# Motion Control Method of Bionic Robot Dog Based on Vision and Navigation Information
## 4 Numerical Results and Analysis
### Simulation Experiment
\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
4 & 160.023 & 276.285 & 148.568 & 450.376 \\ \hline
5 & 136.945 & 276.516 & 148.644 & 450.678 \\ \hline
6 & 136.767 & 276.713 & 148.231 & 450.690 \\ \hline
7 & 136.726 & 276.520 & 148.226 & 450.988 \\ \hline
8 & 136.765 & 276.628 & 148.043 & 450.987 \\ \hline
9 & 160.365 & 276.403 & 148.665 & 450.586 \\ \hline
10 & 160.083 & 276.189 & 148.179 & 450.797 \\ \hline Mean value & 136.967 & 276.408 & 148.765 & 450.664 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 7: Comparison of control accuracy. Analysis of Joint Driving Torque  
The joint drive of the bionic mechanical dog is ultimately driven by the driving torque at the joint, which can be analyzed according to the torque function of each joint [19]. We use the red, dotted line to represent the torque curve of the hip joint and the change in the knee joint. The curve can be viewed as a solid black line for analysis, as shown in Figure 8.  
From the above figures, it can be seen that the driving torque changes suddenly during the walking of the bionic robot dog. The smoother the driving torque curve of the joint, the more stable the motion of the robot [20]. It can be seen from the peak of the driving torque curve of the hip joint and knee joint of the right rear leg that the bionic robot dog has a torque imbalance phenomenon in the process of walking. In addition, when a certain phase is in the ground phase, its torque is much greater than when it is in the swing phase. Therefore, the mutation and vibration of the driving torque can be reduced by optimizing the driver control algorithm to make the driving torque curve smoother, thus improving the stability of the robot. The factors that affect the response time of the angle \(\theta\) and two-dimensional trajectory mainly include the inertia and friction of the mechanical structure, the response speed and accuracy of the sensor, and the complexity and accuracy of the control algorithm [21]. For example, the influence of sensors and control algorithms on the control accuracy and response time of the robot system can be analyzed by establishing schematic diagrams of sensors and control algorithms. Through the analysis and modeling of the schematic diagram, we can better understand the interaction between different factors in the robot system so as to realize optimization and control of the system. The curve simulation diagram is shown in Figure 9.  
Figure 8: Driving moment curves of the hip and knee joints of the right hind leg. Figure 9: Schematic diagram of the angle \(\theta\) response time and two-dimensional trajectory tracking of the PID algorithm when the broken line moves. Through analysis of the \(x\)-axis and the \(y\)-axis, the PID algorithm has a certain following effect in the movement of the linear \(x\)-axis. It starts to stabilize after about 15 s on the \(y\)-axis, the \(\theta\) angle also tends to be stable after 20 s, and the response time is relatively short. The error simulation is shown in Figure 10.  
It can be seen from the broken line error simulation that the anti-interference of the PID controller is not very strong, and the response time is slow. The simulation range of the \(\theta\) axis is \((-0.615\)-\(0.875)\) m. Therefore, considering the error and control precision, we need to find a better algorithm to improve the control precision and reduce the error.