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

Abstract

The invention belongs to the technical field of cross-country path planning, and provides a dynamic cross-country path planning method for land surface state simulation, which comprises the following steps: S1, acquiring a cross-country map, parameters, a starting point and an ending point of a target vehicle, current surface water content, future rainfall data, air temperature data and radiation data; s2, determining the future soil moisture content; s3, rasterizing the cross-country map, and identifying a landform type, a road surface type, a soil type and a gradient; s4, determining the size relationship between the unit grounding pressure and the future soil bearing capacity; s5, dividing 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, based on the maximum driving speed matrix, utilizing an improved A * algorithm to plan a cross-country path.

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 simulating land surface conditions. Background Art

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

[0003] For off-road environments, the land surface state is dynamically affected by real-time changing meteorological and environmental factors. 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] The present application provides a dynamic off-road path planning method for land surface state simulation, comprising:

[0005] S1, obtaining 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 water content, future precipitation data, temperature data, and radiation data;

[0006] S2. determining the future soil moisture content according to the dynamic environmental information;

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

[0008] S4. determining the future soil bearing capacity according to the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determining the unit ground pressure of the target vehicle according to the parameters of the target vehicle, and determining the magnitude 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 the infeasible area within the passable area R0 according to the parameters of the target vehicle and the slope, and update the infeasible area T0 and the infeasible area T1 as the infeasible area T1, and update the remaining area to the passable area R1;

[0011] Determine the infeasible area within the passable area R1 according to the magnitude relationship between the unit ground pressure and the future soil bearing capacity, and update the infeasible area T1 and the infeasible area T2 as the infeasible area T2, and update the rest as the passable 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 according to the parameters of the target vehicle, the rolling friction coefficient and the slope;

[0014] S8, determining the tire / track sinking depth and the soil shear strength coefficient according to the soil parameters corresponding to the soil type, the parameters of the target vehicle and the unit ground contact pressure; and determining the horizontal sinking resistance according to the parameters of the target vehicle, 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, in the passable area R2, calculating the maximum driving speed v of the target vehicle in each grid, wherein the maximum driving speed v is the ratio of the preset power of the target vehicle to the total resistance, wherein the total resistance is the sum of the air resistance, the road resistance, the horizontal subsidence resistance and the slope resistance; setting the speed value of the grid in the impassable area T2 to 0;

[0017] S11, generating 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 in each grid according to the formula:

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

[0022] Among them, 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 daily radiation outside the atmosphere, and its unit is MJ / m 2 / day;

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

[0027] S new =S+R-ET

[0028] Among them, S represents the current surface water content;

[0029] R represents future precipitation data;

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

[0031]

[0032] Among them, V w It represents 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] Further, 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 complies with 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, which indicates the degree of influence of water content related to soil type on 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 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] Further, in step S6, determining the air resistance includes:

[0050] Calculate the air resistance F air It complies with the following formula:

[0051]

[0052] 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; V is the relative speed between the vehicle and the air.

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

[0054] Road resistance F rolling It complies with 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 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 cohesive modulus of the soil in kN / m n+1 ;

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

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

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

[0065] S82. Determine the shear strength coefficient k of the soil z It complies with the following formula:

[0066] k z =c+σ·tan(φ);

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

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

[0069] F sinking =k z ·A·z

[0070] Where A is the contact area of ​​the target vehicle.

[0071] Further, in step S9, it is determined that the slope resistance meets the following formula:

[0072] F slope =mgsin(α)

[0073] 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.

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

[0075] S121. Calculate the total cost f(n) of the target vehicle at the current node n according to the maximum driving speed matrix:

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

[0077] Among them, g(n) represents the time consumed by the target vehicle to move from the starting point to the current node n, which conforms to 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. According to 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] Further, 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, reading the off-road map, the dynamic environment information, and the parameters of the target vehicle, converting the map into a grid, where each grid unit represents a node; establishing an open table and a closed table of grid nodes, where the open table is used to record the 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, and the closed table is used to record the 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;

[0086] S124, traverse the adjacent nodes of the current node to determine whether it belongs to the passable area R2; if it does, and it is 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, and after the current node is processed, 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] This application simulates and calculates the dynamic surface state (water content) and the vehicle driving resistance under its influence based on the parameters of the target vehicle, a rasterized off-road map (ground type, soil type, soil parameters, etc.), and dynamically changing meteorological environmental factors, 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the conventional technology, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0091] Figure 1 It is a flow chart of a dynamic off-road path planning method for simulating land surface conditions in one embodiment of the present application.

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

[0093] Figure 3 It is a structural diagram of a dynamic off-road path planning system for simulating land surface conditions in one embodiment of the present application. DETAILED DESCRIPTION

[0094] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the embodiments of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined with each other and quoted from each other without contradiction.

[0095] At present, there is a problem in the prior art of how to avoid risks and plan an off-road path with the best time according to the impact of the dynamic land surface state under the influence of multiple environmental factors on the target vehicle's travel speed.

[0096] To solve the above problems, this application analyzes the mechanism of the impact of environmental factors on vehicle speed, or constructs a vehicle speed calculation system model with comprehensive impact of multiple environmental factors; Figure 1 As shown, on the one hand, a dynamic off-road path planning method for land surface state simulation is provided, comprising:

[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 of the land surface state according to the changes in the regional meteorological environment elements. The specific implementation can be completed by constructing a land surface environment simulation model; it should be noted that the "S" label in the following steps is only for ease of reading and understanding. Steps with strict calculation logic or using previously acquired 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, obtaining 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] Among them, dynamic environmental information refers to the natural factors that affect the state of the surface in real time, including current surface moisture content, future precipitation data, temperature data and radiation data; dynamic environmental information can be obtained by: the current surface moisture content can be obtained by inversion of 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. From the European Center for Medium-Range Numerical Weather Forecasts (ECMWF), you can download and obtain 4 daily forecast hours, each covering the next 4 to 15 days of global 0.4° grid forecast data, including temperature and humidity, air pressure, precipitation, wind speed, radiation and many other forecast variables. After obtaining the above data, statistical downscaling methods can be used to obtain higher-resolution regional data.

[0100] The parameters of the target vehicle mainly include vehicle type, power, weight, tire / track width and contact area, windward 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 in 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. determining the future soil bearing capacity according to the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determining the unit ground pressure of the target vehicle according to the parameters of the target vehicle, and determining the magnitude relationship between the unit ground pressure and the future soil bearing capacity;

[0105] 2. Identify passable areas: In addition to being affected by the type of landform, the passable area also needs to consider the parameters and slope of the target vehicle, as well as the impact of dynamic environmental factors on the traffic conditions. Therefore, based on the above factors, the passable and impassable areas are identified. 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 remaining areas can be classified into the passable area R0; and assign a grid value of 1 to the passable area R0, and assign 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 with the passable area R1; specifically, the slope has restrictions on vehicle passage. In this step, the corresponding feasible area is identified according to the vehicle model. Different vehicle models have different climbing capabilities. If the maximum climbing threshold corresponding to the vehicle model is less than the surface slope, the area is classified as the inaccessible area T1, and if it is greater than the surface slope, it is classified as the passable area R1; usually, the maximum climbing ability of a general car, its climbable slope threshold is 20°; the maximum climbing ability of an off-road vehicle, its climbable slope threshold is 35°; the maximum climbing ability of a tracked vehicle, its climbable slope threshold is 30°. Taking a general car as an example, based on the regional terrain slope data, on the basis of the passable area R0, if the slope is greater than 20°, the grid is assigned a value of 0, and the passable area R1 is updated.

[0108] Furthermore, the infeasible area within the passable area R1 is determined according to the relationship between the unit ground pressure and the future soil bearing capacity, and it and the infeasible area T1 are updated to the infeasible area T2, and the rest are updated to the passable area R2; the unit ground pressure of the vehicle is calculated based on the vehicle specifications, and the corresponding feasible area is identified according to the criterion of "the vehicle sinks when the ground pressure is greater than the soil bearing capacity"; specifically, the area in the passable area R1 where the unit ground pressure of the target vehicle is less than the future soil bearing capacity is updated to the passable area R2, and the area where the unit ground pressure of the target vehicle 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 passable 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 driving state of the target vehicle: Different land surface conditions produce different resistances to the vehicle. Under constant preset power, the dynamic land surface conditions affect the driving state of the vehicle (mainly considering the speed). The driving state of the target vehicle is calculated based on the parameters of the target vehicle, 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 the road surface resistance according to the parameters of the target vehicle, the rolling friction coefficient and the slope;

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

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

[0115] S10. In the passable area R2, the maximum driving speed v of the target vehicle in each grid is calculated, where the maximum driving speed v is the ratio of the preset power of the target vehicle to the total resistance, where the total resistance is the sum of air resistance, road resistance, horizontal subsidence resistance and slope resistance; the speed value of the grid in the impassable area T2 is set to 0;

[0116] 4. Off-road path planning: This application uses the improved A* algorithm to plan the off-road path. The steps are as follows:

[0117] S11, generating 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, the improved A* algorithm is used to calculate the shortest time from the starting point to the end point, and the corresponding path is recorded.

[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 (weather) 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 according to the dynamic environmental information includes the following steps:

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

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

[0123] Among them, 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 daily radiation outside the atmosphere, and its unit is MJ / m 2 / day, which can be obtained based on the latitude of the time grid;

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

[0128] S new =S+R-ET

[0129] Among them, 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. The future soil moisture content can be obtained by calculating the soil moisture content per unit grid area and substituting it into the following formula. Calculate the future soil moisture content θ new , in accordance with the formula:

[0132]

[0133] Among them, V w It represents 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 moisture content of the soil will affect 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 complies with 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, which indicates the degree of influence of water content related to soil type on 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 under water-containing conditions, θ new The soil bearing capacity at zero is taken as the soil bearing capacity in the dry state, so q u It can be used to indicate future soil bearing capacity.

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

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

[0148]

[0149] In one embodiment, in step S4, the unit ground contact pressure of the target vehicle is determined according to 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 unit ground pressure, in kN / m 2 ;

[0152] m represents the weight of the target vehicle; the unit is 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] According to the criterion of "sinking occurs when the ground pressure of the vehicle is greater than the soil bearing capacity", the corresponding feasible area is identified; the area in the passable area R1 where the unit ground pressure of the target vehicle is less than the future soil bearing capacity is updated to the passable 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 subject to the influence of 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 preset output 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 complies with 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] Further, in step S7, determining the road resistance includes:

[0167] Road resistance F rolling It complies with the following formula:

[0168] F rolling =μmgcos(θ),

[0169] Wherein, m is the vehicle mass, g is the acceleration of 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 in combination with 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, with a value of 0.7-1.2;

[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 complies with 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 (cohesion modulus, internal friction coefficient, deformation index, cohesion, internal friction angle, etc.) involved in steps 81, 82, and 83 can be obtained from the following Table 3:

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

[0190]

[0191] Further, 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 environmental information, soil parameters, parameters of the target vehicle, road type, soil type, etc. are connected to calculate the maximum driving speed of the target vehicle under the preset power P:

[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 around 0.28 to 0.4.

[0197] Based on the identification results of the target vehicle's passable and impassable areas, the maximum driving speed matrix of the target vehicle is initialized, and the speed value corresponding to the grid assigned 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 vehicle type, weight, tire / track contact area, power, etc. are substituted, and the maximum driving speed matrix of the vehicle traveling at the preset power in the map can be obtained by traversing and calculating all the grids in the grid map.

[0198] In off-road path planning, in step S12, the present application improves the traditional A* algorithm.

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

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

[0201] Among them, g(n) represents the actual cost consumed by the vehicle to move from the starting point to the current node. h(n) is the estimated value of the path cost from the current node to the end point, also known as the heuristic function. f(n) represents the total cost consumed by the car 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 chooses a path with the minimum cost to reach the destination. In general, the heuristic function in the A* algorithm selects Euclidean distance or Manhattan distance. The implementation process of the A* algorithm depends on two lists, open and closed. The former is used to save the grid nodes to be checked, and save the parent nodes of the grid nodes for subsequent path search; the latter is used to save the nodes or obstacles that have been visited to speed up the search process.

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

[0203] However, the above traditional A* algorithm has two defects: first, the key to path search is to find the shortest path from the starting point to the end point, and the quantitative impact of multiple environmental factors on the vehicle's travel speed is not taken into account, making it difficult to accurately provide the travel speed values ​​of each part of the path and the cumulative travel time of the vehicle; second, the existing off-road path planning technical solutions are mostly static, with unchanging map data as input, and lack the ability to dynamically plan off-road paths under future road condition changes.

[0204] In a specific embodiment, the present application improves the traditional A* algorithm, and the improved A* algorithm includes:

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

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

[0207] In order to simulate the target vehicle's driving speed more realistically, the grid obstacle matrix of the traditional A* algorithm is replaced by the maximum driving speed matrix. The maximum driving speed matrix takes into account the speed drop caused by various resistances such as vehicle parameters and environmental information. The A* algorithm is optimized with time cost as the cost to achieve the 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] Further, 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 parameters of the target vehicle, convert the map into a grid, and each grid unit represents a node; establish an open table and a closed table of the grid nodes, the open table is used to record the 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, and the closed table is used to record the 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;

[0219] S124, traverse the adjacent nodes of the current node to determine whether it belongs to the passable area R2: if it does, and it is 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, and after the current node is processed, 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 reaching the starting point; if not, return to step S124 until reaching the end point.

[0221] The dynamic off-road path planning method for land surface state simulation of the present 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: a land surface environment simulation module, a traffic area identification module, a vehicle driving state calculation module and an off-road path planning module.

[0223] 1. The land surface environment simulation module is mainly used to estimate the change of surface moisture content in the region 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, involving the following steps:

[0224] S1, obtaining 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, the dynamic environmental information including current surface water 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 in each grid;

[0227] S4. determining the future soil bearing capacity according to the soil bearing capacity of the soil type in a dry state and the future soil moisture content; determining the unit ground pressure of the target vehicle according to the parameters of the target vehicle, and determining the magnitude relationship between the unit ground pressure and the future soil bearing capacity;

[0228] 2. The passable area identification module is mainly used to distinguish the passable area grid and the impassable area grid according to the landform type, slope and future soil bearing capacity, and assign a value of 1 to the passable area grid and a value of 0 to the impassable area grid. The specific steps are as follows:

[0229] S5, dividing the off-road map into a passable area R0 and an impassable area T0 according to the landform 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 according to 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 state on 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 the road surface resistance according to the parameters of the target vehicle, the rolling friction coefficient and the slope;

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

[0236] S9, determining the slope resistance according to the 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, where the maximum driving speed v is the ratio of the preset power of the target vehicle to the total resistance, where the total resistance is the sum of air resistance, road resistance, horizontal subsidence resistance and slope resistance; the speed value of the grid in the impassable area T2 is set to 0;

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

[0239] S11, generating 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;

[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 end point, and the corresponding path is recorded.

[0241] It should be understood that the above specific embodiments of the present application are only used to illustrate or explain the principles of the present application, and do not constitute a limitation to the present application. Therefore, any modifications, equivalent substitutions, improvements, etc. made without departing from the spirit and scope of the present application should 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 that fall within the scope and boundaries of the attached claims, or the equivalent forms of such scope and boundaries.

Claims

1. A dynamic off-road path planning method for land surface state simulation, characterized in that: include: S1, obtaining 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 water content, future precipitation data, temperature data, and radiation data; S2. determining the future soil moisture content according to the dynamic environmental information; S3, rasterizing the off-road map, and identifying the landform type, road surface type, soil type and slope in each grid; S4, determining the future soil bearing capacity according to 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 according to 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 the infeasible area within the passable area R0 according to the parameters of the target vehicle and the slope, and update the infeasible area T0 and the infeasible area T1 as the infeasible area T1, and update the remaining area to the passable area R1; Determine the infeasible area within the passable 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, and update the remaining areas to the passable 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 according to the parameters of the target vehicle, the rolling friction coefficient and the slope; S8, determining the tire / track sinking depth and the soil shear strength coefficient according to the soil parameters corresponding to the soil type, the parameters of the target vehicle and the unit ground contact pressure; and determining the horizontal sinking resistance according to the parameters of the target vehicle, 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, in the passable area R2, calculating the maximum driving speed v of the target vehicle in each grid, wherein the maximum driving speed v is the ratio of the preset power of the target vehicle to the total resistance, wherein the total resistance is the sum of the air resistance, the road resistance, the horizontal subsidence resistance and the slope resistance; setting the speed value of the grid in the impassable area T2 to 0; S11, generating 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 in each grid according to the formula: ET=0.0023×(T mean +17.8)×(T max -T min ) 0.5 ×Ra Where ET is evapotranspiration, in 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 daily radiation outside the atmosphere, and its unit is MJ / m 2 / day; S22. Calculate the future soil moisture content S new , in accordance with the formula: S new =S+R-ET Among them, S represents the current surface water content; R represents future precipitation data; S23. Calculate the future soil moisture content θ new , in accordance with the formula: Among them, V w It represents 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 complies with 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, which indicates the degree of influence of water content related to soil type on future soil bearing capacity; θ new 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 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 complies with the following formula: 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; 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 complies with the following formula: F rollling =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 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, with a value of 0.7-1.2; S82. Determine the shear strength coefficient k of the soil z It complies with 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 complies with the following formula: F sinking =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 according to 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. According to 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, reading the off-road map, the dynamic environment information, and the parameters of the target vehicle, converting the map into a grid, where each grid unit represents a node; establishing an open table and a closed table of grid nodes, where the open table is used to record the 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, and the closed table is used to record the 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; S124, traverse the adjacent nodes of the current node to determine whether it belongs to the passable area R2: if it does, and it is 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, and after the current node is processed, 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 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.

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