A trajectory planning method and device for a pure electric unmanned mining dump truck

Abstract

The present invention discloses a pure electric unmanned mining dump truck trajectory planning method and equipment, and the technical solution thereof includes the following steps: S1, setting the initial position and terminal position of the pure electric unmanned mining dump truck through a communication module; S2, constructing a vehicle driving dynamics model and establishing vehicle dynamics constraints; S3, planning a path from the starting point to the end point through a global path planning algorithm; S4, post-processing the planned initial path to make the path smooth and meet the dynamic constraints of the pure electric unmanned mining dump truck; S5, when the GPS signal is good, the vehicle is positioned in real time through GPS. This method combines global path planning with local path planning. When the laser radar does not detect an obstacle, local path planning is not required, thereby reducing the computational burden; when there is an obstacle near the vehicle, a new local path is planned in real time so that the vehicle can bypass the obstacle.

Description

Technical Field

[0001] The present invention relates to the field of smart mines, and in particular to a trajectory planning method and device for a pure electric unmanned mining dump truck. Background Art

[0002] Smart mines are based on the digitalization and informatization of mines. They actively perceive, automatically analyze, and quickly process mine production, occupational health and safety, technical support, and logistics support, so as to build smart mines and ultimately realize the construction of safe mines, unmanned mines, efficient mines, and clean mines. Due to the harsh working environment of mines, operators must endure harsh working environments such as noise, dust, and bumps every day. The high temperature, high humidity, noise, and vibration in the working environment cause great harm to the health of mine equipment operators. The realization of completely unmanned and autonomous operation of open-pit mine transportation operations is conducive to improving production efficiency, reducing production costs, and reducing the number of operators. Existing technologies rarely carry out trajectory planning for mining vehicles.

[0003] Heavy mining vehicles have poor maneuverability and flexibility due to their large inertia and high center of gravity. The tires tilt significantly during driving. When driving at high speeds, the handling stability is poor. In emergency situations such as turning and braking, the vehicle is prone to tilt or even rollover accidents. Once an accident occurs, the loss is greater than that of an ordinary car. Therefore, improving the energy utilization rate of road vehicles is one of the important tasks of automotive powertrain control. Different speed curves have an important impact on the handling stability, driving economy, and battery life of unmanned mining vehicles.

[0004] In the prior art, the kinematic model and the dynamic model are usually considered separately, so that the planned speed curve does not meet the dynamic constraints or the speed curve has jumps and is not smooth. Summary of the invention

[0005] In view of the problems mentioned in the background technology, the purpose of the present invention is to provide a trajectory planning method and device for a pure electric unmanned mining dump truck to solve the problems mentioned in the background technology.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] A trajectory planning method for a pure electric unmanned mining dump truck comprises the following steps:

[0008] S1. Setting the initial position and final position of the pure electric unmanned mining dump truck through the communication module;

[0009] S2. Construct a vehicle driving dynamics model and establish vehicle dynamics constraints;

[0010] S3, plan the path from the starting point to the end point through the global path planning algorithm;

[0011] S4, post-processing the planned initial path to make the path smooth and meet the dynamic constraints of the pure electric unmanned mining dump truck;

[0012] S5. When the GPS signal is good, the vehicle is positioned in real time through GPS and travels along the global path; when the GPS signal strength is lower than a given threshold, the vehicle is positioned by sensing the surrounding environment in real time through the lidar; when an obstacle appears in front of the vehicle, a new local path is planned through the online local path planning method so that the vehicle can bypass the obstacle and return to the original path.

[0013] Preferably, the dynamic and kinematic parameters of the pure electric unmanned mining dump truck are obtained according to the vehicle-mounted sensors, and the specific acquisition steps are as follows:

[0014] Establish the longitudinal, lateral, yaw, and roll dynamics and kinematics models of the vehicle:

[0015]

[0016]

[0017]

[0018]

[0019] Among them, v x is the lateral velocity of the vehicle, v y is the vehicle longitudinal velocity, is the vehicle yaw angle, φ is the vehicle roll angle, θ is the road longitudinal slope angle, φ t is the transverse slope angle of the road, F xf 、F yf 、F xr and F yr are the components of the front and rear wheels on the x-axis and y-axis of the vehicle coordinate system respectively;

[0020] Select the state quantity as The control quantity is u=δ f , the model is linearized and discretized into a state space expression, and we get:

[0021] ξ(k+1)=A k ξ(k)+B k u(k)

[0022] η(k)=Cξ(k);

[0023] The speed planning model is:

[0024]

[0025] Where a(k) is the vehicle acceleration, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration.

[0026] The sideslip angle of the center of mass is:

[0027]

[0028] The dynamic constraints during vehicle driving are established to limit the driving acceleration and speed of the pure electric unmanned mining dump truck. The constraints set are:

[0029] ξ(k+1)=A k ξ(k)+B k u(k)

[0030] S min ≤S(k+1)≤S max

[0031] LTR(k+1)≤LTRd

[0032] β min ≤β≤β max

[0033] v 2 (k)≤2a min |d(k)-d st |;

[0034] The lateral load transfer rate of the vehicle is:

[0035]

[0036] The speed planning performance indicators considering battery life are:

[0037]

[0038] Among them, M1 and M2 are constants; Q loss (t) is the total capacity decay rate of the battery, which is a constant at the initial moment; n is the motor speed; T is the motor torque; B is the exponential factor; z is the power exponential factor; R g is the gas constant; E a is the material activation energy; D is the battery temperature; S0 is the rated capacity of the single battery;

[0039] The performance indicators considering battery energy consumption are:

[0040]

[0041] Preferably, the real-time speed planning problem of the pure electric unmanned mining dump truck is expressed as:

[0042] min J=J1+J2

[0043] stξ(k+1)=A k ξ(k)+B k u(k)

[0044] S min ≤S(k+1)≤S max

[0045] |LTR(k+1)|≤LTR max

[0046] β min ≤β≤β max

[0047] v 2 (k)≤2|a|(d(k)-d st );

[0048] The relationship between motor speed and vehicle speed is:

[0049]

[0050] The motor torque is:

[0051]

[0052] Where β is the sideslip angle of the center of mass, ξ(k+1) is the state space expression, LTR is the lateral load transfer rate of the vehicle, n is the motor speed, T is the motor torque, θ is the longitudinal slope angle of the road, φ t is the transverse slope angle of the road, n(k) is the state space expression parameter, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration.

[0053] Preferably, the dynamic and kinematic parameters of the pure electric unmanned mining dump truck during driving include: road adhesion coefficient, center of mass sideslip angle, tire force, vehicle mass, road slope, obstacle information, vehicle coordinates and other information.

[0054] Preferably, the road adhesion coefficient estimates the longitudinal slip rate and tire sideslip angle of the wheel by detecting the tire motion state, including wheel speed and steering angle, and then estimates the road peak adhesion coefficient through a tire model or slip characteristic curve.

[0055] Preferably, the center of mass sideslip angle is obtained by a method of fusion estimation of GPS and INS information. The longitudinal and lateral vehicle speed and yaw angle signals measured by NS are first compared with the true values ​​to obtain noise and deviation during long-term integration. The GPS and INS information are then fused, and a Kalman filter is established based on the vehicle dynamics model to estimate the center of mass sideslip angle.

[0056] Preferably, when the vehicle mass and road slope are obtained, acceleration signals are collected through GPS and INS and based on the vehicle longitudinal dynamics and kinematics model, the data is fitted to obtain the mass and ramp angle, and the steady-state Kalman filtering method is used to estimate the mass and ramp angle.

[0057] Preferably, when obtaining the various parameters of the pure electric unmanned mining dump truck power battery, a second-order RC equivalent circuit model or electrochemical model of the battery is first established, and then a statistical filtering algorithm is introduced to achieve the optimal estimation of the model parameters, and characteristic parameters with trend are extracted therefrom to achieve the characterization of performance degradation, thereby realizing the estimation of the battery's health status.

[0058] Preferably, when the speed curve is obtained, the speed curve is solved by sequential quadratic programming, dynamic programming, genetic algorithm, or intelligent algorithm according to the established vehicle dynamics model, constraints, and performance indicators.

[0059] The present invention also provides a trajectory planning device for a pure electric unmanned mining dump truck, comprising:

[0060] A communication module, which is used to remotely interact with the pure electric unmanned mining dump truck, issue commands, and perform other operations;

[0061] On-board GPS, which provides three-dimensional vehicle speed and heading angle information of a pure electric unmanned mining dump truck;

[0062] An inertial navigation system, wherein the inertial navigation system provides yaw rate and longitudinal and lateral acceleration information;

[0063] A wheel speed sensor, wherein the wheel speed sensor provides tire rotation speed information;

[0064] A trajectory planning module, which receives vehicle status information sent back by various sensors of the pure electric mining dump truck and plans a speed curve in real time;

[0065] The battery management system monitors the battery status in real time and sends the voltage, current and temperature information of the battery pack to the trajectory planning module via the CAN bus.

[0066] In summary, the present invention mainly has the following beneficial effects:

[0067] This method combines global path planning with local path planning. When the laser radar does not detect an obstacle, there is no need for local path planning, which reduces the computational burden. When there is an obstacle near the vehicle, a new local path is planned in real time so that the vehicle can bypass the obstacle and return to the original path. The vehicle kinematic model is combined with the dynamic model, and the rate of change of acceleration is used as the control quantity, so that the changes in acceleration, speed and driving distance are smoother and without jumps. A four-degree-of-freedom dynamic model of the vehicle is established. Through inertial navigation devices such as wheel speed sensors and battery management systems, the operating status of the vehicle is observed in real time in combination with state observation technology to ensure the accuracy of planning. Lateral load transfer rate constraints and center of mass sideslip angle constraints are added to avoid rollover of mining dump trucks and ensure the smooth operation of unmanned mining dump trucks. Performance indicators that take into account battery life and energy consumption are added to reduce energy consumption during vehicle driving and reduce battery life loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 It is one of the system block diagrams of the present invention;

[0069] Figure 2 This is the second system block diagram of the present invention;

[0070] Figure 3 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0071] 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.

[0072] Example 1

[0073] refer to Figures 1 to 3 , a trajectory planning method for a pure electric unmanned mining dump truck, comprising the following steps:

[0074] S1. Setting the initial position and final position of the pure electric unmanned mining dump truck through the communication module;

[0075] S2. Construct a vehicle driving dynamics model and establish vehicle dynamics constraints;

[0076] S3, plan the path from the starting point to the end point through the global path planning algorithm;

[0077] S4, post-processing the planned initial path to make the path smooth and meet the dynamic constraints of the pure electric unmanned mining dump truck;

[0078] S5. When the GPS signal is good, the vehicle is positioned in real time through GPS and travels along the global path; when the GPS signal strength is lower than a given threshold, the vehicle is positioned by sensing the surrounding environment in real time through the lidar; when an obstacle appears in front of the vehicle, a new local path is planned through the online local path planning method so that the vehicle can bypass the obstacle and return to the original path.

[0079] Among them, the dynamic and kinematic parameters of the pure electric unmanned mining dump truck are obtained according to the on-board sensors. The specific acquisition steps are as follows;

[0080] Establish the longitudinal, lateral, yaw, and roll dynamics and kinematics models of the vehicle:

[0081]

[0082]

[0083]

[0084]

[0085] Among them, v x is the lateral velocity of the vehicle, v y is the vehicle longitudinal velocity, is the vehicle yaw angle, φ is the vehicle roll angle, θ is the road longitudinal slope angle, φ t is the transverse slope angle of the road, F xf 、F yf 、F xr and F yr are the components of the front and rear wheels on the x-axis and y-axis of the vehicle coordinate system respectively;

[0086] Select the state quantity as The control quantity is u=δ f , the model is linearized and discretized into a state space expression, and we get:

[0087] ξ(k+1)=A k ξ(k)+B k u(k)

[0088] η(k)=Cξ(k);

[0089] The speed planning model is:

[0090]

[0091] Where a(k) is the vehicle acceleration, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration.

[0092] The sideslip angle of the center of mass is:

[0093]

[0094] The dynamic constraints during vehicle driving are established to limit the driving acceleration and speed of the pure electric unmanned mining dump truck. The constraints set are:

[0095] ξ(k+1)=A k ξ(k)+B k u(k)

[0096] S min ≤S(k+1)≤S max

[0097] LTR(k+1)≤LTRd

[0098] β min ≤β≤β max

[0099] v 2 (k)≤2a min |d(k)-d st |;

[0100] The lateral load transfer rate of the vehicle is:

[0101]

[0102] The speed planning performance indicators considering battery life are:

[0103]

[0104] Among them, M1 and M2 are constants; Q loss (t) is the total capacity decay rate of the battery, which is a constant at the initial moment; n is the motor speed; T is the motor torque; B is the exponential factor; z is the power exponential factor; R g is the gas constant; E a is the material activation energy; D is the battery temperature; S0 is the rated capacity of the single battery;

[0105] The performance indicators considering battery energy consumption are:

[0106]

[0107] Among them, the real-time speed planning problem of pure electric unmanned mining dump truck is expressed as:

[0108] min J=J1+J2

[0109] stξ(k+1)=A kξ(k)+B k u(k)

[0110] S min ≤S(k+1)≤S max

[0111] |LTR(k+1)|≤LTR max

[0112] β min ≤β≤β max

[0113] v 2 (k)≤2|a|(d(k)-d st );

[0114] The relationship between motor speed and vehicle speed is:

[0115]

[0116] The motor torque is:

[0117]

[0118] Where β is the sideslip angle of the center of mass, ξ(k+1) is the state space expression, LTR is the lateral load transfer rate of the vehicle, n is the motor speed, T is the motor torque, θ is the longitudinal slope angle of the road, φ t is the transverse slope angle of the road, n(k) is the state space expression parameter, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration.

[0119] Among them, the dynamic and kinematic parameters of the pure electric unmanned mining dump truck during driving include: road adhesion coefficient, center of mass sideslip angle, tire force, vehicle mass, road slope, obstacle information, vehicle coordinates and other information.

[0120] Among them, the road adhesion coefficient estimates the longitudinal slip rate and tire sideslip angle of the wheel by detecting the tire movement state, including wheel speed and steering angle, and then estimates the road peak adhesion coefficient through the tire model or slip characteristic curve.

[0121] Among them, the center of mass sideslip angle is obtained by the method of fusion estimation of GPS and INS information. The longitudinal and lateral vehicle speed and yaw angle signals measured by NS are first compared with the true values ​​to obtain the noise and deviation in the long-term integration. Then the GPS and INS information are fused, and a Kalman filter is established according to the vehicle dynamics model to estimate the center of mass sideslip angle.

[0122] Among them, when obtaining the vehicle mass and road slope, the acceleration signal is collected through GPS and INS, and based on the vehicle longitudinal dynamics and kinematics model, the data is fitted to obtain the mass and ramp angle, and the steady-state Kalman filtering method is used to estimate the mass and ramp angle.

[0123] Among them, when obtaining the various parameters of the power battery of the pure electric unmanned mining dump truck, the second-order RC equivalent circuit model or electrochemical model of the battery is first established, and then the statistical filtering algorithm is introduced to achieve the optimal estimation of the model parameters, and the characteristic parameters with trend are extracted from them to realize the characterization of performance degradation, thereby realizing the estimation of the battery's health status.

[0124] Among them, when the speed curve is obtained, according to the established vehicle dynamics model, constraints and performance indicators, sequential quadratic programming, dynamic programming, genetic algorithm and intelligent algorithm are used to solve the speed curve.

[0125] Among them, this method combines global path planning with local path planning. When the laser radar does not detect an obstacle, there is no need to perform local path planning, which reduces the computational burden. When there is an obstacle near the vehicle, a new local path is planned in real time so that the vehicle can bypass the obstacle and return to the original path. The vehicle kinematic model is combined with the dynamic model, and the rate of change of acceleration is used as the control quantity, so that the changes in acceleration, speed and driving distance are smoother and without jumps. A four-degree-of-freedom dynamic model of the vehicle is established. Through inertial navigation, wheel speed sensor, battery management system and other equipment, combined with state observation technology, the running state of the vehicle is observed in real time to ensure the accuracy of planning. Lateral load transfer rate constraints and center of mass sideslip angle constraints are added to avoid rollover of mining dump trucks and ensure the smooth operation of unmanned mining dump trucks. Performance indicators that consider battery life and energy consumption are added to reduce energy consumption during vehicle driving and reduce battery life loss.

[0126] Example 2

[0127] This embodiment also provides a pure electric unmanned mining dump truck trajectory planning device, including:

[0128] Communication module: The communication module is used to remotely interact with the pure electric unmanned mining dump truck, issue commands, and perform other operations;

[0129] On-board GPS: The on-board GPS provides three-dimensional vehicle speed and heading angle information for pure electric unmanned mining dump trucks;

[0130] Inertial navigation system, which provides yaw rate and longitudinal and lateral acceleration information;

[0131] Wheel speed sensor, which provides tire speed information;

[0132] Trajectory planning module: The trajectory planning module receives vehicle status information sent back by various sensors of the pure electric mining dump truck and plans the speed curve in real time;

[0133] Battery Management System,The battery management system monitors the battery status in real time, and sends the voltage, current, and temperature information of the battery pack to the trajectory planning module through the CAN bus.

[0134] Example 3

[0135] refer to Figures 1 to 3 , this embodiment provides a trajectory planning method for a pure electric unmanned mining dump truck, comprising the following steps:

[0136] Step 1: Establish the longitudinal, lateral, yaw and roll dynamics and kinematics models of the vehicle, and their expressions are:

[0137]

[0138]

[0139]

[0140]

[0141] In the formula, v x is the lateral velocity of the vehicle, v y is the vehicle longitudinal velocity, is the vehicle yaw angle, φ is the vehicle roll angle, θ is the road longitudinal slope angle, φ t is the transverse slope angle of the road, F xf 、F yf 、F xr and F yr are the components of the front and rear wheels on the x-axis and y-axis of the vehicle coordinate system respectively;

[0142] Select the state quantity as The control quantity is u=δ f , the model is linearized and discretized into a state space expression:

[0143] ξ(k+1)=A k ξ(k)+B k u(k)

[0144] η(k)=Cξ(k);

[0145] The speed planning model is:

[0146]

[0147] The sideslip angle of the center of mass is:

[0148]

[0149] Among them, a(k) is the vehicle acceleration, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration.

[0150] Step 2: The operator interacts with the vehicle through the wireless communication module to set the starting and ending positions of the mining dump truck as well as constraints such as speed, acceleration, and lateral load transfer rate. According to the vehicle roll dynamics model, the lateral load transfer rate of the vehicle is:

[0151]

[0152] Furthermore, the constraints set are:

[0153] ξ(k+1)=A k ξ(k)+B k u(k)

[0154] S min ≤S(k+1)≤S max

[0155] |LTR(k+1)|≤LTR max

[0156] β min ≤β≤β max

[0157] v 2 (k)≤2|a|(d(k)-d st );

[0158] Among them, d st The distance between the starting point and the end point;

[0159] Step 2: The trajectory planning unit runs the pre-stored path planning program to perform global path planning from the starting position to the end position according to the relevant settings of step 1. It should be noted that the path planning algorithm can be selected according to actual needs. The optional path planning algorithms include RRT algorithm, A* algorithm, artificial potential field method, ant colony algorithm, Dijkstra algorithm, etc.

[0160] Step 3, obtaining the vehicle's dynamics, kinematics information, and the vehicle's surrounding environment information according to the sensor;

[0161] Step 4: For some mining dump truck state information that is difficult to obtain directly, the state estimation technology is used to estimate it;

[0162] Step 5: Determine whether there are obstacles around the vehicle based on the laser radar. If there are no obstacles, jump to step 6; if there are obstacles, perform local path planning based on the global path planning to plan a path that allows the mining dump truck to avoid obstacles;

[0163] Step 6: Perform real-time speed planning based on the planned path and the constraints set in step 1, combined with performance indicators that consider battery life and battery energy consumption. The speed planning performance indicators that consider battery life are:

[0164]

[0165]

[0166]

[0167]

[0168] Among them, M1 and M2 are constants; Q loss is the total capacity decay rate of the battery, which is a constant at the initial moment; n is the motor speed; T is the motor torque; B is the exponential factor; z is the power exponential factor; R g is the gas constant; E a is the material activation energy; D is the battery temperature; S0 is the rated capacity of the single battery;

[0169] Furthermore, the motor speed, torque and The relationship between them can be obtained:

[0170]

[0171] The performance indicators considering battery energy consumption are:

[0172]

[0173]

[0174] In the formula, I b is the battery pack discharge current, V oc is the open circuit voltage of the battery pack, R0 is the equivalent internal resistance of the battery pack, P b0 is the instantaneous output power of the battery;

[0175] Furthermore, the motor speed, torque and V oc I b The relationship between them can be obtained:

[0176]

[0177] Optionally, the numerical fitting method includes polynomial fitting, spline curve fitting, least square method, neural network, etc.;

[0178] Furthermore, the real-time speed planning problem can be expressed as:

[0179] min J=J1+J2

[0180] stξ(k+1)=A k ξ(k)+B k u(k)

[0181] S min ≤S(k+1)≤S max

[0182] |LTR(k+1)|≤LTR max

[0183] β min ≤β≤β max

[0184] v 2 (k)≤2|a|(d(k)-d st );

[0185] The relationship between motor speed and vehicle speed is:

[0186]

[0187] The motor torque is:

[0188]

[0189] In the formula, G is the mass of the vehicle, f is the rolling resistance coefficient, C D is the air resistance coefficient, A is the frontal area, δ is the rotation mass conversion coefficient, and i0 is the main reduction ratio.

[0190] Step 7: transmit the planned speed curve to the electric drive system via the CAN bus, and the electric drive system tracks the planned speed.

[0191] Step 8: During driving, positioning is performed using GPS when the GPS signal is above a certain threshold; when the GPS signal is weak, positioning is performed using a lidar.

[0192] Step 9: Make a judgment based on the positioning information. If the vehicle reaches the destination, the planning ends, otherwise jump to step 3.

[0193] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A trajectory planning method for a pure electric unmanned mining dump truck, characterized in that: The following steps are involved: S1. Setting the initial position and final position of the pure electric unmanned mining dump truck through the communication module; S2. Construct a vehicle driving dynamics model and establish vehicle dynamics constraints; S3, plan the path from the starting point to the end point through the global path planning algorithm; S4, post-processing the planned initial path to make the path smooth and meet the dynamic constraints of the pure electric unmanned mining dump truck; S5. When the GPS signal is good, the vehicle is positioned in real time through GPS and travels along the global path; when the GPS signal strength is lower than a given threshold, the vehicle is positioned in real time through the laser radar to perceive the surrounding environment; when an obstacle appears in front of the vehicle, a new local path is planned through the online local path planning method so that the vehicle can bypass the obstacle and return to the original path; The dynamic and kinematic parameters of the pure electric unmanned mining dump truck are obtained according to the vehicle-mounted sensors, and the specific acquisition steps are as follows: Establish the longitudinal, lateral, yaw, and roll dynamics and kinematics models of the vehicle: Among them, v x is the lateral velocity of the vehicle, v y is the vehicle longitudinal velocity, is the vehicle yaw angle, φ is the vehicle roll angle, θ is the road longitudinal slope angle, φ t is the transverse slope angle of the road, F xf 、F yf 、F xr and F yr are the components of the front and rear wheels on the x-axis and y-axis of the vehicle coordinate system respectively; Select the state quantity as The control quantity is u=δ f , the model is linearized and discretized into a state space expression, and we get: ξ(k+1)=A k ξ(k)+B k u(k) η(k)=Cξ(k); The speed planning model is: Where a(k) is the vehicle acceleration, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration. The sideslip angle of the center of mass is: The dynamic constraints during vehicle driving are established to limit the driving acceleration and speed of the pure electric unmanned mining dump truck. The constraints set are: ξ(k+1)=A k ξ(k)+B k u(k) S min ≤S(k+1)≤S max LTR(k+1)≤LTRd β min ≤β≤β max v 2 (k)≤2a min |d(k)-d st |; The lateral load transfer rate of the vehicle is: The speed planning performance indicators considering battery life are: Among them, M1 and M2 are constants; Q loss (t) is the total capacity decay rate of the battery, which is a constant at the initial moment; n is the motor speed; T is the motor torque; B is the exponential factor; z is the power exponential factor; R g is the gas constant; E a is the material activation energy; D is the battery temperature; S0 is the rated capacity of the single battery; The performance indicators considering battery energy consumption are: The real-time speed planning problem of the pure electric unmanned mining dump truck is expressed as: min J=J1+J2 stξ(k+1)=A k ξ(k)+B k u(k) S min ≤S(k+1)≤S max |LTR(k+1)|≤LTR max β min ≤β≤β max v 2 (k)≤2|a|(d(k)-d st ); The relationship between motor speed and vehicle speed is: The motor torque is: Where β is the sideslip angle of the center of mass, ξ(k+1) is the state space expression, LTR is the lateral load transfer rate of the vehicle, n is the motor speed, T is the motor torque, θ is the longitudinal slope angle of the road, φ t is the transverse slope angle of the road, n(k) is the state space expression parameter, v(k) is the vehicle speed, d(k) is the vehicle distance traveled, and j(k) is the rate of change of the vehicle acceleration; The dynamic and kinematic parameters of the pure electric unmanned mining dump truck during driving include: road adhesion coefficient, center of mass sideslip angle, tire force, vehicle mass, road slope, obstacle information, and vehicle coordinate information; The road adhesion coefficient estimates the longitudinal slip rate and tire side slip angle of the wheel by detecting the tire motion state, including wheel speed and steering angle, and then estimates the road peak adhesion coefficient through a tire model or a slip characteristic curve; The center of mass sideslip angle is obtained by using the method of GPS and INS information fusion estimation. The longitudinal and lateral vehicle speed and yaw angle signals measured by NS are first compared with the true values ​​to obtain the noise and deviation during long-term integration. Then, the GPS and INS information are fused, and a Kalman filter is established according to the vehicle dynamics model to estimate the center of mass sideslip angle. When the vehicle mass and road slope are obtained, the acceleration signal is collected by GPS and INS and based on the vehicle longitudinal dynamics and kinematics model, the data is fitted to obtain the mass and ramp angle, and the mass and ramp angle are estimated using the steady-state Kalman filter method; When obtaining various parameters of the power battery of the pure electric unmanned mining dump truck, a second-order RC equivalent circuit model or an electrochemical model of the battery is first established, and then a statistical filtering algorithm is introduced to achieve optimal estimation of the model parameters, and characteristic parameters with trend are extracted therefrom to achieve the characterization of performance degradation, thereby achieving battery health status estimation; When the speed curve is obtained, according to the established vehicle dynamics model, constraints and performance indicators, sequential quadratic programming, dynamic programming, genetic algorithm and intelligent algorithm are used to solve the speed curve.

2. The trajectory planning method for a pure electric unmanned mining dump truck according to claim 1, characterized in that: The pure electric unmanned mining dump truck comprises: A communication module, which is used to remotely interact with the pure electric unmanned mining dump truck and issue commands; On-board GPS, which provides three-dimensional vehicle speed and heading angle information of a pure electric unmanned mining dump truck; An inertial navigation system, wherein the inertial navigation system provides yaw rate and longitudinal and lateral acceleration information; A wheel speed sensor, wherein the wheel speed sensor provides tire rotation speed information; A trajectory planning module, which receives vehicle status information sent back by various sensors of the pure electric mining dump truck and plans a speed curve in real time; The battery management system monitors the battery status in real time and sends the voltage, current and temperature information of the battery pack to the trajectory planning module via the CAN bus.

Images