# Evaluation of PID-Based Algorithms for UGVs
###### Abstract  
The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV's navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV's position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper. PID control; UGV; algorithms; real environment tests +
Footnote †: journal: _articles  
0000-0001-9399-4000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-000000-000000-00000-000000-000000-000000-0000000-0000000-00000000-0000000000000000000000000000000000000000000000 application of UGVs in specific agricultural tasks [1; 2]. Additionally, in forestry, although advancements are more limited compared to agriculture, the literature already highlights some progress, as demonstrated in [3; 4]. The navigation of UGVs in unstructured outdoor environments has been challenging due to the presence of adversities/unpredictable factors such as lighting, shadows, obstructions, dynamic obstacles, and rugged or demanding terrains [5; 6]. Given these constraints, the focus of research by many investigators in the field of mobile robotics has been on developing UGVs capable of navigating autonomously and efficiently. Such autonomous systems in challenging environments require a computational component to enable UGVs to perform trajectory planning. The ability of a UGV to plan a trajectory that not only reaches a target destination but also avoids obstacles with time and efficiency guarantees are basic functions that UGVs must possess, regardless of the application scenario. We can identify three types of controllers commonly used in this field: classical control, referred to as Proportional, Integral, and Derivative (PID); intelligent and adaptive control (Fuzzy); and predictive control [7]. However, for the work addressed in this article, the PID controller is used. Thus, the autonomous navigation technology of UGVs in unstructured environments involves trajectory planning and control, precise positioning, and other functions. The current literature reveals the existence of various navigation systems, with the most commonly used being the real-time kinematic global satellite navigation system (RTK-GNSS). This type of system achieves centimeter-scale accuracy in satellite positioning, but requires a clear line of sight [8]. In addition to this navigation system, other, less predominant systems also appear in the literature, which collects different types of sensory data. These systems include GNSS/INS, which result from the combination of a Global Navigation Satellite System (GNSS) and an Inertial Navigation System (INS) [9; 10]; GNSS/VISUAL/INS systems, which combine GNSSs, visual information, and INSs [11]; GNSS/VISUAL/IMU/MAP systems [12]; and others. For the work in question, the GNSS-RTK/INS navigation system was used, which combines data from GNSS + RTK + IMU sensors to obtain the position, as well as a magnetometer to determine the heading or yaw (referring to the robot's rotation along the \(Z\)-axis of the UGV).