Dynamic off-road path planning method for land surface state simulation

Abstract

The present application belongs to the technical field of cross-country path planning, and proposes a dynamic cross-country path planning method for land surface state simulation, comprising: S1, obtaining a cross-country map, parameters of a target vehicle, a starting point and an ending point, and current land surface water content, future precipitation data, air temperature data and radiation data; S2, determining future soil water content; S3, rasterizing the cross-country map, and identifying landform types, road surface types, soil types and slopes; S4, determining the size relationship between unit ground pressure and future soil bearing capacity; S5, dividing into a passable area R2 and an impassable area T2; S6-S9, determining air resistance, road surface resistance, horizontal subsidence resistance and slope resistance; S10, calculating the maximum driving speed of the vehicle in each grid; S11, generating a maximum driving speed matrix based on the maximum driving speed in the passable area R2 and the speed value in the impassable area T2; and S12, planning a cross-country path based on the maximum driving speed matrix and using an improved A* algorithm.

Description

Technical Field

[0001] The present application belongs to the technical field of off-road path planning, and in particular relates to a dynamic off-road path planning method for land surface state simulation. Background Art

[0002] Path planning is an important research direction in fields such as autonomous driving and robot navigation. It is a multidisciplinary research field that intersects operations research, computer science, geographic information science, and other disciplines. Its goal is to provide safe and convenient path selection for vehicles or equipment through path planning algorithms, taking into account complex and changing posture and environmental factors.

[0003] For off-road environments, the land surface state is affected by real-time meteorological and environmental factors and changes dynamically. How to consider the impact of dynamic land surface state changes on the target vehicle's travel speed and then plan the most time-efficient off-road route has become a concern for technicians. Summary of the Invention

[0004] This application provides a dynamic off-road path planning method for land surface state simulation, including:

[0005] S1. Obtain an off-road map of a target scene, parameters of a target vehicle, a starting point and an end point of the target vehicle, and dynamic environmental information corresponding to the off-road map, wherein the dynamic environmental information includes current surface moisture content, future precipitation data, temperature data, and radiation data;

[0006] S2. determining the future soil moisture content based on the dynamic environmental information;

[0007] S3, rasterizing the off-road map, and identifying the landform type, road surface type, soil type, and slope within each grid;

[0008] S4. Determine the future soil bearing capacity based on the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determine the unit ground pressure of the target vehicle based on the parameters of the target vehicle, and determine the relationship between the unit ground pressure and the future soil bearing capacity;

[0009] S5. Dividing the off-road map into a passable area R0 and an impassable area T0 according to the landform type;

[0010] Determine an infeasible area within the traversable area R0 according to the parameters of the target vehicle and the slope, and update the infeasible area T0 and the infeasible area T1 into the infeasible area T1, and update the remaining areas into the traversable area R1;

[0011] Determine the infeasible area within the traversable area R1 according to the relationship between the unit ground pressure and the future soil bearing capacity, and update the infeasible area T1 and the infeasible area T2 into the infeasible area T2, and update the rest into the traversable area R2;

[0012] S6. Obtaining air density and air resistance coefficient, and determining air resistance according to the parameters of the target vehicle, air density, and air resistance coefficient;

[0013] S7. Obtaining a rolling friction coefficient corresponding to a road surface type, and determining a road surface resistance based on the parameters of the target vehicle, the rolling friction coefficient, and the slope;

[0014] S8. Determine a tire / track sinking depth and a soil shear strength coefficient based on soil parameters corresponding to the soil type, parameters of the target vehicle, and the unit ground contact pressure; and determine a horizontal sinking resistance based on the target vehicle parameters, the tire / track sinking depth, and the soil shear strength coefficient.

[0015] S9. determining a slope resistance according to the parameters of the target vehicle and the slope;

[0016] S10. Within the traversable region R2, calculate the maximum speed v of the target vehicle within each grid, where the maximum speed v is the ratio of the target vehicle's preset power to the total resistance, where the total resistance is the sum of the air resistance, the road resistance, the horizontal subsidence resistance, and the slope resistance; set the speed value of the grid within the impassable region T2 to 0;

[0017] S11. Generate a maximum driving speed matrix based on the maximum driving speed v in each grid in the passable area R2 and the speed value of the grid in the impassable area T2;

[0018] S12. Based on the maximum driving speed matrix, use an improved A* algorithm to calculate the shortest time from the starting point to the end point, and record the corresponding path.

[0019] Furthermore, in step S2, determining the future soil moisture content includes the following steps:

[0020] S21. Calculate the evapotranspiration within each grid according to the formula:

[0021] ET=0.0023×(T mean +17.8)×(T max -T min ) 0.5 ×Ra

[0022] Where, ET is evapotranspiration, unit is mm / day;

[0023] Tmean is the daily average temperature in degrees Celsius;

[0024] T max The highest temperature of the day, T min The lowest temperature of the day, in degrees Celsius;

[0025] Ra is the solar radiation or the extra-atmospheric solar radiation, with the unit of MJ / m 2 / day;

[0026] S22. Calculate future soil moisture content S new , in accordance with the formula:

[0027] S new =S+R-ET

[0028] Where S represents the current surface water content;

[0029] R represents future precipitation data;

[0030] S23. Calculate future soil moisture θ new , in accordance with the formula:

[0031]

[0032] Among them, V w Indicates the volume of water in the soil;

[0033] V t represents the total volume of soil;

[0034] D is the surface soil depth;

[0035] is the total porosity of the soil.

[0036] Furthermore, the road surface type in step S3 includes good asphalt or concrete road surface, gravel road surface, pothole road surface, compacted dirt road, icy road surface, and compacted snow road;

[0037] Soil types include sand, clay, and loam.

[0038] Furthermore, in step S4, determining the future soil bearing capacity includes:

[0039] Future soil bearing capacity q u It conforms to the following formula:

[0040] q u =q dry (1-k·θ new )

[0041] Among them, q dry Indicates the bearing capacity of soil in dry state;

[0042] k is an empirical coefficient that indicates the degree to which the water content associated with the soil type affects the future soil bearing capacity;

[0043] θ new The future soil moisture content.

[0044] Furthermore, in step S4, determining the target vehicle unit ground contact pressure includes:

[0045]

[0046] σ represents the target vehicle's unit ground pressure, in kN / m 2 ;

[0047] m represents the weight of the target vehicle in kg; g is the acceleration due to gravity;

[0048] A: The contact area of ​​the target vehicle's tires / tracks, in m 2 .

[0049] Furthermore, in step S6, determining the air resistance includes:

[0050] Calculate the air resistance F air It conforms to the following formula:

[0051]

[0052] Where ρ is the air density; C d is the air resistance coefficient, which ranges from 0.28 to 0.4; S is the frontal area; and V is the relative speed between the vehicle and the air.

[0053] Furthermore, in step S7, determining the road resistance includes:

[0054] Road resistance F rolling It conforms to the following formula:

[0055] F rolling = μmgcos(α);

[0056] Where m is the vehicle mass, g is the acceleration due to gravity, α is the slope angle, and μ is the rolling friction coefficient corresponding to the road surface type.

[0057] Furthermore, in step S8, determining the sinking depth of the tire / track includes the following steps:

[0058] S81, the subsidence depth z conforms to the following formula:

[0059]

[0060] Where b represents the width of the tire / track of the target vehicle in meters;

[0061] k c is the cohesion modulus of soil, in units of kN / m n+1 ;

[0062] k θ is the internal friction coefficient of soil, in units of kN / m n+2 ;

[0063] σ is the unit ground pressure of the target vehicle, in units of kN / m 2 ;

[0064] n is the deformation index, taking a value of 0.7-1.2;

[0065] S82, determine the shear strength coefficient k of soil z complies with the following formula:

[0066] k z =c+σ·tan(φ);

[0067] c is the cohesion of soil; φ is the internal friction angle of soil;

[0068] S83, horizontal subsidence resistance F sinking complies with the following formula:

[0069] F sinking =k z ·A·z

[0070] wherein A is the ground area of the target vehicle.

[0071] Further, in step S9, the slope resistance is determined to comply with the following formula:

[0072] F slope =mgsin(α)

[0073] wherein F slope is the slope resistance, m is the weight of the target vehicle, g is the acceleration of gravity, and α is the slope angle.

[0074] Further, in step S12, the improved A* algorithm is used, comprising:

[0075] S121, according to the maximum travel speed matrix, calculate the total cost f(n) of the target vehicle at the current node n:

[0076] f(n)=g(n)+h(n);

[0077] wherein g(n) represents the time consumed by the target vehicle moving from the starting point to the current node n, complying with the formula:

[0078]

[0079] g(n) 父节点 represents the cumulative time from the starting point to the parent node of the current node n, S is the size of a single grid, V[i][j] is the maximum driving speed of the current node, obtained from the maximum driving speed matrix, and g(n) represents the cumulative time from the starting point to the current node;

[0080] h(n) is the estimated time from the current node to the end point, which conforms to the formula:

[0081]

[0082] Where D is the Euclidean distance from the current node to the end point, is the average speed of all non-zero speed nodes in the maximum driving speed matrix;

[0083] S122. Based on the total cost f(n) of the target vehicle at the current node n, plan the shortest time required for the target vehicle to travel from the starting point to the end point, and record the corresponding path.

[0084] Furthermore, in step S12, the shortest time required for the target vehicle to travel from the starting point to the end point is calculated, including:

[0085] S123. Read the off-road map, the dynamic environment information, and the parameters of the target vehicle, and convert the map into a grid, where each grid cell represents a node; establish an open table and a closed table for the grid nodes, wherein the open table is used to record nodes to be explored, and the nodes are sorted by the total cost f(n) value, with the nodes with the smallest f(n) value being expanded first; and the closed table is used to record nodes that have been explored to avoid repeated searches; initialize the closed table based on the impassable area T2, and the areas in the closed table are no longer added to the open table for search;

[0086] S124. Traverse the adjacent nodes of the current node to determine whether they belong to the traversable area R2; if so, and if they are not in the closed table, add the child node to the open table; calculate the values ​​of g(n), h(n), and f(n) in sequence. After processing the current node, add it to the closed table and mark it as an explored node;

[0087] S125. Search the grid with the smallest value of f(n) among all the current child nodes as the next node to be expanded; if the current node is the end node, end the search, start from the end node, and trace back along the parent node of each node until the starting node is reached; if not, return to step S124 until the end node is reached.

[0088] The above technical solution of the present invention has at least the following beneficial technical effects:

[0089] The application simulates the dynamic ground surface state (moisture content) and vehicle driving resistance under the influence of the dynamic ground surface state based on the parameters of the target vehicle, the rasterized off-road map (ground type, soil type, soil parameters, etc.), and the dynamically changing meteorological environmental factors, analyzes whether the target vehicle can pass and the passing speed, constructs a maximum driving speed matrix, and plans an off-road path with the shortest time by using an optimized A* algorithm. BRIEF DESCRIPTION OF DRAWINGS

[0090] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0091] Figure 1 It is a flow chart of the dynamic off-road path planning method of the land surface state simulation in an embodiment of the present application.

[0092] Figure 2 It is a rasterized landform type map of the target scene map in an embodiment of the present application.

[0093] Figure 3 It is a structure diagram of the dynamic off-road path planning system of the land surface state simulation in an embodiment of the present application. DETAILED DESCRIPTION

[0094] In order to make the purposes, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be described in detail below with reference to the drawings. However, those skilled in the art can understand that in the embodiments of the present application, many technical details are proposed in order to make the readers better understand the present application. However, the technical solutions claimed by the present application can be realized even without these technical details and various changes and modifications based on the following embodiments. The division of the following embodiments is for the convenience of description, and should not constitute any limitation on the specific implementation of the present application. The embodiments can be combined with each other and mutually referenced without contradiction.

[0095] At present, there is a problem in the prior art that how to influence the passing speed of the target vehicle under the influence of the dynamic land surface state of multiple environmental factors, avoid risks, and plan an optimal off-road path.

[0096] To solve the above problems, the present application analyzes the mechanism of the influence of environmental factors on vehicle passing speed, or constructs a vehicle passing speed calculation system model under the comprehensive influence of multiple environmental factors; as shown in the figure, on the one hand, a dynamic off-road path planning method of land surface state simulation is provided, which comprises: Figure 1 ​

[0097] 1. Simulate the dynamic land surface environment: In an off-road environment, in addition to the inherent landform type, the land surface state changes dynamically with the meteorological environment, which in turn affects the vehicle's passability and speed in the area. Therefore, it is necessary to simulate the dynamic changes in the land surface state according to the changes in the regional meteorological environment elements. The specific implementation can be completed by building a land surface environment simulation model; it should be noted that the "S" label in the following steps is only for the convenience of reading and understanding. Steps with strict calculation logic or using previously obtained data / results have a sequence. Other steps have no execution order or can be performed simultaneously; for example, various resistance calculations in steps S6 to S9 can be performed simultaneously. The specific steps of the method are as follows:

[0098] S1. Obtain an off-road map of a target scene, parameters of a target vehicle, a starting point and an end point of the target vehicle, and dynamic environment information corresponding to the off-road map;

[0099] Dynamic environmental information refers to natural factors that affect the state of the Earth's surface in real time, including current surface moisture, future precipitation data, temperature data, and radiation data. Dynamic environmental information can be obtained by: Current surface moisture can be inverted from satellite remote sensing data. The US SMAP remote sensing satellite is the latest soil moisture monitoring satellite in orbit and can be downloaded through NASA's Earthdata portal. Four daily forecasts are available from the European Centre for Medium-Range Numerical Weather Forecasts (ECMWF), each covering the next 4 to 15 days at a global 0.4° grid point. These forecasts include numerous forecast variables, such as temperature, humidity, air pressure, precipitation, wind speed, and radiation. Once these data are obtained, statistical downscaling can be used to obtain higher-resolution regional data.

[0100] The parameters of the target vehicle mainly include vehicle model, power, weight, tire / track width and contact area, frontal area, etc.

[0101] S2. Determine the future soil moisture content based on dynamic environmental information;

[0102] S3, rasterizing the off-road map and determining the size of a single grid, and identifying the landform type, road surface type, soil type, and slope within each grid;

[0103] like Figure 2 As shown, landform types include buildings (obstacles), forests (vegetation), water bodies (water systems), sand, soil roads, hard roads, grasslands and other landform data.

[0104] S4. Determine the future soil bearing capacity based on the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determine the unit ground pressure of the target vehicle based on the parameters of the target vehicle, and determine the relationship between the unit ground pressure and the future soil bearing capacity;

[0105] 2. Identify traversable areas: In addition to being affected by the terrain type, traversable areas also need to consider the target vehicle's parameters and slope, as well as the impact of dynamic environmental factors on traversability. Therefore, based on the aforementioned factors, identify traversable and impassable areas. The steps are as follows:

[0106] S5. Divide the off-road map into a passable area R0 and an impassable area T0 according to the landform type; optionally, buildings, forests, water bodies, etc. in the landform type can be classified into the impassable area T0, and the rest of the areas can be classified into the passable area R0; and assign a grid value of 1 to the passable area R0 and a grid value of 0 to the impassable area T0.

[0107] Furthermore, the infeasible area in the passable area R0 is determined according to the parameters and slope of the target vehicle, and the infeasible area T0 is updated to the infeasible area T 1， The remaining areas are updated as traversable areas R1. Specifically, slopes restrict vehicle access. In this step, the corresponding traversable areas are identified based on the vehicle type. Different vehicle types have different climbing capabilities. If the maximum climbing threshold corresponding to the vehicle type is less than the surface slope, the area is classified as impassable area T1; if it is greater, it is classified as traversable area R1. Generally, the maximum climbing capability of a typical car has a climbable slope threshold of 20°; the maximum climbing capability of an off-road vehicle has a climbable slope threshold of 35°; and the maximum climbing capability of a tracked vehicle has a climbable slope threshold of 30°. Taking a typical car as an example, based on the regional terrain slope data, if the grid cell with a slope greater than 20° is assigned a value of 0 based on the traversable area R0, the traversable area R1 is updated.

[0108] Furthermore, the infeasible area within the traversable area R1 is determined based on the relationship between the unit ground pressure and the future soil bearing capacity, and this area and the infeasible area T1 are updated to the infeasible area T2, while the rest are updated to the traversable area R2. The vehicle's unit ground pressure is calculated based on the vehicle specifications, and the corresponding feasible area is identified according to the criterion that "the vehicle sinks when its ground pressure is greater than the soil bearing capacity." Specifically, the area within the traversable area R1 where the target vehicle's unit ground pressure is less than the future soil bearing capacity is updated to the traversable area R2, and the area where the target vehicle's unit ground pressure is less than the future soil bearing capacity and the infeasible area T1 are updated to the infeasible area T2.

[0109] In this step, the grids of the traversable area and the impassable area are hierarchically divided and assigned values ​​based on the landform type, the parameters of the target vehicle, the future soil bearing capacity, etc. This has the effect of avoiding risks and makes the path planned by the improved A* algorithm in the subsequent steps more accurate.

[0110] 3. Calculate the target vehicle's driving state: Different land surface conditions create different resistance forces on the vehicle. Under constant preset power, the dynamic land surface conditions affect the vehicle's driving state (primarily considering speed). Calculate the target vehicle's driving state based on the target vehicle's parameters, ground type, soil type, and dynamic environmental information. The steps are as follows:

[0111] S6. Obtaining air density and air resistance coefficient, and determining air resistance according to the parameters of the target vehicle, air density, and air resistance coefficient;

[0112] S7. Obtaining a rolling friction coefficient corresponding to the road surface type, and determining a road surface resistance based on the target vehicle parameters, the rolling friction coefficient, and the slope;

[0113] S8. Determine the tire / track sinking depth and the soil shear strength coefficient based on soil parameters corresponding to the soil type, parameters of the target vehicle, and unit ground contact pressure; and determine the horizontal sinking resistance based on the target vehicle parameters, the tire / track sinking depth, and the soil shear strength coefficient;

[0114] S9. determining a slope resistance according to parameters of the target vehicle and the slope;

[0115] S10. Within the traversable region R2, calculate the maximum speed v of the target vehicle within each grid. The maximum speed v is the ratio of the target vehicle's preset power to the total resistance, where the total resistance is the sum of air resistance, road resistance, horizontal subsidence resistance, and slope resistance. Set the speed value of the grid within the impassable region T2 to 0.

[0116] IV. Off-road Path Planning: This application uses an improved A* algorithm to plan an off-road path. The steps are as follows:

[0117] S11. Generate a maximum driving speed matrix based on the maximum driving speed v in each grid in the passable area R2 and the speed value of the grid in the impassable area T2;

[0118] S12. Based on the maximum driving speed matrix, use the improved A* algorithm to calculate the shortest time from the start point to the end point, and record the corresponding path.

[0119] This application simulates and calculates dynamic surface state (moisture content) prediction data based on the parameters of the target vehicle, ground type, soil type, and dynamically changing environmental information (meteorology) in a rasterized off-road map, analyzes whether the target vehicle can pass and the passing speed, constructs a maximum driving speed matrix, and uses the optimized and improved A* algorithm to plan the shortest off-road path.

[0120] In a specific embodiment, in step S2, determining the future soil moisture content based on the dynamic environmental information includes the following steps:

[0121] S21. Estimate the evapotranspiration within each grid. The Hogreaves equation is used to estimate regional evapotranspiration in accordance with the formula:

[0122] ET=0.0023×(T mean +17.8)×(T max -T min ) 0.5 ×Ra

[0123] Where, ET is evapotranspiration, in mm / day;

[0124] T mean is the daily average temperature in degrees Celsius;

[0125] T max The highest temperature of the day, T min The lowest temperature of the day, in degrees Celsius;

[0126] Ra is the solar radiation or the extra-atmospheric solar radiation, and its unit is MJ / m 2 / day, which can be obtained based on the latitude of the time grid;

[0127] S22. Calculate future soil moisture content S new , in accordance with the formula:

[0128] S new =S+R-ET

[0129] Where S represents the current surface water content;

[0130] R represents future precipitation data;

[0131] S23. Soil moisture content is usually expressed as the ratio of the volume of water in the soil to the total volume of the soil. By calculating the soil moisture content per unit grid area and substituting it into the following formula, the future soil moisture content can be obtained by traversing. Calculate the future soil moisture content θ new , in accordance with the formula:

[0132]

[0133] Among them, V w Indicates the volume of water in the soil;

[0134] V t represents the total volume of soil;

[0135] D is the surface soil depth;

[0136] is the total porosity of the soil.

[0137] In one embodiment, the road surface type in step S3 includes good asphalt or concrete road surface, gravel road surface, pothole road surface, compacted dirt road, icy road surface, and compacted snow road;

[0138] Soil types include sand, clay, and loam.

[0139] Furthermore, the soil moisture content affects its cohesion and internal friction angle, thereby affecting the soil bearing capacity. Based on the obtained future soil moisture content and combined with the soil type, a simplified empirical formula is used to calculate the soil bearing capacity. Specifically, in step S4, determining the future soil bearing capacity based on the soil bearing capacity of the soil type in a dry state and the future soil moisture content includes:

[0140] Future soil bearing capacity q u It conforms to the following formula:

[0141] q u =q dry (1-k·θ new )

[0142] Among them, q dry Indicates the bearing capacity of soil in dry state;

[0143] k is an empirical coefficient that indicates the degree to which the water content associated with the soil type affects the future soil bearing capacity;

[0144] θ new is the future soil moisture content (percentage, converted to decimal form, such as 30% is 0.30).

[0145] q u The calculation formula expresses the soil bearing capacity in the water-containing state, θ new The soil bearing capacity in the dry state is taken as zero, so q u It can be used to indicate the future bearing capacity of soil.

[0146] Among them, the value of the empirical coefficient k is related to the soil bearing capacity q in dry state. dry Refer to the table below:

[0147] Table 1 Correspondence between soil types and empirical coefficient k

[0148]

[0149] In one embodiment, in step S4, the unit ground contact pressure of the target vehicle is determined based on the parameters of the target vehicle, and the relationship between the unit ground contact pressure and the future soil bearing capacity is determined, including:

[0150]

[0151] σ represents the target vehicle's unit ground pressure, in kN / m 2 ;

[0152] m represents the weight of the target vehicle in kg; g is the acceleration due to gravity;

[0153] A represents the contact area of ​​the target vehicle's tires / tracks, in m 2 .

[0154] Based on the criterion of "sinking occurs when the vehicle's ground pressure is greater than the soil bearing capacity," the corresponding feasible area is identified. Within the traversable area R1, the area where the target vehicle's unit ground pressure is less than the future soil bearing capacity is updated to the traversable area R2, and the remaining area in R1 and T1 are updated to the impassable area T2.

[0155] When calculating the vehicle's driving state, the vehicle is affected by different road conditions (such as slope, friction, etc.) under the condition of unchanged preset power output.

[0156] The preset power P indicates that the vehicle is running at a specific power output. The relationship between power, vehicle speed and driving resistance is as follows:

[0157]

[0158] Where: P is the vehicle's output preset power (constant), F total is the total resistance of the target vehicle, including air resistance, rolling resistance, sinking resistance, gravity component caused by slope, etc.; v is the speed of the vehicle, corresponding to the maximum driving speed in this application.

[0159] Total resistance of target vehicle F total It can be expressed as:

[0160] F total =F air +F rolling +F sinking +F slope

[0161] Among them, F air Indicates air resistance, F rolling Indicates road resistance; F slope Indicates slope resistance; F sinking Indicates horizontal subsidence resistance. Various resistances are obtained or calculated as follows:

[0162] In step S6, determining the air resistance according to the parameters of the target vehicle, the air density, and the air resistance coefficient includes:

[0163] Calculate the air resistance F air It conforms to the following formula:

[0164]

[0165] Where ρ is the air density; C d is the air resistance coefficient, which takes a value of 0.28-0.4; S is the frontal area, and V is the relative speed between the vehicle and the air. For simplicity, the general driving speed of the vehicle is taken as 15m / s.

[0166] Furthermore, in step S7, determining the road resistance includes:

[0167] Road resistance F rolling It conforms to the following formula:

[0168] F rolling =μmgcos(θ),

[0169] Where m is the vehicle mass, g is the acceleration due to gravity, θ is the slope angle, and μ is the rolling friction coefficient corresponding to the road surface type. The rolling friction coefficient can be obtained from Table 2 below.

[0170] Table 2 Road rolling friction coefficient

[0171]

[0172]

[0173] Horizontal subsidence resistance F sinking , the Bekker model can be used to first calculate the sinking depth of the target vehicle (tire / track), and then the horizontal sinking resistance can be calculated based on the soil shear strength.

[0174] Specifically, in step S8, determining the sinking depth of the tire / track includes the following steps:

[0175] S81, the subsidence depth z conforms to the following formula:

[0176]

[0177] Where b represents the width of the tire / track of the target vehicle in meters;

[0178] k c is the cohesive modulus of the soil in kN / m n+1 ;

[0179] k θ is the internal friction coefficient of the soil, in kN / m n+2 ;

[0180] σ is the target vehicle unit ground pressure, in kN / m 2 ;

[0181] n is the deformation index, which ranges from 0.7 to 1.2;

[0182] S82. Calculate the shear strength coefficient k of the soil z , which represents the soil's ability to resist shear deformation and can be calculated using the Mohr-Coulomb shear strength formula:

[0183] k z =c+σ·tan(φ);

[0184] Where c is the cohesion of the soil; φ is the internal friction angle of the soil; σ is the unit ground pressure of the target vehicle;

[0185] S83, horizontal subsidence resistance F sinking It conforms to the following formula:

[0186] F sinking =k z ·A·z

[0187] Where A is the contact area of ​​the target vehicle, z is the tire / track sinking depth calculated by the Bekker model, and k z is the shear strength coefficient of the soil.

[0188] The soil parameters involved in steps 81, 82, and 83 (cohesive modulus, internal friction coefficient, deformation index, cohesion, internal friction angle, etc.) can be obtained from Table 3 below:

[0189] Table 3 Soil parameters of 3 types of ground soil

[0190]

[0191] Furthermore, the slope resistance is calculated: In step S9, the slope resistance is caused by the component of gravity on the slope, which conforms to the following formula:

[0192] F slope =mgsin(α)

[0193] Among them, F slope Slope resistance, m is the weight of the target vehicle, and α is the slope angle.

[0194] After calculating the air resistance, road resistance, horizontal subsidence resistance, and slope resistance in the above scheme, the maximum speed of the target vehicle under the preset power P is calculated by taking into account environmental information, soil parameters, target vehicle parameters, road type, soil type, etc.

[0195]

[0196] The solution of this formula is the maximum speed of the target vehicle at the grid; the air density is 1.2258 kg / m 3The vehicle air resistance coefficient Cd is generally about 0.28-0.4.

[0197] Based on the identification result of the passable area and the impassable area of the target vehicle, a maximum driving speed matrix of the target vehicle is initialized, and the speed value corresponding to the grid with a value of 0 in the passable area R2 in the speed matrix is set to 0; for the remaining grids, the parameters of the target vehicle such as the vehicle type, the weight, the ground contact area of the tire / track, and the power are substituted, and the maximum driving speed matrix of the vehicle driving on the map at a preset power can be obtained through traversal calculation on all grids in the grid map.

[0198] In the off-road path planning, in step S12, the traditional A* algorithm is improved.

[0199] The core expression of the traditional A* algorithm is:

[0200] f(n)=g(n)+h(n)

[0201] Wherein, g(n) represents the actual value of the vehicle moving from the starting point to the current node. h(n) is the path cost estimation value of the current node to the terminal, also known as the heuristic function. f(n) represents the total cost value of the vehicle at the current node n. The vehicle calculates the f(n) value of the node that can be reached at the next moment in real time, and finds the minimum value of f(n) from the grid nodes allowed to move to move, and finally selects a path with the minimum cost value to reach the destination. In general A* algorithm, the heuristic function selects the Euclidean distance or Manhattan distance. The implementation process of A* algorithm depends on the open and closed lists, the former is used to save the grid nodes to be checked, and the parent node of the grid node is saved for subsequent search path; the latter is used to save the nodes or obstacles that have been visited to realize the speed-up of the search process.

[0202] The heuristic cost h(n) of the traditional A* algorithm is the straight-line distance from the current node to the terminal.

[0203] But the traditional A* algorithm has two defects: first, the key of path search is to find the shortest path from the starting point to the terminal, and the quantitative influence of multiple environmental factors on the vehicle passing speed is not added, so it is difficult to accurately provide the passing speed value of each part of the path and the cumulative passing time of the vehicle; second, the existing off-road path planning technical solution is static, and the input is the unchanged map data, which lacks the dynamic off-road path planning ability under the future road surface state change scenario.

[0204] In one specific embodiment, the traditional A* algorithm is improved, and the improved A* algorithm includes:

[0205] S121, according to the maximum driving speed matrix, the total cost f(n) of the target vehicle at the current node n is calculated:

[0206] f(n)=g(n)+h(n);

[0207] To simulate the target vehicle's speed more realistically, the grid obstacle matrix of the traditional A* algorithm is replaced with a maximum speed matrix. The maximum speed matrix takes into account the speed drop caused by various obstacles such as vehicle parameters and environmental information, as well as the ability to pass. The A* algorithm is optimized using time as the cost to achieve optimal path planning with the minimum travel time.

[0208] The A* algorithm based on travel time considers the impact of various ground obstacle elements on the algorithm results, makes corresponding improvements to the heuristic function and cost function, and expands the existing open list and closed list contents based on the established vehicle maximum driving speed matrix.

[0209] The g(n) cost of the traditional A* algorithm is the sum of the distances from the starting point to the current node;

[0210] The improved g(n) in this application represents the cost of the target vehicle moving from the starting point to the current node n, expressed in terms of the consumed time, in accordance with the formula:

[0211]

[0212] g(n) 父节点 represents the cumulative time from the starting point to the parent node of the current node n, S is the size of a single grid, V[i][j] is the maximum driving speed of the current node, obtained from the maximum driving speed matrix, and g(n) represents the cumulative time from the starting point to the current node;

[0213] The h(n) heuristic cost of the traditional A* algorithm is the straight-line distance from the current node to the end point; the h(n) improved in this application is the estimated time from the current node to the end point, which conforms to the formula:

[0214]

[0215] Where D is the Euclidean distance from the current node to the end point, is the average speed of all non-zero speed nodes in the maximum driving speed matrix;

[0216] S122. Calculate the shortest time required for the target vehicle to travel from the starting point to the end point based on the total cost f(n) of the target vehicle at the current node n, and record the corresponding path.

[0217] Furthermore, in step S12, the shortest time required for the target vehicle to travel from the starting point to the end point is calculated, including:

[0218] S123. Read the off-road map, dynamic environment information, and target vehicle parameters, convert the map into a grid, where each grid cell represents a node; establish an open table and a closed table for the grid nodes. The open table is used to record nodes to be explored. The nodes are sorted by the total cost f(n) value, and the nodes with the smallest f(n) value are expanded first. The closed table is used to record nodes that have been explored to avoid repeated searches; the closed table is initialized based on the impassable area T2, and the areas in the closed table are no longer added to the open table for search;

[0219] S124. Traverse the adjacent nodes of the current node to determine whether they belong to the traversable area R2. If so, and if they are not in the closed table, add the child node to the open table. Calculate the values ​​of g(n), h(n), and f(n) in sequence. After processing the current node, add it to the closed table and mark it as an explored node.

[0220] S125. Search the grid with the smallest value of f(n) among all the current child nodes as the next node to be expanded; if the current node is the end node, end the search, start from the end node, and trace back along the parent node of each node until the starting point is reached; if not, return to step S124 until the end point is reached.

[0221] The dynamic off-road path planning method for land surface state simulation in this application takes into account the impact of future dynamically changing environmental information on the passage of the target vehicle. On this basis, the kinematic equations of vehicle parameters, dynamic road surface state under the influence of dynamic environmental information and vehicle driving state are added to accurately calculate the difference in vehicle driving speed under changing road surface state, and finally construct an optimal passage cost function f(n) that is closer to the actual situation to plan the off-road path, thereby improving the accuracy of the off-road path planning algorithm while ensuring computational efficiency and practicality.

[0222] like Figure 3 As shown, the present application also proposes a dynamic off-road path planning system for land surface state simulation. The system model mainly includes four modules: land surface environment simulation module, traffic area identification module, vehicle driving state calculation module and off-road path planning module.

[0223] 1. The land surface environment simulation module is mainly used to estimate the change in regional surface moisture content based on the current surface moisture content, future precipitation data, temperature data, and radiation data of the region, and obtain the future soil moisture content. It involves the following steps:

[0224] S1. Obtain an off-road map of the target scene, parameters of the target vehicle, the starting and ending points of the target vehicle, and dynamic environmental information corresponding to the off-road map, the dynamic environmental information including current surface moisture content, future precipitation data, temperature data, and radiation data;

[0225] S2. Determine the future soil moisture content based on dynamic environmental information;

[0226] S3, rasterizing the off-road map and identifying the landform type, road surface type, soil type, and slope within each grid;

[0227] S4. Determine the future soil bearing capacity based on the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determine the unit ground pressure of the target vehicle based on the parameters of the target vehicle, and determine the relationship between the unit ground pressure and the future soil bearing capacity;

[0228] Second, the passable area identification module is mainly used to distinguish passable area grids from impassable area grids based on landform type, slope, and future soil bearing capacity. The passable area grid is assigned a value of 1, and the impassable area grid is assigned a value of 0. The specific steps are as follows:

[0229] S5. Divide the off-road map into a passable area R0 and an impassable area T0 according to the terrain type;

[0230] The infeasible area in the passable area R0 is determined according to the parameters and slope of the target vehicle, and it is updated with the infeasible area T0 to form the infeasible area T 1， The remaining areas are updated to the passable area R1;

[0231] Determine the infeasible area within the traversable area R1 based on the relationship between the unit ground pressure and the future soil bearing capacity, and update it and the infeasible area T1 into the infeasible area T2, and the rest are updated into the traversable area R2;

[0232] 3. The vehicle driving state calculation module is mainly used to simulate the influence of vehicle parameters and dynamic land surface conditions on the driving state under constant preset power output conditions, and calculate the maximum driving speed matrix. The specific steps are as follows:

[0233] S6. Obtaining air density and air resistance coefficient, and determining air resistance according to the parameters of the target vehicle, air density, and air resistance coefficient;

[0234] S7. Obtaining a rolling friction coefficient corresponding to the road surface type, and determining a road surface resistance based on the target vehicle parameters, the rolling friction coefficient, and the slope;

[0235] S8. Determine the tire / track sinking depth and the soil shear strength coefficient based on the soil parameters corresponding to the soil type, the target vehicle parameters, and the unit ground contact pressure; and determine the horizontal sinking resistance based on the target vehicle parameters, the tire / track sinking depth, and the soil shear strength coefficient.

[0236] S9. determining a slope resistance according to parameters of the target vehicle and the slope;

[0237] S10, in the passable area R2, the maximum driving speed v of the target vehicle in each grid is calculated, the maximum driving speed v is the ratio of the preset power of the target vehicle and the total resistance, the total resistance is the sum of the air resistance, the road surface resistance, the horizontal subsidence resistance and the slope resistance; the speed value of the grid in the impassable area T2 is set to 0;

[0238] Four, the off-road path planning module mainly based on the maximum driving speed matrix, the improved A* algorithm is used to plan the off-road path with the minimum time:

[0239] S11, based on the maximum driving speed v in each grid in the passable area R2 and the speed value of the grid in the impassable area T2, the maximum driving speed matrix is generated;

[0240] S12, based on the maximum driving speed matrix, the improved A* algorithm is used to calculate the shortest time from the starting point to the ending point, and the corresponding path is recorded.

[0241] It should be understood that the above specific embodiments of the present application are only used for illustrative or explanatory purposes of the principles of the present application, and do not constitute a limitation on the present application. Therefore, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present application shall be included in the protection scope of the present application. In addition, the claims attached to the present application are intended to cover all changes and modifications falling within the scope and boundary of the appended claims, or the equivalent forms of such scope and boundary.

Claims

1. A dynamic off-road path planning method for land surface state simulation, characterized in that: include: S1. Obtain an off-road map of a target scene, parameters of a target vehicle, a starting point and an end point of the target vehicle, and dynamic environmental information corresponding to the off-road map, wherein the dynamic environmental information includes current surface moisture content, future precipitation data, temperature data, and radiation data; S2. determining the future soil moisture content based on the dynamic environmental information; S3, rasterizing the off-road map, and identifying the landform type, road surface type, soil type, and slope within each grid; S4. determining the future soil bearing capacity based on the soil bearing capacity of the soil type in a dry state and the future soil moisture content; Determine the unit ground pressure of the target vehicle based on the parameters of the target vehicle, and determine the relationship between the unit ground pressure and the future soil bearing capacity; S5. Dividing the off-road map into a passable area R0 and an impassable area T0 according to the landform type; Determine an infeasible area within the traversable area R0 according to the parameters of the target vehicle and the slope, and update the infeasible area T0 and the infeasible area T1 into the infeasible area T1, and update the remaining areas into the traversable area R1; Determine the infeasible area within the traversable area R1 according to the relationship between the unit ground pressure and the future soil bearing capacity, and update the infeasible area T1 and the infeasible area T2 into the infeasible area T2, and update the remaining areas into the traversable area R2; S6. Obtaining air density and air resistance coefficient, and determining air resistance according to the parameters of the target vehicle, air density, and air resistance coefficient; S7. Obtaining a rolling friction coefficient corresponding to a road surface type, and determining a road surface resistance based on the parameters of the target vehicle, the rolling friction coefficient, and the slope; S8. Determine a tire / track sinking depth and a soil shear strength coefficient based on soil parameters corresponding to the soil type, parameters of the target vehicle, and the unit ground contact pressure; and determine a horizontal sinking resistance based on the target vehicle parameters, the tire / track sinking depth, and the soil shear strength coefficient. S9. determining a slope resistance according to the parameters of the target vehicle and the slope; S10. Within the traversable region R2, calculate the maximum speed v of the target vehicle within each grid, where the maximum speed v is the ratio of the target vehicle's preset power to the total resistance, where the total resistance is the sum of the air resistance, the road resistance, the horizontal subsidence resistance, and the slope resistance; set the speed value of the grid within the impassable region T2 to 0; S11. Generate a maximum driving speed matrix based on the maximum driving speed v in each grid in the passable area R2 and the speed value of the grid in the impassable area T2; S12. Based on the maximum driving speed matrix, use an improved A* algorithm to calculate the shortest time from the starting point to the end point, and record the corresponding path.

2. The method according to claim 1, characterized in that In step S2, determining the future soil moisture content includes the following steps: S21. Calculate the evapotranspiration within each grid according to the formula: ET=0.0023×(T mean +17.8)×(T max -T min ) 0.5 ×Ra Where, ET is evapotranspiration, unit is mm / day; T mean is the daily average temperature in degrees Celsius; T max The highest temperature of the day, T min The lowest temperature of the day, in degrees Celsius; Ra is the solar radiation or the extra-atmospheric solar radiation, and its unit is MJ / m 2 / day; S22. Calculate future soil moisture content S new , in accordance with the formula: S new =S+R-ET Where S represents the current surface water content; R represents future precipitation data; S23. Calculate future soil moisture content θ new , in accordance with the formula: Among them, V w Indicates the volume of water in the soil; V t represents the total volume of soil; D is the surface soil depth; is the total porosity of the soil.

3. The method according to claim 1, characterized in that The road surface types in step S3 include good asphalt or concrete road surface, gravel road surface, pothole road surface, compacted dirt road, icy road surface, and compacted snow road; Soil types include sand, clay, and loam.

4. The method according to claim 2, characterized in that In step S4, determining the future soil bearing capacity includes: Future soil bearing capacity q u It conforms to the following formula: q u =q dry (1-k·θ new ) Among them, q dry Indicates the bearing capacity of soil in dry state; k is an empirical coefficient that indicates the degree to which the water content associated with the soil type affects the future soil bearing capacity; θ ne w is the future soil moisture content.

5. The method according to claim 4, characterized in that In step S4, determining the target vehicle unit ground contact pressure includes: σ represents the target vehicle's unit ground pressure, in kN / m 2 ; m represents the weight of the target vehicle in kg; g is the acceleration due to gravity; A: The contact area of ​​the target vehicle's tires / tracks, in m 2 .

6. The method according to claim 1, characterized in that In step S6, determining the air resistance includes: Calculate the air resistance F air It conforms to the following formula: Where ρ is the air density; C d is the air resistance coefficient, which ranges from 0.28 to 0.4; S is the frontal area; and V is the relative speed between the vehicle and the air.

7. The method according to claim 1, characterized in that In step S7, determining the road resistance includes: Road resistance F rolling It conforms to the following formula: F rolling =μmgcos(α), Where m is the vehicle mass, g is the acceleration due to gravity, α is the slope angle, and μ is the rolling friction coefficient corresponding to the road surface type.

8. The method according to claim 5, characterized in that In step S8, determining the sinking depth of the tire / track includes the following steps: S81, the subsidence depth z conforms to the following formula: Where b represents the width of the tire / track of the target vehicle in meters; k c is the cohesive modulus of the soil in kN / m n+1 ; k θ is the internal friction coefficient of the soil, in kN / m n+2 ; σ is the target vehicle unit ground pressure, in kN / m 2； n is the deformation index, which ranges from 0.7 to 1.2; S82. Determine the shear strength coefficient k of the soil z It conforms to the following formula: k z =c+σ·tan(φ); c is the cohesion of the soil; φ is the internal friction angle of the soil; S83, horizontal subsidence resistance F sinking It conforms to the following formula: F sinkin g=k z ·A·z Where A is the contact area of ​​the target vehicle.

9. The method according to claim 1, characterized in that In step S9, it is determined that the slope resistance conforms to the following formula: F slope =mgsin(a) Among them, F slope is the slope resistance, m is the weight of the target vehicle, g is the acceleration due to gravity, and α is the slope angle.

10. The method according to claim 1, characterized in that In step S12, the improved A* algorithm is used, including: S121. Calculate the total cost f(n) of the target vehicle at the current node n based on the maximum driving speed matrix: f(n)=g(n)+h(n); Among them, g(n) represents the cost of the target vehicle moving from the starting point to the current node n, expressed in terms of the consumed time, which conforms to the formula: g(n) 父节点 represents the cumulative time from the starting point to the parent node of the current node n; S is the size of a single grid; V[i][j] is the maximum driving speed of the current node, obtained from the maximum driving speed matrix; g(n) represents the cumulative time from the starting point to the current node; h(n) is the estimated time from the current node to the end point, which conforms to the formula: Where D is the Euclidean distance from the current node to the end point; is the average speed of all non-zero speed nodes in the maximum driving speed matrix; S122. Based on the total cost f(n) of the target vehicle at the current node n, plan the shortest time required for the target vehicle to travel from the starting point to the end point, and record the corresponding path.

11. The method according to claim 10, characterized in that In step S12, the shortest time required for the target vehicle to travel from the starting point to the end point is calculated, including: S123. Read the off-road map, the dynamic environment information, and the parameters of the target vehicle, and convert the map into a grid, where each grid cell represents a node; establish an open table and a closed table for the grid nodes, wherein the open table is used to record nodes to be explored, and the nodes are sorted by the total cost f(n) value, with the nodes with the smallest f(n) value being expanded first; and the closed table is used to record nodes that have been explored to avoid repeated searches; initialize the closed table based on the impassable area T2, and the areas in the closed table are no longer added to the open table for search; S124. Traverse the adjacent nodes of the current node to determine whether they belong to the traversable area R2. If so, and if they are not in the closed table, add the adjacent node to the open table. Calculate the values ​​of g(n), h(n), and f(n) in sequence. After processing the current node, add it to the closed table and mark it as an explored node. S125. Search the grid with the smallest value of f(n) among all the adjacent nodes of the current node, and use it as the next node to be expanded; if the current node is the end node, end the search, start from the end node, and trace back along the parent node of each node until the starting node is reached; if not, return to step S124 until the end node is reached.

Images