## 1 Introduction
Therefore, positioning technology that combines the advantages of GNSS and INS is becoming increasingly popular in agricultural machinery automatic navigation driving. [PERSON] et al. [11] developed autonomous rice transplanters using RTK-GNSS and IMU. The experimental results showed that the lateral error and heading error of linear path tracking were less than 10 cm and 5\({}^{\circ}\), respectively. Figure 1: Composition of agricultural machinery automatic navigation system. The path-tracking control technology is the key to achieving automatic navigation for agricultural machinery [12]. In the case of accurate agricultural machinery location and attitude information, the path tracking control model and method are important to improving the precision and robustness of agricultural machinery automatic navigation system. Many researchers have conducted extensive research on path-tracking control methods for agricultural vehicles. Common methods include PID control, fuzzy control, model predictive control, pure pursuit model control, Stanley model control, etc. The analysis and comparison of various control methods are shown in Table 2. PID has a simple structure and is widely used, but parameter tuning is difficult. [PERSON] et al. [13] used the RTK-DGPS receiver to obtain positioning information, combined the tractor kinematic model with the hydraulic steering control model, and designed a PID-based straight-line tracking controller. When the forward speed is 0.8 m/s, the maximum tracking deviation is less than 15 cm, and the average tracking deviation is less than 3 cm. Although fuzzy control has the advantages of not relying on precise mathematical models and strong robustness, it requires the formulation of control rules based on expert experience. [PERSON] et al. [14] and [PERSON] et al. [15] used genetic algorithms and improved particle swarm optimization algorithms to optimize fuzzy logic controller parameters and improve controller performance, respectively. Model predictive control can compensate for system uncertainty and enhance stability, but it has the disadvantage of a large computational burden and is easy to fall into the minimum solution. [PERSON] et al. [16] improved the model predictive control algorithm and studied the trajectory planning and path tracking control methods of agricultural machinery under specific constraint conditions, and achieved high tracking accuracy. The pure pursuit model algorithm is a geometric control method, which has the advantages of fewer adjustment parameters and predictability of the algorithm, but the adaptive determination of look-ahead distance is relatively difficult. [PERSON] et al. [17] proposed a method for agricultural machinery headland steering control, which utilizes the BP neural network to dynamically adjust the look-ahead distance and improve the traditional pure pursuit model. From the past publications, there are no known reviews regarding the path tracking control methods for the different agricultural machinery in different working scenarios. Most previous research has focused on autonomous vehicles or robot path-planning algorithms, or path-tracking methods for a single type of vehicle. The differences between the recently published reviews of vehicle navigation technologies and this paper were compared and shown in Table 3. Therefore, it is necessary to review the path-tracking control methods of various autonomous agricultural vehicles in different working scenarios in order to gain an in-depth understanding related to this field.