Unmanned mud head vehicle intelligent driving control method, system, equipment and medium

Abstract

The invention discloses an intelligent driving control method, system and device for an unmanned mud-head vehicle and a medium. The method comprises the steps that the real-time speed difference of the unmanned mud-head vehicle is obtained; the real-time speed difference is analyzed based on a PID longitudinal control algorithm, and the target total torque of the unmanned mud head vehicle is obtained; the target total torque is analyzed based on an energy optimization distribution strategy, and the target torque of each driving motor of the unmanned mud head vehicle is obtained; a target control rotation angle of the unmanned mud head vehicle is obtained based on a PID transverse control algorithm; and performing intelligent driving control on the unmanned mud head vehicle based on the target torque and the target control rotating angle of each driving motor. By adopting the PID control algorithm, the target total torque and the target control rotation angle of the unmanned mud-head vehicle are obtained, the unmanned mud-head vehicle is controlled based on the target total torque and the target control rotation angle, the target of stable running of the vehicle is met, the method has the advantages of being high in precision and energy utilization efficiency, and the endurance mileage of the unmanned mud-head vehicle is effectively increased.

Description

Technical Field

[0001] The present invention relates to the field of unmanned driving control technology, and in particular to an unmanned mud truck intelligent driving control method, system, equipment and medium. Background Art

[0002] In recent years, with the rapid development of science and technology, the state and the government have successively issued some policies to vigorously encourage and promote the development of the unmanned driving industry and accelerate the implementation of unmanned vehicles. However, at this stage, the law explicitly stipulates the liability for unmanned vehicle accidents and the types of liability insurance are limited. There is still a long way to go for the implementation of unmanned passenger vehicles. As the main means of transportation for engineering projects, especially mining dump trucks, they are often used in special scenes such as construction sites, and are an important scene for the implementation of unmanned vehicles. Since special scenes such as construction sites often have problems such as uneven sites and inconvenient replenishment, how to maintain the smooth driving of unmanned dump trucks and a higher cruising range is the key to ensuring the working efficiency of unmanned dump trucks. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides an unmanned dump truck intelligent driving control method, system, equipment and medium. By adopting a PID control algorithm, the target total torque and target control angle of the unmanned dump truck are respectively obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of smooth driving of the vehicle. It has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck.

[0004] The present invention provides an intelligent driving control method for an unmanned mud truck, the method comprising:

[0005] Get the real-time speed difference of the unmanned dump truck;

[0006] Analyze the real-time speed difference based on the PID longitudinal control algorithm to obtain the target total torque of the unmanned mud truck;

[0007] Analyze the target total torque based on the energy optimization allocation strategy to obtain the target torque of each driving motor of the unmanned mud truck;

[0008] Obtain the target control angle of the unmanned mud truck based on the PID lateral control algorithm;

[0009] Intelligent driving control of the unmanned mud truck is performed based on the target torque and target control angle of each driving motor.

[0010] Furthermore, the step of obtaining the real-time speed difference of the unmanned mud truck includes:

[0011] Based on the speed sensor, the real-time speed value of the unmanned mud truck during automatic driving is obtained;

[0012] Based on the design planning data of the unmanned dump truck, the planned speed value of the unmanned dump truck under the same road conditions is obtained;

[0013] The real-time speed value and the planned speed value are sorted to obtain the real-time speed difference of the unmanned dump truck.

[0014] Furthermore, the analyzing the real-time speed difference based on the PID longitudinal control algorithm to obtain the target total torque of the unmanned mud truck includes:

[0015] The driving and braking torque equations of the unmanned mud truck are established based on the PID longitudinal control algorithm;

[0016] Calculating the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equation based on the load mass parameter, speed parameter and turning angle parameter of the unmanned mud truck;

[0017] Based on the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations, combined with the real-time speed difference, the target total torque of the unmanned mud truck is calculated.

[0018] Furthermore, the expressions of the driving and braking torque equations of the unmanned dump truck include:

[0019]

[0020] Where, T t is the total driving and braking torque, P m Solve for the proportionality factor for the driving and braking torques, I m Solve for the integral time coefficient for the driving and braking torques, D m Solve for the differential time coefficients for the drive and brake torques, T f To overcome the rolling friction torque, v diff The real-time speed difference.

[0021] Furthermore, the target total torque is analyzed based on the energy optimization allocation strategy to obtain the target torque of each driving motor of the unmanned mud truck, including:

[0022] The driving motor operation data of the unmanned mud truck is obtained, and the target total torque is distributed based on the energy optimization allocation strategy combined with the driving motor operation data to obtain the target torque of each driving motor of the unmanned mud truck.

[0023] Furthermore, the method of obtaining the target control angle of the unmanned mud truck based on the PID lateral control algorithm includes:

[0024] Calculating the lateral deviation of the unmanned mud truck, and calculating the lateral deviation angle based on the lateral deviation;

[0025] Calculating a heading deviation of the unmanned mud truck, and calculating a heading deviation angle based on the heading deviation;

[0026] The target control angle of the unmanned mud truck is obtained by combining the lateral deviation angle and the heading deviation angle.

[0027] Furthermore, the intelligent driving control of the unmanned mud truck based on the target torque and target control angle of each driving motor includes:

[0028] The actuator of the unmanned dump truck controls each driving motor to execute the target torque, and the controller of the unmanned dump truck controls the unmanned dump truck to execute the target control angle.

[0029] The present invention also provides an unmanned dump truck intelligent driving control system, the unmanned dump truck intelligent driving control system is used to implement the unmanned dump truck intelligent driving control method described above, the system comprises:

[0030] A real-time speed difference acquisition module, which is used to acquire the real-time speed difference of the unmanned mud truck;

[0031] A target total torque acquisition module, which is used to analyze the real-time speed difference based on a PID longitudinal control algorithm to acquire a target total torque of the unmanned mud truck;

[0032] A drive motor target torque acquisition module, the drive motor target torque acquisition module is used to analyze the target total torque based on the energy optimization allocation strategy to obtain the target torque of each drive motor of the unmanned mud truck;

[0033] A target control angle acquisition module, wherein the target control angle acquisition module is used to acquire a target control angle of the unmanned mud truck based on a PID lateral control algorithm;

[0034] An intelligent driving control module is used to perform intelligent driving control on the unmanned mud truck based on the target torque and target control angle of each driving motor.

[0035] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned unmanned dump truck intelligent driving control method.

[0036] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned unmanned mud truck intelligent driving control method is implemented.

[0037] The present invention provides an unmanned dump truck intelligent driving control method, system, equipment and medium. By adopting PID control algorithm in both longitudinal and lateral directions of the unmanned dump truck, the real-time speed difference between the actual speed and the planned speed of the unmanned dump truck is analyzed in the longitudinal direction to obtain the target total torque of the unmanned dump truck. In the lateral direction, the lateral deviation and the heading deviation are considered respectively to obtain the target control angle of the unmanned dump truck. At the same time, based on the energy optimization allocation strategy, the target torque of each driving motor of the unmanned dump truck is obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of stable driving of the vehicle. The method has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 is a flow chart of the intelligent driving control method of an unmanned mud truck in the first embodiment of the present invention;

[0040] Figure 2 This is a flow chart of obtaining the real-time speed difference of an unmanned mud truck in the first embodiment of the present invention;

[0041] Figure 3 is a flow chart of obtaining the target total torque of the unmanned mud truck in the first embodiment of the present invention;

[0042] Figure 4 is a flow chart of obtaining the target control angle of the unmanned mud truck in the first embodiment of the present invention;

[0043] Figure 5 It is an architecture diagram of the intelligent driving control system of the unmanned mud truck in the second embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In the present invention, it should be understood that terms such as "include" or "have" are intended to indicate the existence of features, numbers, steps, behaviors, components, parts or a combination thereof disclosed in the specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, behaviors, components, parts or a combination thereof exist or are added.

[0046] It should also be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0047] Embodiment 1

[0048] Embodiment 1 of the present invention provides an intelligent driving control method for an unmanned dump truck, the method comprising: obtaining a real-time speed difference of the unmanned dump truck; analyzing the real-time speed difference based on a PID longitudinal control algorithm to obtain a target total torque of the unmanned dump truck; analyzing the target total torque based on an energy optimization allocation strategy to obtain a target torque of each drive motor of the unmanned dump truck; obtaining a target control angle of the unmanned dump truck based on a PID lateral control algorithm; and performing intelligent driving control on the unmanned dump truck based on the target torque and target control angle of each drive motor.

[0049] In an optional implementation of this embodiment, Figure 1 As shown, Figure 1 The flowchart of the intelligent driving control method of the unmanned mud truck in the first embodiment of the present invention is shown, which includes the following steps:

[0050] S101, obtaining the real-time speed difference of the unmanned mud truck;

[0051] In an optional implementation of this embodiment, the real-time speed difference of the unmanned dump truck is obtained by analyzing the real-time speed value and the planned speed value of the unmanned dump truck.

[0052] Specifically, Figure 2 As shown, Figure 2 The flowchart of obtaining the real-time speed difference of the unmanned mud truck in the first embodiment of the present invention is shown, including the following steps:

[0053] S201, obtaining a real-time speed value of the unmanned mud truck during automatic driving based on a speed sensor;

[0054] In an optional implementation of this embodiment, based on the speed sensor installed on the unmanned mud truck and the speed sensors installed on both sides of the road, the real-time speed value of the unmanned mud truck when it is driving automatically at the current moment is obtained.

[0055] S202, obtaining a planned speed value of the unmanned dump truck under the same road conditions based on the unmanned dump truck design planning data;

[0056] In an optional implementation of this embodiment, the planned speed value of the unmanned dump truck when traveling under the same road conditions as the real-time road conditions is extracted from the design planning data of the unmanned dump truck.

[0057] S203: Arrange the real-time speed value and the planned speed value to obtain the real-time speed difference of the unmanned dump truck.

[0058] In an optional implementation of this embodiment, the difference between the real-time speed value and the planned speed value is used as the real-time speed difference of the unmanned dump truck, and the expression includes:

[0059] v diff =|v real -v ref |;

[0060] In the formula, v diff is the real-time speed difference, v real is the real-time speed value, v ref is the planned speed value.

[0061] S102, analyzing the real-time speed difference based on a PID longitudinal control algorithm to obtain a target total torque of the unmanned mud truck;

[0062] In an optional implementation of this embodiment, the driving and braking torque equations of the unmanned dump truck are established based on the dynamic PID longitudinal control algorithm, and the target total torque of the unmanned dump truck is obtained by analyzing the real-time speed difference.

[0063] Specifically, Figure 3 As shown, Figure 3 A flow chart of obtaining the target total torque of an unmanned dump truck in the first embodiment of the present invention is shown, comprising the following steps:

[0064] S301, establishing driving and braking torque equations of the unmanned mud truck based on the PID longitudinal control algorithm;

[0065] In an optional implementation of this embodiment, the real-time speed difference of the unmanned dump truck is analyzed by a dynamic PID longitudinal control algorithm to establish the driving and braking torque equations of the unmanned dump truck, and the expressions include:

[0066]

[0067] Where, T t is the total driving and braking torque, P m Solve for the proportionality factor for the driving and braking torques, I m Solve for the integral time coefficient for the driving and braking torques, D m Solve for the differential time coefficients for the drive and brake torques, T f To overcome the rolling friction torque, vdiff The real-time speed difference.

[0068] S302, calculating the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equation based on the load mass parameter, speed parameter and turning angle parameter of the unmanned mud truck;

[0069] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations are mainly affected by the load mass parameters, speed parameters and steering angle parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID longitudinal control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters, speed parameters and steering angle parameters.

[0070] Specifically, the expression for solving the proportional coefficient of the driving and braking torques includes:

[0071] P m =P0+k pm ΔP m +k pv ΔP v +k ps ΔP s ;

[0072] Where P m Solve the proportional coefficient for the driving and braking torques, P0 is the initial proportional value, k pm is the proportional load mass coefficient, ΔP m is the proportional load mass value, k pv is the proportional speed coefficient, ΔP v is the proportional speed value, k ps is the proportional rotation coefficient, ΔP s is the proportional angle value.

[0073] Furthermore, the expressions for solving the integral time coefficients of the driving and braking torques include:

[0074] I m =I0+k im ΔI m +k iv ΔI v +k is ΔI s ;

[0075] In the formula, I m Solve for the integral time coefficients for the drive and brake torques, is the initial integral value, k im is the integral load mass coefficient, ΔI m is the integral load mass value, k iv is the integral speed coefficient, ΔI v is the integral speed value, kis is the integral rotation coefficient, ΔI s is the integral angle value.

[0076] Furthermore, the expressions for solving the differential time coefficients of the driving and braking torques include:

[0077] D m =D0+k dm ΔD m +k dv ΔD v +k ds ΔD s ;

[0078] Where D m Solve for the differential time coefficients for the drive and brake torques, is the initial differential value, k dm is the differential load mass coefficient, ΔD m is the differential load mass value, k dv is the differential speed coefficient, ΔD v is the differential speed value, k ds is the differential rotation coefficient, ΔD s is the differential angle value.

[0079] S303, based on the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations, combined with the real-time speed difference, calculate the target total torque of the unmanned mud truck.

[0080] In an optional implementation of this embodiment, the proportional coefficient, integral coefficient and differential coefficient obtained in step S302 are combined with the real-time speed difference and the torque to overcome rolling friction, and substituted into the driving and braking torque equation in step S301 to calculate the target total torque of the unmanned mud truck.

[0081] S103, analyzing the target total torque based on the energy optimization allocation strategy to obtain the target torque of each driving motor of the unmanned mud truck;

[0082] In an optional implementation of this embodiment, the driving motor operating data of the unmanned mud truck is obtained, and the target total torque is distributed based on the energy optimization allocation strategy combined with the driving motor operating data to obtain the target torque of each driving motor of the unmanned mud truck.

[0083] Specifically, according to the position, direction and power of each drive motor, the target total torque is distributed based on the energy optimization distribution strategy, and the driving torque or braking torque of each drive motor is distributed. The expressions include:

[0084]

[0085] Where, Tti is the required torque of the i-th drive motor, α i is the torque distribution coefficient required by the i-th drive motor, T t is the total driving and braking torque.

[0086] It should be noted that the required torque distribution coefficient of each drive motor is determined by the position, direction and power of the drive motor.

[0087] In an optional implementation of this embodiment, for a single drive motor, the relationship expression between its drive efficiency, torque and speed includes:

[0088] λ t =f(T t , n t );

[0089] For multiple motors, the optimization equation is as follows:

[0090]

[0091] In the formula, λ t is the driving efficiency of a single driving motor, T t is the total driving and braking torque, λ opt is the total drive efficiency, T ti is the required torque of the i-th drive motor, λ i is the driving efficiency of the i-th driving motor.

[0092] S104, obtaining a target control angle of the unmanned mud truck based on a PID lateral control algorithm;

[0093] In an optional implementation of this embodiment, based on a dynamic PID lateral control algorithm, a control strategy taking into account lateral deviation and heading deviation is used to analyze the real-time point coordinates of the unmanned dump truck and the coordinates of the nearest point on the planned trajectory, obtain the lateral deviation angle and heading deviation angle, and then obtain the target control angle of the unmanned dump truck.

[0094] Specifically, Figure 4 As shown, Figure 4 The flowchart of obtaining the target control angle of the unmanned mud truck in the first embodiment of the present invention is shown, including the following steps:

[0095] S401, calculating the lateral deviation of the unmanned mud truck, and calculating the lateral deviation angle based on the lateral deviation;

[0096] In an optional implementation of this embodiment, the expression of the lateral deviation includes:

[0097] θ lat_er =θ lat_veh -θ lat_ref ;

[0098] In the formula, θ lat_er is the lateral deviation, θ lat_veh is the real-time point coordinate, θ lat_ref is the coordinate of the closest point from the real-time point to the planned trajectory.

[0099] In an optional implementation of this embodiment, the expression for the change in lateral deviation angle caused by the lateral deviation includes:

[0100]

[0101] In the formula, θ lat is the lateral deviation angle, P lat Solve for the proportionality factor for the lateral deviation angle, I lat Solve for the integration time coefficient for the lateral deviation angle, D lat Solve for the differential time coefficient for the lateral deviation angle, θ lat_er is the lateral deviation.

[0102] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the lateral deviation angle change expression are mainly affected by the load mass parameters and speed parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID lateral control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters and speed parameters.

[0103] Specifically, the expression for solving the proportionality coefficient of the lateral deviation angle includes:

[0104] P lat =P lat_0 +k lat_pm ΔP lat_pm +k lat_pv ΔP lat_pv ;

[0105] Where P lat Solve for the proportionality factor, P, for the lateral deviation angle lat_0 is the initial scale value when solving the lateral deviation angle, k lat_pm is the proportional load mass coefficient when solving the lateral deviation angle, ΔP lat_pm is the proportional load mass value when solving the lateral deviation angle, k lat_pv is the proportional speed coefficient when solving the lateral deviation angle, ΔP lat_pv The proportional velocity value when solving for the lateral deviation angle.

[0106] Furthermore, the expression for solving the integral time coefficient of the lateral deviation angle includes:

[0107] I lat =I lat_0 +klat_im ΔI lat_im +k lat_iv ΔI lat_iv ;

[0108] In the formula, I lat Solve for the integration time coefficient for the lateral deviation angle, I lat_0 is the initial integral value when solving the lateral deviation angle, k lat_im is the integral load mass coefficient when solving the lateral deviation angle, ΔI lat_im is the integral load mass value when solving the lateral deviation angle, k lat_iv is the integral velocity coefficient when solving the lateral deviation angle, ΔI lat_iv It is the integrated velocity value when solving the lateral deviation angle.

[0109] Furthermore, the expression for solving the differential time coefficient of the lateral deviation angle includes:

[0110] D lat =D lat_0 +k lat_dm ΔD lat_dm +k lat_dv ΔD lat_dv ;

[0111] Where D lat Solve for the differential time coefficient for the lateral deviation angle, D lat_0 is the initial differential value when solving the lateral deviation angle, k lat_dm is the differential load mass coefficient when solving for the lateral deviation angle, ΔD lat_dm is the differential load mass value when solving the lateral deviation angle, k lat_dv is the differential velocity coefficient when solving the lateral deviation angle, ΔD lat_dv It is the differential velocity value when solving for the lateral deviation angle.

[0112] S402, calculating the heading deviation of the unmanned mud truck, and calculating the heading deviation angle based on the heading deviation;

[0113] In an optional implementation of this embodiment, the heading deviation angle change expression caused by the heading deviation includes:

[0114]

[0115] In the formula, θ head is the heading deviation angle, P head Solve the proportional coefficient for the heading deviation angle, I head Solve the integral time coefficient for the heading deviation angle, D head Solve for the differential time coefficient for the heading deviation angle.

[0116] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the heading deviation angle change expression are mainly affected by the load mass parameters and speed parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID heading control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters and speed parameters.

[0117] Specifically, the expression for solving the proportional coefficient of the heading deviation angle includes:

[0118] P head =P head_0 +k head_pm ΔP head_pm +k head_pv ΔP head_pv ;

[0119] Where P head Solve the proportional coefficient for the heading deviation angle, P head_0 is the initial scale value when solving the heading deviation angle, k head_pm is the proportional load mass coefficient when solving the heading deviation angle, ΔP head_pm k is the proportional load mass value when solving the heading deviation angle, head_pv is the proportional speed coefficient when solving the heading deviation angle, ΔP head_pv The proportional speed value when solving for the heading deviation angle.

[0120] Furthermore, the expression for solving the integral time coefficient of the heading deviation angle includes:

[0121] I head =I head_0 +k head_im ΔI head_im +k head_iv ΔI head_iv ;

[0122] In the formula, I head Solve the integral time coefficient for the heading deviation angle, I head_0 is the initial integral value when solving the heading deviation angle, k head_im is the integral load mass coefficient when solving the heading deviation angle, ΔI head_im is the integral load mass value when solving the heading deviation angle, k head_iv is the integral speed coefficient when solving the heading deviation angle, ΔI head_iv It is the integrated speed value when solving the heading deviation angle.

[0123] Furthermore, the expression for solving the differential time coefficient of the heading deviation angle includes:

[0124] D head =D head_0+k head_dm ΔD head_dm +k head_ d v ΔD head_ d v ;

[0125] Where D head Solve the differential time coefficient for the heading deviation angle, D head_0 is the initial differential value when solving the heading deviation angle, k head_dm is the differential load mass coefficient when solving the heading deviation angle, ΔD head_dm is the differential load mass value when solving the heading deviation angle, k head_dv is the differential speed coefficient when solving the heading deviation angle, ΔD head_ d v The differential speed value when solving the heading deviation angle.

[0126] S403: Acquire a target control angle of the unmanned mud truck by combining the lateral deviation angle and the heading deviation angle.

[0127] In an optional implementation of this embodiment, the expression of the target control angle of the unmanned mud truck includes:

[0128] θ c =θ lat ±θ head ;

[0129] In the formula, θ c is the target control angle, θ lat is the lateral deviation angle, θ head is the heading deviation angle.

[0130] It should be noted that when the lateral deviation angle and the heading deviation angle are in the same direction, the value is "+", and when the directions are opposite, the value is "-".

[0131] S105: Perform intelligent driving control on the unmanned mud truck based on the target torque and target control angle of each driving motor.

[0132] In an optional implementation of this embodiment, the unmanned mud truck is intelligently controlled based on the target torque and target control angle of each driving motor obtained in the above steps.

[0133] In summary, embodiment 1 of the present invention provides an intelligent driving control method for an unmanned dump truck. By adopting PID control algorithm in both the longitudinal and lateral directions of the unmanned dump truck, the real-time speed difference between the actual speed and the planned speed of the unmanned dump truck is analyzed in the longitudinal direction to obtain the target total torque of the unmanned dump truck. In the lateral direction, the lateral deviation and the heading deviation are considered respectively to obtain the target control angle of the unmanned dump truck. At the same time, based on the energy optimization allocation strategy, the target torque of each driving motor of the unmanned dump truck is obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of smooth driving of the vehicle. It has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck.

[0134] Embodiment 2

[0135] Embodiment 2 of the present invention provides an unmanned dump truck intelligent driving control system, which is used to implement the unmanned dump truck intelligent driving control method described in Embodiment 1. The system includes a real-time speed difference acquisition module, a target total torque acquisition module, a drive motor target torque acquisition module, a target control angle acquisition module, and an intelligent driving control module.

[0136] In an optional implementation of this embodiment, Figure 5 As shown, Figure 5 The following is a diagram showing the architecture of the intelligent driving control system for an unmanned mud truck in the second embodiment of the present invention, including the following modules:

[0137] A real-time speed difference acquisition module 10, wherein the real-time speed difference acquisition module 10 is used to acquire the real-time speed difference of the unmanned mud truck;

[0138] In an optional implementation of the present embodiment, obtaining the real-time speed difference of the unmanned dump truck includes: obtaining the real-time speed value of the unmanned dump truck during automatic driving based on a speed sensor; obtaining the planned speed value of the unmanned dump truck under the same road conditions based on the design planning data of the unmanned dump truck; and collating the real-time speed value and the planned speed value to obtain the real-time speed difference of the unmanned dump truck.

[0139] In an optional implementation of this embodiment, the difference between the real-time speed value and the planned speed value is used as the real-time speed difference of the unmanned dump truck, and the expression includes:

[0140] v diff =|v real -v ref |;

[0141] In the formula, v diff is the real-time speed difference, v real is the real-time speed value, v ref is the planned speed value.

[0142] A target total torque acquisition module 20, the target total torque acquisition module 20 is used to analyze the real-time speed difference based on a PID longitudinal control algorithm to acquire a target total torque of the unmanned mud truck;

[0143] In an optional implementation of the present embodiment, analyzing the real-time speed difference based on the PID longitudinal control algorithm to obtain the target total torque of the unmanned dump truck includes: establishing the driving and braking torque equations of the unmanned dump truck based on the PID longitudinal control algorithm; calculating the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations based on the load mass parameters, speed parameters and steering angle parameters of the unmanned dump truck; calculating the target total torque of the unmanned dump truck based on the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations in combination with the real-time speed difference.

[0144] In an optional implementation of this embodiment, the real-time speed difference of the unmanned dump truck is analyzed by a dynamic PID longitudinal control algorithm to establish the driving and braking torque equations of the unmanned dump truck, and the expressions include:

[0145]

[0146] Where, T t is the total driving and braking torque, P m Solve for the proportionality factor for the driving and braking torques, I m Solve for the integral time coefficient for the driving and braking torques, D m Solve for the differential time coefficients for the drive and brake torques, T f To overcome the rolling friction torque, v diff The real-time speed difference.

[0147] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations are mainly affected by the load mass parameters, speed parameters and steering angle parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID longitudinal control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters, speed parameters and steering angle parameters.

[0148] Specifically, the expression for solving the proportional coefficient of the driving and braking torques includes:

[0149] P m =P0+k pm ΔP m +k pv ΔP v +k ps ΔP s ;

[0150] Where P mSolve the proportional coefficient for the driving and braking torques, P0 is the initial proportional value, k pm is the proportional load mass coefficient, ΔP m is the proportional load mass value, k pv is the proportional speed coefficient, ΔP v is the proportional speed value, k ps is the proportional rotation coefficient, ΔP s is the proportional angle value.

[0151] Furthermore, the expressions for solving the integral time coefficients of the driving and braking torques include:

[0152] I m =I0+k im ΔI m +k iv ΔI v +k is ΔI s ;

[0153] In the formula, I m Solve for the integral time coefficients for the drive and brake torques, is the initial integral value, k im is the integral load mass coefficient, ΔI m is the integral load mass value, k iv is the integral speed coefficient, ΔI v is the integral speed value, k is is the integral rotation coefficient, ΔI s is the integral angle value.

[0154] Furthermore, the expressions for solving the differential time coefficients of the driving and braking torques include:

[0155] D m =D0+k dm ΔD m +k dv ΔD v +k ds ΔD s ;

[0156] Where D m Solve for the differential time coefficients for the drive and brake torques, is the initial differential value, k dm is the differential load mass coefficient, ΔD m is the differential load mass value, k dv is the differential speed coefficient, ΔD v is the differential speed value, k ds is the differential rotation coefficient, ΔD s is the differential angle value.

[0157] A drive motor target torque acquisition module 30, the drive motor target torque acquisition module 30 is used to analyze the target total torque based on the energy optimization allocation strategy to obtain the target torque of each drive motor of the unmanned mud truck;

[0158] In an optional implementation of this embodiment, the driving motor operating data of the unmanned mud truck is obtained, and the target total torque is distributed based on the energy optimization allocation strategy combined with the driving motor operating data to obtain the target torque of each driving motor of the unmanned mud truck.

[0159] Specifically, according to the position, direction and power of each drive motor, the target total torque is distributed based on the energy optimization distribution strategy, and the driving torque or braking torque of each drive motor is distributed. The expressions include:

[0160]

[0161] Where, T ti is the required torque of the i-th drive motor, α i is the torque distribution coefficient required by the i-th drive motor, T t is the total driving and braking torque.

[0162] It should be noted that the required torque distribution coefficient of each drive motor is determined by the position, direction and power of the drive motor.

[0163] In an optional implementation of this embodiment, for a single drive motor, the relationship expression between its drive efficiency, torque and speed includes:

[0164] λ t =f(T t , n t );

[0165] For multiple motors, the optimization equation is as follows:

[0166]

[0167] In the formula, λ t is the driving efficiency of a single driving motor, T t is the total driving and braking torque, λ opt is the total drive efficiency, T ti is the required torque of the i-th drive motor, λ i is the driving efficiency of the i-th driving motor.

[0168] A target control angle acquisition module 40, wherein the target control angle acquisition module 40 is used to acquire a target control angle of the unmanned mud truck based on a PID lateral control algorithm;

[0169] In an optional implementation of the present embodiment, the target control angle of the unmanned dump truck is obtained based on the PID lateral control algorithm, including: calculating the lateral deviation of the unmanned dump truck, and calculating the lateral deviation angle based on the lateral deviation; calculating the heading deviation of the unmanned dump truck, and calculating the heading deviation angle based on the heading deviation; and obtaining the target control angle of the unmanned dump truck by combining the lateral deviation angle and the heading deviation angle.

[0170] In an optional implementation of this embodiment, the expression of the lateral deviation includes:

[0171] θ lat_er =θ lat_veh -θ lat_ref ;

[0172] In the formula, θ lat_er is the lateral deviation, θ lat_veh is the real-time point coordinate, θ lat_ref is the coordinate of the closest point from the real-time point to the planned trajectory.

[0173] In an optional implementation of this embodiment, the expression for the change in lateral deviation angle caused by the lateral deviation includes:

[0174]

[0175] In the formula, θ lat is the lateral deviation angle, P lat Solve for the proportionality factor for the lateral deviation angle, I lat Solve for the integration time coefficient for the lateral deviation angle, D lat Solve for the differential time coefficient for the lateral deviation angle, θ lat_er is the lateral deviation.

[0176] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the lateral deviation angle change expression are mainly affected by the load mass parameters and speed parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID lateral control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters and speed parameters.

[0177] Specifically, the expression for solving the proportionality coefficient of the lateral deviation angle includes:

[0178] P lat =P lat_0 +k lat_pm ΔP lat_pm +k lat_pv ΔP lat_pv ;

[0179] Where P lat Solve for the proportionality factor, P, for the lateral deviation anglelat_0 is the initial scale value when solving the lateral deviation angle, k lat_pm is the proportional load mass coefficient when solving the lateral deviation angle, ΔP lat_pm is the proportional load mass value when solving the lateral deviation angle, k lat_pv is the proportional speed coefficient when solving the lateral deviation angle, ΔP lat_pv The proportional velocity value when solving for the lateral deviation angle.

[0180] Furthermore, the expression for solving the integral time coefficient of the lateral deviation angle includes:

[0181] I lat =I lat_0 +k lat_im ΔI lat_im +k lat_iv ΔI lat_iv ;

[0182] In the formula, I lat Solve for the integration time coefficient for the lateral deviation angle, I lat_0 is the initial integral value when solving the lateral deviation angle, k lat_im is the integral load mass coefficient when solving the lateral deviation angle, ΔI lat_im is the integral load mass value when solving the lateral deviation angle, k lat_iv is the integral velocity coefficient when solving the lateral deviation angle, ΔI lat_iv It is the integrated velocity value when solving the lateral deviation angle.

[0183] Furthermore, the expression for solving the differential time coefficient of the lateral deviation angle includes:

[0184] D lat =D lat_0 +k lat_dm ΔD lat_dm +k lat_dv ΔD lat_dv ;

[0185] Where D lat Solve for the differential time coefficient for the lateral deviation angle, D lat_0 is the initial differential value when solving the lateral deviation angle, k lat_dm is the differential load mass coefficient when solving for the lateral deviation angle, ΔD lat_dm is the differential load mass value when solving the lateral deviation angle, k lat_dv is the differential velocity coefficient when solving the lateral deviation angle, ΔD lat_dv It is the differential velocity value when solving for the lateral deviation angle.

[0186] In an optional implementation of this embodiment, the heading deviation angle change expression caused by the heading deviation includes:

[0187]

[0188] In the formula, θ head is the heading deviation angle, P head Solve the proportional coefficient for the heading deviation angle, I head Solve the integral time coefficient for the heading deviation angle, D head Solve for the differential time coefficient for the heading deviation angle.

[0189] In an optional implementation of the present embodiment, the proportional coefficient, integral coefficient and differential coefficient in the heading deviation angle change expression are mainly affected by the load mass parameters and speed parameters of the unmanned dump truck, and the proportional, integral and differential values ​​in the dynamic PID heading control algorithm are composed of the initial value and the influencing values ​​of the load mass parameters and speed parameters.

[0190] Specifically, the expression for solving the proportional coefficient of the heading deviation angle includes:

[0191] P head =P head_0 +k head_pm ΔP head_pm +k head_pv ΔP head_pv ;

[0192] Where P head Solve the proportional coefficient for the heading deviation angle, P head_0 is the initial scale value when solving the heading deviation angle, k head_pm is the proportional load mass coefficient when solving the heading deviation angle, ΔP head_pm k is the proportional load mass value when solving the heading deviation angle, head_pv is the proportional speed coefficient when solving the heading deviation angle, ΔP head_pv The proportional speed value when solving for the heading deviation angle.

[0193] Furthermore, the expression for solving the integral time coefficient of the heading deviation angle includes:

[0194] I head =I head_0 +k head_im ΔI head_im +k head_iv ΔI head_iv ;

[0195] In the formula, I head Solve the integral time coefficient for the heading deviation angle, I head_0is the initial integral value when solving the heading deviation angle, k head_im is the integral load mass coefficient when solving the heading deviation angle, ΔI head_im is the integral load mass value when solving the heading deviation angle, k head_iv is the integral speed coefficient when solving the heading deviation angle, ΔI head_iv It is the integrated speed value when solving the heading deviation angle.

[0196] Furthermore, the expression for solving the differential time coefficient of the heading deviation angle includes:

[0197] D head =D head_0 +k head_dm ΔD head_dm +k head_dv ΔD head_dv ;

[0198] Where D head Solve the differential time coefficient for the heading deviation angle, D head_0 is the initial differential value when solving the heading deviation angle, k head_dm is the differential load mass coefficient when solving the heading deviation angle, ΔD head_dm is the differential load mass value when solving the heading deviation angle, k head_dv is the differential speed coefficient when solving the heading deviation angle, ΔD head_dv The differential speed value when solving the heading deviation angle.

[0199] In an optional implementation of this embodiment, the expression of the target control angle of the unmanned mud truck includes:

[0200] θ c =θ lat ±θ head ;

[0201] In the formula, θ c is the target control angle, θ lat is the lateral deviation angle, θ head is the heading deviation angle.

[0202] It should be noted that when the lateral deviation angle and the heading deviation angle are in the same direction, the value is "+", and when the directions are opposite, the value is "-".

[0203] The intelligent driving control module 50 is used to perform intelligent driving control on the unmanned mud truck based on the target torque and target control angle of each driving motor.

[0204] In summary, embodiment 2 of the present invention provides an unmanned dump truck intelligent driving control system, which is used to implement the unmanned dump truck intelligent driving control method described in embodiment 1. By adopting PID control algorithm in the longitudinal and lateral directions of the unmanned dump truck, the real-time speed difference between the actual speed and the planned speed of the unmanned dump truck is analyzed in the longitudinal direction to obtain the target total torque of the unmanned dump truck. In the lateral direction, the lateral deviation and the heading deviation are considered respectively to obtain the target control angle of the unmanned dump truck. At the same time, based on the energy optimization allocation strategy, the target torque of each driving motor of the unmanned dump truck is obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of smooth driving of the vehicle. It has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck.

[0205] Embodiment 3

[0206] Embodiment 3 of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent driving control method for the unmanned dump truck described in Embodiment 1.

[0207] In summary, embodiment three of the present invention provides an electronic device for implementing the intelligent driving control method of the unmanned dump truck in embodiment one. By adopting PID control algorithm in both the longitudinal and lateral directions of the unmanned dump truck, the real-time speed difference between the actual speed and the planned speed of the unmanned dump truck is analyzed in the longitudinal direction to obtain the target total torque of the unmanned dump truck. In the lateral direction, the lateral deviation and the heading deviation are considered respectively to obtain the target control angle of the unmanned dump truck. At the same time, based on the energy optimization allocation strategy, the target torque of each driving motor of the unmanned dump truck is obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of smooth driving of the vehicle. It has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck.

[0208] Embodiment 4

[0209] Embodiment 4 of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the unmanned mud truck intelligent driving control method described in Embodiment 1 is implemented.

[0210] In summary, embodiment four of the present invention provides a computer-readable storage medium for executing the unmanned dump truck intelligent driving control method described in embodiment one. By adopting PID control algorithm in both the longitudinal and lateral directions of the unmanned dump truck, the real-time speed difference between the actual speed and the planned speed of the unmanned dump truck is analyzed in the longitudinal direction to obtain the target total torque of the unmanned dump truck. In the lateral direction, the lateral deviation and the heading deviation are considered respectively to obtain the target control angle of the unmanned dump truck. At the same time, based on the energy optimization allocation strategy, the target torque of each driving motor of the unmanned dump truck is obtained, and the unmanned dump truck is controlled based on the target total torque and the target control angle to meet the goal of smooth driving of the vehicle. It has the advantages of high precision and high energy utilization efficiency, and effectively improves the cruising range of the unmanned dump truck.

[0211] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0212] In addition, the embodiments of the present invention are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. An intelligent driving control method for an unmanned mud truck, characterized in that: The method comprises: Get the real-time speed difference of the unmanned dump truck; Analyze the real-time speed difference based on the PID longitudinal control algorithm to obtain the target total torque of the unmanned mud truck; Analyze the target total torque based on the energy optimization allocation strategy to obtain the target torque of each driving motor of the unmanned mud truck; Obtain the target control angle of the unmanned mud truck based on the PID lateral control algorithm; Intelligent driving control of the unmanned mud truck is performed based on the target torque and target control angle of each driving motor.

2. The intelligent driving control method of an unmanned mud truck according to claim 1, characterized in that: The method of obtaining the real-time speed difference of the unmanned mud truck includes: Based on the speed sensor, the real-time speed value of the unmanned mud truck during automatic driving is obtained; Based on the design planning data of the unmanned dump truck, the planned speed value of the unmanned dump truck under the same road conditions is obtained; The real-time speed value and the planned speed value are sorted to obtain the real-time speed difference of the unmanned dump truck.

3. The intelligent driving control method of an unmanned mud truck according to claim 1, characterized in that: The analyzing the real-time speed difference based on the PID longitudinal control algorithm to obtain the target total torque of the unmanned mud truck includes: The driving and braking torque equations of the unmanned mud truck are established based on the PID longitudinal control algorithm; Calculating the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equation based on the load mass parameter, speed parameter and turning angle parameter of the unmanned mud truck; Based on the proportional coefficient, integral coefficient and differential coefficient in the driving and braking torque equations, combined with the real-time speed difference, the target total torque of the unmanned mud truck is calculated.

4. The intelligent driving control method of an unmanned mud truck according to claim 3, characterized in that: The expressions of the driving and braking torque equations of the unmanned mud truck include: Where, T t is the total driving and braking torque, P m Solve for the proportionality factor for the driving and braking torques, I m Solve for the integral time coefficient for the driving and braking torques, D m Solve for the differential time coefficients for the drive and brake torques, T f To overcome the rolling friction torque, v diff The real-time speed difference.

5. The intelligent driving control method of an unmanned dump truck according to claim 1, characterized in that: Analyzing the target total torque based on the energy optimization allocation strategy to obtain the target torque of each driving motor of the unmanned mud truck includes: The driving motor operation data of the unmanned mud truck is obtained, and the target total torque is distributed based on the energy optimization allocation strategy combined with the driving motor operation data to obtain the target torque of each driving motor of the unmanned mud truck.

6. The intelligent driving control method of an unmanned mud truck according to claim 1, characterized in that: The method of obtaining the target control angle of the unmanned mud truck based on the PID lateral control algorithm includes: Calculating the lateral deviation of the unmanned mud truck, and calculating the lateral deviation angle based on the lateral deviation; Calculating a heading deviation of the unmanned mud truck, and calculating a heading deviation angle based on the heading deviation; The target control angle of the unmanned mud truck is obtained by combining the lateral deviation angle and the heading deviation angle.

7. The intelligent driving control method of an unmanned mud truck according to claim 1, characterized in that: The intelligent driving control of the unmanned mud truck based on the target torque and target control angle of each driving motor includes: The actuator of the unmanned dump truck controls each driving motor to execute the target torque, and the controller of the unmanned dump truck controls the unmanned dump truck to execute the target control angle.

8. An intelligent driving control system for an unmanned mud truck, characterized in that: The unmanned dump truck intelligent driving control system is used to implement the unmanned dump truck intelligent driving control method described in any one of claims 1 to 7, and the system includes: A real-time speed difference acquisition module, which is used to acquire the real-time speed difference of the unmanned mud truck; A target total torque acquisition module, which is used to analyze the real-time speed difference based on a PID longitudinal control algorithm to acquire a target total torque of the unmanned mud truck; A drive motor target torque acquisition module, the drive motor target torque acquisition module is used to analyze the target total torque based on the energy optimization allocation strategy to obtain the target torque of each drive motor of the unmanned mud truck; A target control angle acquisition module, wherein the target control angle acquisition module is used to acquire a target control angle of the unmanned mud truck based on a PID lateral control algorithm; An intelligent driving control module is used to perform intelligent driving control on the unmanned mud truck based on the target torque and target control angle of each driving motor.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the intelligent driving control method for an unmanned mud truck according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the unmanned dump truck intelligent driving control method according to any one of claims 1 to 7 is implemented.

Images