Method for generating optimal vehicle traveling path in field environment

Abstract

The invention discloses a method for generating an optimal driving path of a vehicle in a field environment, and the method comprises the steps: selecting a field environment region, extracting field obstacle information, and building a field digital map containing soil characteristics and landform characteristic information; the method comprises the following steps: establishing a wheel and soil coupling mechanical model in a field environment based on a vehicle ground mechanics theory, and deducing a vehicle and soil coupling motion equation in the field environment; the method comprises the following steps: randomly generating multiple groups of paths based on a field environment digital map, embedding a wheel and soil coupling mechanical model in a field environment, and calculating an initial automatic obstacle avoidance path; establishing a path optimization algorithm, and obtaining an optimal path in the field environment digital map under the field environment; according to the method, the coupling effect of the wheels and the soil is considered, so that the mechanical characteristics and the motion characteristics of the vehicle in the field environment are calculated, the authenticity of the optimal vehicle advancing path generated in the field environment is improved, the optimal vehicle advancing path in the field can be rapidly obtained, and various obstacles can be smoothly avoided.

Description

Technical Field

[0001] The invention belongs to the technical field of path planning, and in particular relates to a method for generating an optimal path for a vehicle in a field environment. Background Art

[0002] The optimal path for vehicle travel in a wild environment plays an important role in field travel, field search and rescue, and transportation of materials. It also has broad prospects in working environments such as extraterrestrial ground landing, detection, and collection.

[0003] The research on vehicle trafficability and path planning on urban structured roads is relatively mature, but the outdoor environment is more complex, with various potholes, mud and various unknown threats that hinder the movement of vehicles. The complex environment causes troubles and uncertainty to the movement of vehicles. Therefore, it is necessary to quickly analyze the outdoor environment, determine efficient and safe outdoor travel paths, and reduce the uncertainty of outdoor vehicle movement.

[0004] At present, path planning in complex field environments rarely considers the coupled mechanical properties between wheels and soil. Therefore, the optimal path is judged based on the required distance of the path, without considering the actual field environment and factors such as field threats. This will lead to problems such as low passability and poor accuracy of the predicted optimal path when it is applied in actual complex field environments, making it difficult to adapt to the field environment. Summary of the invention

[0005] The present invention provides a method for generating an optimal path for vehicle travel in a field environment, embedding a wheel-soil coupling mechanical model into a path optimization algorithm, and accurately calculating a safe and efficient optimal path for a complex field environment.

[0006] In order to solve the above technical problems, the present invention provides a method for generating an optimal path for a vehicle in a field environment, comprising the following steps:

[0007] Step 100, selecting a field environment area, extracting field obstacle information, and establishing a field digital map containing soil characteristics and landform feature information;

[0008] Step 200: Based on the vehicle-terrain mechanics theory, a wheel-soil coupling mechanics model in a field environment is established, and a motion equation of the vehicle-soil coupling in a field environment is derived;

[0009] Step 300: randomly generate multiple groups of paths based on the digital map of the field environment, embed a wheel and soil coupling mechanical model in the field environment, and calculate an initial automatic obstacle avoidance path;

[0010] Step 400: Establish a path optimization algorithm to obtain the optimal path in the outdoor environment digital map under the outdoor environment.

[0011] As a preferred solution of the present invention, in step 100, based on the geographic information data of the current field environment area, Matlab software is used to convert the geographic information data into a field environment digital map containing elevations of different coordinate points, obstacle distribution and soil property information;

[0012] According to the range of obstacles, the obstacles are simplified into circular and polygonal geometric shapes.

[0013] As a preferred solution of the present invention, in step 200, the wheel and soil coupling mechanical model is established based on the Lagrange equation;

[0014] Based on the wheel-soil coupling mechanical model, the variation laws of settlement, slip rate, hook traction and travel resistance parameters are obtained, and the motion equation of vehicle-soil coupling in field environment is established.

[0015] As a preferred solution of the present invention, in step 300, the wheel and soil coupling mechanical model embedding method is:

[0016] Fix the starting point and the end point on the digital map of the outdoor environment, randomly generate multiple groups of coordinate points within the coordinate range of the starting point and the end point, and integrate all the coordinate points to form a generated path, wherein the coordinate points represent the horizontal coordinate and the vertical coordinate of the point on the path respectively;

[0017] Using the motion equation of the vehicle and soil coupling in the field environment, the time required for the vehicle to pass on the generated path is calculated;

[0018] The distance between each coordinate point on the generated path and the starting point coordinate is calculated, and each generated path is sorted in order according to the distance, and then the N generated paths are formed into an initial path population.

[0019] As a preferred solution of the present invention, in step 300, after the initial path population is generated, it is cyclically determined whether the generated path individuals in the initial path population intersect with the obstacle until a path capable of automatically avoiding obstacles is generated. The specific implementation method is as follows:

[0020] Based on the coordinate information of obstacles and soil, the obstacle area and the soft soil area are divided into zones in the field environment digital map; for the obstacle area, the passability thereof is set to 0;

[0021] When generating a path, determine whether the path intersects with the obstacle area. If so, remove the path and generate a new path.

[0022] As a preferred solution of the present invention, a method for determining whether the generated path in the initial path population intersects with the obstacle is as follows:

[0023] Take 50 points from each segment of each generated path to determine whether each point falls in the middle of the circular obstacle. At the same time, use the vector cross product calculation method to determine whether each point falls in the middle of the rectangular obstacle.

[0024] As a preferred solution of the present invention, in step 400, a path optimization algorithm is established to obtain the optimal path in the digital map of the outdoor environment in the outdoor environment in the following manner:

[0025] Based on the dynamic model, a motion equation of the vehicle in a field environment is obtained, and the travel time of the vehicle on each generated path in the initial path population is calculated;

[0026] A fitness function is established based on the travel time to find the minimum value of the travel time, so the fitness function is as follows:

[0027]

[0028] Among them, F(x i ) is the fitness of the i-th generated path, t i The time required for vehicle passage.

[0029] Based on the roulette wheel selection method, N generation paths with large fitness are selected, and crossover operations and mutation operations are performed to form new N generation paths.

[0030] As a preferred embodiment of the present invention, the selection method based on the roulette selection method is:

[0031] The probability of each generated path in the initial path population being selected is calculated, and the selection probability of the i-th generated path is calculated based on the fitness function as follows:

[0032]

[0033] Among them, p(x i ) is the probability of the i-th generated path being selected, F(x i ) is the fitness of the i-th generated path, and N is the population size;

[0034] Calculate the cumulative probability of each generated path, then the cumulative probability of the i-th generated path is:

[0035]

[0036] Among them, q i is the cumulative probability of the i-th generation path, that is, the sum of the probabilities of all generation paths before the i-th generation path.

[0037] A number r is randomly generated in the interval [0, 1], and the random number r is compared with the cumulative probability q of the i-th generated path in the initial path population. i Compare them one by one. If r>q i , then skip the i-th generated path and compare it with the next generated path until r≤q j , then the j-th generated path is selected as the object;

[0038] A crossover operation and a mutation operation are performed on the N generated paths to form new N child paths.

[0039] As a preferred solution of the present invention, the roulette wheel selection method performs crossover and mutation operations on the N generated paths to form new N child paths in the following manner:

[0040] The crossover operation uses a two-point crossover method and sets the crossover probability P c , generate a random number r in the range [0,1] c , judge each generation path in the N generation paths as the parent, if r c <P c , then the i-th generated path is intersected with the i+1-th generated path;

[0041] The specific crossover operation is: randomly select two coordinate points for the i-th generated path selected for crossover, and exchange the coordinates in the i-th generated path that are within the range of the two coordinate points with the i+1-th generated path;

[0042] The mutation operation adopts the single-point mutation method and sets the mutation probability P m , generate a random number r in the range [0, 1] m , judge each generation path in the N generation paths as the parent, if r m <P m , then the i-th generated path will be mutated, otherwise no mutation will occur;

[0043] The specific mutation operation is: randomly generate two mutation points for the i-th generation path selected for mutation, and then reverse the coordinate points of the two mutation points to obtain the offspring chromosome.

[0044] As a preferred solution of the present invention, the fitness of the generated N offspring chromosomes is calculated, and the selection, crossover and mutation operations are repeated on the N offspring chromosomes until the maximum evolutionary generation is reached to obtain the optimal path.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention proposes a method for calculating the mechanical characteristics of a vehicle in a field environment. Aiming at the complex real environment in the field, a method considering the coupling effect of wheels and soil is proposed, so as to calculate the mechanical characteristics and motion characteristics of the vehicle in the field environment and improve the authenticity of the optimal path of the vehicle generated in the field environment.

[0047] The present invention also proposes a method for calculating the optimal path of a vehicle in a wild environment, which integrates the mechanical characteristics of the vehicle traveling in the wild, can quickly obtain the optimal path of the vehicle traveling in the wild, and can successfully avoid various obstacles. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0049] Figure 1 is a flow chart of finding the optimal travel path in a field environment provided by an embodiment of the present invention;

[0050] Figure 2 is a schematic diagram of a circular obstacle in a field environment provided by an embodiment of the present invention;

[0051] Figure 3 This is an optimal path result diagram in a complex outdoor environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Path planning in a field environment is an important method to ensure that equipment can pass efficiently, safely and quickly in a complex field environment. However, the calculation of path planning in actual engineering currently does not take into account field soil characteristics and vehicle mechanical behavior. The present invention solves the above problems and realizes accurate obstacle avoidance and efficient travel of vehicles in a field environment. The present invention is further described below in conjunction with embodiments and drawings.

[0054] like Figure 1 As shown, this embodiment takes a vehicle in a field environment with multiple obstacles as a specific application background, and provides a method for generating an optimal path for the vehicle in the field environment, including the following steps:

[0055] Step 100: Select a field environment area, extract field obstacle information, and establish a field digital map containing soil characteristics and landform feature information.

[0056] In step 100, based on the geographic information data of the current field environment area, Matlab software is used to convert the geographic information data into a field environment digital map containing elevations of different coordinate points, obstacle distribution and soil property information; according to the range of the obstacles, the obstacles are simplified into circular and polygonal geometric shapes.

[0057] Preliminary information processing is performed based on on-site exploration or satellite radar information, and the obstacle area is analyzed and merged to determine the location information and geometric information of the obstacle. In the example, the map size is 50×50, the center of the vehicle is located at the starting point (0, 0), and the end point is located at (50, 50).

[0058] Circular obstacles with radii of 5, 5 and 3 are set at (4, 15), (20, 20) and (35, 15) respectively. There are also two rectangular obstacles with vertex coordinates of {(12, 9), (12, 11), (18, 11), (18, 9)} and {(35, 30), (35, 35), (40, 35), (40, 30)} respectively.

[0059] Therefore, in this embodiment, Figure 2 and Figure 3 As shown, four circular obstacles and three circular obstacles and two rectangular obstacles are set in the field environment. There is no limit on the number of obstacles during actual operation. The fitness calculated in the embodiment is related to vehicle performance and soil characteristics, and the vehicle travel time is then calculated.

[0060] Step 200: Based on the vehicle-terrain mechanics theory, a wheel-soil coupling mechanics model in a field environment is established, and a motion equation of the vehicle-soil coupling in a field environment is derived.

[0061] Specifically, a wheel-soil coupling mechanical model is established based on the Lagrange equation. Based on the wheel-soil coupling mechanical model, the change laws of settlement, slip rate, hook traction and travel resistance parameters are obtained, and the motion equation of vehicle-soil coupling in the field environment is established.

[0062] Step 300: randomly generate multiple groups of paths based on the digital map of the field environment, embed the wheel and soil coupling mechanical model in the field environment, and calculate the initial automatic obstacle avoidance path.

[0063] Among them, the embedding method of the wheel and soil coupling mechanical model is:

[0064] (1) The starting and ending positions are fixed on the digital map of the field environment, and multiple groups of coordinate points within the coordinate range of the starting and ending points are randomly generated. All the coordinate points are integrated to form a generated path, where the coordinate points represent the horizontal and vertical coordinates of the points on the path, respectively.

[0065] (2) Using the motion equation of the vehicle-soil coupling in the field environment, the time required for the vehicle to travel on the generated path is calculated.

[0066] (3) Calculate the distance between each coordinate point on the generated path and the starting point coordinate, sort each generated path in order of distance, and then form the N generated paths into an initial path population.

[0067] As an example of this implementation, the initial path population size is set to 1000, the evolution generation number is 10000, the initial chromosome number is 4, and 1000 groups of horizontal coordinates and vertical coordinates of the randomly generated path are generated. Each generated path is 4 groups of point coordinates, that is, the horizontal coordinate generation path genx i =[genxx i,j , genxx i,j+1 , genxx i,j+2 , genxx i,j+3 ], vertical coordinate generation path geny i =[genyy i,j ,genyy i,j+1 ,genyy i,j+2 ,genyy i,j+3 ] to represent the direction of the path.

[0068] The starting point coordinates (x s ,y s ) and target coordinates (x t ,y t ) is added to the population, then the horizontal coordinate of each generated path becomes genx i =[x s , genxx i,j , genxx i,j+1 , genxx i,j+2 , genxx i,j+3 , x t ], the vertical coordinate of each generated path becomes geny i =[y s ,genyy i,j ,genyy i,j+1 ,genyy i,j+2 ,genyy i,j+3 ,y t ].

[0069] After the initial path population is generated, it is necessary to perform cyclic judgment processing on the generated path individuals in the initial path population until a path that can automatically avoid obstacles is generated. The specific implementation method is as follows:

[0070] Based on the coordinate information of obstacles and soil, the obstacle area and soft soil area are divided in the digital map of the field environment; for the obstacle area, its passability is set to 0;

[0071] When generating a path, determine whether the path intersects with the obstacle area. If so, remove the path and generate a new path. The specific method for determining whether the generated path in the initial path population intersects with the obstacle is as follows:

[0072] Take 50 points from each segment of each generated path to determine whether each point falls in the middle of the circular obstacle. At the same time, use the vector cross product calculation method to determine whether each point falls in the middle of the rectangular obstacle.

[0073] In combination with the example of this implementation mode, it is determined whether each point falls in the middle of the circular obstacle, that is:

[0074] (x-xobs(t)) 2 +(y-yobs(t)) 2 -robs(t) 2 ≤0

[0075] Among them, x and y are the horizontal and vertical coordinates of the taken point respectively, xobs(t) is the horizontal coordinate of the center of the t-th obstacle circle, yobs(t) is the vertical coordinate of the center of the t-th obstacle circle, and robs(t) is the radius of the t-th obstacle.

[0076] At the same time, to determine whether each point falls in the middle of the rectangular obstacle, the vector cross product calculation method is used, that is:

[0077] (P2-P1)×(P-P1)*(P4-P3)×(P-P3)≥0

[0078] and,

[0079] (P3-P2)×(P-P2)*(P1-P4)×(P-P4)≥0

[0080] Among them, P1~P4 are the four vertices of the rectangle, P is the point taken on each path in the individual, the symbol × is the vector cross multiplication, and the symbol * is the multiplication sign between numbers.

[0081] If it is determined that the individual intersects with the obstacle, the work of step 300 is repeated to regenerate a different path, and continue to determine whether the generated path in the initial path population intersects with the obstacle, until each generated route in the generated population does not intersect with the obstacle.

[0082] Step 400: Establish a path optimization algorithm to obtain the optimal path in the outdoor environment digital map under the outdoor environment.

[0083] The overall process of establishing a path optimization algorithm is:

[0084] (1) Calculate the travel distance of each generated path, calculate the vehicle travel time t(i) based on vehicle dynamics, and then obtain the fitness F(x i );

[0085] (2) Use the roulette wheel method to filter out the generated paths with low fitness and form a new path population;

[0086] (3) Retain the optimal generated path in the new path population, judge each generated path, and form a new path population through crossover and mutation operations;

[0087] (4) Repeat the above steps (1), (2) and (3) for the new path population, perform cyclic iterations, and obtain the final optimal path.

[0088] The specific implementation method of establishing a path optimization algorithm and obtaining the optimal path in the digital map of the outdoor environment is as follows:

[0089] Based on the dynamic model, the motion equation of the vehicle in the field environment is obtained, and the travel time of the vehicle on each generated path in the initial path population is calculated;

[0090] A fitness function is established based on the travel time to find the minimum travel time, so the fitness function is as follows:

[0091]

[0092] Among them, F(x i ) is the fitness of the i-th generated path, t i The time required for vehicle passage.

[0093] Based on the roulette wheel selection method, N generation paths with large fitness are selected, and crossover and mutation operations are performed to form new N generation paths.

[0094] The selection method based on the roulette wheel selection method is:

[0095] Calculate the probability of each generated path in the initial path population being selected. Specifically, the selection probability of the i-th generated path is calculated based on the fitness function:

[0096]

[0097] Among them, p(x i ) is the probability of the i-th generated path being selected, F(xi ) is the fitness of the i-th generated path, and N is the population size;

[0098] Calculate the cumulative probability of each generated path, then the cumulative probability of the i-th generated path is:

[0099]

[0100] Among them, q i is the cumulative probability of the i-th generated path, that is, the sum of the probabilities of all generated paths before the i-th generated path.

[0101] Generate a random number r in the interval [0, 1] and compare the random number r with the cumulative probability q of the i-th generated path in the initial path population. i Compare them one by one, and select N generation paths with large fitness based on the comparison results. Specifically, if r>q i , then skip the i-th generated path and compare it with the next generated path until r≤q j , then the jth generated path is selected.

[0102] Perform crossover and mutation operations on the N generated paths to form new N child paths.

[0103] The implementation method of performing crossover and mutation operations on the N generation paths generated by the roulette wheel selection method to form new N child paths is as follows:

[0104] The crossover operation uses a two-point crossover method and sets the crossover probability P c , the general crossover probability range is [0.6, 0.9], and then generate a random number r in the range of [0,1] c , judge each generation path in the N generation paths as the parent, if r c <P c , then the i-th generated path is intersected with the i+1-th generated path;

[0105] The specific crossover operation is: randomly select two coordinate points for the i-th generated path selected for crossover, and exchange the coordinates in the i-th generated path that are within the range of the two coordinate points with the i+1-th generated path;

[0106] The mutation operation adopts the single-point mutation method and sets the mutation probability P m The mutation probability range is generally [0.0001, 0.1], generating a random number r in the range of [0, 1]. m , judge each generation path in the N generation paths as the parent, if r m <P m, then the i-th generated path will be mutated, otherwise no mutation will occur;

[0107] The specific mutation operation is: randomly generate two mutation points for the i-th generation path selected for mutation, and then reverse the coordinates of the two mutation points to obtain the offspring chromosomes.

[0108] Calculate the fitness of the N generated offspring chromosomes, repeat the selection, crossover and mutation operations on the N offspring chromosomes until the maximum number of evolutionary generations is reached and the optimal path is obtained.

[0109] The final calculation results can be found in Figure 3 ,The results show that the outdoor path planning algorithm proposed in ,the present invention can achieve efficient obstacle avoidance and has good ,solving capability.

[0110] Based on the above, this embodiment proposes a method for calculating the mechanical characteristics of a vehicle in a wild environment. In view of the complex real environment in the wild, a method considering the coupling effect of wheels and soil is proposed to calculate the mechanical characteristics and motion characteristics of the vehicle in the wild environment. Based on the method of the mechanical characteristics of the vehicle in the wild environment, a method for calculating the optimal path of the vehicle in the wild environment is also proposed. By integrating the mechanical characteristics of the vehicle in the wild, the optimal path of the vehicle in the wild can be quickly obtained, and various obstacles can be avoided smoothly.

[0111] The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application. The protection scope of the present application is defined by the claims. Those skilled in the art may make various modifications or equivalent substitutions to the present application within the essence and protection scope of the present application, and such modifications or equivalent substitutions shall also be deemed to fall within the protection scope of the present application.

Claims

1. A method for generating an optimal path for a vehicle in a field environment, the method comprising the following steps: Step 100: Select a field environment area, extract field obstacle information, and establish a field digital map containing soil characteristics and landform feature information; Step 200: Based on the vehicle-terrain mechanics theory, a wheel-soil coupling mechanics model in a field environment is established, and a motion equation of the vehicle-soil coupling in a field environment is derived; Step 300: randomly generate multiple groups of paths based on the digital map of the field environment, embed a wheel and soil coupling mechanical model in the field environment, and calculate an initial automatic obstacle avoidance path; Step 400: Establish a path optimization algorithm to obtain the optimal path in the outdoor environment digital map under the outdoor environment.

2. The method for generating an optimal path for a vehicle in a field environment according to claim 1, characterized in that: In the step 100, based on the geographic information data of the current field environment area, Matlab software is used to convert the geographic information data into a field environment digital map containing the elevation of different coordinate points, obstacle distribution and soil property information; According to the range of obstacles, the obstacles are simplified into circular and polygonal geometric shapes.

3. The method for generating an optimal path for a vehicle in a field environment according to claim 1, characterized in that: In the step 200, the wheel and soil coupling mechanical model is established based on the Lagrange equation; Based on the wheel-soil coupling mechanical model, the variation laws of settlement, slip rate, hook traction and travel resistance parameters are obtained, and the motion equation of vehicle-soil coupling in field environment is established.

4. A method for generating an optimal path for a vehicle in a field environment according to claim 1 or 3, characterized in that: In step 300, the wheel and soil coupling mechanical model embedding method is: Fix the starting point and the end point on the digital map of the outdoor environment, randomly generate multiple groups of coordinate points within the coordinate range of the starting point and the end point, and integrate all the coordinate points to form a generated path, wherein the coordinate points represent the horizontal coordinate and the vertical coordinate of the point on the path respectively; Using the motion equation of the vehicle and soil coupling in the field environment, the time required for the vehicle to pass on the generated path is calculated; The distance between each coordinate point on the generated path and the starting point coordinate is calculated, and each generated path is sorted in order according to the distance, and then the N generated paths are formed into an initial path population.

5. The method for generating an optimal path for a vehicle in a field environment according to claim 4, characterized in that: In step 300, after the initial path population is generated, it is cyclically determined whether the generated path individuals in the initial path population intersect with the obstacle until a path capable of automatically avoiding obstacles is generated. The specific implementation method is as follows: Based on the coordinate information of obstacles and soil, the obstacle area and the soft soil area are divided into zones in the field environment digital map; for the obstacle area, the passability thereof is set to 0; When generating a path, determine whether the path intersects with the obstacle area. If so, remove the path and generate a new path.

6. The method for generating an optimal path for a vehicle in a field environment according to claim 5, characterized in that: The implementation method of determining whether the generated path in the initial path population intersects with the obstacle is: Take 50 points from each segment of each generated path to determine whether each point falls in the middle of the circular obstacle. At the same time, use the vector cross product calculation method to determine whether each point falls in the middle of the rectangular obstacle.

7. The method for generating an optimal path for a vehicle in a field environment according to claim 1, characterized in that: In step 400, a path optimization algorithm is established to obtain the optimal path in the outdoor environment digital map in the outdoor environment in the following manner: Based on the dynamic model, a motion equation of the vehicle in a field environment is obtained, and the travel time of the vehicle on each generated path in the initial path population is calculated; A fitness function is established based on the travel time to find the minimum value of the travel time, so the fitness function is as follows: Among them, F(x i ) is the fitness of the i-th generated path, t i The time required for vehicle passage. Based on the roulette wheel selection method, N generation paths with large fitness are selected, and crossover operations and mutation operations are performed to form new N generation paths.

8. The method for generating an optimal path for a vehicle in a field environment according to claim 7, characterized in that: The selection method based on the roulette wheel selection method is: The probability of each generated path in the initial path population being selected is calculated, and the selection probability of the i-th generated path is calculated based on the fitness function as follows: Among them, p(x i ) is the probability of the i-th generated path being selected, F(x i ) is the fitness of the i-th generated path, and N is the population size; Calculate the cumulative probability of each generated path, then the cumulative probability of the i-th generated path is: Among them, q i is the cumulative probability of the i-th generation path, that is, the sum of the probabilities of all generation paths before the i-th generation path. A number r is randomly generated in the interval [0, 1], and the random number r is compared with the cumulative probability q of the i-th generated path in the initial path population. i Compare them one by one. If r>q i , then skip the i-th generated path and compare it with the next generated path until r≤q j , then the j-th generated path is selected as the object; A crossover operation and a mutation operation are performed on the N generated paths to form new N child paths.

9. The method for generating an optimal path for a vehicle in a field environment according to claim 8, characterized in that: The implementation method of performing crossover operation and mutation operation on the N generated paths of the roulette wheel selection method to form new N child paths is as follows: The crossover operation uses a two-point crossover method and sets the crossover probability P c , generate a random number r in the range [0,1] c , judge each generation path in the N generation paths as the parent, if r c <P c , then the i-th generated path is intersected with the i+1-th generated path; The specific crossover operation is: randomly select two coordinate points for the i-th generated path selected for crossover, and exchange the coordinates in the i-th generated path that are within the range of the two coordinate points with the i+1-th generated path; The mutation operation adopts a single-point mutation method and sets the mutation probability P m , generate a random number r in the range [0, 1] m , judge each generation path in the N generation paths as the parent, if r m <P m , then the i-th generated path will be mutated, otherwise no mutation will occur; The specific mutation operation is: randomly generate two mutation points for the i-th generation path selected for mutation, and then reverse the coordinate points of the two mutation points to obtain the offspring chromosome.

10. The method for generating an optimal path for a vehicle in a field environment according to claim 9, characterized in that: The fitness of the generated N offspring chromosomes is calculated, and the selection, crossover and mutation operations are repeated on the N offspring chromosomes until the maximum number of evolutionary generations is reached to obtain the optimal path.

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