Caragana korshinskii stumping mobile platform path planning method and planning system thereof

Abstract

The invention belongs to the technical field of path planning, and particularly relates to a caragana microphylla stumping mobile platform path planning method and a planning system thereof. On the basis of an A * algorithm, a grid fixed distance is improved in combination with operation requirements, a dynamic weight and a corner optimization function are introduced to complete global path planning of the caragana microphylla stumping mobile platform, the improved algorithm improves the operation efficiency, and the overall motion stability of the caragana microphylla stumping mobile platform is improved. The device is used for solving the problem that the stumping effect is poor in the caragana korshinskii stumping operation environment with changeable terrains and complex obstacles. The robot can adapt to the working environment with changeable terrains and complex obstacles in shallow mountains and hills.

Description

Technical Field

[0001] The invention belongs to the technical field of path planning, and in particular relates to a path planning method and a planning system of a mobile platform for caragana striata pruning. Background Art

[0002] It is of great strategic significance to prevent and control land desertification and sustainably manage desertified land. Caragana korshinskii is an important windbreak and sand-fixing plant, but it will decline after 3-5 years of growth and needs to be regularly pruned and rejuvenated. At present, there are several ways to prune Caragana korshinskii in the Northwest region: one is to manually use tools to plan, chop, cut or use a brush cutter for pruning, but the operation efficiency is low and the labor intensity is high; the second is to use self-propelled machinery or large machinery similar to silage machines for pruning. This method does not have the ability to adapt to the terrain and is only suitable for flat terrain. The stubble height after pruning is inconsistent, the incision is not smooth, and it is difficult to meet the agronomic requirements of pruning. In the first case, there are problems such as high labor demand, difficulty in recruiting workers, and high labor costs. In the second case, large-scale silage equipment and shrub cutting equipment are only suitable for flat terrain, not suitable for the complex terrain of Caragana korshinskii pruning operation environment, and the pruning effect is not good.

[0003] The operating environment of the mobile platform for Caragana sutchuenensis stubble is a hilly area with changeable terrain and complex obstacles. The geometric feature method is sensitive to environmental changes, and the topological map does not accurately describe the shape and boundary of obstacles. Summary of the invention

[0004] The invention provides a path planning method for a mobile platform for caragana strife trimming, which is used to solve the problem of poor trimming effect in a caragana strife trimming operation environment with changeable terrain and complex obstacles.

[0005] The invention provides a path planning system for a mobile platform for pruning caragana korshinskii, which is used for realizing a path planning method for a mobile platform for pruning caragana korshinskii.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for path planning of a mobile platform for pruning caragana korshinskii, the method comprising the following steps:

[0008] Step 1: Build an environment model;

[0009] Step 2: Based on the environmental model constructed in step 1, a global path planning algorithm for the mobile platform for caragana shavings is constructed;

[0010] Step 3: Reconstruct the local path planning algorithm of the mobile platform for caragana striata stubble;

[0011] Step 4: Based on the global path planning algorithm of step 2 and the local path planning algorithm of step 3, the path planning of the mobile platform for caragana striata pruning is realized.

[0012] Furthermore, the step 1 specifically includes the following steps:

[0013] Step 1.1: Set the grid cells;

[0014] Step 1.2: Expand the obstacles;

[0015] Step 1.3: Create a map based on the grid cells in step 1.1 and the obstacles in step 1.2.

[0016] Furthermore, the step 2 specifically includes the following steps:

[0017] Step 2.1: Based on the setting of grid cells in step 1.1, fix the distance of grid cells;

[0018] Step 2.2: Introduce dynamic weights to the existing A* algorithm;

[0019] Step 2.3: Add corner optimization function based on step 2.2;

[0020] Step 2.4: Based on steps 2.1 to 2.3, a global path planning algorithm is constructed.

[0021] Furthermore, the step 2.1 is specifically to change the distance between grid cells from a random number of 1 to 11 to a fixed value of 10 in order to make the path more in line with intuitive logic and improve the search speed.

[0022] Furthermore, the step 2.2 is specifically as follows:

[0023] Introducing weights on h(n) is the dynamic weighting heuristic A*, expressed as:

[0024] f(n)=g(n)+ω·h(n) (5)

[0025] Where: n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current node to the target destination;

[0026] If h(n) is usually less than the actual path cost, increasing h(n) reduces the number of expansion nodes and increases the search speed;

[0027] If h(n) is equal to the actual path cost, the shortest path will be searched quickly and accurately; if h(n) is usually greater than the actual path cost, it is not the optimal path but the search is fast;

[0028] Increasing the h(n) weight when there are many obstacles can further effectively speed up the search speed. Reducing the h(n) weight when there are fewer obstacles can improve the path quality while ensuring the search speed.

[0029] Furthermore, the step 2.3 is specifically to add the corner cost c(n) to equation (5), namely:

[0030] f(n)=g(n)+ω·h(n)+λc(n) (6)

[0031] c(n) calculates the position angle of (n-1)-n-(n+1). The larger the angle, the greater the cost. A smoother path will be selected when judging the optimal path. Corner optimization has obvious advantages in maps with multiple obstacles.

[0032] Furthermore, the step 3 uses a dynamic window method to obtain the optimal path, specifically,

[0033] The speed space under the limit speed constraint can be expressed as:

[0034] V s ={(v,ω)|v∈[v min ,v max ],ω∈[ω min ,ω max ]} (7)

[0035] Where: v min ,ω min represents the minimum speed, angular velocity; v max ,ω max Represents maximum speed, angular velocity;

[0036] The velocity space under dynamic constraints such as torque and site friction can be expressed as:

[0037]

[0038] Where: v c ,ω c Represents the current movement speed and angular velocity; Represents the maximum acceleration and deceleration; represents the maximum angular acceleration; Δt is the sampling interval;

[0039] The velocity space of the mobile platform can be expressed as:

[0040]

[0041] Where: dist(v,ω) represents the Euclidean distance between the obstacle and the mobile platform for stubble removal;

[0042] The velocity space intersection is obtained under the constraint:

[0043] V r =V s ∩V d ∩V a(10)

[0044] The trajectory evaluation function in the velocity sampling space is:

[0045] G(v,w)=s(a×heading(v,w)+b×dist(v,w)+g×velocity(v,w)) (11)

[0046] Where: heading(v,ω) is the azimuth evaluation; dist(v,ω) is the distance evaluation; velocity(v,ω) is the speed evaluation; σ is the smoothing function; α, β, γ are weighting functions.

[0047] A path planning system for a mobile platform for pruning caragana korshinskii. The system uses the path planning method for a mobile platform for pruning caragana korshinskii as described above. The system comprises:

[0048] An environment building unit, used for building an environment model;

[0049] The path planning unit is used to construct a global path planning algorithm for the mobile platform for Caragana striata pruning, and then construct a local path planning algorithm for the mobile platform for Caragana striata pruning; and realize the path planning of the mobile platform for Caragana striata pruning.

[0050] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the above method is implemented.

[0051] A computer-readable storage medium stores a computer program, and the computer program implements the above method when executed by a processor.

[0052] The beneficial effects of the present invention are:

[0053] The present invention can adapt to the working environment in hilly areas with changeable terrain and complex obstacles.

[0054] The present invention can complete the stubble work in an operating environment with changeable terrain and complex obstacles.

[0055] The present invention can increase movement stability and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a schematic diagram of generating a grid map according to the present invention.

[0057] Figure 2 This is a schematic diagram of the overall flow of the existing A* algorithm.

[0058] Figure 3Schematic diagram of the simulation results of the existing A* algorithm examples, where (a) is the simulation result of Example 1, and (b) is the simulation result of Example 2.

[0059] Figure 4 1 is a schematic diagram of simulation results of the improved grid distance A* algorithm of the present invention, wherein (a) is the simulation result of Example 1, and (b) is the simulation result of Example 2.

[0060] Figure 5 It is a schematic diagram of the algorithm simulation results when h(n)=0 and g(n)=0 of the A* algorithm of the present invention, wherein (a) is the simulation result of the Dijkstra algorithm, and (b) is the simulation result of the greedy best-first search.

[0061] Figure 6 It is a schematic diagram of simulation results of different weights h(n) for maps with different numbers of obstacles of the present invention, wherein (a) is the simulation result of a map with many obstacles of ω=1.05, (b) is the simulation result of a map with many obstacles of ω=2, (c) is the simulation result of a map with few obstacles of ω=1.05, and (d) is the simulation result of a map with few obstacles of ω=2.

[0062] Figure 7 It is a schematic diagram of the simulation comparison results of the final optimization algorithm and the basic optimization algorithm of the present invention, wherein (a) is the simulation result of the basic A* algorithm, and (b) is the simulation result of the final optimization algorithm.

[0063] Figure 8 It is a schematic diagram of the overall flow of the dynamic window method of the present invention.

[0064] Fig. 9 It is a schematic diagram of the velocity sampling space of the present invention.

[0065] Fig.10 1 is a schematic diagram of the local dynamic obstacle avoidance simulation of discrete small obstacles of the present invention, wherein (a) is the overall simulation result, (b) is the simulation exploration process, and (c) is the simulation optimal path.

[0066] Fig.11 1 is a schematic diagram of the local dynamic obstacle avoidance simulation of discrete large obstacles of the present invention, wherein (a) is the simulation exploration process and (b) is the simulation optimal path.

[0067] Fig.12 1 is a schematic diagram of the local dynamic obstacle avoidance simulation of a complex combination of obstacles of the present invention, wherein (a) is the simulation exploration process, and (b) is the simulation optimal path.

[0068] Fig.13 It is a flow chart of the method of the present invention.

[0069] Fig.14 It is a schematic diagram of the mobile obstacle avoidance simulation result of the present invention. DETAILED DESCRIPTION

[0070] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0071] It should be understood that when used in this specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0072] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, the singular forms "a", "an" and "the" are intended to include plural forms unless the context clearly indicates otherwise.

[0073] The following is attached to this application specification Figure 1-13 , the technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0074] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0075] Implementation Method 1

[0076] This embodiment provides a path planning method for a mobile platform for tamarisk stubble. The invention is based on the A* algorithm, improves the fixed distance of the grid in combination with the operation requirements, introduces dynamic weights and corner optimization functions to complete the global path planning of the mobile platform for tamarisk stubble. The improved algorithm improves the operation efficiency and increases the overall motion stability. The local path planning of the mobile platform for tamarisk stubble is completed by the DWA algorithm to reduce the path deviation.

[0077] The method comprises the following steps,

[0078] Step 1: Build an environment model;

[0079] Step 2: Based on the environmental model constructed in step 1, a global path planning algorithm for the mobile platform for caragana shavings is constructed;

[0080] Step 3: Reconstruct the local path planning algorithm of the mobile platform for caragana striata stubble;

[0081] Step 4: Based on the global path planning algorithm of step 2 and the local path planning algorithm of step 3, the path planning of the mobile platform for caragana striata pruning is realized.

[0082] Furthermore, the step 1 specifically includes the following steps:

[0083] Step 1.1: Set the grid cells;

[0084] The constructed environmental model should have good accuracy while ensuring computational efficiency and reducing data processing time. Therefore, it is necessary to set an appropriate grid cell size. The grid cell size d setting condition is:

[0085]

[0086] Where: d is the grid unit size; s0 is the total area of ​​the obstacle; s m is the total area of ​​the environment; d max is the maximum obstacle length; d min is the minimum obstacle length.

[0087] Step 1.2: Expand the obstacles;

[0088] In the process of path planning, the mobile platform of Caragana suturing is usually regarded as a mass point. In order to avoid collision caused by its own physical size when the mobile platform of Caragana suturing approaches obstacles, the obstacle position is taken as the base point, and the obstacle is expanded according to the size of the mobile platform of Caragana suturing and the safety distance. The expansion grid is:

[0089] N=[(R r +D s ) / 2l g ]+1 (2)

[0090] Where: N is the number of expansions; R r D is the maximum radius of the mobile platform for cutting caragana korshinskii. s is a safe distance; g is the grid cell size.

[0091] Step 1.3: Create a map based on the grid cells in step 1.1 and the obstacles in step 1.2.

[0092] After rasterizing the collected map information, assign the grid with obstacles as 1 and the grid that can be passed smoothly as 0. Take the grid unit size d = 1m, the obstacle extension N = 1, and generate a 20×20 map as shown below Figure 1 shown.

[0093] Furthermore, the step 2 specifically includes the following steps:

[0094] Step 2.1: Based on the setting of grid cells in step 1.1, fix the distance of grid cells;

[0095] Step 2.2: Introduce dynamic weights to the existing A* algorithm;

[0096] Step 2.3: Add corner optimization function based on step 2.2;

[0097] Step 2.4: Based on steps 2.1 to 2.3, a global path planning algorithm is constructed.

[0098] Furthermore, the step 2.1 is specifically as follows: Figure 3 It can be seen that the existing A* algorithm has many search paths, the search trend is not obvious, and the overall calculation efficiency is low. In order to make the path more in line with intuitive logic and improve the search speed, the distance between grid cells is changed from a random number of 1 to 11 to a fixed value of 10. This can reduce the complex conditional judgment caused by calculation variables and uncertainty, reduce random sensitivity, simplify the calculation process, make the algorithm converge quickly, and the calculated path cost is more in line with the actual path cost. The simulation results after improvement are as follows Figure 4 .

[0099] It can be seen that the predictability and consistency of the improved algorithm are significantly improved, which effectively improves the efficiency and inspiration of path search.

[0100] Furthermore, the existing A* algorithm is specifically as follows:

[0101] The cost of the position node in the A* algorithm evaluation space is:

[0102] f(n)=g(n)+h(n) (3)

[0103] Where n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current node to the target destination.

[0104] g(n) is a known term representing the cost of the searched path, and h(n) is an unknown term that determines the overall search efficiency. The calculation formula for h(n) is:

[0105]

[0106] Where: (xn ,y n ) is the node coordinate of the current position; (x goal ,y goal ) is the target location node coordinate, the overall algorithm flow is as follows Figure 2 .

[0107] Generate multiple 100×100 grid maps with different obstacle locations and starting and target locations, using A*

[0108] The algorithm completes the simulation, and the simulation results are as follows: Figure 3 .

[0109] The step 2.2 is specifically as follows:

[0110] In order to increase the adaptability of the algorithm in the operating environment of hilly and complex terrain, improve the efficiency of the algorithm, and exclude infeasible and costly paths among the alternative paths, the weight is introduced into formula (3) h(n), that is, the dynamic weight heuristic A*, which is expressed as:

[0111] f(n)=g(n)+ω·h(n) (5)

[0112] Where: n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current node to the target destination;

[0113] When h(n) = 0, only g(n) is valid, which is the Dijkstra algorithm; when g(n) = 0, only h(n) is valid, which is the greedy best first search. The simulation results of h(n) = 0 and g(n) = 0 are as follows Figure 5 ,Dijkstra takes a long time to ensure the best path, ,greedy best first search is fast ,but the path quality is low. Figure 4 (b)

[0114] The A* algorithm combines the advantages of both in searching for the same obstacle and starting point target position;

[0115] If h(n) is usually less than the actual path cost, increasing h(n) can reduce the number of extended nodes and increase the search speed; if h(n) is equal to the actual path cost, the shortest path will be searched quickly and accurately; if h(n) is usually greater than the actual path cost, it is not the optimal path but the search is fast; adjust the h(n) weight to ω=1.05 and ω=2 in

[0116] Figure 4 (b) Simulation results on a map with many obstacles and a map with few obstacles are shown in the figure below. Figure 6 .

[0117] Figure 6 The algorithm can be compared after adjusting the weights Figure 5 (b) The search speed is significantly increased, and when there are many obstacles, the h(n) weight can be appropriately increased, which can further effectively speed up the search speed. When there are fewer obstacles, the h(n) weight can be reduced, which can improve the path quality while ensuring the search speed.

[0118] Furthermore, the step 2.3 is as follows: the terrain of the mobile platform for tamarisk stubble cutting is undulating. Frequent or sharp turns during the movement not only affect the stability of the mobile platform for tamarisk stubble cutting, but also increase the driving time and energy consumption. Therefore, a corner optimization function is added to the algorithm to improve the path smoothness, improve the integrity and reduce energy consumption. Add the corner cost c(n) to equation (5):

[0119] f(n)=g(n)+ω·h(n)+λc(n) (6)

[0120] c(n) calculates the position angle of (n-1)-n-(n+1). The larger the angle, the greater the cost. When judging the optimal path, a smoother path will be selected. Corner optimization has obvious advantages in multi-obstacle maps. We select a multi-obstacle map and use the basic A* algorithm and the algorithm simulation comparison after adding the corner optimization function. The results are as follows: Figure 7 , operating parameters are shown in Table 4.1.

[0121] Table 4.1 Comparison of simulation parameters of the final optimization algorithm and the basic A* algorithm

[0122]

[0123] Depend on Figure 7 As shown in Table 4.1, the path length of the final optimization algorithm is the same as that of the basic A* algorithm, but the running time is greatly reduced, the algorithm efficiency is significantly improved, and at the same time, the number of corners in the constructed path is significantly reduced, and the path smoothness is better, which can effectively increase the stability of the mobile platform for Caragana sutchuenensis pruning and reduce energy consumption.

[0124] Furthermore, the step 3 uses the dynamic window method to obtain the optimal path. Specifically, due to the structure and control accuracy of the mobile platform of the tamarisk stubble, a certain path deviation will occur during the movement. In order to avoid collision accidents caused by path deviation, local path planning should be performed. The dynamic window method (DWA) can quickly calculate the path by limiting the search space to accelerate the decision, and at the same time considers the kinematic and dynamic parameters such as the maximum speed and maximum acceleration of the mobile platform of the tamarisk stubble, ensuring the feasibility of the path.

[0125] The core of the dynamic window method is to convert the position constraints of the wheeled caragana stubble mobile platform into speed constraints, sample the possible speeds based on these speed constraints, and generate a series of candidate motion trajectories. The trajectory is evaluated through the evaluation function, and the trajectory with the highest score is selected as the optimal path. The overall process is as follows: Figure 8 .

[0126] The speed space under the limit speed constraint can be expressed as:

[0127] V s ={(v,ω)|v∈[v min ,v max ],ω∈[ω min ,ω max ]} (7)

[0128] Where: v min ,ω min represents the minimum speed, angular velocity; v max ,ω max Represents maximum speed, angular velocity;

[0129] The velocity space under dynamic constraints such as torque and site friction can be expressed as:

[0130]

[0131] Where: v c ,ω c Represents the current movement speed and angular velocity; Represents the maximum acceleration and deceleration; represents the maximum angular acceleration; Δt is the sampling interval;

[0132] The velocity space of the mobile platform can be expressed as:

[0133]

[0134] Where: dist(v,ω) represents the Euclidean distance between the obstacle and the mobile platform for stubble removal;

[0135] The velocity sampling space is Fig. 9 , is the velocity space intersection under the constraint:

[0136] V r =V s ∩V d ∩V a (10)

[0137] The trajectory evaluation function in the velocity sampling space is:

[0138] G(v,w)=s(a×heading(v,w)+b×dist(v,w)+g×velocity(v,w)) (11)

[0139] Where: heading(v,ω) is the azimuth evaluation; dist(v,ω) is the distance evaluation; velocity(v,ω) is the speed evaluation; σ is the smoothing function; α, β, γ are weighting functions.

[0140] Generate a 20×20 map, set the starting point to (0,0), the target point to (20,20), and take the maximum speed v max =1.6m / s, maximum acceleration a max =0.2m / s 2 , maximum angular velocity ω=60° / s, maximum angular acceleration ω a =15° / s 2 , set the obstacle safety distance to 1m, and take the best weighting coefficients α=0.05, β=0.2, γ=0.1 after multiple simulation experiments. Set multiple discrete small obstacles to simulate staff, livestock, etc. to perform local dynamic obstacle avoidance simulation. The simulation results are as follows Fig.10 .

[0141] Set up discrete large obstacles to simulate vehicles, equipment, etc. to perform local dynamic obstacle avoidance simulation. The simulation results are as follows Fig.11 .

[0142] According to the simulation results, in the local dynamic obstacle avoidance simulation of discrete small and large obstacles, the algorithm can find the path to the target location and maintain a safe distance from the obstacles, and the path quality is good. Increasing the difficulty of obstacles makes the simulation closer to the most complex actual situation. The simulation is set up with discrete and tightly combined large and small obstacles. The simulation results are as follows: Fig.12 In the complex combined obstacle map, the algorithm can also find a path to the target location with good path quality. The path does not directly pass through the gap at the obstacle, while maintaining a safe distance from the obstacle. In this case, the path chooses to bypass the upper part instead of trying to pass through the gap between the discrete obstacles and the combined obstacles below. Otherwise, the path search may stagnate or take a big detour to reach the target point. The rationality of the weight ratio can be verified. Therefore, the DWA algorithm can effectively complete the local path planning of the Caragana stubble mobile platform and significantly improve the dynamic obstacle avoidance capability of the Caragana stubble mobile platform.

[0143] The specific simulation experiment is as follows:

[0144] The operating environment of the caragana pruning robot is complex. In order to detect whether the algorithm can effectively plan a safe operating path, the terrain obstacles are simulated and the motion obstacle avoidance simulation is completed to verify the reliability of the operation.

[0145] After setting the starting point, target point, and obstacles, the simulation results of the path planning algorithm are as follows Fig.14It can be seen that a path that avoids collision with obstacles can be effectively found from the starting point to the target point, and the search process is guided, the path quality is good, and the safe distance between the center of the vehicle and the obstacle is kept above 1, which verifies the reliability of the algorithm.

[0146] Implementation Method 2

[0147] This embodiment provides a path planning system for a mobile platform for pruning caragana korshinskii. The system uses the path planning method for a mobile platform for pruning caragana korshinskii as described in the first embodiment. The system includes:

[0148] An environment building unit, used for building an environment model;

[0149] The path planning unit is used to construct a global path planning algorithm and then a local path planning algorithm to realize the path planning of the mobile platform for pruning.

[0150] Furthermore, the specific working principle of the environment construction unit is:

[0151] Set grid cells;

[0152] The constructed environmental model should have good accuracy while ensuring computational efficiency and reducing data processing time. Therefore, it is necessary to set an appropriate grid cell size. The grid cell size d setting condition is:

[0153]

[0154] Where: d is the grid unit size; s0 is the total area of ​​the obstacle; s m is the total area of ​​the environment; d max is the maximum obstacle length; d min is the minimum obstacle length.

[0155] Extended obstacles;

[0156] In the process of path planning, the mobile platform of Caragana suturing is usually regarded as a mass point. In order to avoid collision caused by its own physical size when the mobile platform of Caragana suturing approaches obstacles, the obstacle position is taken as the base point, and the obstacle is expanded according to the size of the mobile platform of Caragana suturing and the safety distance. The expansion grid is:

[0157] N=[(R r +D s ) / 2l g ]+1 (2)

[0158] Where: N is the number of expansions; R r D is the maximum radius of the mobile platform for cutting caragana korshinskii. s is a safe distance; g is the grid cell size.

[0159] Map creation based on grid cells and obstacles.

[0160] After rasterizing the collected map information, assign the grid with obstacles as 1 and the grid that can be passed smoothly as 0. Take the grid unit size d = 1m, the obstacle extension N = 1, and generate a 20×20 map as shown below Figure 1 shown.

[0161] Furthermore, the global path planning algorithm constructed in the path planning unit is specifically:

[0162] Based on setting the grid unit, fix the distance of the grid unit;

[0163] Introducing dynamic weights into the existing A* algorithm;

[0164] Then, we introduce the corner optimization function based on the A* algorithm with dynamic weights;

[0165] The global path planning algorithm is constructed.

[0166] Furthermore, the distance between the fixed grid units is specifically: Figure 3 It can be seen that the existing A* algorithm has many search paths, the search trend is not obvious, and the overall calculation efficiency is low. In order to make the path more in line with intuitive logic and improve the search speed, the distance between grid cells is changed from a random number of 1 to 11 to a fixed value of 10. This can reduce the complex conditional judgment caused by calculation variables and uncertainty, reduce random sensitivity, simplify the calculation process, make the algorithm converge quickly, and the calculated path cost is more in line with the actual path cost. The simulation results after improvement are as follows Figure 4 .

[0167] It can be seen that the predictability and consistency of the improved algorithm are significantly improved, which effectively improves the efficiency and inspiration of path search.

[0168] Furthermore, the existing A* algorithm is specifically as follows:

[0169] The cost of the position node in the A* algorithm evaluation space is:

[0170] f(n)=g(n)+h(n) (3)

[0171] Where n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current node to the target destination.

[0172] g(n) is a known term representing the cost of the searched path, and h(n) is an unknown term that determines the overall search efficiency. The calculation formula for h(n) is:

[0173]

[0174] Where: (x n ,y n ) is the node coordinate of the current position; (x goal ,y goal ) is the target location node coordinate, the overall algorithm flow is as follows Figure 2 .

[0175] Generate multiple 100×100 grid maps with different obstacle locations and starting and target locations, using A*

[0176] The algorithm completes the simulation, and the simulation results are as follows: Figure 3 .

[0177] The specific method of introducing dynamic weights into the existing A* algorithm is as follows:

[0178] In order to increase the adaptability of the algorithm in the operating environment of hilly and complex terrain, improve the efficiency of the algorithm, and exclude infeasible and costly paths among the alternative paths, the weight is introduced into formula (3) h(n), that is, the dynamic weight heuristic A*, which is expressed as:

[0179] f(n)=g(n)+ω·h(n) (5)

[0180] Where: n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current node to the target destination;

[0181] When h(n) = 0, only g(n) is valid, which is the Dijkstra algorithm; when g(n) = 0, only h(n) is valid, which is the greedy best first search. The simulation results of h(n) = 0 and g(n) = 0 are as follows Figure 5 ,Dijkstra takes a long time to ensure the best path, ,greedy best first search is fast ,but the path quality is low. Figure 4 (b) The A* algorithm combines the advantages of both algorithms in searching for the same obstacle and starting point location;

[0182] If h(n) is usually less than the actual path cost, increasing h(n) can reduce the number of extended nodes and increase the search speed; if h(n) is equal to the actual path cost, the shortest path will be searched quickly and accurately; if h(n) is usually greater than the actual path cost, it is not the optimal path but the search is fast; adjust the h(n) weight to ω=1.05 and ω=2 in Figure 4 (b) Simulation results on a map with many obstacles and a map with few obstacles are shown in the figure below. Figure 6 .

[0183] Figure 6 The algorithm can be compared after adjusting the weights Figure 5(b) The search speed is significantly increased, and when there are many obstacles, the h(n) weight can be appropriately increased, which can further effectively speed up the search speed. When there are fewer obstacles, the h(n) weight can be reduced, which can improve the path quality while ensuring the search speed.

[0184] Furthermore, the corner optimization function is added on the basis of the A* algorithm with dynamic weights. Specifically, the terrain of the mobile platform for tamarisk stubble operation is undulating. Frequent or sharp turns during movement not only affect the stability of the mobile platform for tamarisk stubble but also increase the driving time and energy consumption. Therefore, a corner optimization function is added on the basis of the algorithm to improve the path smoothness, improve the integrity and reduce energy consumption. Adding the corner cost c(n) on the basis of formula (5) is:

[0185] f(n)=g(n)+ω·h(n)+λc(n) (6)

[0186] c(n) calculates the position angle of (n-1)-n-(n+1). The larger the angle, the greater the cost. When judging the optimal path, a smoother path will be selected. Corner optimization has obvious advantages in multi-obstacle maps. We select a multi-obstacle map and use the basic A* algorithm and the algorithm simulation comparison after adding the corner optimization function. The results are as follows: Figure 7 , operating parameters are shown in Table 4.1.

[0187] Table 4.1 Comparison of simulation parameters of the final optimization algorithm and the basic A* algorithm

[0188]

[0189] Depend on Figure 7 As shown in Table 4.1, the path length of the final optimization algorithm is the same as that of the basic A* algorithm, but the running time is greatly reduced, the algorithm efficiency is significantly improved, and at the same time, the number of corners in the constructed path is significantly reduced, and the path smoothness is better, which can effectively increase the stability of the mobile platform for Caragana sutchuenensis pruning and reduce energy consumption.

[0190] Furthermore, the dynamic window method is used in the local path planning algorithm to obtain the optimal path. Specifically, due to the structure and control accuracy of the mobile platform of the Caragana striata, a certain path deviation will occur during the movement. In order to avoid collision accidents caused by path deviation, local path planning should be performed. The dynamic window method (DWA) can quickly calculate the path by limiting the search space to accelerate the decision, and at the same time considers the kinematic and dynamic parameters such as the maximum speed and maximum acceleration of the mobile platform of the Caragana striata, ensuring the feasibility of the path.

[0191] The core of the dynamic window method is to convert the position constraints of the wheeled caragana stubble mobile platform into speed constraints, sample the possible speeds based on these speed constraints, and generate a series of candidate motion trajectories. The trajectory is evaluated through the evaluation function, and the trajectory with the highest score is selected as the optimal path. The overall process is as follows: Figure 8 .

[0192] The speed space under the limit speed constraint can be expressed as:

[0193] V s ={(v,ω)|v∈[v min ,v max ],ω∈[ω min ,ω max ]} (7)

[0194] Where: v min ,ω min represents the minimum speed, angular velocity; v max ,ω max Represents maximum speed, angular velocity;

[0195] The velocity space under dynamic constraints such as torque and site friction can be expressed as:

[0196]

[0197] Where: v c ,ω c Represents the current movement speed and angular velocity; Represents the maximum acceleration and deceleration; represents the maximum angular acceleration; Δt is the sampling interval;

[0198] The velocity space of the mobile platform can be expressed as:

[0199]

[0200] Where: dist(v,ω) represents the Euclidean distance between the obstacle and the mobile platform for stubble removal;

[0201] The velocity sampling space is Fig. 9 , is the velocity space intersection under the constraint:

[0202] V r =V s ∩V d ∩V a (10)

[0203] The trajectory evaluation function in the velocity sampling space is:

[0204] G(v,w)=s(a×heading(v,w)+b×dist(v,w)+g×velocity(v,w)) (11)

[0205] Where: heading(v,ω) is the azimuth evaluation; dist(v,ω) is the distance evaluation; velocity(v,ω) is the speed evaluation; σ is the smoothing function; α, β, γ are weighting functions.

[0206] Generate a 20×20 map, set the starting point to (0,0), the target point to (20,20), and take the maximum speed v max =1.6m / s, maximum acceleration a max =0.2m / s 2 , maximum angular velocity ω=60° / s, maximum angular acceleration ω a =15° / s 2 , set the obstacle safety distance to 1m, and take the best weighting coefficients α=0.05, β=0.2, γ=0.1 after multiple simulation experiments. Set multiple discrete small obstacles to simulate staff, livestock, etc. to perform local dynamic obstacle avoidance simulation. The simulation results are as follows Fig.10 .

[0207] Set up discrete large obstacles to simulate vehicles, equipment, etc. to perform local dynamic obstacle avoidance simulation. The simulation results are as follows Fig.11 .

[0208] According to the simulation results, in the local dynamic obstacle avoidance simulation of discrete small and large obstacles, the algorithm can find the path to the target location and maintain a safe distance from the obstacles, and the path quality is good. Increasing the difficulty of obstacles makes the simulation closer to the most complex actual situation. The simulation is set up with discrete and tightly combined large and small obstacles. The simulation results are as follows: Fig.12 In the complex combined obstacle map, the algorithm can also find a path to the target location with good path quality. The path does not directly pass through the gap at the obstacle, while maintaining a safe distance from the obstacle. In this case, the path chooses to bypass the upper part instead of trying to pass through the gap between the discrete obstacles and the combined obstacles below. Otherwise, the path search may stagnate or take a big detour to reach the target point. The rationality of the weight ratio can be verified. Therefore, the DWA algorithm can effectively complete the local path planning of the Caragana stubble mobile platform and significantly improve the dynamic obstacle avoidance capability of the Caragana stubble mobile platform.

[0209] Implementation Method 3

[0210] An embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the memory is used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory and the processor are connected via a bus. Specifically, the processor implements any step in the first embodiment above by running the computer program stored in the memory.

[0211] It should be understood that in the embodiments of the present invention, the processor referred to may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0212] The memory may include a read-only memory, a flash memory, and a random access memory, and provides instructions and data to the processor. A part or all of the memory may also include a nonvolatile random access memory.

[0213] It can be seen from the above that the electronic device provided in the embodiment of the present invention can implement the path planning method of the mobile platform for tamarisk pruning as described in the first embodiment by running a computer program, thereby improving the operation efficiency and increasing the overall movement stability.

[0214] It should be understood that if the above-mentioned integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method implementations when executed by the processor. Among them, the above-mentioned computer program includes computer program code, and the above-mentioned computer program code can be in source code form, object code form, executable file or some intermediate form. The above-mentioned computer-readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the above-mentioned computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction.

[0215] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest range consistent with the principles and novel features disclosed herein.

[0216] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the above-mentioned device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0217] It should be noted that the methods and detailed examples provided in the above embodiments can be combined with the devices and equipment provided in the embodiments, and references can be made to each other, and no further details will be given.

[0218] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0219] In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal equipment and method can be implemented in other ways. For example, the device / equipment implementation described above is only illustrative, for example, the division of the above modules or units is only a logical function division, and in actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0220] The above-mentioned embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above-mentioned embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A path planning method for a mobile platform for pruning caragana korshinskii, characterized in that: The method comprises the following steps, Step 1: Build an environment model; Step 2: Based on the environmental model constructed in step 1, a global path planning algorithm for the mobile platform for caragana shavings is constructed; Step 3: Reconstruct the local path planning algorithm of the mobile platform for caragana striata stubble; Step 4: Based on the global path planning algorithm of step 2 and the local path planning algorithm of step 3, the path planning of the mobile platform for caragana striata pruning is realized.

2. According to claim 1, a method for path planning of a mobile platform for trimming caragana korshinskii is characterized in that: The step 1 specifically includes the following steps: Step 1.1: Set the grid cells; Step 1.2: Expand the obstacles; Step 1.3: Create a map based on the grid cells in step 1.1 and the obstacles in step 1.

2.

3. According to claim 2, a method for path planning of a mobile platform for trimming caragana korshinskii is characterized in that: The step 2 specifically includes the following steps: Step 2.1: Based on the setting of grid cells in step 1.1, fix the distance of grid cells; Step 2.2: Introduce dynamic weights to the existing A* algorithm; Step 2.3: Add corner optimization function based on step 2.2; Step 2.4: Based on steps 2.1 to 2.3, a global path planning algorithm is constructed.

4. According to claim 3, a method for path planning of a mobile platform for trimming caragana korshinskii is characterized in that: Specifically, step 2.1 is to change the distance between grid cells from a random number of 1 to 11 to a fixed value of 10 in order to make the path more logically intuitive and improve the search speed.

5. According to claim 3, a method for path planning of a mobile platform for trimming caragana korshinskii is characterized in that: The step 2.2 is specifically as follows: Introducing weights on h(n) is the dynamic weighting heuristic A*, expressed as: f(n)=g(n)+ω·h(n) (5) Where: n is the coordinate of the current node; f(n) is the total cost of the current node; g(n) is the actual cost from the starting point to the current node; h(n) is the estimated cost from the current location node to the target end point; If h(n) is usually less than the actual path cost, increasing h(n) reduces the number of expansion nodes and increases the search speed; If h(n) is equal to the actual path cost, the shortest path will be searched quickly and accurately; if h(n) is usually greater than the actual path cost, it is not the optimal path but the search is fast; Increasing the h(n) weight when there are many obstacles can further effectively speed up the search speed. Reducing the h(n) weight when there are fewer obstacles can improve the path quality while ensuring the search speed.

6. A method for path planning of a mobile platform for trimming caragana according to claim 5, characterized in that: Specifically, step 2.3 is to add the corner cost c(n) to equation (5), namely: f(n)=g(n)+ω·h(n)+λc(n) (6) c(n) calculates the position angle of (n-1)-n-(n+1). The larger the angle, the greater the cost. A smoother path will be selected when judging the optimal path. Corner optimization has obvious advantages in maps with multiple obstacles.

7. The path planning method for a mobile platform for trimming caragana korshinskii according to claim 1, characterized in that: The step 3 uses a dynamic window method to obtain the optimal path, specifically, The speed space under the limit speed constraint can be expressed as: V s ={(v,ω)|v∈[v min ,v max ],ω∈[ω min ,ω max ]} (7) Where: v min ,ω min represents the minimum speed, angular velocity; v max ,ω max Represents maximum speed, angular velocity; The velocity space under dynamic constraints such as torque and site friction can be expressed as: Where: v c ,ω c Represents the current movement speed and angular velocity; Represents the maximum acceleration and deceleration; represents the maximum angular acceleration; Δt is the sampling interval; The velocity space of the mobile platform can be expressed as: Where: dist(v,ω) represents the Euclidean distance between the obstacle and the mobile platform for stubble removal; The velocity space intersection is obtained under the constraint: V r =V s ∩V d ∩V a (10) The trajectory evaluation function in the velocity sampling space is: G(v,w)=s(a×heading(v,w)+b×dist(v,w)+g×velocity(v,w)) (11) Where: heading(v,ω) is the azimuth evaluation; dist(v,ω) is the distance evaluation; velocity(v,ω) is the speed evaluation; σ is the smoothing function; α, β, γ are weighting functions.

8. A path planning system for a mobile platform for trimming caragana korshinskii, characterized in that: The system uses the path planning method for a mobile platform for pruning caragana as described in any one of claims 1 to 7, and the system includes: An environment building unit, used for building an environment model; The path planning unit is used to construct a global path planning algorithm for the mobile platform for Caragana striata pruning, and then construct a local path planning algorithm for the mobile platform for Caragana striata pruning; and realize the path planning of the mobile platform for Caragana striata pruning.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images