Methods and control systems for suppressing high-speed airborne movement of electric vehicles in low-gravity environments

Abstract

This invention discloses a method and control system for suppressing high-speed airborne movement of electric vehicles in low-gravity environments, relating to the field of motion control technology for satellite surface exploration vehicles. The method involves collecting terrain data in front of the electric vehicle and extracting terrain parameters. These terrain parameters are input into a low-gravity-corrected two-degree-of-freedom vertical motion model to obtain the normal force on each wheel of the electric vehicle. Based on the normal force on each wheel and the real-time collected actual vehicle speed, an airborne risk prediction is performed to determine whether an airborne warning should be triggered. When triggered, real-time vehicle attitude data is collected, and an airborne suppression strategy is triggered based on the actual vehicle speed, terrain parameters, and frontal terrain data to adjust the vehicle speed and motor output torque, thereby suppressing airborne movement. This invention is applicable to the high-speed stability control of wheeled electric vehicles in complex terrain under low-gravity conditions.

Description

Technical Field

[0001] This invention relates to the field of motion control technology for star surface exploration vehicles, and more specifically to a method and control system for suppressing high-speed airborne movement of electric vehicles in low-gravity environments. Background Technology

[0002] Existing wheeled electric vehicles suitable for low-gravity environments are all low-speed patrol vehicles with speeds not exceeding 400 m / h. Their movement can be approximated as quasi-static, without involving air-lift suppression issues. However, as the speed of wheeled electric vehicles adapted to low-gravity environments increases (reaching over 10 km / h), high-speed stable operation on rugged terrain becomes crucial. In low-gravity fields, rugged terrain significantly impacts vehicle stability, especially the air-lift phenomenon caused by vertical impacts, which greatly reduces vehicle safety. Current research in this area is limited, and methods for suppressing vehicle air-lift often rely on the second-order spring oscillator model widely used in traditional ground vehicles. However, this approach has the following problems:

[0003] (1) Insufficient adaptability to low gravity environment; the existing model does not consider the segmented influence characteristics of low gravity field conditions on the normal load of the wheel, which makes it impossible for vibration analysis to break through the critical stress state, resulting in a certain model mismatch.

[0004] (2) Lack of terrain prediction capability; Traditional methods rely on suspension feedback signals for passive adjustment, which cannot detect the terrain undulation characteristics within 5-10m ahead in advance, resulting in response delay.

[0005] (3) The control dimension is singular; it only suppresses airborne movement by adjusting the suspension damping, without combining the vehicle speed-terrain matching strategy for active prevention.

[0006] Therefore, how to effectively suppress the high-speed airborne movement of electric vehicles in low-gravity environments is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of the above problems, the present invention is proposed to provide a method and control system for suppressing high-speed takeoff of electric vehicles in low-gravity environments to overcome or at least partially solve the above problems, which can effectively suppress high-speed takeoff of electric vehicles in low-gravity environments and improve vehicle stability.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, embodiments of the present invention provide a method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment, comprising the following steps:

[0010] S1: Collect terrain data in front of the electric vehicle and extract terrain parameters;

[0011] S2: Input the terrain parameters into the two-degree-of-freedom vertical motion model after low gravity correction to obtain the normal force of each wheel of the electric vehicle. Based on the normal force of each wheel and the real-time collected actual speed of the electric vehicle, generate a risk prediction result for the airborne risk and determine whether an airborne warning is triggered. If an airborne warning is triggered, proceed to S3; otherwise, drive normally and proceed to S1.

[0012] S3: Real-time acquisition of vehicle attitude data of electric vehicles, combined with actual vehicle speed, front terrain data and terrain parameters to trigger airborne suppression strategy, adjust the vehicle speed and motor output torque of electric vehicles to suppress high-speed airborne movement of electric vehicles.

[0013] Preferably, in S1, a time-of-flight camera is used to scan the terrain ahead at a set frequency to obtain terrain data, and point cloud data is generated based on the terrain data. After the point cloud data is registered, feature matching is performed with a prior database to extract terrain parameters. The terrain parameters include: slope, curvature, and ground adhesion coefficient.

[0014] Preferably, in S2, the two-degree-of-freedom vertical motion model after low-gravity correction is expressed as:

[0015] when At that time, there were:

[0016] ;

[0017] in, Indicates the switching threshold distance; Indicates the mass of the wheel; Indicates the vehicle's mass; as well as These represent the vertical displacement, vertical velocity, and vertical acceleration of the wheel, respectively. , as well as These represent the vertical displacement of the vehicle body, the vertical velocity of the vehicle body, and the vertical acceleration of the vehicle body, respectively. Indicates the wheel reference displacement; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient;

[0018] when At that time, there were:

[0019] ;

[0020] The formula for calculating the normal force of each wheel of an electric vehicle is as follows:

[0021] ;

[0022] in, Indicates the first The normal force of each wheel; This represents the acceleration due to gravity in a low-gravity environment. Indicates the total mass of the electric vehicle; This represents the overall stiffness coefficient of an electric vehicle, which is related to the wheel stiffness coefficient and the vehicle body suspension stiffness coefficient. This represents the overall damping coefficient of an electric vehicle, which is related to the suspension damping coefficient. and The first Vertical displacement and vertical velocity of each wheel; This represents the change in ground elevation caused by topographic relief, calculated from the slope parameter in the topographic data. ; This indicates the vertical velocity of the ground relative to the wheels when an electric vehicle is in motion.

[0023] Preferably, in S2, the airborne risk prediction includes: when the normal force of all wheels is greater than or equal to a set threshold, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state, returning to S1 for continuous monitoring; when the normal force of any wheel is lower than the set threshold, a risk prediction result is generated based on the low-gravity corrected two-degree-of-freedom vertical motion model, and a decision is made on whether to trigger an airborne warning based on the risk prediction result; the risk prediction result includes the airborne moment, ground clearance height, and duration. If the ground clearance height at the airborne moment exceeds a preset height threshold, and the duration exceeds a preset time limit threshold, an airborne warning is triggered; otherwise, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state, returning to S1 for continuous monitoring; the specific process for calculating the airborne moment, ground clearance height, and duration is as follows:

[0024] S21: The two-degree-of-freedom vertical motion model after low-gravity correction is transformed into a state-space equation. as follows:

[0025] ;

[0026] ;

[0027] ;

[0028] in, Represents state variables, ; Indicates control variables, ; Represents the coefficient matrix of state variables; Represents the coefficient matrix of the control variables; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient; Indicates the mass of the wheel; Indicates the vehicle's mass;

[0029] S22: State-space equations Discretize;

[0030] Set the distance from the walk to be The backward Euler method is used to discretize the coefficient matrix of the state variables and the coefficient matrix of the control variables;

[0031] make: to replace ;

[0032] in, and They represent the first Step and the first The state variables of the step; Indicates the first The control variables of the step;

[0033] The following discrete recurrence relation is obtained, and the discretized state-space equation is expressed as:

[0034] ;

[0035] ;

[0036] ;

[0037] in, and They represent the first Step and the first Discrete state variables of the step; and These represent the discrete state variable coefficient matrix and the discrete control variable coefficient matrix, respectively. This represents the control variable at step k; Represents the identity matrix. Represents a variable structure matrix;

[0038] when Sometimes, ;

[0039] when Sometimes, ;

[0040] S23: Calculate the takeoff time, ground clearance, and duration based on the discretized state-space equation; calculate the longitudinal reference speed based on the actual vehicle speed; iteratively update the discrete state variables of the discretized state-space equation using the longitudinal reference speed; generate the corresponding ground clearance reference value; take the moment when the ground clearance reference value is greater than 0 as the takeoff time; take the maximum value of the ground clearance reference value as the ground clearance; and take the time interval when the ground clearance reference value changes from greater than 0 to less than or equal to 0 as the duration.

[0041] Preferably, the specific implementation process of S3 is as follows:

[0042] S31: Real-time acquisition of vehicle attitude data and actual vehicle speed;

[0043] S32: Determine the road conditions ahead based on the terrain data ahead;

[0044] S33: Calculate the maximum safe speed based on terrain parameters;

[0045] S34: Generate a takeoff suppression strategy based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed.

[0046] Preferably, the vehicle attitude data includes pitch angle and roll angle. When the pitch angle or roll angle is greater than a set angle threshold, the secondary response control command in the takeoff suppression strategy is triggered.

[0047] Preferably, the peak value of the road within the field of view is determined by the changes in terrain undulation based on the terrain data ahead. The specific process for determining the road conditions ahead based on the peak value of the road within the field of view is as follows:

[0048] If the number of road peaks is 1, then record the distance from the current position of the electric vehicle to the peak point where the highest point of the road peaks represented by the terrain undulations within the field of view is located. If the number of road peaks is 2, then record the distance between the two road peaks. , which is the distance between the peak points of two road peaks; if the number of road peaks is n and n is greater than 2, then record . Average distance between the peak points of each road peak The recorded distance is used as the feature distance parameter. Average distance It is expressed as follows:

[0049] ;

[0050] in, Indicates the first From the first peak point to the second The distance between the peak points;

[0051] Compare feature distance parameters Vehicle wheelbase compared to electric vehicles The relative size between them; if If the signal is positive, it is determined to be a long-wave road condition; otherwise, it is determined to be a short-wave road condition.

[0052] The preferred formula for calculating the maximum safe speed is as follows:

[0053] ;

[0054] in, Indicates the maximum safe speed. Indicates the ground adhesion coefficient. This represents the approximate radius of curvature of the long-wave path. This indicates the height of the vehicle's center of gravity. The radius of curvature is obtained by calculating the reciprocal of the curvature.

[0055] Preferably, the takeoff suppression strategy in S3 includes:

[0056] When the road conditions ahead are determined to be long-wave conditions, a speed control command is generated based on the maximum safe speed to keep the actual speed below the maximum safe speed.

[0057] When the road conditions ahead are determined to be shortwave conditions, the vehicle's motor response or graded trigger response is initiated based on the maximum safe speed, generating motor output torque control commands or graded response control commands; the specific process is as follows:

[0058] When the actual vehicle speed is lower than the maximum safe vehicle speed, at the moment the air-to-ground warning is triggered, a motor output torque control command is generated based on the motor output torque at the moment the air-to-ground warning is triggered, so that the motor output torque at the moment the air-to-ground warning is triggered is not greater than the maximum motor output torque, thereby suppressing the motor output torque;

[0059] When the actual vehicle speed is higher than or equal to the maximum safe vehicle speed, the graded trigger response is as follows:

[0060] Level 1 response control command: Reduce motor output torque and collect actual vehicle speed within a first set time. When the actual vehicle speed drops below the maximum safe speed, maintain the current motor output torque. When the actual vehicle speed does not drop below the maximum safe speed, activate Level 2 response.

[0061] Level 2 response control command: Directly reduce vehicle speed and collect actual vehicle speed within a second set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque and vehicle speed. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 3 response.

[0062] Level 3 response control command: Control the motor to brake and stop, and send alarm information.

[0063] Secondly, embodiments of the present invention provide a high-speed airborne suppression control system for electric vehicles in low-gravity environments, comprising:

[0064] The data acquisition module collects terrain data in front of the electric vehicle;

[0065] The terrain prediction module extracts terrain parameters based on the terrain data ahead.

[0066] The takeoff prediction module deploys a two-degree-of-freedom vertical motion model after low gravity correction, calculates the normal force of each wheel of the electric vehicle based on terrain parameters, and performs takeoff risk prediction based on the normal force to obtain the risk prediction result, determines whether a takeoff warning is triggered, and starts the two-stage controller if a takeoff warning is triggered.

[0067] Inertial measurement unit (IMU) is used to collect vehicle attitude data and actual vehicle speed.

[0068] The two-level controller triggers the air-launch suppression strategy based on vehicle attitude data, actual vehicle speed, and terrain data ahead.

[0069] The vehicle drive motor controller regulates the output torque and vehicle speed of the vehicle drive motor according to the air-to-ground suppression strategy.

[0070] Preferably, it also includes a maximum safe speed calculation module to calculate the maximum safe speed based on terrain parameters.

[0071] Preferably, it also includes a priori database for storing typical terrain feature parameters to assist the terrain prediction module in extracting terrain parameters and the dual-level controller in identifying terrain features.

[0072] Preferably, the two-level controller includes a terrain assessment calculation controller and a vehicle motion planning controller; the terrain assessment calculation controller is used to identify terrain features based on the typical terrain feature parameters stored in the prior database using the terrain data ahead, to determine the road conditions ahead, and to obtain the road conditions; the vehicle motion planning controller triggers an airborne suppression strategy based on the maximum safe vehicle speed, combined with road conditions, vehicle attitude data and actual vehicle speed.

[0073] Preferably, the terrain prediction module generates point cloud data based on the terrain data ahead, registers the point cloud data and performs feature matching with the prior database to extract terrain parameters, including: slope, curvature and ground adhesion coefficient.

[0074] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and control system for suppressing high-speed airborne movement of electric vehicles in low-gravity environments, which has the following advantages:

[0075] (1) Based on the actual low gravity environment, a two-degree-of-freedom vertical motion model after low gravity correction was adopted to accurately estimate the normal force, which is applicable to low gravity environments such as the Moon and Mars.

[0076] (2) By collecting terrain data in front of the electric vehicle, the response speed is improved by actively adjusting the response.

[0077] (3) The vehicle speed and motor output torque were adjusted based on terrain data, air-to-ground risk prediction results and vehicle attitude data, which improved the control dimension and implemented proactive prevention strategies. Attached Figure Description

[0078] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0079] Figure 1 This is a flowchart of a method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment, provided in an embodiment of the present invention.

[0080] Figure 2 This is a diagram of the takeoff suppression response process provided in an embodiment of the present invention;

[0081] Figure 3 This is a block diagram of a high-speed airborne suppression control system for electric vehicles in a low-gravity environment provided in an embodiment of the present invention.

[0082] Figure 4 This is a long-wave road condition characteristic curve provided in an embodiment of the present invention;

[0083] Figure 5 The following diagrams are provided in the embodiments of the present invention: (a) is a diagram of wheel dynamic load under Earth's gravity; (b) is a diagram of suspension dynamic travel under Earth's gravity; and (c) is a diagram of wheel ground clearance under Earth's gravity.

[0084] Figure 6 The following diagrams are provided in the embodiments of the present invention: (a) is a diagram of wheel dynamic load under low gravity conditions; (b) is a diagram of suspension dynamic travel under low gravity conditions; and (c) is a diagram of wheel ground clearance under low gravity conditions.

[0085] Figure 7The following are motion state curves of an uncontrolled vehicle provided in this embodiment of the invention: (a) is a wheel dynamic load curve of an uncontrolled vehicle; (b) is a suspension dynamic travel curve of an uncontrolled vehicle; and (c) is a wheel ground clearance curve of an uncontrolled vehicle.

[0086] Figure 8 The following are motion state curves of a vehicle with a control system provided in the embodiments of the present invention: (a) is a wheel dynamic load curve of a vehicle with a control system; (b) is a suspension dynamic travel curve of a vehicle with a control system; and (c) is a wheel ground clearance curve of a vehicle with a control system.

[0087] Figure 9 This is a time-of-flight curve provided in an embodiment of the present invention;

[0088] Figure 10 This is a bar chart showing the optimized hang time ratio provided in this embodiment of the invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] This invention discloses a method for suppressing high-speed airborne movement of electric vehicles in low-gravity environments, such as... Figure 1 As shown, the specific steps are as follows:

[0091] S1: Collect terrain data in front of the electric vehicle and extract terrain parameters;

[0092] S2: Input the terrain parameters into the two-degree-of-freedom vertical motion model after low gravity correction to obtain the normal force of each wheel of the electric vehicle. Based on the normal force of each wheel and the real-time collected actual speed of the electric vehicle, perform airborne risk prediction to generate risk prediction results, determine whether airborne warning is triggered, if airborne warning is triggered, proceed to S3, otherwise drive normally and proceed to S1.

[0093] S3: Real-time acquisition of vehicle attitude data of electric vehicles, combined with actual vehicle speed, front terrain data and terrain parameters to trigger airborne suppression strategy, adjust the vehicle speed and motor output torque of electric vehicles to suppress high-speed airborne movement of electric vehicles.

[0094] In one specific embodiment, in S1, a time-of-flight camera is used to scan the terrain ahead at a set frequency to obtain terrain data ahead, and point cloud data is generated based on the scanned terrain data ahead. After the point cloud data is registered, feature matching is performed with a prior database to extract terrain parameters, including slope, curvature and ground adhesion coefficient.

[0095] In one specific embodiment, the two-degree-of-freedom vertical motion model after low gravity correction in S2 is as follows:

[0096] when Sometimes:

[0097] ;

[0098] in, Indicates the switching threshold distance. Indicates the mass of the wheel. Indicates vehicle body mass. as well as These represent the vertical displacement, vertical velocity, and vertical acceleration of the wheel, respectively. , as well as These represent the vertical displacement of the vehicle body, the vertical velocity of the vehicle body, and the vertical acceleration of the vehicle body, respectively. Indicates the wheel reference displacement; Indicates the wheel stiffness coefficient. This indicates the vehicle suspension stiffness coefficient. Indicates the suspension damping coefficient;

[0099] when Sometimes:

[0100] ;

[0101] The formula for calculating the normal force of each wheel of an electric vehicle is as follows:

[0102] ;

[0103] in, Indicates the first The normal force of each wheel This represents the acceleration due to gravity in a low-gravity environment. This indicates the total mass of the electric vehicle. This represents the overall stiffness coefficient of an electric vehicle, which is related to the wheel stiffness coefficient and the vehicle body suspension stiffness coefficient. This represents the overall damping coefficient of an electric vehicle, which is related to the suspension damping coefficient. and They represent the first Vertical displacement and vertical velocity of each wheel, This represents the change in ground elevation caused by terrain undulations, calculated based on point cloud data and slope. , This indicates the vertical velocity of the ground relative to the wheels when an electric vehicle is in motion.

[0104] Furthermore, the process of predicting the risk of airborne injury based on the normal force of each wheel is as follows:

[0105] When the normal force of all wheels is greater than or equal to the set threshold, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state. The system returns to S1 for continuous monitoring. When the normal force of any wheel is lower than the set threshold, a risk prediction result is generated based on the low-gravity corrected two-degree-of-freedom vertical motion model. The system then determines whether to trigger an airborne warning based on the risk prediction result. The risk prediction result includes the airborne moment, ground clearance, and duration. If the ground clearance at the airborne moment exceeds a preset height threshold and the duration exceeds a preset time limit threshold, an airborne warning is triggered; otherwise, no airborne warning is triggered, and the electric vehicle is in a reasonable driving state. The system returns to S1 for continuous monitoring. The calculation methods for the airborne moment, ground clearance, and duration are as follows:

[0106] S21: Based on the two-degree-of-freedom vertical motion model modified for low gravity, it is transformed into a state-space equation. The state-space equations are as follows:

[0107] ;

[0108] Wherein, state variables represent Control variables represent ;

[0109] State variable coefficient matrix and control variable coefficient matrix The expressions are as follows:

[0110] ;

[0111] ;

[0112] S22: After selecting the discretization step size, the above formula is discretized using the backward Euler method, i.e., using... to replace ;

[0113] The following discrete recurrence relation is obtained:

[0114] ;

[0115] in, ; ;

[0116] It is a variable structure matrix; defining the matrix for:

[0117] ;

[0118] when Sometimes, ,when Sometimes, ;

[0119] S23: At the time of takeoff warning, the state variables are calculated through the above discrete recursive relationship. The longitudinal reference speed is calculated based on the actual vehicle speed. The discrete state variables of the discretized state space equation are updated iteratively using the longitudinal reference speed, and the corresponding ground reference value is generated. The moment when the ground reference value is greater than 0 is taken as the takeoff moment. The maximum value of the ground reference value is taken as the ground height. The time interval when the ground reference value changes from greater than 0 to less than or equal to 0 is taken as the duration.

[0120] The calculation process is as follows:

[0121] S231: Let the number of iterations be... =1, record the longitudinal reference speed at the current moment based on the actual vehicle speed. Record the current discrete state variables as Define the first flag bit. Second tag bit ;Calculate the longitudinal reference speed based on the actual vehicle speed using existing estimation algorithms;

[0122] S232: Based on the distance from the walk length and the current longitudinal reference speed Calculate the position at the next moment The calculation formula is as follows:

[0123] ;

[0124] The position at the next moment Represents the distance ahead relative to the current electric vehicle;

[0125] S233: Calculate terrain elevation-distance data from point cloud data generated based on the terrain data ahead, and determine the position at the next moment based on the terrain elevation-distance data. The elevation value at the next moment is obtained by linear interpolation using the independent variable. And set the control variable at step k to the elevation value at the next moment. Based on the point cloud data in S1, terrain elevation-distance data are obtained by considering only the longitudinal undulation state.

[0126] S234: Based on the control variables of step k And the discrete state variables from the previous step Update the discrete state variables at step k. And calculate the ground reference value at step k. , represented as:

[0127] ;

[0128] Let C represent the ground reference value at step k; C represents the row vector, C=[1,0,0,0]; X represents the discrete state variable.

[0129] S235: Update ,renew ;

[0130] S236: If ,determination Check if the condition is true or false. If not, return to S232; if true, record the condition. And modify the flag bit ;

[0131] S237: If ,determination Check if the condition is true or false. If not, return to S232; if true, record the condition. And modify the flag bit ;

[0132] S238: The Moment of Takeoff Height above ground Duration This constitutes the risk prediction results.

[0133] In one specific embodiment, the specific implementation process of S3 is as follows:

[0134] S31: Real-time acquisition of vehicle attitude data and actual vehicle speed;

[0135] S32: Determine the road conditions ahead based on the terrain data ahead;

[0136] S33: Calculate the maximum safe speed based on terrain parameters;

[0137] S34: Generate a takeoff suppression strategy based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed.

[0138] Furthermore, such as Figure 2As shown, the system determines the peak value of the road within the field of view based on the terrain data ahead and the changes in terrain undulation. Road conditions are then assessed based on the peak value of the road within the visible area. If the peak value is 1, the system records the distance from the current position of the electric vehicle to the highest point of the road peak value represented by the terrain undulation within the field of view. If the number of road peaks is 2, then record the distance between the two road peaks. , which is the distance between the peak points of two road peaks; if the number of road peaks is n and n is greater than 2, then record . Average distance between the peak points of each road peak The recorded distance is used as the feature distance parameter. Average distance It is expressed as follows:

[0139] ;

[0140] in, Indicates the first From the first peak point to the second The distance between the peak points;

[0141] Compare feature distance parameters Vehicle wheelbase compared to electric vehicles Relative size;

[0142] if If the signal is positive, it is determined to be a long-wave road condition; otherwise, it is determined to be a short-wave road condition.

[0143] Furthermore, vehicle attitude data is collected through an inertial measurement unit, including pitch and roll angles. When the pitch or roll angle exceeds a set angle threshold, a secondary response control command in the takeoff suppression strategy is triggered.

[0144] Furthermore, the formula for calculating the maximum safe speed is as follows:

[0145] ;

[0146] in, Indicates the maximum safe speed. Indicates surface roughness. This represents the radius of curvature of the long-wave road condition calculated based on the curvature. This indicates the height of the vehicle's center of gravity.

[0147] Furthermore, a takeoff suppression strategy is generated based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed. This strategy includes:

[0148] When the road conditions ahead are determined to be long-wave conditions, keep the actual speed below the maximum safe speed.

[0149] When the road conditions ahead are determined to be shortwave conditions, the vehicle's motor response or graded trigger response is initiated based on the maximum safe speed, generating motor output torque control commands or graded response control commands; the specific process is as follows:

[0150] When the actual vehicle speed is lower than the maximum safe vehicle speed, the motor output torque is suppressed at the moment the air-to-ground warning is triggered, so that the motor output torque at the moment the air-to-ground warning is triggered is not greater than the maximum motor output torque.

[0151] When the actual vehicle speed is higher than or equal to the maximum safe vehicle speed, the graded trigger response is as follows:

[0152] Level 1 response control command: Reduce motor output torque by more than 30%, and collect actual vehicle speed within a first set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 2 response.

[0153] Level 2 response control command: Directly reduce vehicle speed and collect actual vehicle speed within a second set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque and vehicle speed. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 3 response.

[0154] Level 3 response control command: Control the motor to brake and stop, and send alarm information.

[0155] In this embodiment, active deceleration is used instead of emergency braking, which reduces system power consumption by 40%.

[0156] On the other hand, such as Figure 3 As shown, the control system based on the above-mentioned method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment includes:

[0157] The data acquisition module collects terrain data in front of the electric vehicle;

[0158] The terrain prediction module transforms and extracts terrain parameters based on the terrain data ahead;

[0159] The takeoff prediction module deploys a two-degree-of-freedom vertical motion model after low gravity correction, calculates the normal force of each wheel of the electric vehicle based on terrain parameters, and performs takeoff risk prediction based on the normal force to obtain the risk prediction result, determines whether a takeoff warning is triggered, and starts the two-stage controller if a takeoff warning is triggered.

[0160] Inertial measurement unit (IMU) is used to collect vehicle attitude data and actual vehicle speed.

[0161] The two-level controller triggers the air-launch suppression strategy based on vehicle attitude data, actual vehicle speed, terrain parameters, and terrain data ahead.

[0162] The vehicle drive motor controller regulates the output torque and vehicle speed of the vehicle drive motor according to the air-to-ground suppression strategy.

[0163] Furthermore, it also includes a maximum safe speed calculation module, which calculates the maximum safe speed based on terrain parameters.

[0164] Furthermore, a priori database is used to store typical terrain feature parameters to assist the terrain prediction module in extracting terrain parameters and the two-stage controller in identifying terrain features; typical terrain feature parameters include friction coefficient coverage. Slope Coverage wait.

[0165] Furthermore, the two-level controller includes a terrain assessment calculation controller and a vehicle motion planning controller; the terrain assessment calculation controller is used to identify terrain features based on the typical terrain feature parameters stored in the prior database using the terrain data ahead, to determine the road conditions ahead, and to obtain the road conditions; the vehicle motion planning controller generates an airborne suppression strategy based on the maximum safe speed, road conditions, vehicle attitude data, and actual vehicle speed.

[0166] Furthermore, the terrain prediction module generates point cloud data based on the terrain data ahead, registers the point cloud data and performs feature matching with the prior database to extract terrain parameters, including slope, curvature and ground adhesion coefficient.

[0167] Furthermore, the data acquisition module employs a time-of-flight camera to scan the terrain ahead and acquire terrain point cloud data; A wide-angle TOF camera (1024×768 resolution, 30Hz frame rate) acquires elevation data within 15m in front of the camera in real time.

[0168] On the other hand, to verify the effectiveness of the technical solution of this invention, system function testing and verification based on hardware-in-the-loop simulation were conducted. The test scenario was implemented using a high-fidelity digital terrain device, and the relevant controllers used prototypes with the same computing power. Long-wave paths at different altitudes were selected for testing based on terrain features, as shown in the following description. Figure 4 As shown. To verify the differentiated effects of low gravity and ground gravity on vehicle airborne characteristics, impact tests were conducted under both conventional and low gravity conditions. Significant differences were observed, such as... Figure 5 and Figure 6 As shown, the horizontal axis represents time; Figure 5 Figure (a) shows the dynamic load curve of the wheel under Earth's gravity conditions. Figure 6 (a) is a curve of wheel dynamic load under low gravity conditions, where the vertical axis represents wheel dynamic load; Figure 5 Figure (b) shows the suspension dynamic travel curve under Earth's gravity conditions. Figure 6(b) is a graph of the suspension dynamic travel under low gravity conditions, where the vertical axis represents the suspension dynamic travel; Figure 5 (c) is a graph showing the wheel's height above the ground under Earth's gravity. Figure 6 (c) is a graph showing the wheel height above the ground under low gravity conditions, where the vertical axis represents the wheel height above the ground. Figure 5 and Figure 6 This demonstrates the rationality of the two-degree-of-freedom model with modification terms in this invention.

[0169] like Figure 7 and Figure 8 As shown, the horizontal axis represents time; Figure 7 (a) is a graph showing the wheel dynamic load curves of a vehicle without a control system. Figure 8 (a) is a graph of wheel dynamic loads for a vehicle with a control system, where the vertical axis represents the wheel dynamic load. Figure 7 (b) is a diagram showing the suspension travel curve of a vehicle without a control system. Figure 8 (b) is a graph of the suspension travel curve of a vehicle with a control system, where the vertical axis represents the suspension travel. Figure 7 (c) is a graph showing the wheel height from the ground for a vehicle without a control system. Figure 8 (c) is a graph showing the wheel height from the ground of a vehicle with a control system, where the vertical axis represents the wheel height from the ground. Figure 7 and Figure 8 The results demonstrate that vehicles equipped with the control system of this invention can effectively improve their motion characteristics on rough terrain compared to vehicles without a control system, effectively reduce the maximum wheel ground clearance and hang time, and reduce suspension travel, thereby reducing mechanical shock. Key performance indicators comparing vehicles with and without a control system are shown in Table 1.

[0170] Table 1 Comparison of Key Indicators with and without Control Systems (Table 1)

[0171]

[0172] Statistical results show that the optimization efficiency for hovering time is highest at a vehicle speed of 3 m / s. As the vehicle speed increases, the optimization ratio gradually decreases and stabilizes above 40%. Figure 9 and Figure 10 As shown, Figure 9 The horizontal axis represents vehicle speed, and the vertical axis represents time spent in the air. Figure 10 The horizontal axis represents vehicle speed, and the vertical axis represents the optimization ratio of air time, indicating that the technical solution of the present invention can effectively reduce the wheel lift-off time, which plays an important role in the high-speed, stable and safe driving of vehicles.

[0173] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0174] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment, characterized in that, Includes the following steps: S1: Collect terrain data in front of the electric vehicle and extract terrain parameters; S2: Input the terrain parameters into the two-degree-of-freedom vertical motion model after low gravity correction to obtain the normal force of each wheel of the electric vehicle. Based on the normal force of each wheel and the real-time collected actual speed of the electric vehicle, generate a risk prediction result for the airborne risk and determine whether an airborne warning is triggered. If an airborne warning is triggered, proceed to S3; otherwise, drive normally and proceed to S1. S3: Real-time acquisition of vehicle posture data of electric vehicles, combined with actual vehicle speed, front terrain data and terrain parameters to trigger airborne suppression strategy, and adjust the vehicle speed and motor output torque of electric vehicles. In S2, the two-degree-of-freedom vertical motion model after low-gravity correction is expressed as: when At that time, there were: ； in, Indicates the switching threshold distance; Indicates the mass of the wheel; Indicates the vehicle's mass; These represent the vertical displacement, vertical velocity, and vertical acceleration of the wheel, respectively. , as well as These represent the vertical displacement of the vehicle body, the vertical velocity of the vehicle body, and the vertical acceleration of the vehicle body, respectively. Indicates the reference displacement of the wheel; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient; when At that time, there were: ； The normal force of each wheel of an electric vehicle is expressed as: ； in, Indicates the first The normal force of each wheel; This represents the acceleration due to gravity in a low-gravity environment. Indicates the total mass of the electric vehicle; This represents the overall stiffness coefficient of an electric vehicle, which is related to the wheel stiffness coefficient and the vehicle body suspension stiffness coefficient. The overall damping coefficient of an electric vehicle is related to the suspension damping coefficient; and The first Vertical displacement and vertical velocity of each wheel; This represents the change in ground elevation caused by terrain undulations, calculated based on point cloud data and slope parameters from the terrain parameters. ; This indicates the vertical velocity of the ground relative to the wheels when an electric vehicle is in motion.

2. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 1, characterized in that, In S1, a time-of-flight camera is used to scan the terrain ahead at a set frequency to obtain terrain data, and point cloud data is generated based on the terrain data. After the point cloud data is registered, feature matching is performed with the prior database to extract terrain parameters. Terrain parameters include: slope, curvature, and ground adhesion coefficient.

3. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 1, characterized in that, In S2, when the normal force of all wheels is greater than or equal to a set threshold, no takeoff warning is triggered. When the normal force of any wheel is lower than the set threshold, a risk prediction result is generated based on the low-gravity corrected two-degree-of-freedom vertical motion model. The risk prediction result is used to determine whether to trigger a takeoff warning. The risk prediction result includes the takeoff time, ground clearance height, and duration. If the ground clearance height at the takeoff time exceeds a preset height threshold and the duration exceeds a preset time limit threshold, a takeoff warning is triggered; otherwise, no takeoff warning is triggered. The specific process for calculating the takeoff time, ground clearance height, and duration is as follows: S21: The two-degree-of-freedom vertical motion model after low-gravity correction is transformed into a state-space equation. as follows: ； ； ； in, Represents state variables; Indicates control variables, ; Represents the coefficient matrix of state variables; Represents the coefficient matrix of the control variables; Indicates the wheel stiffness coefficient; Indicates the stiffness coefficient of the vehicle body suspension; Indicates the suspension damping coefficient; Indicates the mass of the wheel; Indicates the vehicle's mass; S22: State-space equations Discretize; Set the distance from the walk to be The backward Euler method is used to discretize the coefficient matrices of the state variables and the coefficient matrices of the control variables. The discretized state-space equation is expressed as: ； ； ； in, and They represent the first Step and the first Discrete state variables of the step; and These represent the discrete state variable coefficient matrix and the discrete control variable coefficient matrix, respectively. This represents the control variable at step k; Represents the identity matrix. Represents a variable structure matrix; when Sometimes, ; when Sometimes, ; S23: Calculate the takeoff time, ground clearance, and duration based on the discretized state-space equation; calculate the longitudinal reference speed based on the actual vehicle speed; iteratively update the discrete state variables of the discretized state-space equation using the longitudinal reference speed; generate the corresponding ground clearance reference value; take the moment when the ground clearance reference value is greater than 0 as the takeoff time; take the maximum value of the ground clearance reference value as the ground clearance; and take the time interval when the ground clearance reference value changes from greater than 0 to less than or equal to 0 as the duration.

4. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 1, characterized in that, The specific implementation process of S3 is as follows: S31: Real-time acquisition of vehicle attitude data; S32: Determine the road conditions ahead based on the terrain data ahead; S33: Calculate the maximum safe speed based on terrain parameters; S34: Generate a takeoff suppression strategy based on road conditions, vehicle attitude data, actual vehicle speed, and maximum safe vehicle speed.

5. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 1, characterized in that, Vehicle attitude data includes pitch angle and roll angle. When the pitch angle or roll angle is greater than the set angle threshold, the takeoff suppression strategy is triggered.

6. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 4, characterized in that, Based on the terrain data ahead, the peak value of the road within the field of view is determined by the changes in terrain undulation. The specific process of judging the road conditions ahead based on the peak value of the road within the field of view is as follows: If the number of road peaks is 1, then record the distance from the current position of the electric vehicle to the peak point where the highest road peak is located. ; If the number of peaks is 2, then record the distance between the peak points of the two road peaks. If the number of peaks is n and n is greater than 2, then record... Average distance between the peak points of each road peak The recorded distance is used as the feature distance parameter. ; Compare feature distance parameters Vehicle wheelbase compared to electric vehicles The size, if If the signal is positive, it is determined to be a long-wave road condition; otherwise, it is determined to be a short-wave road condition.

7. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 4, characterized in that, The formula for calculating the maximum safe speed is as follows: ； in, Indicates the maximum safe speed. Indicates the ground adhesion coefficient. This represents the approximate radius of curvature of the long-wave path. This indicates the height of the vehicle's center of gravity.

8. The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in claim 6, characterized in that, The takeoff suppression strategies in S3 include: When the road conditions ahead are determined to be long-wave conditions, a speed control command is generated based on the maximum safe speed to keep the actual speed below the maximum safe speed. When the road conditions ahead are determined to be shortwave conditions, the vehicle's motor response or graded trigger response is initiated based on the maximum safe speed, generating motor output torque control commands or graded response control commands; the specific process is as follows: When the actual vehicle speed is lower than the maximum safe vehicle speed, at the moment the air-to-ground warning is triggered, a motor output torque control command is generated based on the motor output torque at the moment the air-to-ground warning is triggered, so that the motor output torque at the moment the air-to-ground warning is triggered is not greater than the maximum motor output torque, thereby suppressing the motor output torque; When the actual vehicle speed is higher than or equal to the maximum safe vehicle speed, the graded trigger response is as follows: Level 1 response control command: Reduce motor output torque and collect actual vehicle speed within a first set time. When the actual vehicle speed drops below the maximum safe speed, maintain the current motor output torque. When the actual vehicle speed does not drop below the maximum safe speed, activate Level 2 response. Level 2 response control command: Directly reduce vehicle speed and collect actual vehicle speed within a second set time. When the actual vehicle speed drops below the maximum safe vehicle speed, maintain the current motor output torque and vehicle speed. When the actual vehicle speed does not drop below the maximum safe vehicle speed, activate Level 3 response. Level 3 response control command: Control the motor to brake and stop, and send alarm information.

9. A high-speed airborne suppression control system for electric vehicles in low-gravity environments, characterized in that, The method for suppressing high-speed airborne movement of electric vehicles in a low-gravity environment as described in any one of claims 1-8 includes: The data acquisition module collects terrain data in front of the electric vehicle; The terrain prediction module extracts terrain parameters based on the terrain data ahead. The takeoff prediction module deploys a two-degree-of-freedom vertical motion model after low gravity correction, calculates the normal force of each wheel of the electric vehicle based on terrain parameters, and performs takeoff risk prediction based on the normal force to obtain the risk prediction result, determines whether a takeoff warning is triggered, and starts the two-stage controller if a takeoff warning is triggered. Inertial measurement unit (IMU) is used to collect vehicle attitude data and actual vehicle speed. The two-level controller triggers the air-launch suppression strategy based on vehicle attitude data, actual vehicle speed, terrain parameters, and terrain data ahead. The vehicle drive motor controller regulates the output torque and vehicle speed of the vehicle drive motor according to the air-to-ground suppression strategy.

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