Open-pit mine unmanned heavy-load vehicle and path tracking control method and device thereof

Abstract

The application discloses an open-pit mine unmanned heavy-load vehicle and a path tracking control method and device thereof, which comprises: an advancing path tracking step: according to preset path information and vehicle state information, a new Stanley controller is used to control path tracking control when the open-pit mine heavy-load vehicle advances, and the new Stanley controller considers vehicle steering lag characteristics, vehicle size and advancing driving speed; and a retreating path tracking step: a new rear wheel feedback controller is used to control path tracking control when the open-pit mine heavy-load vehicle retreats, and the new rear wheel feedback controller considers vehicle steering lag characteristics, vehicle size and retreating driving speed. The application can realize path tracking control when the heavy-load vehicle advances and retreats, and can adaptively adjust control parameters under different working conditions, so that better control effect is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automatic driving, and in particular to an open-pit mine unmanned heavy vehicle and a path tracking control method and device thereof. BACKGROUND

[0002] As one of the key links of the open-pit mine unmanned system, the path tracking control performance directly affects the operation safety and transportation efficiency of the unmanned vehicle, and has important academic research significance and social and economic value. The classic path tracking control algorithm includes PID (Proportional Integral Derivative, proportional integral derivative control), pure pursuit, Stanley, etc. The PID control parameters have an important influence on the control effect, and the optimal effect can be obtained under ideal conditions based on the model to set the PID parameters, but when the model is uncertain, the trial-and-error method is usually used to set the parameters, which is time-consuming. In addition, PID cannot be compensated in a targeted manner when both system uncertainty and external disturbance exist. The pure pursuit control algorithm is a classic path tracking control algorithm, and its core idea is to use the rear axle center of the vehicle as the tangent point and the longitudinal center line as the tangent line based on the single-track model of the vehicle, and to control the front wheel steering angle to make the vehicle run along the circular path passing through the preview point, which is a control method based on geometric tracking. The pure pursuit algorithm has good robustness to road curvature disturbance, but its tracking performance is seriously dependent on the selection of the preview distance, and the optimal performance is difficult to guarantee.

[0003] With the development of artificial intelligence, many intelligent control algorithms have also been applied to the field of unmanned path tracking control, such as data-driven and neural networks. However, the control accuracy of these intelligent control algorithms is seriously dependent on massive data, and further prediction and analysis of the dynamic behavior of the system cannot be performed. MPC (Model Predictive Control) is widely used in unmanned path tracking control, and has significant advantages for mine truck path tracking control. The model predictive controller is a multivariable controller that can control the complex multiple-input multiple-output system of the mine truck. However, the calculation amount of the MPC algorithm is large and the required calculation time is long, and the real-time requirement of the algorithm for autonomous vehicles is high, so it is difficult to directly apply the above method to the actual vehicle. In order to solve the practical problems encountered in engineering applications, a path tracking control method and system for an open-pit mine unmanned heavy vehicle are researched. SUMMARY

[0004] The present application aims to provide an open-pit mine unmanned heavy vehicle and a path tracking control method and device thereof to overcome or at least alleviate at least one of the above-mentioned deficiencies of the prior art.

[0005] To achieve the above object, the present invention provides a path tracking control method for an unmanned heavy-load vehicle in an open-pit mine, which comprises:

[0006] Forward path tracking step: Based on the preset path information and vehicle state information, the new Stanley controller shown in the following equation (1) is used to control the path tracking control of the heavy-load vehicle in the open-pit mine. The new Stanley controller takes into account the vehicle steering gear lag characteristics, vehicle size and forward driving speed:

[0007]

[0008] Where, δ c is the front wheel steering angle command, e p is the lateral distance error, v f is the forward speed of the vehicle, is the angle error, Δt is the system sampling time, τ δ is the time lag factor, k is the adaptive adjustment control gain, is the forward acceleration of the vehicle, δ is the front wheel turning angle of the vehicle, C rp is the curvature corresponding to the nearest path point;

[0009] Reverse path tracking steps: Use the new rear wheel feedback controller shown in the following equation to control the path tracking control of the heavy-load vehicle in the open-pit mine when reversing. The new rear wheel feedback controller takes into account the vehicle steering gear lag characteristics, vehicle size and reverse driving speed:

[0010]

[0011] Where, δ c is the front wheel steering angle command, L is the wheelbase, c r is the curvature corresponding to the nearest path point, is the rate of change of the curvature corresponding to the nearest path point, is the yaw angle error, e r is the lateral distance error, is the proportional factor corresponding to the yaw angle error, k e is the proportional shadow corresponding to the lateral distance error, τ δ is the lag factor, v r The vehicle's reverse speed is positive. is the vehicle yaw angular velocity.

[0012] Furthermore, the new Stanley controller also determines the preview distance d based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following equations (6) to (8): p :

[0013] d p =max{dpv , d pc} (6)

[0014] d pv = k pv v f Delta t (7)

[0015]

[0016] In the formula, d pv is a preview distance obtained according to v f , k pv is a proportional factor, d pc is a preview distance calibrated according to c rp , d hc , d mc , and d lc are preview distances calibrated according to different values of c rp .

[0017] Further, the new Stanley controller also determines an adaptive adjustment control gain k according to vehicle information and driving characteristics of the heavy load vehicle in an open-pit mine by using the following formula (11):

[0018]

[0019] wherein k1 and k2 are non-dimensional proportional factors.

[0020] Further, the new rear wheel feedback controller also determines a preview distance according to vehicle information and driving characteristics of the heavy load vehicle in an open-pit mine by using a dynamic selection method provided by the following formula (12):

[0021]

[0022] In the formula, d pr is a preview distance calibrated according to c rp , d hr , d mr , and d lr are preview distances calibrated according to different values of c rp .

[0023] The application also provides a path tracking control device for an unmanned heavy load vehicle in an open-pit mine, which comprises:

[0024] a new Stanley controller for controlling path tracking control when the heavy load vehicle in an open-pit mine advances according to vehicle information and driving characteristics of the heavy load vehicle in an open-pit mine, the new Stanley controller considering vehicle steering lag characteristics, as shown in the following formula (1):

[0025]

[0026] Where, δ c is the front wheel steering angle command, e p is the lateral distance error, v f is the forward speed of the vehicle, is the angle error, Δt is the system sampling time, τ δ is the time lag factor, k is the adaptive adjustment control gain, is the vehicle's forward acceleration, δ is the vehicle's front wheel turning angle, c rp is the curvature corresponding to the nearest path point;

[0027] The new rear wheel feedback controller is used to control the path tracking control of heavy-load vehicles in open-pit mines when they are retreating. The new rear wheel feedback controller takes into account the time lag characteristics of the vehicle steering gear and the characteristics of the vehicle when it is retreating, as shown in the following equation:

[0028]

[0029] Where, δ c is the front wheel steering angle command, L is the wheelbase, c r is the curvature corresponding to the nearest path point, is the rate of change of the curvature corresponding to the nearest path point, is the yaw angle error, e r is the lateral distance error, is the proportional factor corresponding to the yaw angle error, k e is the proportional shadow corresponding to the lateral distance error, τ δ is the lag factor, v r The vehicle's reverse speed is positive. is the vehicle yaw angular velocity.

[0030] Furthermore, the new Stanley controller also determines the preview distance d based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following equations (6) to (8): p :

[0031] d p =max{d pv , d pc} (6)

[0032] d pv =k pv v f Δt (7)

[0033]

[0034] Where, d pv According to v f The preview distance obtained, kpv is the scale factor, d pc Based on c rp Calibrated preview distance, d hc d mc d lc According to the different values ​​of c rp Calibrated preview distance.

[0035] Furthermore, the new Stanley controller also uses the following formula (11) to determine the adaptive adjustment control gain k based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine:

[0036]

[0037] Among them, κ1 and κ2 are dimensionless proportional factors.

[0038] Furthermore, the new rear wheel feedback controller also determines the preview distance based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following formula (12):

[0039]

[0040] Where, d pr Based on c rp Calibrated preview distance, d hr d mr d lr According to the different values ​​of c rp Calibrated preview distance.

[0041] The present invention also provides an unmanned heavy-load vehicle for open-pit mines, comprising a vehicle body having the following dimensions: a vehicle body length of 14.75 meters, a vehicle body width of 7.44 meters, a front bumper ground clearance of 1.42 meters, a wheelbase of 6.35 meters, a front overhang length of 4.25 meters, a rear overhang length of 3.43 meters, a maximum front wheel turning angle of 31 degrees, a forward vehicle speed range of 0-30 kilometers per hour, and a reverse vehicle speed range of 0-10 kilometers per hour; and further comprising:

[0042] Inertial navigation combined equipment, used to obtain vehicle status information;

[0043] The unmanned driving computing platform is pre-installed with the above-mentioned open-pit mine unmanned heavy-load vehicle path tracking control device, which calculates the front wheel angle instruction δ c , and δ c Send to the vehicle wire control interface unit;

[0044] The wire control steering unit is used to receive the delta signal received via the CAN bus. c , and according to δ cControl the vehicle to travel along the desired path.

[0045] Furthermore, the steer-by-wire unit includes a fully hydraulic steering gear with an electronic control system.

[0046] Aiming at the operating conditions of unmanned heavy-loaded vehicles in open-pit mines, the present invention takes into account the time-lag characteristics of the vehicle steering gear and proposes a new Stanley controller and a new rear-wheel feedback controller to achieve path tracking control of the heavy-loaded vehicle when moving forward and backward, and adaptively adjusts the control parameters under different working conditions to achieve better control effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of the connection relationship between modules provided in an embodiment of the present invention.

[0048] Figure 2 The figure is a schematic diagram of the size and structure of a heavy-load vehicle for open-pit mines applicable to the present invention.

[0049] Figure 3 A schematic diagram of the framework principle of path tracking control for unmanned heavy-load vehicles in open-pit mines provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The present invention will be described in detail below with reference to the accompanying drawings and embodiments.

[0051] An embodiment of the present invention provides a path tracking control method and system for unmanned heavy-load vehicles in open-pit mines. By considering the time lag characteristics of the heavy-load vehicle's steering actuator, Stanley and rear-wheel feedback models are used to design path tracking controllers for forward and reverse respectively. Pre-aiming and control parameter settings are performed based on the open-pit mine road environment, heavy-load vehicles, and reference trajectory characteristics. Finally, precise path tracking control of unmanned heavy-load vehicles in open-pit mines is achieved.

[0052] like Figure 1 As shown, the path tracking control method for unmanned heavy-load vehicles in open-pit mines provided by the embodiment of the present invention includes:

[0053] Forward path tracking step: Based on the preset path information and vehicle state information, the new Stanley controller shown in the following equation (1) is used to control the path tracking control of the heavy-load vehicle in the open-pit mine. The new Stanley controller takes into account the vehicle steering gear lag characteristics, vehicle size and forward driving speed:

[0054]

[0055] Where, δ c is the front wheel steering angle command, e p is the lateral distance error, v fVf is the forward driving speed of the vehicle, is the angle error, At is the system sampling time, τ δ is the time delay factor, τ δ The value of k is obtained by calibration, the time delay factor of the heavy-duty vehicle in open pit is about 1 second, k is the adaptive adjustment control gain, Vf is the forward driving acceleration of the vehicle, δ is the front wheel steering angle of the vehicle, c rp is the curvature corresponding to the nearest path point.

[0056] The backward path tracking step: the path tracking control of the heavy-duty vehicle in open pit when backing up is controlled by a new rear wheel feedback controller shown in the following formula (2), which takes into account the time delay characteristics of the vehicle steering gear, the size of the vehicle and the backward driving speed:

[0057]

[0058] In the formula, δ c is the front wheel steering angle command, L is the wheelbase, c r is the curvature corresponding to the nearest path point, is the rate of change of the curvature corresponding to the nearest path point, is the yaw angle error, e r is the lateral distance error, is the proportional factor corresponding to the yaw angle error, as shown in the following formula (3), k e is the proportional factor corresponding to the lateral distance error, as shown in the following formula (4), τ δ is the time delay factor, v r is the positive value of the vehicle backward driving speed, is the vehicle yaw rate.

[0059]

[0060] k e = a 2 (4)

[0061] Wherein, a is a positive number, ζ is a damping coefficient, for example: ζ = 1.85, a = 0.28.

[0062] In one embodiment, the specific value of the curvature c r corresponding to the nearest path point is determined according to the positive and negative of the steering wheel.

[0063] In the above embodiment, the nearest path point selection angle constraint is set as the following formula (5):

[0064]

[0065] Wherein, is the path point heading angle from the path information, is the vehicle yaw angle from the inertial navigation system.

[0066] Based on the vehicle's forward speed feedback from the inertial navigation system and the curvature of the nearest path point from the path information, the nearest path point is determined using Equation (5). The path point contains information such as coordinates, heading angle, curvature, and speed. First, the nearest path point is found using the path point's coordinates and heading angle information. Then, all the information about this nearest path point is used for path tracking control.

[0067] In one embodiment, the new Stanley controller also determines the preview distance d based on the vehicle information and driving characteristics of the open-pit mine heavy-load vehicle using the dynamic selection method provided by the following equations (6) to (8): p :

[0068] d p =max{d pv , d pc} (6)

[0069] d pv =k pv v f Δt (7)

[0070]

[0071] Where, d pv According to v f The preview distance obtained, k pv is the proportional factor, its value can be 0.5, for example, d pc Based on c rp Calibrated preview distance, d hc d mc d lc According to the different values ​​of c rp Calibrated preview distance.

[0072] According to the preview distance d p Select preview point: Traverse the waypoints in the direction of travel in sequence until you find a waypoint where the sum of the distances between this waypoint and the nearest waypoint is greater than or equal to the preview distance. This waypoint is then considered the preview point.

[0073] Then use the following formula (9) to calculate the angle error Then use the following formula (10) to calculate the lateral distance error e p :

[0074]

[0075] in, is the vehicle yaw angle, is the heading angle of the preview point.

[0076]

[0077] wherein x0 and y0 are the lateral and longitudinal coordinates of the front axle center of the vehicle in the global coordinate system, x p and y p are the lateral and longitudinal coordinates of the preview point in the global coordinate system.

[0078] Generally, k can be a specific value calibrated by experiment. However, in an embodiment, to ensure the path tracking accuracy, the control gain k is adaptively adjusted according to the preview path point curvature and the lateral distance error, as shown in the following formula (11):

[0079]

[0080] wherein κ1 and κ2 are non-dimensional proportional factors, and the specific values thereof are usually empirical values, such as κ1 = 12 and κ2 = 0.15.

[0081] In an embodiment, the new rear wheel feedback controller also determines the preview distance by using the dynamic selection method provided by the following formula (12) according to the vehicle information and the driving characteristics of the heavy load vehicle in open-pit mine:

[0082]

[0083] wherein d pr is the preview distance calibrated according to c rp , d hr , d mr and d lr are the preview distances calibrated according to the different values of c rp .

[0084] The preview point is selected according to the preview distance d pr , and the lateral distance error e r is calculated by using the following formula (13):

[0085]

[0086] wherein x r and y r are the lateral and longitudinal coordinates of the rear axle center of the vehicle in the global coordinate system, x pr and y pr are the lateral and longitudinal coordinates of the preview point in the global coordinate system.

[0087] The present application also provides a path tracking control device for unmanned heavy load vehicle in open-pit mine, which comprises a new Stanley controller and a new rear wheel feedback controller, wherein:

[0088] The new Stanley controller is used to control the path tracking control of the heavy load vehicle of the open-pit mine when the heavy load vehicle of the open-pit mine advances according to the vehicle information and the driving characteristics of the heavy load vehicle of the open-pit mine, and the new Stanley controller considers the time delay characteristics of the vehicle steering gear, as shown in the above formula (1).

[0089] The new rear wheel feedback controller is used to control the path tracking control of the heavy load vehicle of the open-pit mine when the heavy load vehicle of the open-pit mine retreats, and the new rear wheel feedback controller considers the time delay characteristics of the vehicle steering gear and the characteristics of the vehicle when the vehicle retreats, as shown in the above formula.

[0090] The embodiment of the present application also provides an unmanned heavy load vehicle of an open-pit mine, which comprises a vehicle body. Figure 2 As shown in the figure, the size structure of the vehicle body is that the length of the vehicle body is 14.75 meters, the width of the vehicle body is 7.44 meters, the ground clearance of the front bumper is 1.42 meters, the wheelbase is 6.35 meters, the front suspension length is 4.25 meters, the rear suspension length is 3.43 meters, the maximum rotation angle of the front wheel is 31 degrees, the driving speed range of the vehicle when advancing is 0-30 kilometers per hour, and the driving speed range of the vehicle when retreating is 0-10 kilometers per hour.

[0091] As shown in the figure, the embodiment of the present application provides an unmanned heavy load vehicle of an open-pit mine, which comprises a vehicle body. Figure 3 The unmanned heavy load vehicle of the open-pit mine provided by the embodiment of the present application further comprises an inertial navigation combination device, an unmanned computing platform and a steer-by-wire steering unit, wherein:

[0092] The inertial navigation combination device is used to obtain vehicle state information;

[0093] The unmanned computing platform is provided with the path tracking control device of the unmanned heavy load vehicle of the open-pit mine as described in the above embodiment in advance, and the front wheel rotation angle instruction δ c is calculated. c The δ c is sent to the vehicle steer-by-wire interface unit.

[0094] The steer-by-wire steering unit is used to receive the δ c received through the can bus, and controls the vehicle to drive along the expected path according to the δ c . In the embodiment, the steer-by-wire steering unit comprises a full hydraulic steering gear with an electric control system.

[0095] The path tracking controller in each of the above embodiments is designed based on the Stanley and rear wheel feedback algorithm, and can be replaced by a path tracking control based on a vehicle dynamics model.

[0096] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit the same. Those skilled in the art should understand that the technical solutions described in the foregoing embodiments can be modified, or some technical features thereof can be replaced by equivalent ones; these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A path tracking control method for unmanned heavy-load vehicles in open-pit mines, characterized in that: include: Forward path tracking step: Based on the preset path information and vehicle state information, the new Stanley controller shown in the following equation (1) is used to control the path tracking control of the heavy-load vehicle in the open-pit mine when it is moving forward. The new Stanley controller takes into account the vehicle steering gear lag characteristics, vehicle size and forward driving speed: Where, δ c is the front wheel steering angle command, e p is the lateral distance error, v f is the forward speed of the vehicle, is the angle error, Δt is the system sampling time, τ δ is the time lag factor, k is the adaptive adjustment control gain, is the vehicle's forward acceleration, δ is the vehicle's front wheel turning angle, c rp is the curvature corresponding to the nearest path point; Reverse path tracking steps: Use the new rear wheel feedback controller shown in the following equation to control the path tracking control of the heavy-load vehicle in the open-pit mine when reversing. The new rear wheel feedback controller takes into account the vehicle steering gear lag characteristics, vehicle size and reverse driving speed: Where, δ c is the front wheel steering angle command, L is the wheelbase, c r is the curvature corresponding to the nearest path point, is the rate of change of the curvature corresponding to the nearest path point, is the yaw angle error, e r is the lateral distance error, is the proportional factor corresponding to the yaw angle error, k e is the proportional shadow corresponding to the lateral distance error, τ δ is the lag factor, v r The vehicle's reverse speed is positive. is the vehicle yaw angular velocity.

2. The path tracking control method for an unmanned heavy-load vehicle in an open-pit mine according to claim 1, characterized in that: The new Stanley controller also determines the preview distance d based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following equations (6) to (8): p : d p =max{d pv ,d pc } (6) d pv =k pv v f Δt (7) Where, d pv According to v f The preview distance obtained, k pv is the scale factor, d pc Based on c rp Calibrated preview distance, d hc d mc d lc According to the different values ​​of c rp Calibrated preview distance.

3. The path tracking control method for an unmanned heavy-load vehicle in an open-pit mine according to claim 2, characterized in that: The new Stanley controller also uses the following formula (11) to determine the adaptive adjustment control gain k based on the vehicle information and driving characteristics of heavy-load vehicles in open-pit mines: Among them, κ1 and κ2 are dimensionless proportional factors.

4. The path tracking control method for unmanned heavy-load vehicles in open-pit mines according to any one of claims 1 to 3, characterized in that: The new rear wheel feedback controller also determines the preview distance based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following formula (12): Where, d pr Based on c rp Calibrated preview distance, d hr d mr d lr According to the different values ​​of c rp Calibrated preview distance.

5. A path tracking control device for unmanned heavy-load vehicles in open-pit mines, characterized in that: include: The new Stanley controller is used to control the path tracking control of the open-pit mine heavy-load vehicle when it moves forward according to the vehicle information and driving characteristics of the open-pit mine heavy-load vehicle. The new Stanley controller takes into account the time lag characteristics of the vehicle steering gear, as shown in the following equation (1): Where, δ c is the front wheel steering angle command, e p is the lateral distance error, v f is the forward speed of the vehicle, is the angle error, Δt is the system sampling time, τ δ is the time lag factor, k is the adaptive adjustment control gain, is the vehicle's forward acceleration, δ is the vehicle's front wheel turning angle, c rp is the curvature corresponding to the nearest path point; The new rear wheel feedback controller is used to control the path tracking control of heavy-load vehicles in open-pit mines when they are retreating. The new rear wheel feedback controller takes into account the time lag characteristics of the vehicle steering gear and the characteristics of the vehicle when it is retreating, as shown in the following equation: Where, δ c is the front wheel steering angle command, L is the wheelbase, c r is the curvature corresponding to the nearest path point, is the rate of change of the curvature corresponding to the nearest path point, is the yaw angle error, e r is the lateral distance error, is the proportional factor corresponding to the yaw angle error, k e is the proportional shadow corresponding to the lateral distance error, τ δ is the lag factor, v r The vehicle's reverse speed is positive. is the vehicle yaw angular velocity.

6. The open-pit mine unmanned heavy-load vehicle path tracking control device according to claim 5, characterized in that: The new Stanley controller also determines the preview distance d based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following equations (6) to (8): p : d p =max{d pv ,d pc } (6) d pv =k pv v f Δt (7) Where, d pv According to v f The preview distance obtained, k pv is the scale factor, d pc Based on c rp Calibrated preview distance, d hc d mc d lc According to the different values ​​of c rp Calibrated preview distance.

7. The open-pit mine unmanned heavy-load vehicle path tracking control device according to claim 6, characterized in that: The new Stanley controller also uses the following formula (11) to determine the adaptive adjustment control gain k based on the vehicle information and driving characteristics of heavy-load vehicles in open-pit mines: Among them, κ1 and κ2 are dimensionless proportional factors.

8. The open-pit mine unmanned heavy-load vehicle path tracking control device according to any one of claims 5 to 7, characterized in that: The new rear wheel feedback controller also determines the preview distance based on the vehicle information and driving characteristics of the heavy-load vehicle in the open-pit mine using the dynamic selection method provided by the following formula (12): Where, d pr Based on c rp Calibrated preview distance, d hr d mr d lr According to the different values ​​of c rp Calibrated preview distance.

9. An unmanned heavy-duty open-pit mine vehicle, comprising a vehicle body, having dimensions of 14.75 meters in length, 7.44 meters in width, 1.42 meters in front bumper clearance, 6.35 meters in wheelbase, 4.25 meters in front overhang, 3.43 meters in rear overhang, a maximum front wheel turning angle of 31 degrees, a forward speed range of 0-30 kilometers per hour, and a reverse speed range of 0-10 kilometers per hour; characterized in that: Also includes: Inertial navigation combined equipment, used to obtain vehicle status information; The unmanned driving computing platform is pre-equipped with a path tracking control device for an unmanned heavy-load vehicle in an open-pit mine according to any one of claims 5 to 8, and calculates the front wheel turning angle instruction δ c , and δ c Send to the vehicle wire control interface unit; The wire control steering unit is used to receive the delta signal received via the CAN bus. c , and according to δ c Control the vehicle to travel along the desired path.

10. The unmanned heavy-load vehicle for open-pit mines according to claim 9, characterized in that: The steer-by-wire unit comprises a fully hydraulic steering gear with an electronic control system.

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