# Evaluation of PID-Based Algorithms for UGVs
## 5 Conclusions  
In summary, the comprehensive research on the autonomous navigation of unmanned ground vehicles (UGVs) within unstructured environments, such as those commonly  
Figure 19: Pareto front between ITAE and IAE objectives. Figure 20: Pareto front between ITAE and ISE objectives. Figure 19: Pareto front between ITAE and IAE objectives. encountered in agriculture and forestry, has highlighted the vital importance of various PID control algorithms in enhancing their operational effectiveness. The capability of UGVs to navigate independently in these intricate environments is not merely a technological enhancement, but an essential requirement for efficiency, safety, and sustainability in tasks that are frequently hazardous or overly labor-intensive for human operators. The study thoroughly assessed multiple PID control algorithms, including the ON-OFF controller, PID Heading controller, PID Cross Track Error (CTE) controller, PID CTE + Heading controller, and PID Vector Field controller. Each algorithm underwent rigorous testing in real-world scenarios using a forestry tractor converted into a UGV, equipped with advanced sensors for precise data acquisition. The evaluations revealed significant variations in performance metrics among the different control strategies. Notably, the PID Vector Field controller was identified as the optimal approach, demonstrating exceptional performance regarding navigation efficiency, trajectory tracking precision, and adaptability to the unpredictable conditions inherent in unstructured terrain. Utilizing the multi-objective optimization framework confirmed that this method properly balanced stability, adaptability, and computing efficiency. This algorithm's capacity to derive its error from the vector sum of virtual forces enabled dynamic adjustments during navigation, ensuring that the UGV could stay close to its intended path despite obstacles or irregular terrain features. Additionally, the integration of high-accuracy GNSS-RTK positioning alongside inertial measurement units (IMUs) supplied the UGV with crucial data for the continuous real-time monitoring of its position and orientation. This confluence of technologies not only elevated the precision of the navigation system, but also fostered the formulation of reactive control strategies prioritizing safety and efficiency. However, the performance of a PID control algorithm also depends on environmental factors. Variability in terrain conditions, such as soil, mud, or uneven surfaces, can affect the traction of the UGV, thereby complicating its navigation. Since the scope of this article focuses on unstructured environments, specifically forested areas, the presence of dense vegetation and trees may reduce the reliability of GNSS-RTK signals, potentially impacting the PID control algorithms. The practical implications of these results are substantial. The effective adaptation of PID control algorithms for UGVs represents a significant advancement in robotics applications within sectors such as agriculture and forestry, where the demand for autonomous systems continues to grow. The capacity for UGVs to function effectively in challenging environments can reduce labor costs, lower environmental impacts, and enhance safety for human operators. Furthermore, this research establishes a foundational framework for future progress in mobile robotics. By creating a performance evaluation framework based on criteria such as Integral of Absolute Error (IAE), Integral of Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE), along with metrics like mean, standard deviation, maximum position errors, and navigation time, the study allows for a thorough analysis of control algorithms. These metrics may serve as benchmarks for future research, facilitating the ongoing enhancement of UGV navigation technologies. Conceptualization, T.G., T.P. and H.M.; Methodology, T.G., T.P., F.D.G. and S.S.; Software, T.G., T.P., H.M. and F.D.G. ; Validation, T.P., N.F., J.F., S.S. and A.V.