure 7. It can be seen from the analysis that the fitting error of the robot's end position and attitude parameters is low, the dynamic tracking performance is good, and the robustness is high when using this method to control the bionic robot.