# Evaluation of PID-Based Algorithms for UGVs
## 4 Experimental Results
### PID Algorithm's Control Performance  
To visualize the data from the experiments carried out for each control system, we created a visual presentation to provide a clear and comparative perspective of the performance of each controller. This practical approach aims to provide more practical and \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Controller**} & \multicolumn{3}{c}{**PID Linear Coefficient**} & \multicolumn{3}{c}{**PID Angular Coefficient**} \\ \cline{2-7}  & **Kp** & **Ki** & **Kd** & **Kp** & **Ki** & **Kd** \\ \hline PID H & 0.642 & 0.071 & 0.90 & 0.101 & 0.0054 & 0.0040 \\ PID CTE & 1.882 & 0.607 & 0.83 & 0.084 & 0.0295 & 0.0376 \\ PID CTE + H & 0.298 & 0.021 & 0.69 & 0.297 & 0.411 & 0.0546 \\ PID (VF) & & Uses S* & & 0.385 & 0.1026 & 0.0211 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters obtained for each PID control algorithm. valuable insights into the behavior and effectiveness of navigation controllers in the real world. All the tests conducted can be observed in Figure 15.  
After thoroughly analyzing the results obtained from the field tests, we decided to conduct additional tests to more precisely identify the most promising methods to be incorporated into an obstacle avoidance algorithm during navigation. Through this careful analysis, we aimed to select approaches that demonstrate consistency and effectiveness in a variety of situations. Figure 16 is a visual representation of the various paths taken under different control types. We observed that the proposed destinations were successfully reached; however, since this was not an ideal environment and contained disturbances, additional comparisons were made by repeating the same trajectories for all algorithms. In our case, we considered the square movement, where the robot passed through the coordinates [(8.0, 0.0); (8.0, 8.0); (0.0, 8.0); (0.0, 0.0)], respectively. All tests started with the UGV facing east before navigation. In all tests, the robot used GPS + RTK technology, providing an accuracy of up to 2 cm. For all navigation tests, an offset of 0.2 m was considered until the destination was reached. For each of these tests, an analysis of the robot's position error relative to the trajectory it should follow was performed. That is, the mean error, maximum error, and standard deviation were calculated for each situation. In the graphs, a position translation of 1.1 m is noticeable. This means that the robot's location, instead of being at the \"Sentry\" point where the GPS is located, was now situated at the robot's center of rotation, affecting the observed trajectories. This occurred because the position did not start from the origin point (0,0), but from the point (1,0). Figure 16: Tests conducted in a real environment. To better clarify the different paths taken by the UGV, Figure 17 shows the impacts that the robot took and moved. This visualization enables an accurate analysis by presenting the observed data with the context given in Figure 16.  
Table 6 presents the performance evaluation criteria used to evaluate and compare the proposed algorithms, focusing on their effectiveness in terms of error reduction, routing accuracy, and navigation efficiency. The analysis of the data presented in the table reveals that the CTE (Cross Track Error) and Vector Field Control (VFC) algorithms outperform the others in terms of overall performance. In particular, the VFC algorithm exhibits the best results in key quantitative performance parameters, including ITAE (Integral Time Absolute Error), IAE (Integral Absolute Error), and ISE (Integral Square Error). The VFC algorithm obtains the lowest values of ITAE (520.96), IAE (66.989), ISE (11.8488), indicating stability and accuracy with minimal accumulated errors over time. Moreover, the stationary error (Mean [m] = 0.18) and the mean (Standard Deviation [m]. = 0.17) show low values, indicating that a route is always closer to the correct route.