Dynamic path planning method for cluster trolley

Abstract

The invention provides a dynamic path planning method for a cluster trolley, and the method comprises the steps: obtaining an environment image, carrying out the rasterizing of the environment image, and obtaining a rasterized map; performing path planning on the rasterized map based on a Bransen-Hamm straight line algorithm to obtain an initial planning path; and performing local path optimization on the initial planning path based on the artificial potential field to obtain an optimal planning path. Compared with the prior art, the method has the advantages that on the basis of the Bransen-Hamm straight line algorithm, the path of path planning cannot be attached to the obstacle, and collision caused by obstacle size change due to control delay or camera shaking is reduced; the thought of turning points and commutation points also abandons a large amount of unnecessary node operation so as to ensure extremely high search efficiency, and the problem of local minimum is solved through an artificial potential field method.

Description

Technical Field

[0001] The present invention is applicable to the field of automatic control technology, and in particular relates to a dynamic path planning method for cluster vehicles. Background Art

[0002] Cluster car technology has developed rapidly in recent years and is widely used in logistics, warehousing, smart manufacturing and other fields. These cars are usually equipped with sensors, controllers and actuators, and can autonomously complete tasks such as path planning, obstacle avoidance and navigation. However, with the complexity of application scenarios, traditional path planning methods can no longer meet actual needs. The current path planning methods mainly include the following three methods:

[0003] Artificial potential field path planning: The technical principle is simple and easy to use for real-time control at the bottom layer. It has advantages in real-time obstacle avoidance and smooth trajectory control of robots. However, artificial potential field path planning methods usually have local minima. Although there are many improved methods for local minima, no completely satisfactory answer has been found so far. In addition, there are human uncertainties in the design of gravitational and repulsive fields, and the amount of calculation is too large when there are many obstacles.

[0004] Genetic algorithm: It is the earliest intelligent optimization algorithm applied to combinatorial optimization problems. This algorithm and its derivative algorithms have been applied in the field of robot path planning research. The ant colony algorithm is good at solving the traveling salesman problem (TSP). The disadvantage is that the computational efficiency is not high.

[0005] Traditional A* algorithm: When faced with complex environments and highly dynamic tasks in path planning, the search speed is slow and there are many redundant nodes. In addition, the dynamic route has many broken lines, many turns, and large turning angles. Moreover, it is close to obstacles, which may cause collisions due to control delays or changes in the size of obstacles caused by camera shaking.

[0006] Therefore, a new dynamic path planning method for cluster vehicles is urgently needed to solve the above technical problems. Summary of the invention

[0007] The present invention provides a dynamic path planning method for cluster vehicles, aiming to improve the efficiency and accuracy of path planning for cluster vehicles.

[0008] The present invention provides a dynamic path planning method for cluster vehicles, and the dynamic path planning method comprises the following steps:

[0009] S1, acquiring an environment image, and performing rasterization processing on the environment image to obtain a rasterized map;

[0010] S2, performing path planning on the rasterized map based on the Bresenham straight line algorithm to obtain an initial planned path;

[0011] S3. Perform local path optimization on the initial planned path based on the artificial potential field to obtain an optimal planned path.

[0012] Preferably, step S2 includes the following sub-steps:

[0013] S21, setting a starting point and a target point in the rasterized map, and adding a safety window according to obstacles in the rasterized map;

[0014] S22, emitting a plurality of rays from a preset direction of the starting point by using the Bresenham line algorithm to determine a visible area in the rasterized map;

[0015] S23, marking the coordinate points where the light encounters the obstacle to obtain a plurality of key points; wherein the key points include turning points and reversal points, the key points being points where the light changes direction due to the influence of the obstacle, and the reversal points being points where the direction of the light is adjusted;

[0016] S24, determining whether the light reaches the target point;

[0017] If not, the costs of the multiple key points are calculated, and the key point with the smallest cost is used as a new starting point, and the process returns to step S22;

[0018] If so, multiple key points with the lowest cost are integrated to obtain the initial planning path.

[0019] Preferably, when the obstacle exists in the upper left or lower right of the key point and there is no obstacle in the horizontal direction or the vertical direction, the key point is a turning point.

[0020] Preferably, when the obstacle exists to the upper left of the key point and there is no obstacle above or to the left of the key point, the key point is a turning point;

[0021] The reversing point includes a horizontal reversing point and a vertical reversing point. The horizontal reversing point is the point where the direction of the light needs to be adjusted after the horizontal path of the light is intercepted by the obstacle. The vertical reversing point is the point where the direction of the light needs to be adjusted after the vertical path of the light is intercepted by the obstacle.

[0022] Preferably, in step S3, a repulsive potential field is provided to the cluster vehicles at key points in the initial planned path based on the artificial potential field, so as to achieve local path optimization of the initial planned path.

[0023] Preferably, the repulsive potential field satisfies the following rules:

[0024]

[0025] Among them, K r represents a constant, d represents the distance between the cluster car and the obstacle, d m represents a preset distance threshold, and F represents the repulsive potential field.

[0026] Compared with the prior art, the present invention obtains an environmental image, performs rasterization processing on the environmental image, and obtains a rasterized map; performs path planning on the rasterized map based on the Bresenham line algorithm to obtain an initial planned path; performs local path optimization on the initial planned path based on an artificial potential field to obtain an optimal planned path. The present invention is based on the Bresenham line algorithm, which can achieve that the route of path planning will not be close to obstacles, reducing collisions caused by changes in the size of obstacles due to control delays or camera jitters; the idea of ​​turning points and reversal points also reduces a large number of unnecessary node operations to ensure extremely fast search efficiency, and solves the local minimum problem through the artificial potential field method. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention will be described in detail below in conjunction with the accompanying drawings. The above and other aspects of the present invention will become clearer and easier to understand through the detailed description made in conjunction with the following drawings. In the accompanying drawings:

[0028] Figure 1 It is a flowchart of a dynamic path planning method for cluster vehicles provided by an embodiment of the present invention;

[0029] Figure 2 It is a rasterized map of the dynamic path planning method of cluster vehicles provided by an embodiment of the present invention;

[0030] Figure 3 It is a path planning schematic diagram of a dynamic path planning method for cluster vehicles provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] Please refer to Figure 1-Figure 3 The present invention provides a dynamic path planning method for cluster vehicles, and the dynamic path planning method comprises the following steps:

[0033] S1, acquiring an environment image, and performing rasterization processing on the environment image to obtain a rasterized map;

[0034] S2. Performing path planning on the rasterized map based on the Bresenham straight line algorithm to obtain an initial planned path;

[0035] S3. Perform local path optimization on the initial planned path based on the artificial potential field to obtain an optimal planned path.

[0036] In this embodiment of the present invention, step S2 includes the following sub-steps:

[0037] S21, setting a starting point and a target point in the rasterized map, and adding a safety window according to obstacles in the rasterized map;

[0038] S22, emitting a plurality of rays from the starting point in preset directions (up, down, left, right, upper left, lower left, upper right, lower right) by using the Bresenham line algorithm to determine a visible area in the rasterized map;

[0039] S23, marking the coordinate points where the light encounters the obstacle to obtain a plurality of key points; wherein the key points include turning points and reversal points, the key points being points where the light changes direction due to the influence of the obstacle, and the reversal points being points where the direction of the light is adjusted;

[0040] S24, determining whether the light reaches the target point;

[0041] If not, the costs of the multiple key points are calculated, and the key point with the smallest cost is used as a new starting point, and the process returns to step S22;

[0042] If so, multiple key points with the lowest cost are integrated to obtain the initial planning path.

[0043] In the embodiment of the present invention, when the obstacle exists in the upper left or lower right of the key point and does not exist in the horizontal direction or the vertical direction, the key point is a turning point.

[0044] In the embodiment of the present invention, when the obstacle exists to the upper left of the key point and the obstacle does not exist above or to the left of the key point, the key point is a reversal point;

[0045] The reversing point includes a horizontal reversing point and a vertical reversing point. The horizontal reversing point is the point where the direction of the light needs to be adjusted after the horizontal path of the light is intercepted by the obstacle. The vertical reversing point is the point where the direction of the light needs to be adjusted after the vertical path of the light is intercepted by the obstacle.

[0046] In an embodiment of the present invention, in step S3, a repulsive potential field is provided to the cluster vehicles at key points in the initial planned path based on the artificial potential field, thereby achieving local path optimization of the initial planned path.

[0047] In this embodiment of the present invention, the repulsive potential field satisfies the following rules:

[0048]

[0049] Among them, K r represents a constant, d represents the distance between the cluster car and the obstacle, d m represents a preset distance threshold, and F represents the repulsive potential field.

[0050] Specifically, due to the initial planning path provided by step S2, the repulsive force of the obstacle that causes the local minimum is essentially reduced, that is: in the actual operation process, generally there is only one gravitational force to guide the car to move, and this force is obtained by substituting the turning point, reversal point and straight line interpolation point provided by step S2, that is, adding virtual sub-targets into the artificial potential field formula, which fundamentally eliminates the local minimum, that is, the balance of gravity and repulsion leads to the problem of being unable to reach the target point, because there is no repulsion at all. The above is the general case of global static obstacle avoidance. In the problem of local dynamic obstacle avoidance, that is, when the car is very close to the obstacle, the artificial potential field provides a huge repulsive force to guide the car to avoid the obstacle, returning to the general situation.

[0051] The present invention performs rasterization processing on the environment image: the controlled target is regarded as a point-like object moving on a two-dimensional platform, such as Figure 2 and Figure 3 As shown in the figure, obstacles are white blocks on the rasterized image, and the feasible area is represented by gray blocks. Then, path planning is performed, and the grid is traversed along each straight line (up, down, left, right, upper left, lower left, upper right, lower right) at the starting point until the current node encounters a lower left obstacle, no obstacles at the bottom and left, or an upper right obstacle, no obstacles at the top and right, marked as a turning point, and an upper left obstacle, no obstacles at the top and left, marked as a turning point. The point with the minimum cost is calculated, and then the point with the minimum cost is traversed along each straight line until it reaches the end. At this point, the global static path planning has been completed, and the key points of path planning are obtained. Then, the improved artificial potential field method is used to perform local path planning for the cluster car, and the local minimum problem is solved by adding virtual sub-goals as the key points of path planning.

[0052] Compared with the prior art, the present invention obtains an environmental image, performs rasterization processing on the environmental image, and obtains a rasterized map; performs path planning on the rasterized map based on the Bresenham line algorithm to obtain an initial planned path; performs local path optimization on the initial planned path based on an artificial potential field to obtain an optimal planned path. The present invention is based on the Bresenham line algorithm, which can achieve that the route of path planning will not be close to obstacles, reducing collisions caused by changes in the size of obstacles due to control delays or camera jitters; the idea of ​​turning points and reversal points also reduces a large number of unnecessary node operations to ensure extremely fast search efficiency, and solves the local minimum problem through the artificial potential field method.

[0053] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0054] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only the preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation manner. The above-mentioned specific implementation manner is only illustrative rather than restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, all of which are within the protection of the present invention.

Claims

1. A dynamic path planning method for cluster vehicles, characterized in that: The dynamic path planning method comprises the following steps: S1, acquiring an environment image, and performing rasterization processing on the environment image to obtain a rasterized map; S2, performing path planning on the rasterized map based on the Bresenham straight line algorithm to obtain an initial planned path; S3. Perform local path optimization on the initial planned path based on the artificial potential field to obtain an optimal planned path.

2. The dynamic path planning method for cluster vehicles as claimed in claim 1, characterized in that: Step S2 includes the following sub-steps: S21, setting a starting point and a target point in the rasterized map, and adding a safety window according to obstacles in the rasterized map; S22, emitting a plurality of rays from a preset direction of the starting point by using the Bresenham line algorithm to determine a visible area in the rasterized map; S23, marking the coordinate points where the light encounters the obstacle to obtain a plurality of key points; wherein the key points include turning points and reversal points, the key points being points where the light changes direction due to the influence of the obstacle, and the reversal points being points where the direction of the light is adjusted; S24, determining whether the light reaches the target point; If not, the costs of the multiple key points are calculated, and the key point with the smallest cost is used as a new starting point, and the process returns to step S22; If so, multiple key points with the lowest cost are integrated to obtain the initial planning path.

3. The dynamic path planning method for cluster vehicles as claimed in claim 2, characterized in that: When the obstacle exists in the upper left or lower right of the key point and there is no obstacle in the horizontal direction or the vertical direction, the key point is a turning point.

4. The dynamic path planning method for cluster vehicles as claimed in claim 2, characterized in that: When the obstacle exists at the upper left of the key point and there is no obstacle above or to the left of the key point, the key point is a reversal point; The reversing point includes a horizontal reversing point and a vertical reversing point. The horizontal reversing point is the point where the direction of the light needs to be adjusted after the horizontal path of the light is intercepted by the obstacle. The vertical reversing point is the point where the direction of the light needs to be adjusted after the vertical path of the light is intercepted by the obstacle.

5. The dynamic path planning method for cluster vehicles as described in claim 2, characterized in that: In step S3, based on the artificial potential field, a repulsive potential field is provided to the cluster vehicles at key points in the initial planned path to achieve local path optimization of the initial planned path.

6. The dynamic path planning method for cluster vehicles as claimed in claim 5, characterized in that: The repulsive potential field satisfies the following rules: Among them, K r represents a constant, d represents the distance between the cluster car and the obstacle, d m represents a preset distance threshold, and F represents the repulsive potential field.

Images