A simulation and optimization method for robot's sand driving performance

Abstract

The invention provides a simulation and optimization method for the sand driving performance of a robot, comprising: collecting terrain information and sand quality information of the sand, combining physical mechanics principles and geological principles to construct a virtual sand model of the sand, simulating several driving modes of the robot, obtaining each corresponding driving key coefficient, evaluating the driving performance of the robot, screening the high-drive driving modes corresponding to the robot in different sand areas, optimizing the conversion process between different high-drive driving modes based on physical mechanics principles, generating several coherent driving modes, simulating each of the coherent driving modes of the robot in the virtual sand model, obtaining stability information, speed information and energy consumption information corresponding to each coherent driving mode and displaying them, simulating and evaluating the driving performance of the robot in advance based on physical mechanics and geological principles, and performing corresponding optimization when necessary to ensure that the robot can drive freely on the sand.

Description

Technical Field

[0001] The invention relates to the technical field of sand driving simulation, and in particular to a sand driving performance simulation and optimization method for a robot. Background Art

[0002] Surveying and exploring deserts and desertified areas requires robots to be able to drive in the sand to collect data such as terrain and resource distribution. This data is very important for environmental protection, resource development, and scientific research. However, robots need to overcome many challenges when driving in the sand, including terrain complexity, traction problems, stability requirements, and environmental impacts. Modern robotics technology continues to advance, and solutions to these challenges are also constantly evolving, enabling robots to perform increasingly complex tasks in a variety of complex environments. Therefore, special designs and strategies are usually required to deal with the characteristics of sand and environmental uncertainties. Generally speaking, manual surveys are carried out first, and then the terrain and topography of the desert are analyzed to plan the driving path for the robot and analyze its driving performance. However, this method not only requires a high degree of cooperation from professional surveyors and analysts, but also requires a lot of survey time and analysis time, resulting in a lot of work delays.

[0003] Therefore, the present invention provides a method for simulating and optimizing the sand driving performance of a robot. Summary of the invention

[0004] The present invention provides a simulation and optimization method for robot driving performance on sand, which simulates and evaluates the driving performance of the robot in advance based on physical mechanics and geological principles, and performs corresponding optimization when necessary to ensure that the robot can drive freely on the sand.

[0005] The present invention provides a method for simulating and optimizing the sand driving performance of a robot, comprising:

[0006] Step 1: Collecting the terrain information and sand quality information of the sand and building a virtual sand model of the sand in combination with the principles of physical mechanics and geology;

[0007] Step 2: Simulate several driving modes of the robot in the virtual sand model to obtain a driving key coefficient corresponding to each driving mode;

[0008] Step 3: Evaluate the driving performance of the robot according to the driving key coefficients, and select the high-drive driving modes corresponding to the robot in different sandy areas;

[0009] Step 4: Optimizing the conversion process between different high-drive driving modes based on the physical mechanics principle to generate several coherent driving modes of the robot in the sand;

[0010] Step 5: Simulate each of the continuous driving modes of the robot in the virtual sand model, obtain stability information, speed information and energy consumption information corresponding to each of the continuous driving modes and display them.

[0011] In one practicable manner,

[0012] Also includes:

[0013] Topographic survey and sand quality sampling are carried out on the sand land to obtain topographic information and sand quality information of the sand land.

[0014] In one practicable manner,

[0015] The step 1 comprises:

[0016] Step 11: Perform terrain analysis on the terrain information using the geological principle, draw a relief map of the sandy land, and map the sand quality information onto the relief map based on the physical mechanics principle to generate a three-dimensional sandy land image of the sandy land;

[0017] Step 12: analyzing the three-dimensional sand image to determine several types of sand deformation features contained in the sand and the characteristic distribution corresponding to each of the sand deformation features, and marking each of the sand deformation features in the three-dimensional sand image based on the characteristic distribution to generate a three-dimensional characteristic image;

[0018] Step 13: construct corresponding model blocks according to each of the characteristic distributions, combine the model blocks according to the three-dimensional characteristic images, and use computer graphics technology to perform scene rendering on the combination results to generate a virtual sand model of the sand.

[0019] In one practicable manner,

[0020] The step 2 comprises:

[0021] Step 21: Acquire the driving mechanism data of the robot, construct a virtual robot according to the driving mechanism data, calibrate the driving starting point and the driving end point in the virtual sand model, simulate the robot using each driving mode according to the virtual sand model and the virtual robot, and track the driving trajectory and driving speed corresponding to each driving mode in the virtual sand model;

[0022] Step 22: drawing a standard track in the virtual sand model based on the driving starting point and the driving end point, drawing each driving track in the virtual sand model as a simulated track, and adding a corresponding mode identifier to each simulated track according to the corresponding driving mode;

[0023] Step 23: obtaining the track intersection and separation information between each of the simulated tracks and the standard track in the virtual sand model, calling the corresponding driving speed according to the track mode identifier, and determining the first average speed of the virtual robot corresponding to each intersection track area and the second average speed corresponding to each separation track area according to the driving speed and the track intersection and separation information;

[0024] Step 24: respectively obtain the separation amount between each separation trajectory area and the standard trajectory in the same simulation trajectory, and establish the driving key coefficient of the corresponding driving mode in combination with the first average speed and the second average speed.

[0025] In one practicable manner,

[0026] The step 3 comprises:

[0027] Step 31: constructing a qualified driving area and a non-qualified driving area corresponding to each of the driving modes according to the key driving coefficients, marking each of the qualified driving areas and each of the non-qualified driving areas in the virtual sand model, and roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas;

[0028] Step 32: Analyze the driving qualification rate corresponding to each driving mode according to the qualified driving area and the unqualified driving area corresponding to each driving mode, record it as the first evaluation factor, and record the X driving feature, Y driving feature and Z driving feature of the driving information of the robot in each unqualified area as the second evaluation factor;

[0029] Step 33: adding a stability evaluation factor to each qualified driving area corresponding to the driving mode, and combining the first evaluation factor and the second evaluation factor corresponding to the unqualified driving area of ​​each driving mode to evaluate the driving performance characteristics of the robot when implementing each driving mode;

[0030] Step 34: Counting a number of driving performance characteristics corresponding to each of the sandy areas, generating a characteristic sequence for each of the sandy areas, and determining a number of high-speed driving modes corresponding to the robot in different sandy areas in combination with the qualified driving information of the robot.

[0031] In one practicable manner,

[0032] The process of roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas includes:

[0033] Step a: determining a number of overlapping regions included in the virtual sand model according to the marking result, obtaining the overlapping area of ​​each pair of overlapping regions, and setting corresponding ratio weights for corresponding overlapping regions according to the overlapping areas;

[0034] Step b: adding a region expansion condition to an expandable region whose ratio weight is greater than the average ratio weight, adding a region contraction condition to a plurality of to-be-contracted regions adjacent to the expandable region, and adding the to-be-contracted regions to the corresponding expandable regions based on the region expansion condition and the region contraction condition, to generate a plurality of expanded regions;

[0035] Step c: reorganizing the area of ​​each of the expanded areas to obtain a plurality of divided areas contained in the virtual sand model, determining the terrain slope information of the sand according to the virtual sand model, and determining the regional terrain and regional slope corresponding to each of the divided areas according to the terrain slope information;

[0036] Step d: regional edge fusion is performed on a plurality of first divided regions containing the same regional topography, and regional edge fusion is performed on a plurality of second divided regions containing the same regional slope, and a plurality of sandy areas are obtained according to the fusion results.

[0037] In one practicable manner,

[0038] Also includes:

[0039] Find isolated overlapping regions that do not contain both region expansion conditions and region contraction conditions;

[0040] The area distance between the isolated overlapping area and each of the expandable areas is obtained, and the isolated overlapping area is expanded by using the target expandable area with the smallest area distance.

[0041] In one practicable manner,

[0042] The step 4 comprises:

[0043] Step 41: respectively obtaining driving force change information and driving direction change information corresponding to each high-drive driving mode executed by the robot, and determining the starting driving force and the terminal driving force as well as the starting driving direction and the terminal driving direction corresponding to each high-drive driving mode;

[0044] Step 43: Analyze each of the combinations to determine the corresponding intra-group driving force and intra-group driving direction of the robot at different moments, locate the conversion intra-group driving force and conversion intra-group driving direction corresponding to the intra-group conversion moment, and determine the conversion smoothness information of the combination;

[0045] Step 44: when the message passing process information is insufficient, the first stability of the intra-group driving force and the intra-group driving direction and the second stability of the converted intra-group driving force and the converted intra-group driving direction are analyzed by using the principle of physical mechanics, and the conversion process is adjusted according to the first stability and the second stability;

[0046] Step 44: Analyze the first stability of the intra-group driving force and the intra-group driving direction and the second stability of the converted intra-group driving force and the converted intra-group driving direction by using the principle of physical mechanics, and adjust the conversion process according to the first stability and the second stability;

[0047] Step 45: until the first stability and the second stability are consistent, a plurality of continuous driving modes of the robot in the sand are obtained.

[0048] In one practicable manner,

[0049] The step 5 comprises:

[0050] Step 51: simulating each of the continuous driving modes in the virtual sand model, and collecting driving data corresponding to each of the continuous driving modes;

[0051] Step 52: Classify the driving data to obtain a stability data class, a speed data class, and an energy consumption data class corresponding to each robot;

[0052] Step 53: constructing stability information corresponding to the robot in each of the continuous driving modes according to the stability data class, constructing speed information corresponding to the robot in each of the continuous driving modes according to the speed data class, and constructing energy consumption information corresponding to the robot in each of the continuous driving modes according to the energy consumption data class;

[0053] Step 54: Count and display the stability information, speed information and energy consumption information corresponding to each of the continuous driving modes.

[0054] In one practicable manner,

[0055] Also includes:

[0056] Obtain the tracking instruction issued by the management platform, locate the real-time position of the robot according to the tracking instruction and display it.

[0057] The achievable beneficial effects of the above technical solution are as follows: in order to realize rapid survey and thus establish the driving path of the robot, a virtual sand model of the sand is first constructed according to the terrain information and sand quality information of the sand combined with the principles of physical mechanics and geology, and then each driving mode that the simulation robot can realize is simulated therein, thereby obtaining the driving key coefficient corresponding to each driving mode, thereby completing the preliminary survey and simulation work, and obtaining the coefficients that can be used for subsequent analysis, and then using the driving key coefficients to evaluate the driving performance of the robot. After the evaluation, the high-drive driving mode of the robot in different sand areas can be screened, and then the conversion process between the high-drive driving modes is optimized, thereby obtaining the coherent driving mode of the robot in the sand, and finally by simulating each coherent driving mode to reflect the stability information, speed information and energy consumption information of the robot, so that the performance of the robot can be comprehensively evaluated, and various conditions of the sand are studied during the evaluation process to ensure the accuracy and reliability of the simulation and evaluation results, and improve the feasibility of the coherent driving mode.

[0058] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 A schematic diagram of the workflow of a method for simulating and optimizing the sand driving performance of a robot in an embodiment of the present invention;

[0062] Figure 2 Schematic diagram of the workflow of step 1 of a method for simulating and optimizing the sand driving performance of a robot in an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0064] Example 1

[0065] This embodiment provides a method for simulating and optimizing the sand driving performance of a robot. Figure 1 As shown, including:

[0066] Step 1: Collecting the terrain information and sand quality information of the sand and building a virtual sand model of the sand in combination with the principles of physical mechanics and geology;

[0067] Step 2: Simulate several driving modes of the robot in the virtual sand model to obtain a driving key coefficient corresponding to each driving mode;

[0068] Step 3: Evaluate the driving performance of the robot according to the driving key coefficients, and select the high-drive driving modes corresponding to the robot in different sandy areas;

[0069] Step 4: Optimizing the conversion process between different high-drive driving modes based on the physical mechanics principle to generate several coherent driving modes of the robot in the sand;

[0070] Step 5: Simulate each of the continuous driving modes of the robot in the virtual sand model, obtain stability information, speed information and energy consumption information corresponding to each of the continuous driving modes and display them.

[0071] In this example, the driving key coefficients represent the data presented by the robot in different driving modes;

[0072] In this example, the high-drive driving mode indicates that the robot exhibits high-quality driving in a sandy area;

[0073] In this example, the continuous driving mode refers to a driving mode composed of different high-drive driving directions;

[0074] In this example, the sand quality information includes the sand friction coefficient and the sand stability index;

[0075] In this example, the robot can correspond to one or more high-drive driving modes in a sandy area. The high-drive driving mode means that the robot can drive in the sandy area with high quality, that is, it can reach the basic driving speed and can control its movement in all directions;

[0076] In this example, the stability information indicates the stability of the robot when it is traveling on the sand, the speed information indicates the speed of the robot when it is traveling on the sand, and the energy consumption information indicates the electrical energy consumed by the robot when it is traveling on the sand.

[0077] The working principle and beneficial effects of the above technical solution are as follows: in order to achieve rapid survey and thus establish the driving path of the robot, a virtual sand model of the sand is first constructed according to the terrain information and sand quality information of the sand combined with the principles of physical mechanics and geology, and then each driving mode that the simulation robot can realize is simulated therein, thereby obtaining the driving key coefficient corresponding to each driving mode, thereby completing the preliminary survey and simulation work, and obtaining the coefficients that can be used for subsequent analysis, and then using the driving key coefficients to evaluate the driving performance of the robot. After the evaluation, the high-drive driving mode of the robot in different sand areas can be screened, and then the conversion process between the high-drive driving modes is optimized, thereby obtaining the robot's coherent driving mode in the sand, and finally by simulating each coherent driving mode to reflect the robot's stability information, speed information and energy consumption information, so that the robot's performance can be comprehensively evaluated, and various conditions of the sand are studied during the evaluation process to ensure the accuracy and reliability of the simulation and evaluation results, and improve the feasibility of the coherent driving mode.

[0078] Example 2

[0079] On the basis of Example 1, the method for simulating and optimizing the sand driving performance of a robot further includes:

[0080] Topographic survey and sand quality sampling are carried out on the sand land to obtain topographic information and sand quality information of the sand land.

[0081] The working principle and beneficial effects of the above technical solution: The topographic information and sand quality information of the sandy land are obtained through investigation and sampling, which lays the foundation for subsequent modeling work.

[0082] Example 3

[0083] Based on claim 1, the method for simulating and optimizing the sand driving performance of a robot is as follows: Figure 2 As shown, the step 1 includes:

[0084] Step 11: Perform terrain analysis on the terrain information using the geological principle, draw a relief map of the sandy land, and map the sand quality information onto the relief map based on the physical mechanics principle to generate a three-dimensional sandy land image of the sandy land;

[0085] Step 12: analyzing the three-dimensional sand image to determine several types of sand deformation features contained in the sand and the characteristic distribution corresponding to each of the sand deformation features, and marking each of the sand deformation features in the three-dimensional sand image based on the characteristic distribution to generate a three-dimensional characteristic image;

[0086] Step 13: construct corresponding model blocks according to each of the characteristic distributions, combine the model blocks according to the three-dimensional characteristic images, and use computer graphics technology to perform scene rendering on the combination results to generate a virtual sand model of the sand.

[0087] In this example, the three-dimensional sand image represents an image of sand constructed in three-dimensional space;

[0088] In this example, the feature distribution represents the distribution of the same sand deformation feature in the three-dimensional sand image;

[0089] In this example, one characteristic distribution corresponds to one model block, and the model block represents the distribution of the same sand deformation characteristic expressed by the model;

[0090] In this example, the computer graphics technology refers to a method of acquiring an image of sand using a computer and then rendering based on the image of the sand.

[0091] The working principle and beneficial effects of the above technical solution are as follows: first, the terrain information is analyzed using geological principles, and then a relief map of the sand is drawn. A three-dimensional sand image of the sand is generated by fusing the sand quality information with the relief map. In this way, the constructed three-dimensional sand image can be in line with the actual situation, reducing the randomness of subsequent modeling work. Then, a three-dimensional feature image is constructed through the distribution of different sand deformation characteristics in the sand. A model block is constructed by constructing each feature distribution. The model blocks are combined under the guidance of the three-dimensional feature image and rendered at the same time to generate a model of the virtual sand. In this way, not only a model that conforms to the actual situation of the sand can be constructed, but also rendering elements are added to make the generated virtual sand model more aesthetically pleasing.

[0092] Example 4

[0093] On the basis of Example 1, the method for simulating and optimizing the sand driving performance of a robot, step 2 comprises:

[0094] Step 21: Acquire the driving mechanism data of the robot, construct a virtual robot according to the driving mechanism data, calibrate the driving starting point and the driving end point in the virtual sand model, simulate the robot using each driving mode according to the virtual sand model and the virtual robot, and track the driving trajectory and driving speed corresponding to each driving mode in the virtual sand model;

[0095] Step 22: drawing a standard track in the virtual sand model based on the driving starting point and the driving end point, drawing each driving track in the virtual sand model as a simulated track, and adding a corresponding mode identifier to each simulated track according to the corresponding driving mode;

[0096] Step 23: obtaining the track intersection and separation information between each of the simulated tracks and the standard track in the virtual sand model, calling the corresponding driving speed according to the track mode identifier, and determining the first average speed of the virtual robot corresponding to each intersection track area and the second average speed corresponding to each separation track area according to the driving speed and the track intersection and separation information;

[0097] Step 24: respectively obtain the separation amount between each separation trajectory area and the standard trajectory in the same simulation trajectory, and establish the driving key coefficient of the corresponding driving mode in combination with the first average speed and the second average speed.

[0098] In this example, the driving mechanism data represents the data of the wheels and feet of the robot;

[0099] In this example, each driving mode corresponds to a driving track and a driving speed, and different driving modes can correspond to the same driving track and driving speed;

[0100] In this example, the mode identifier represents a means for distinguishing different driving modes;

[0101] In this example, the track intersection and separation information indicates the information of intersection and separation between the simulation track and the standard track. When the track intersects, it corresponds to the intersection track, and when the track separates, it corresponds to the separation track.

[0102] In this example, the separation amount indicates the maximum distance between the corresponding track in the separation track area and the standard track.

[0103] The working principle and beneficial effects of the above technical solution are as follows: by simulating different driving processes of the robot in a virtual desert model, the driving trajectory and driving speed of the robot in different driving modes are determined, and then the distance between different simulated trajectories and the standard trajectory is analyzed to divide the simulated trajectory into an intersecting trajectory area and a separated trajectory area, and the speed of the robot in different trajectory areas is analyzed in combination with the corresponding driving speeds, and finally the driving key coefficient of each driving mode is established in combination with the separation amount of the separated trajectory area. In this way, multiple driving modes can be simulated at the same time, and the key coefficients of the robot's driving process can be analyzed according to the intersection and separation information between the trajectories, thereby achieving the purpose of synchronous simulation and synchronous analysis.

[0104] Example 5

[0105] On the basis of Example 1, the method for simulating and optimizing the sand driving performance of a robot, step 3, comprises:

[0106] Step 31: constructing a qualified driving area and a non-qualified driving area corresponding to each of the driving modes according to the key driving coefficients, marking each of the qualified driving areas and each of the non-qualified driving areas in the virtual sand model, and roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas;

[0107] Step 32: Analyze the driving qualification rate corresponding to each driving mode according to the qualified driving area and the unqualified driving area corresponding to each driving mode, record it as the first evaluation factor, and record the X driving feature, Y driving feature and Z driving feature of the driving information of the robot in each unqualified area as the second evaluation factor;

[0108] Step 33: adding a stability evaluation factor to each qualified driving area corresponding to the driving mode, and combining the first evaluation factor and the second evaluation factor corresponding to the unqualified driving area of ​​each driving mode to evaluate the driving performance characteristics of the robot when implementing each driving mode;

[0109] Step 34: Counting a number of driving performance characteristics corresponding to each of the sandy areas, generating a characteristic sequence for each of the sandy areas, and determining a number of high-speed driving modes corresponding to the robot in different sandy areas in combination with the qualified driving information of the robot.

[0110] In this example, the qualified driving area corresponds to the intersecting trajectory area in Example 4, and the unqualified driving area corresponds to the unqualified driving area in Example 4;

[0111] In this example, the sandy area means a portion of a sandy area;

[0112] In this example, the driving qualification rate is related to the length of the qualified driving area and the length of the unqualified driving area of ​​the driving mode. The longer the qualified driving area, the higher the driving qualification rate.

[0113] In this example, the first evaluation factor represents the evaluation criteria in terms of driving qualification rate, the second evaluation factor represents the evaluation criteria in terms of driving direction, and the stability evaluation factor represents the evaluation criteria for additional compensation for the qualified driving area;

[0114] In this example, qualified driving information means driving information that the robot can perform normal work;

[0115] In this example, the X drive feature, the Y drive feature, and the Z drive feature represent the features exhibited when the robot is driven forward in the XYZ directions.

[0116] The working principle and beneficial effects of the above technical solution are as follows: in order to obtain multiple high-drive driving modes of the robot, firstly, qualified driving areas and unqualified driving areas contained in each driving mode are constructed according to the key driving coefficients, and then the regions are roughly divided in the virtual sand model to obtain several sand areas. An evaluation factor is constructed by analyzing the driving qualification rate corresponding to each driving mode, and then another evaluation factor is constructed according to the driving characteristics in the unqualified area, and a stable evaluation factor is added to the qualified driving area of ​​each driving mode, so as to evaluate the driving performance characteristics corresponding to the robot when implementing each driving mode, and determine the high-drive driving mode of the robot in different sand areas by counting the qualified driving information of the robot set of driving performance characteristics contained in each sand area. In this way, the driving process of the robot can be comprehensively analyzed and evaluated, the driving process of the robot is simulated, and the high-quality driving modes of the robot in different sand areas are screened out, which lays the foundation for the subsequent construction of a coherent driving mode.

[0117] Example 6

[0118] On the basis of Example 5, the method for simulating and optimizing the sand driving performance of a robot, the process of roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas, includes:

[0119] Step a: determining a number of overlapping regions included in the virtual sand model according to the marking result, obtaining the overlapping area of ​​each pair of overlapping regions, and setting corresponding ratio weights for corresponding overlapping regions according to the overlapping areas;

[0120] Step b: adding a region expansion condition to an expandable region whose ratio weight is greater than the average ratio weight, adding a region contraction condition to a plurality of to-be-contracted regions adjacent to the expandable region, and adding the to-be-contracted regions to the corresponding expandable regions based on the region expansion condition and the region contraction condition, to generate a plurality of expanded regions;

[0121] Step c: reorganizing the area of ​​each of the expanded areas to obtain a plurality of divided areas contained in the virtual sand model, determining the terrain slope information of the sand according to the virtual sand model, and determining the regional terrain and regional slope corresponding to each of the divided areas according to the terrain slope information;

[0122] Step d: regional edge fusion is performed on a plurality of first divided regions containing the same regional topography, and regional edge fusion is performed on a plurality of second divided regions containing the same regional slope, and a plurality of sandy areas are obtained according to the fusion results.

[0123] In this example, the larger the overlapping area, the larger the ratio weight, and the ratio weight represents the proportion of the overlapping area in the virtual sand model;

[0124] In this example, the region expansion condition indicates a condition for executing the region expansion operation, and the region contraction condition indicates a condition for executing the region contraction operation;

[0125] In this example, the purpose of regional edge fusion is to classify the divided areas with the same terrain or slope.

[0126] The working principle and beneficial effects of the above technical solution are as follows: in order to achieve a rough division of the sand, several overlapping areas are first determined in the virtual sand model, and then corresponding ratio weights are set for different overlapping areas according to the area of ​​each overlapping area, and expandable areas that can be expanded are selected, and regional expansion conditions are added thereto, and then the areas to be shrunk adjacent to each expandable area are selected, and regional shrinkage conditions are added thereto, and further expansion is performed accordingly according to the conditions, and then the edges of the areas are fused according to the terrain slope information, so as to obtain the required sand area and divide the sand. In this way, the sand is roughly divided into regions. Due to the variability of the desert terrain, the analysis of the terrain often has a lag, so doing so can not only serve as a basis for the subsequent screening of driving modes, but also reduce the amount of analysis and improve the efficiency of analysis.

[0127] Example 7

[0128] On the basis of Example 6, the method for simulating and optimizing the sand driving performance of a robot further includes:

[0129] Find isolated overlapping regions that do not contain both region expansion conditions and region contraction conditions;

[0130] The area distance between the isolated overlapping area and each of the expandable areas is obtained, and the isolated overlapping area is expanded by using the target expandable area with the smallest area distance.

[0131] The working principle and beneficial effects of the above technical solution are: forced fusion of isolated overlapping areas to reduce the difficulty of subsequent work.

[0132] Example 8

[0133] On the basis of Example 1, the method for simulating and optimizing the sand driving performance of a robot, step 4, comprises:

[0134] Step 41: respectively obtaining driving force change information and driving direction change information corresponding to each high-drive driving mode executed by the robot, and determining the starting driving force and the terminal driving force as well as the starting driving direction and the terminal driving direction corresponding to each high-drive driving mode;

[0135] Step 42: Combining different high-speed driving modes to obtain a plurality of combinations, and determining the intra-group conversion process corresponding to each combination by combining the corresponding starting driving force and terminal driving force as well as the starting driving direction and terminal driving direction;

[0136] Step 43: Analyze each of the combinations to determine the corresponding intra-group driving force and intra-group driving direction of the robot at different moments, locate the conversion intra-group driving force and conversion intra-group driving direction corresponding to the intra-group conversion moment, and determine the conversion smoothness information of the combination;

[0137] Step 44: when the message passing process information is insufficient, the first stability of the intra-group driving force and the intra-group driving direction and the second stability of the converted intra-group driving force and the converted intra-group driving direction are analyzed by using the principle of physical mechanics, and the conversion process is adjusted according to the first stability and the second stability;

[0138] Step 45: until the first stability and the second stability are consistent, a plurality of continuous driving modes of the robot in the sand are obtained.

[0139] In this example, when the first stability and the second stability are inconsistent for a long time, it is easy to cause the robot to be in a bumpy state for a long time, which is prone to malfunction;

[0140] In this example, the intra-group switching time represents the switching time between two high-drive driving modes;

[0141] In this example, multiple combinations can be obtained by combining different high-drive driving modes. Therefore, no matter how the robot changes its driving mode during actual driving, it can correspond to the combination.

[0142] The working principle and beneficial effects of the above technical solution are as follows: the corresponding starting driving force and terminal driving force as well as the starting driving direction and terminal driving direction are determined according to the driving force change information and driving direction change information of the robot when executing different high-drive driving modes, so as to find the intra-group conversion process in different combination modes, and determine the corresponding conversion smoothness information, and further adjust the stability of the intra-group drive, forming a corresponding coherent driving mode and realizing stable conversion.

[0143] Example 9

[0144] On the basis of Example 1, the method for simulating and optimizing the sand driving performance of a robot, step 5, comprises:

[0145] Step 51: simulating each of the continuous driving modes in the virtual sand model, and collecting driving data corresponding to each of the continuous driving modes;

[0146] Step 52: Classify the driving data to obtain a stability data class, a speed data class, and an energy consumption data class corresponding to each robot;

[0147] Step 53: constructing stability information corresponding to the robot in each of the continuous driving modes according to the stability data class, constructing speed information corresponding to the robot in each of the continuous driving modes according to the speed data class, and constructing energy consumption information corresponding to the robot in each of the continuous driving modes according to the energy consumption data class;

[0148] Step 54: Count and display the stability information, speed information and energy consumption information corresponding to each of the continuous driving modes.

[0149] The working principle and beneficial effects of the above technical solution are as follows: by simulating each continuous driving mode and collecting the data generated, the stability information, speed information and energy consumption information of the robot under different continuous driving modes are analyzed, so that users can obtain information at any time.

[0150] Example 10

[0151] On the basis of Example 1, the robot sand driving performance simulation and optimization method further includes:

[0152] Obtain the tracking instruction issued by the management platform, locate the real-time position of the robot according to the tracking instruction and display it.

[0153] The working principle and beneficial effects of the above technical solution: In order to improve the intelligence of the method, the robot can be tracked and displayed according to the tracking instructions to provide convenience for the user.

[0154] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for simulating and optimizing the sand driving performance of a robot, characterized in that: include: Step 1: Collecting the terrain information and sand quality information of the sand and building a virtual sand model of the sand in combination with the principles of physical mechanics and geology; Step 2: Simulate several driving modes of the robot in the virtual sand model to obtain a driving key coefficient corresponding to each driving mode; Step 3: Evaluate the driving performance of the robot according to the driving key coefficient, and select the high-drive driving mode corresponding to the robot in different sandy areas; The high-drive driving mode refers to a high-quality driving mode of the robot in a sandy area, which can achieve a basic driving speed and control its movement in all directions; Step 4: Optimizing the conversion process between different high-drive driving modes based on the physical mechanics principle to generate several coherent driving modes of the robot in the sand; Step 5: Simulate each of the continuous driving modes of the robot in the virtual sand model, obtain stability information, speed information and energy consumption information corresponding to each of the continuous driving modes and display them; The step 2 comprises: Step 21: Acquire the driving mechanism data of the robot, construct a virtual robot according to the driving mechanism data, calibrate the driving starting point and the driving end point in the virtual sand model, simulate the robot using each driving mode according to the virtual sand model and the virtual robot, and track the driving trajectory and driving speed corresponding to each driving mode in the virtual sand model; Step 22: drawing a standard track in the virtual sand model based on the driving starting point and the driving end point, drawing each driving track in the virtual sand model as a simulated track, and adding a corresponding mode identifier to each simulated track according to the corresponding driving mode; Step 23: obtaining the track intersection and separation information between each of the simulated tracks and the standard track in the virtual sand model, calling the corresponding driving speed according to the track mode identifier, and determining the first average speed of the virtual robot corresponding to each intersection track area and the second average speed corresponding to each separation track area according to the driving speed and the track intersection and separation information; Step 24: respectively obtaining the separation amount between each separation trajectory area and the standard trajectory in the same simulation trajectory, and establishing a driving key coefficient of the corresponding driving mode in combination with the first average speed and the second average speed; The step 4 comprises: Step 41: respectively obtaining driving force change information and driving direction change information corresponding to each high-drive driving mode executed by the robot, and determining the starting driving force and the terminal driving force as well as the starting driving direction and the terminal driving direction corresponding to each high-drive driving mode; Step 42: Combining different high-speed driving modes to obtain a plurality of combinations, and determining the intra-group conversion process corresponding to each combination by combining the corresponding starting driving force and terminal driving force as well as the starting driving direction and terminal driving direction; Step 43: Analyze each of the combinations to determine the corresponding intra-group driving force and intra-group driving direction of the robot at different moments, locate the conversion intra-group driving force and conversion intra-group driving direction corresponding to the intra-group conversion moment, and determine the conversion smoothness information of the combination; Step 44: when the conversion smoothness information is insufficient, the first stability of the driving force within the group and the driving direction within the group and the second stability of the converted driving force within the group and the converted driving direction within the group are analyzed by using the principle of physical mechanics, and the conversion process is adjusted according to the first stability and the second stability; Step 45: until the first stability and the second stability are consistent, a plurality of continuous driving modes of the robot in the sand are obtained.

2. A robot sand driving performance simulation and optimization method as claimed in claim 1, characterized in that: Also includes: Topographic survey and sand quality sampling are carried out on the sand land to obtain topographic information and sand quality information of the sand land.

3. A robot sand driving performance simulation and optimization method as claimed in claim 1, characterized in that: The step 1 comprises: Step 11: Perform terrain analysis on the terrain information using the geological principle, draw a relief map of the sandy land, and map the sand quality information onto the relief map based on the physical mechanics principle to generate a three-dimensional sandy land image of the sandy land; Step 12: analyzing the three-dimensional sand image to determine several types of sand deformation features contained in the sand and the characteristic distribution corresponding to each of the sand deformation features, and marking each of the sand deformation features in the three-dimensional sand image based on the characteristic distribution to generate a three-dimensional characteristic image; Step 13: construct corresponding model blocks according to each of the characteristic distributions, combine the model blocks according to the three-dimensional characteristic images, and use computer graphics technology to perform scene rendering on the combination results to generate a virtual sand model of the sand.

4. A robot sand driving performance simulation and optimization method as claimed in claim 1, characterized in that: The step 3 comprises: Step 31: constructing a qualified driving area and a non-qualified driving area corresponding to each of the driving modes according to the key driving coefficients, marking each of the qualified driving areas and each of the non-qualified driving areas in the virtual sand model, and roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas; Step 32: Analyze the driving qualification rate corresponding to each driving mode according to the qualified driving area and the unqualified driving area corresponding to each driving mode, record it as the first evaluation factor, and analyze the X driving feature, Y driving feature and Z driving feature of the driving information of the robot in each unqualified driving area, record it as the second evaluation factor; Step 33: adding a stability evaluation factor to each qualified driving area corresponding to the driving mode, and combining the first evaluation factor and the second evaluation factor corresponding to the unqualified driving area of ​​each driving mode to evaluate the driving performance characteristics of the robot when implementing each driving mode; Step 34: Counting a number of driving performance characteristics corresponding to each of the sandy areas, generating a characteristic sequence for each of the sandy areas, and determining a number of high-speed driving modes corresponding to the robot in different sandy areas in combination with the qualified driving information of the robot.

5. A robot sand driving performance simulation and optimization method as claimed in claim 4, characterized in that: The process of roughly dividing the virtual sand model according to the marking results to obtain a plurality of sand areas includes: Step a: determining a number of overlapping regions included in the virtual sand model according to the marking result, obtaining the overlapping area of ​​each pair of overlapping regions, and setting corresponding ratio weights for corresponding overlapping regions according to the overlapping areas; Step b: adding a region expansion condition to an expandable region whose ratio weight is greater than the average ratio weight, adding a region contraction condition to a plurality of to-be-contracted regions adjacent to the expandable region, and adding the to-be-contracted regions to the corresponding expandable regions based on the region expansion condition and the region contraction condition, to generate a plurality of expanded regions; Step c: reorganizing the area of ​​each of the expanded areas to obtain a plurality of divided areas contained in the virtual sand model, determining the terrain slope information of the sand according to the virtual sand model, and determining the regional terrain and regional slope corresponding to each of the divided areas according to the terrain slope information; Step d: regional edge fusion is performed on a plurality of first divided regions containing the same regional topography, and regional edge fusion is performed on a plurality of second divided regions containing the same regional slope, and a plurality of sandy areas are obtained according to the fusion results.

6. A robot sand driving performance simulation and optimization method as claimed in claim 5, characterized in that: Also includes: Find isolated overlapping regions that do not contain both region expansion conditions and region contraction conditions; The area distance between the isolated overlapping area and each of the expandable areas is obtained, and the isolated overlapping area is expanded by using the target expandable area with the smallest area distance.

7. The method for simulating and optimizing the sand driving performance of a robot as claimed in claim 1, characterized in that: The step 5 comprises: Step 51: simulating each of the continuous driving modes in the virtual sand model, and collecting driving data corresponding to each of the continuous driving modes; Step 52: Classify the driving data to obtain a stability data class, a speed data class, and an energy consumption data class corresponding to each robot; Step 53: constructing stability information corresponding to the robot in each of the continuous driving modes according to the stability data class, constructing speed information corresponding to the robot in each of the continuous driving modes according to the speed data class, and constructing energy consumption information corresponding to the robot in each of the continuous driving modes according to the energy consumption data class; Step 54: Count and display the stability information, speed information and energy consumption information corresponding to each of the continuous driving modes.

8. The method for simulating and optimizing the robot's sand driving performance as claimed in claim 1, characterized in that: Also includes: Obtain the tracking instruction issued by the management platform, locate the real-time position of the robot according to the tracking instruction and display it.

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