Path tracking control method, system and equipment for transfer trolley of mixing pot

Abstract

The invention discloses a path tracking control method for a mixing pot transfer trolley. The method comprises the following steps: 1) establishing an Ackerman steering geometric model; step 2), setting the inner and outer wheel rotating angles of the mixing pot transfer trolley to be the same, and simplifying the Ackerman steering geometric model to obtain a kinematic bicycle model; (3) the course error of the current mixing pot transfer trolley is obtained; 4), calculating a preview distance parameter, adjusting the preview distance, and completing the path tracking control of the transfer trolley of the mixing pot; the system comprises an Ackerman steering geometry module, a kinematic bicycle module, a preview point selection module and a fuzzy controller module. The control device comprises an embedded bottom layer controller, a main control core computing unit processor, a multi-line laser radar, an RTK and a vehicle VCU controller. The output quantity of the fuzzy controller is determined according to the transverse error and the course error, and the parameter of the preview distance is adjusted according to the output quantity; the method can adapt to different paths, reduces the path tracking error of the transfer trolley, and improves the tracking precision and stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic driving of vehicles, and in particular relates to a path tracking control method, system and equipment for a mixing boiler transfer vehicle. Background Art

[0002] With the increasing demand for intelligence and automation in modern industrial production lines, the role of transfer vehicles in production lines is becoming increasingly important. Traditional transfer vehicles mostly rely on manual control or simple trajectory planning, facing problems such as poor path planning accuracy, unstable steering, and low efficiency. Especially in complex production environments, transfer vehicles need to accurately track paths in a limited space to avoid collisions with other equipment or obstacles, so a more efficient and accurate path tracking control method is needed.

[0003] Path tracking control technology refers to controlling the steering and driving of a vehicle and driving it along a predetermined trajectory in a global coordinate system, thereby achieving functions such as avoiding obstacles. The pure tracking algorithm is a widely used path following method. It is based on the Ackerman steering model, selects the target point through the preview distance, calculates the front wheel angle, and allows the center of the vehicle's rear axle to smoothly travel along an arc to the target point. It has strong stability when driving in a straight line at low speed, and is currently widely used in low-speed unmanned vehicles. However, since the pure tracking algorithm has a fixed preview distance, the vehicle's driving stability is easily affected by the vehicle speed and preview distance, so its stability when driving on a curve is weaker than when driving in a straight line. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, the object of the present invention is to provide a path tracking control method, system and device for a hybrid pot transfer vehicle, using lateral error and heading error as input quantities, designing a fuzzy controller preview distance parameter, thereby adapting to different trajectories, further reducing errors and improving path tracking accuracy;

[0005] To achieve the above object, the technical solution adopted by the present invention is:

[0006] The path tracking control method of the mixing pot transfer vehicle comprises the following steps:

[0007] Step 1), according to the wheelbase and track of the mixing pot transfer vehicle, an Ackerman steering geometry model is established for calculating the steering angles of the inner and outer wheels of the vehicle when the transfer vehicle turns;

[0008] Step 2), ignoring the vertical motion of the mixing pot transporter, setting the inner and outer wheel rotation angles of the mixing pot transporter to be the same in the two-dimensional plane, simplifying the Ackerman steering geometry model established in step 1), and obtaining a kinematic bicycle model;

[0009] Step 3), select a point on the driving path of the mixing pot transfer vehicle as the preview point, calculate the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point according to the kinematic bicycle model, and obtain the current heading error of the mixing pot transfer vehicle; the heading error is the deviation between the actual heading of the mixing pot transfer vehicle and the theoretical heading, and the theoretical heading is the direction in which the mixing pot transfer vehicle reaches the preview point; the lateral error is the distance from the preview point to the nearest point on the track;

[0010] Step 4), establish a fuzzy controller and use the lateral error and heading error as input variables to calculate the preview distance parameters, thereby adjusting the preview distance and completing the path tracking control of the mixing pot transfer vehicle.

[0011] The specific method of step 1) is as follows: when the mixing pot transport vehicle turns, the center of the rear axle of the mixing pot transport vehicle is taken as the origin, and the steering angles of the inner and outer wheels of the mixing pot transport vehicle are inconsistent, and the steering angles of the inner and outer wheels are respectively:

[0012]

[0013]

[0014] Among them, δ L represents the inner wheel turning angle, δ R represents the outer wheel turning angle, L represents the wheelbase of the mixing pot transfer vehicle, L w It represents the wheelbase of the mixing pot transfer vehicle, and R represents the turning radius of the mixing pot transfer vehicle.

[0015] The specific method of step 2) is as follows: ignoring the vertical movement of the mixing pot transport vehicle, assuming that the inner and outer wheel turning angles of the mixing pot transport vehicle are the same in the two-dimensional plane, and simplifying the steering angle of the front wheel of the mixing pot transport vehicle to:

[0016]

[0017] Wherein, δ represents the front wheel turning angle of the mixing pot transfer vehicle.

[0018] The specific method of step 3) is as follows: a point is selected on the driving path of the mixing pot transfer vehicle as a preview point, and the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point are calculated according to the kinematic bicycle model:

[0019]

[0020] Among them, E x Indicates the lateral error between the mixing pot transfer vehicle and the preview point; L d It indicates the distance between the steering center of the mixing pot transfer vehicle and the preview point, i.e. the preview distance.

[0021] In the step 4), the fuzzy controller is specifically established as follows: the basic domain of the heading error is set to [-90°, 90°], the basic domain of the lateral error is set to [-3m, 3m], the basic domain of the preview distance is set to [0m, 10m], and the heading error quantization factor, the lateral error quantization factor and the preview distance quantization factor are set to K E =2, K L =1, the defuzzification process is:

[0022]

[0023] Where n represents the number of fuzzy set elements, K p represents the output after defuzzification, K i represents the domain value of each element in the fuzzy set, μ i Represents the membership value of each element in the fuzzy set.

[0024] In the step 4), the lateral error and the heading error are used as input variables to calculate the preview distance parameter, thereby adjusting the preview distance to complete the path tracking control of the hybrid boiler transfer vehicle. Specifically, the heading error is defined as positive to the left, and the wheel angle is defined as positive counterclockwise; the quantization levels of the heading error and the lateral error are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6] according to the basic domain of the heading error, the basic domain of the lateral error, the heading error quantization factor and the lateral error quantization factor; in the fuzzy controller, the input heading error and lateral error are defined as fuzzy sets according to the quantization level

[0025] The output variable of the fuzzy controller is also defined as a subset of the fuzzy [SL, SM, SS, ZO, LS, LM, LL]. In the fuzzy set, SL represents large negative, SM represents medium negative, SS represents small negative, ZO represents zero, LS represents small positive, LM represents medium positive, and LL represents large positive. According to the basic domain of the preview distance and the preview distance quantization factor, the preview distance quantization level is divided into [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], and the preview distance is adaptively adjusted according to the output variable.

[0026] A control system for a hybrid pot transfer vehicle path tracking control method based on pure tracking, comprising: an Ackerman steering geometry module established according to an Ackerman steering geometry model, a kinematic bicycle module established according to a kinematic bicycle model, a preview point selection module established according to a preview point selection model, and a fuzzy controller module established according to a fuzzy controller;

[0027] The Ackerman steering geometry module is used to calculate the steering angles of the inner and outer wheels of the mixing boiler transfer vehicle when the mixing boiler transfer vehicle is turning according to the wheelbase and track of the mixing boiler transfer vehicle;

[0028] The kinematic bicycle module assumes that the vehicle will not move laterally, so the Ackerman steering geometry model of the transport vehicle can be simplified to obtain a kinematic bicycle model;

[0029] The preview point selection module is used to select a point on the driving path of the mixing pot transfer vehicle as a preview point;

[0030] The fuzzy controller module is used to calculate the preview distance parameter, so as to dynamically adjust the preview distance in real time and complete the path tracking control of the mixing pot transfer vehicle.

[0031] A hybrid pot transfer vehicle path tracking control device, comprising an embedded bottom layer controller, a main control core computing unit processor, a multi-line laser radar, a real-time dynamic differentiator, and a vehicle VCU controller;

[0032] The embedded bottom-level controller is connected to the vehicle VCU controller through the CAN protocol, and is connected to the main control core computing unit processor through the serial port 232 protocol; the multi-line laser radar is connected to the main control core computing unit through the TCP protocol; the real-time dynamic differentiator is connected to the main control core computing unit through the 232 protocol.

[0033] The embedded bottom-level controller communicates with the vehicle VCU, reads the vehicle status fed back by the VCU, and issues control instructions;

[0034] The vehicle VCU controller mainly controls the vehicle's bottom actuator, mainly converting the hydraulic transmission system's electrical signals into hydraulic execution signals;

[0035] The multi-line laser radar is mainly used to collect information about the surrounding environment, to construct a three-dimensional point cloud map of the environment, and then to determine its own position in real time based on the constructed environment map;

[0036] The real-time dynamic differentiator is mainly based on differential technology, and determines the position of the vehicle itself based on differential technology according to its own longitude and latitude information and the longitude and latitude information of the fixed station;

[0037] The main control core computing unit processor mainly utilizes the point cloud information provided by the multi-line laser radar module and the longitude and latitude data provided by the real-time dynamic differentiator to provide real-time position for navigation based on multi-sensor fusion. That is, the multi-line laser radar module and the real-time dynamic differentiator are used to complete the real-time control of the hybrid pot transfer vehicle, and is used to run the SLAM mapping and positioning program of the multi-line laser radar, the underlying driver, the multi-sensor fusion positioning program, and the navigation algorithm program.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] Based on the traditional pure tracking algorithm, the present invention adds a fuzzy controller to improve the problem of fixed preview distance of the pure tracking algorithm. The adaptive adjustment of the preview distance can adapt to driving on different paths, reduce the path tracking error of the mixing pot transfer vehicle, and improve the tracking accuracy and stability.

[0040] When the heading angle error and the lateral error of the mixing pot transfer vehicle of the present invention are large, the preview distance can be reduced to make the mixing pot transfer vehicle return to the path faster; when the heading angle error and the lateral error are small, the preview distance can be increased to make the mixing pot transfer vehicle run more smoothly.

[0041] Compared with the prior art, firstly, the present invention constructs a simplified approximate control mathematical model for the hydraulic delay control system of large vehicles such as the mixing boiler transfer vehicle. This model not only performs equivalent simplification on the original more complex hydraulic control system, but also can more realistically reflect the input and output states of the transfer vehicle. Secondly, for this hydraulic control system with a large delay, a unique control algorithm is designed, namely, feedforward control + pure tracking control algorithm, which ultimately achieves precise control of the vehicle. Finally, on the driving trajectory of the transfer vehicle, a third-order Bezier curve is used to approximate the curved part, so that the transfer vehicle can be more accurately close to the trajectory when turning, and more precise trajectory tracking can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 Flow chart of the method of the present invention.

[0043] Figure 2 It is a functional block diagram of the system of the present invention.

[0044] Figure 3 It is a functional block diagram of the device of the present invention. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below with reference to the accompanying drawings and embodiments.

[0046] See also Figure 1 , the path tracking control method of the hybrid pot transfer vehicle based on pure tracking includes the following steps:

[0047] Step 1), according to the wheelbase and track of the mixing pot transfer vehicle, an Ackerman steering geometry model is established for calculating the steering angles of the inner and outer wheels of the vehicle when the transfer vehicle turns;

[0048] Step 2), ignoring the vertical motion of the mixing pot transporter, setting the inner and outer wheel rotation angles of the mixing pot transporter to be the same in the two-dimensional plane, simplifying the Ackerman steering geometry model established in step 1), and obtaining a kinematic bicycle model;

[0049] Step 3), select a point on the driving path of the mixing pot transfer vehicle as the preview point, calculate the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point according to the kinematic bicycle model, and obtain the current heading error of the mixing pot transfer vehicle; the heading error is the deviation between the actual heading of the mixing pot transfer vehicle and the theoretical heading, and the theoretical heading is the direction in which the mixing pot transfer vehicle reaches the preview point; the lateral error is the distance from the preview point to the nearest point on the track;

[0050] Step 4), establish a fuzzy controller and use the lateral error and heading error as input variables to calculate the preview distance parameters, thereby adjusting the preview distance and completing the path tracking control of the mixing pot transfer vehicle.

[0051] The specific method of step 1) is as follows: when the mixing pot transport vehicle turns, the center of the rear axle of the mixing pot transport vehicle is taken as the origin, and the steering angles of the inner and outer wheels of the mixing pot transport vehicle are inconsistent, and the steering angles of the inner and outer wheels are respectively:

[0052]

[0053]

[0054] Among them, δ L represents the inner wheel turning angle, δ R represents the outer wheel turning angle, L represents the wheelbase of the mixing pot transfer vehicle, L w It represents the wheelbase of the mixing pot transfer vehicle, and R represents the turning radius of the mixing pot transfer vehicle.

[0055] The specific method of step 2) is as follows: ignoring the vertical movement of the mixing pot transport vehicle, assuming that the inner and outer wheel turning angles of the mixing pot transport vehicle are the same in the two-dimensional plane, and simplifying the steering angle of the front wheel of the mixing pot transport vehicle to:

[0056]

[0057] Wherein, δ represents the front wheel turning angle of the mixing pot transfer vehicle.

[0058] The specific method of step 3) is as follows: a point is selected on the driving path of the mixing pot transfer vehicle as a preview point, and the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point are calculated according to the kinematic bicycle model:

[0059]

[0060] Among them, E x Indicates the lateral error between the mixing pot transfer vehicle and the preview point; L d It indicates the distance between the steering center of the mixing pot transfer vehicle and the preview point, i.e. the preview distance.

[0061] In the step 4), the fuzzy controller is specifically established as follows: the basic domain of the heading error is set to [-90°, 90°], the basic domain of the lateral error is set to [-3m, 3m], the basic domain of the preview distance is set to [0m, 10m], and the heading error quantization factor, the lateral error quantization factor and the preview distance quantization factor are set to K E =2, K L =1, the defuzzification process is:

[0062]

[0063] Where n represents the number of fuzzy set elements, K p represents the output after defuzzification, K i represents the domain value of each element in the fuzzy set, μ i Represents the membership value of each element in the fuzzy set.

[0064] In the step 4), the lateral error and the heading error are used as input variables to calculate the preview distance parameter, thereby adjusting the preview distance to complete the path tracking control of the hybrid boiler transfer vehicle. Specifically, the heading error is defined as positive to the left, and the wheel angle is defined as positive counterclockwise; the quantization levels of the heading error and the lateral error are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6] according to the basic domain of the heading error, the basic domain of the lateral error, the heading error quantization factor and the lateral error quantization factor; in the fuzzy controller, the input heading error and lateral error are defined as fuzzy sets according to the quantization level

[0065] The output variable of the fuzzy controller is also defined as a subset of the fuzzy [SL, SM, SS, ZO, LS, LM, LL]. In the fuzzy set, SL represents large negative, SM represents medium negative, SS represents small negative, ZO represents zero, LS represents small positive, LM represents medium positive, and LL represents large positive. According to the basic domain of the preview distance and the preview distance quantization factor, the preview distance quantization level is divided into [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], and the preview distance is adaptively adjusted according to the output variable.

[0066] See also Figure 2 , a control system for a hybrid pot transfer vehicle path tracking control method based on pure tracking, characterized in that it comprises: an Ackerman steering geometry module established according to an Ackerman steering geometry model, a kinematic bicycle module established according to a kinematic bicycle model, a preview point selection module established according to a preview point selection model, and a fuzzy controller module established according to a fuzzy controller;

[0067] The Ackerman steering geometry module is used to calculate the steering angles of the inner and outer wheels of the mixing boiler transfer vehicle when the mixing boiler transfer vehicle is turning according to the wheelbase and track of the mixing boiler transfer vehicle;

[0068] The kinematic bicycle module ignores the lateral error of the vehicle during movement and simplifies the Ackerman steering geometry model of the transporter's movement mode to obtain a kinematic bicycle model;

[0069] The preview point selection module is used to select a point on the driving path of the mixing pot transfer vehicle as a preview point;

[0070] The fuzzy controller module is used to calculate the preview distance parameter, so as to dynamically adjust the preview distance in real time and complete the path tracking control of the mixing pot transfer vehicle.

[0071] See also Figure 3 , a hybrid pot transfer vehicle path tracking control device, including an embedded bottom layer controller, a main control core computing unit processor, a multi-line laser radar, a real-time dynamic differentiator (RTK), and a vehicle VCU controller;

[0072] The embedded bottom-level controller is connected to the vehicle VCU controller through the CAN protocol, and is connected to the main control core computing unit processor through the serial port 232 protocol; the multi-line laser radar is connected to the main control core computing unit through the TCP protocol; the real-time dynamic differentiator is connected to the main control core computing unit through the 232 protocol.

[0073] The embedded bottom-level controller communicates with the vehicle VCU, reads the vehicle status fed back by the VCU, and issues control instructions;

[0074] The vehicle VCU controller mainly controls the vehicle's bottom actuator, mainly converting the hydraulic transmission system's electrical signals into hydraulic execution signals;

[0075] The multi-line laser radar is mainly used to collect information about the surrounding environment, to construct a three-dimensional point cloud map of the environment, and then to determine its own position in real time based on the constructed environment map;

[0076] The real-time kinematic differentiator (RTK) is mainly based on differential technology, and determines the position of the vehicle itself based on differential technology according to its own longitude and latitude information and the longitude and latitude information of the fixed station;

[0077] The main control core computing unit processor mainly utilizes the point cloud information provided by the multi-line laser radar module and the longitude and latitude data provided by the real-time dynamic differentiator (RTK) module to provide real-time position for navigation based on multi-sensor fusion. That is, it utilizes the multi-line laser radar module and the RTK (real-time dynamic differential) module to complete the real-time control of the hybrid pot transfer vehicle. It is mainly used to run the SLAM mapping and positioning program of the multi-line laser radar, the underlying driver, the multi-sensor fusion positioning program, and the navigation algorithm program.

Claims

1. A path tracking control method for a mixing pot transfer vehicle, characterized in that: The following steps are involved: Step 1), according to the wheelbase and track of the mixing pot transfer vehicle, an Ackerman steering geometry model is established for calculating the steering angles of the inner and outer wheels of the vehicle when the transfer vehicle turns; Step 2), ignoring the vertical motion of the mixing pot transporter, setting the inner and outer wheel rotation angles of the mixing pot transporter to be the same in the two-dimensional plane, simplifying the Ackerman steering geometry model established in step 1), and obtaining a kinematic bicycle model; Step 3), select a point on the driving path of the mixing pot transfer vehicle as the preview point, calculate the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point according to the kinematic bicycle model, and obtain the current heading error of the mixing pot transfer vehicle; the heading error is the deviation between the actual heading of the mixing pot transfer vehicle and the theoretical heading, and the theoretical heading is the direction in which the mixing pot transfer vehicle reaches the preview point; the lateral error is the distance from the preview point to the nearest point on the track; Step 4), establish a fuzzy controller and use the lateral error and heading error as input variables to calculate the preview distance parameters, thereby adjusting the preview distance and completing the path tracking control of the mixing pot transfer vehicle.

2. The path tracking control method for the mixing pot transfer vehicle according to claim 1, characterized in that: The specific method of step 1) is as follows: when the mixing pot transport vehicle turns, the center of the rear axle of the mixing pot transport vehicle is taken as the origin, and the steering angles of the inner and outer wheels of the mixing pot transport vehicle are inconsistent, and the steering angles of the inner and outer wheels are respectively: Among them, δ L represents the inner wheel turning angle, δ R represents the outer wheel turning angle, L represents the wheelbase of the mixing pot transfer vehicle, L w It represents the wheelbase of the mixing pot transfer vehicle, and R represents the turning radius of the mixing pot transfer vehicle.

3. The path tracking control method for the mixing pot transfer vehicle according to claim 1, characterized in that: The specific method of step 2) is as follows: ignoring the vertical movement of the mixing pot transport vehicle, assuming that the inner and outer wheel turning angles of the mixing pot transport vehicle are the same in the two-dimensional plane, and simplifying the steering angle of the front wheel of the mixing pot transport vehicle to: Wherein, δ represents the front wheel turning angle of the mixing pot transfer vehicle.

4. The path tracking control method for a mixing pot transfer vehicle according to claim 1, characterized in that: The specific method of step 3) is as follows: a point is selected on the driving path of the mixing pot transfer vehicle as a preview point, and the front wheel turning angle and lateral error required for the mixing pot transfer vehicle to reach the preview point are calculated according to the kinematic bicycle model: Among them, E x Indicates the lateral error between the mixing pot transfer vehicle and the preview point; L d It indicates the distance between the steering center of the mixing pot transfer vehicle and the preview point, i.e. the preview distance.

5. The path tracking control method for a mixing pot transfer vehicle according to claim 1, characterized in that: In the step 4), the fuzzy controller is specifically established as follows: the basic domain of the heading error is set to [-90°, 90°], the basic domain of the lateral error is set to [-3m, 3m], the basic domain of the preview distance is set to [0m, 10m], and the heading error quantization factor, the lateral error quantization factor and the preview distance quantization factor are set to K E =2, K L =1, the defuzzification process is: Where n represents the number of fuzzy set elements, K p represents the output after defuzzification, K i represents the domain value of each element in the fuzzy set, μ i Represents the membership value of each element in the fuzzy set.

6. The path tracking control method for the mixing pot transfer vehicle according to claim 5, characterized in that: In the step 4), the lateral error and the heading error are used as input variables to calculate the preview distance parameter, thereby adjusting the preview distance to complete the path tracking control of the hybrid boiler transfer vehicle. Specifically, the heading error is defined as positive to the left, and the wheel angle is defined as positive counterclockwise; the quantization levels of the heading error and the lateral error are divided into [-6, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6] according to the basic domain of the heading error, the basic domain of the lateral error, the heading error quantization factor and the lateral error quantization factor; in the fuzzy controller, the input heading error and lateral error are defined as fuzzy sets according to the quantization level The output variable of the fuzzy controller is also defined as a subset of the fuzzy [SL, SM, SS, ZO, LS, LM, LL]. In the fuzzy set, SL represents large negative, SM represents medium negative, SS represents small negative, ZO represents zero, LS represents small positive, LM represents medium positive, and LL represents large positive. According to the basic domain of the preview distance and the preview distance quantization factor, the preview distance quantization level is divided into [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], and the preview distance is adaptively adjusted according to the output variable.

7. A control system for the path tracking control method of the mixing pot transfer vehicle according to claim 1, characterized in that: include: An Ackerman steering geometry module established according to an Ackerman steering geometry model, a kinematic bicycle module established according to a kinematic bicycle model, a preview point selection module established according to a preview point selection model, and a fuzzy controller module established according to a fuzzy controller; The Ackerman steering geometry module is used to calculate the steering angles of the inner and outer wheels of the mixing boiler transfer vehicle when the mixing boiler transfer vehicle is turning according to the wheelbase and track of the mixing boiler transfer vehicle; The kinematic bicycle module assumes that the kinematic model of the mixing pot transfer vehicle can be simplified in the Ackerman steering geometry model to obtain a kinematic bicycle model when the lateral displacement error of the mixing pot transfer vehicle is not considered during the movement. The preview point selection module is used to select a point on the driving path of the mixing pot transfer vehicle as a preview point; The fuzzy controller module is used to calculate the preview distance parameter, so as to dynamically adjust the preview distance in real time and complete the path tracking control of the mixing pot transfer vehicle.

8. A path tracking control device for a mixing pot transfer vehicle, characterized in that: It includes embedded bottom-level controller, main control core computing unit processor, multi-line laser radar, real-time dynamic differentiator, and vehicle VCU controller; The embedded bottom-level controller is connected to the vehicle VCU controller via the CAN protocol, and is connected to the main control core computing unit processor via the serial port 232 protocol; the multi-line laser radar is connected to the main control core computing unit via the TCP protocol; The real-time dynamic differentiator is connected to the main control core computing unit via 232 protocol.

9. A path tracking control device for a mixing pot transfer vehicle according to claim 8, characterized in that: The embedded bottom-level controller communicates with the vehicle VCU, reads the vehicle status fed back by the VCU, and issues control instructions; The vehicle VCU controller mainly controls the vehicle's bottom actuator, mainly converting the hydraulic transmission system's electrical signals into hydraulic execution signals; The multi-line laser radar is mainly used to collect information about the surrounding environment, to construct a three-dimensional point cloud map of the environment, and then to determine its own position in real time based on the constructed environment map; The real-time dynamic differentiator is mainly based on differential technology, and determines the position of the vehicle itself based on differential technology according to its own longitude and latitude information and the longitude and latitude information of the fixed station; The main control core computing unit processor mainly utilizes the point cloud information provided by the multi-line laser radar module and the longitude and latitude data provided by the real-time dynamic differentiator to provide real-time position for navigation based on multi-sensor fusion. That is, the multi-line laser radar module and the real-time dynamic differentiator are used to complete the real-time control of the hybrid pot transfer vehicle, and is used to run the SLAM mapping and positioning program of the multi-line laser radar, the underlying driver, the multi-sensor fusion positioning program, and the navigation algorithm program.

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