Planning method for parallel multi-target simultaneous delivery path

Abstract

The invention relates to the field of computer science and geographic information science, and particularly discloses a parallel multi-target simultaneous delivery path planning method, which comprises the following steps of S1, acquiring sample data of a starting point and a delivery point; s2, performing strip grouping on the delivery points according to the number of the delivery paths; s3, connecting the delivery points in each strip and then respectively connecting the delivery points with the starting point to obtain a plurality of initial delivery paths; s4, acquiring the length condition of each initial delivery path, calculating the ratio of the standard deviation of the initial delivery path to the mean value, setting a limit value, if the ratio is smaller than the limit value, finishing path planning, if the ratio is larger than the limit value, optimizing the initial delivery path, then re-calculating the ratio of the standard deviation of the initial delivery path to the mean value, and finishing path planning. And finishing path planning until the ratio is smaller than a limit value. According to the parallel multi-target simultaneous delivery path planning method, the parallel multi-target simultaneous delivery path planning efficiency and planning precision can be improved.

Description

Technical Field

[0001] The present invention relates to the fields of computer science and geographic information science, and in particular to a planning method for a parallel multi-target simultaneous delivery path. Background Art

[0002] The parallel multi-target simultaneous delivery path search method is a core content of the UAV mission planning system, and is also widely used in logistics, transportation, urban planning, tourism route planning, emergency prevention and handling and other fields. The problem is solved by cluster thinking such as particle swarm optimization and genetic algorithm, and multi-target problems are analyzed by simulating biological foraging and other behaviors. In recent years, parallel multi-target simultaneous delivery path planning algorithms based on the grey wolf algorithm have emerged, such as Fei Deng et al.'s "Distribution route optimisation of emergency supplies based on improved greywolfoptimisation algorithm", which is a combination of mathematical and biological thinking. The advantage of this type of path planning method is high efficiency but low precision. The average difference given in the case exceeds 20%, and the results are not satisfactory. Summary of the invention

[0003] The present invention aims to solve at least one of the above-mentioned technical problems and provide a method for planning a parallel multi-target simultaneous delivery path, which can improve the planning efficiency and planning accuracy of the parallel multi-target simultaneous delivery path.

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: a method for planning a parallel multi-target simultaneous delivery path, comprising the following steps:

[0005] S1. Obtain sample data of the starting point and the delivery point, and distribute the starting point and the delivery point in the same two-dimensional plane according to the relative position relationship between the starting point and the delivery point;

[0006] S2. Determine the number of delivery paths, and group the delivery points into stripes according to the number of delivery paths;

[0007] S3, connecting the delivery points in each belt and then connecting them with the starting point respectively to obtain several initial delivery paths;

[0008] S4. Obtain the length of each initial delivery path, calculate the ratio of the standard deviation to the mean of the initial delivery path, and set a limit. If the ratio is less than the limit, the path planning is completed. If the ratio is greater than the limit, the initial delivery path is optimized, and then the ratio of the standard deviation to the mean of the initial delivery path is recalculated until the ratio is less than the limit, and the path planning is completed.

[0009] Preferably, in step S3, the delivery points in each strip are connected in sequence along the length direction of the strip, and then the end of the connected path close to the starting point is connected to the starting point to obtain an initial delivery path.

[0010] Preferably, when the step S6 optimizes the initial delivery path, each delivery point on the initial delivery path is filtered, and the filtering method includes:

[0011] S41, obtaining the delivery point to be filtered as the reference delivery point, and constructing a reference triangle through the reference delivery point and two adjacent delivery points on the same initial delivery path as the reference delivery point;

[0012] S42, constructing a Thiessen polygon network through the delivery points, obtaining delivery points in two other Thiessen polygons adjacent to the Thiessen polygon where the reference delivery point is located as comparison delivery points, and the two comparison delivery points are two adjacent delivery points on another initial delivery path, and constructing a comparison triangle through the reference delivery point and the two comparison delivery points;

[0013] S43. Calculate the sum of the two adjacent sides of the reference delivery point in the reference triangle minus the length of the opposite side, and obtain the result L1, and calculate the sum of the two adjacent sides of the comparison delivery point in the comparison triangle minus the length of the opposite side, and obtain the result L2; if L1<L2, retain the two adjacent sides in the reference triangle and the opposite side in the comparison triangle, and delete the opposite side in the reference triangle and the two adjacent sides in the comparison triangle; if L1>L2, retain the opposite side in the reference triangle and the two adjacent sides in the comparison triangle, and delete the two adjacent sides in the reference triangle and the opposite side in the comparison triangle.

[0014] Preferably, the stripe grouping in step S4 comprises the following steps:

[0015] S211, constructing a triangular network through the delivery points, and determining the average distance between the delivery points;

[0016] S212, merging the triangular networks to obtain a merged polygon, and extracting the center point of the merged polygon;

[0017] S213, connecting the starting point and the center point as the center line, constructing several equidistant lines in parallel on both sides of the center line with a distance from the mean, and making all the delivery points located in two adjacent equidistant lines, or in a strip formed between the center line and the adjacent equidistant lines.

[0018] Preferably, the stripe grouping in step S4 comprises the following steps:

[0019] S221, constructing a minimum outer bounding rectangle through the delivery points, so that all the delivery points are located within the minimum outer bounding rectangle;

[0020] S222. Evenly arrange a plurality of mutually parallel dividing lines in the minimum outer bounding rectangle to divide the minimum outer bounding rectangle into the strips.

[0021] Preferably, the path planning method is used for planar path planning.

[0022] The beneficial effect is: compared with the prior art, a parallel multi-target simultaneous delivery path planning method of the present invention groups the delivery points by adopting a strip grouping method, and connects the delivery points in the strip, so that the initial delivery path can be quickly obtained. At the same time, based on the ratio of the standard deviation to the mean of the initial delivery path, the initial delivery path is further optimized, so as to obtain multiple delivery paths with delivery distances that meet the requirements, so as to achieve simultaneous delivery of multiple delivery points, thereby improving the parallel multi-target simultaneous delivery path planning efficiency and planning accuracy that exist in real life. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The specific embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 A schematic diagram of the emergency station and 150 delivery point cases;

[0025] Figure 2 It is a connection diagram of the delivery point after the stripe is grouped;

[0026] Figure 3 A schematic diagram for generating an initial delivery path and obtaining information on the length of the initial delivery path;

[0027] Figure 4 A schematic diagram of the parameter calculation results for each initial delivery path;

[0028] Figure 5 A schematic diagram for optimizing the paths within a strip;

[0029] Figure 6 A schematic diagram to view the optimized path length information;

[0030] Figure 7 This is a schematic diagram of the calculation results of the parameters of the optimized initial delivery path;

[0031] Figure 8 A schematic diagram for optimizing the path within the strip again;

[0032] Fig. 9 A schematic diagram to view the path length information after optimization;

[0033] Fig.10 This is a schematic diagram of the calculation parameter results of the initial delivery path after optimization;

[0034] Fig.11 This is a schematic diagram of the final path planning result;

[0035] Fig.12 Schematic diagram of constructing a triangulated network for delivery points;

[0036] Fig.13 Schematic diagram of merging polygons obtained by merging triangular networks;

[0037] Fig.14 A schematic diagram for extracting the center point of the merged polygon;

[0038] Fig.15 Schematic diagram of generating a strip through a center line. DETAILED DESCRIPTION

[0039] 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.

[0040] It should be noted that when a component is referred to as being "fixed to" another component, it may be directly on the other component or there may be a central component. When a component is considered to be "connected to" another component, it may be directly connected to the other component or there may be a central component at the same time. When a component is considered to be "set on" another component, it may be directly set on the other component or there may be a central component at the same time. When a component is referred to as being "set in the middle", it does not only mean being set in the middle, as long as it is not set at both ends within the range defined by the middle. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0042] As we all know, more than 80% of the problems in human society are related to spatial location. Spatial information is reflected in points, lines and surfaces in computer graphics. Therefore, it can be considered that most human problems are related to points, lines and surfaces: spatial problems are not only affected by logical thinking, but also constrained by spatial relationships. It can be said that spatial topological relationships, which are special constraints that do not depend on image size, shape, position, and direction, are more extensive and common implicit problem constraints. When facing human spatial problems, it is even more necessary to look at the essence of the problem with spatial thinking of points, lines and surfaces, and then explore the root of the problem in order to find the key to solving the problem.

[0043] Human spatial thinking is mostly self-centered, and through vision, from far to near, gradually extends to a limited area. Therefore, the polar coordinate thinking can be used to first reach the nearby target point, and then search for the next nearby point through the nearby target point until all targets are searched. As for the search of nearby points, it can be completed through the Thiessen polygon adjacency search corresponding to the target point. Therefore, multiple initial paths can be constructed through rays, and the Thiessen polygon proximity analysis of the nodes where the initial paths are located is performed to cover the entire domain step by step to obtain multiple paths covering all target points. After completion, according to the connection constraints, the target point paths are optimized one by one to obtain the local optimal solution. Finally, under the connection constraints, each path is optimized for the nodes of the adjacent lines. After multiple single local optimizations and overall optimizations, a parallel multi-target simultaneous delivery path that finally meets the local and overall optimality is obtained.

[0044] To this end, the present application discloses a method for planning a parallel multi-target simultaneous delivery path, comprising the following steps:

[0045] S1. Obtain sample data of the starting point and the delivery point, and distribute the starting point and the delivery point in the same two-dimensional plane according to the relative position relationship between the starting point and the delivery point;

[0046] S2. Determine the number of delivery paths, and group the delivery points into stripes according to the number of delivery paths;

[0047] S3, connecting the delivery points in each belt and then connecting them with the starting point respectively to obtain several initial delivery paths;

[0048] S4. Obtain the length of each initial delivery path, calculate the ratio of the standard deviation to the mean of the initial delivery path, and set a limit. If the ratio is less than the limit, the path planning is completed. If the ratio is greater than the limit, the initial delivery path is optimized, and then the ratio of the standard deviation to the mean of the initial delivery path is recalculated until the ratio is less than the limit, and the path planning is completed.

[0049] The path planning method in this application can be used to optimize logistics delivery paths, as well as to optimize drone paths or travel paths. Specifically, Figure 1 As shown, this application takes the simultaneous delivery of emergency supplies across the river to multiple epidemic areas as an example. It is necessary to use 14 drones to transport the supplies from the emergency station to 150 delivery points. When the path planning method of this application is used to plan the delivery path, the location information of the emergency station and the 150 delivery points can be obtained as sample data of the starting point and the delivery point respectively, and the starting point and the delivery point are distributed in the same two-dimensional plane according to the relative position relationship between the starting point and the delivery point, and then Figure 2 As shown in , all delivery points are grouped into strips so that all delivery points are distributed in 14 strips, and the delivery points in each strip are connected in sequence along the length direction of the strip. Figure 3 As shown in the figure, the end of the path obtained after connection in the strip close to the starting point is connected to the starting point to obtain the initial delivery path, and then as Figure 3 and Figure 4 As shown, the length of each initial delivery path is obtained, the ratio of the standard deviation to the mean of the initial delivery path is calculated, and a limit is set. If the ratio is less than the limit, the path planning is completed. For example, in this case, the limit is set to 4%. At this time, the ratio of the standard deviation to the mean is 6%, which is greater than the limit. Therefore, the initial path needs to be optimized, as shown in Figure 5 As shown, when optimizing the initial delivery path, the path optimization method in the existing patent CN112862186A can be used to filter each delivery point on the initial delivery path, and the delivery points directly connected to the starting point do not need to be filtered. The filtering method includes:

[0050] S61, obtaining the delivery point to be filtered as the reference delivery point, and constructing a reference triangle through the reference delivery point and two adjacent delivery points on the same initial delivery path as the reference delivery point;

[0051] S62, constructing a Thiessen polygon network through the delivery points, obtaining delivery points in two other Thiessen polygons adjacent to the Thiessen polygon where the reference delivery point is located as comparison delivery points, and the two comparison delivery points are two adjacent delivery points on another initial delivery path, and constructing a comparison triangle through the reference delivery point and the two comparison delivery points;

[0052] S63. Calculate the sum of the two adjacent sides of the reference delivery point in the reference triangle minus the length of the opposite side, and obtain the result L1, and calculate the sum of the two adjacent sides of the comparison delivery point in the comparison triangle minus the length of the opposite side, and obtain the result L2; if L1<L2, retain the two adjacent sides in the reference triangle and the opposite side in the comparison triangle, and delete the opposite side in the reference triangle and the two adjacent sides in the comparison triangle; if L1>L2, retain the opposite side in the reference triangle and the two adjacent sides in the comparison triangle, and delete the two adjacent sides in the reference triangle and the opposite side in the comparison triangle.

[0053] like Figure 6 and Figure 7 As shown in the figure, after the optimization of the initial delivery path is completed, the ratio of the standard deviation to the mean of the initial delivery path is recalculated. At this time, the ratio of the standard deviation to the mean is 4.52%. The total length and the error range are both smaller, but the ratio is still greater than the limit. Therefore, it is necessary to continue to optimize the path, such as Figures 8 to 10 As shown, the ratio after optimization is 3.12%. At this time, the ratio is less than the limit, so the path optimization is stopped and the result is Fig.11 The path planning results are shown.

[0054] In the present application, the stripe grouping in step S4 may adopt the following steps:

[0055] S41, such as Fig.12 As shown, a triangular network is constructed through the delivery points, and the mean distance between the delivery points is determined; S42, as Fig.13 As shown, the triangular network is merged to obtain the merged polygon, and as Fig.14 As shown, extract the center point of the merged polygon;

[0056] S43, such as Fig.15 As shown, the starting point and the center point are connected as the center line, and several equidistant lines are constructed in parallel on both sides of the center line with a distance from the mean, so that all delivery points are located in two adjacent equidistant lines, or in the strip formed between the center line and the adjacent equidistant lines. Since the number of strips required in this application is 14, 7 strips can be generated symmetrically on both sides of the center line. When the number of strips is an odd number, (number of strips - 1) / 2 strips can be generated on one side of the center line, and then the corresponding strips are generated symmetrically on the other side of the center line, and then one more strip is generated, so that all delivery points are located in the strip. The strip grouping method in this embodiment is suitable for situations where the delivery points are relatively concentrated.

[0057] Alternatively, the stripe grouping in step S4 may adopt the following steps:

[0058] S221. Construct a minimum outer rectangle through the delivery points so that all the delivery points are located within the minimum outer rectangle. S222. Evenly set a number of mutually parallel dividing lines within the minimum outer rectangle to divide the minimum outer rectangle into the strips. In this embodiment, the dividing lines are parallel to the length of the rectangle and are set in 13 numbers, thereby dividing the minimum outer rectangle into 14 strips in the width direction. The strip grouping method in this embodiment is not only applicable to the case where the delivery points are relatively concentrated, but also to the case where the delivery points are relatively dispersed.

[0059] Compared with the prior art, the method for planning a parallel multi-target simultaneous delivery path of the present invention has the following characteristics:

[0060] 1. The parallel multi-target delivery path search method is a point connection problem, but extending it to the surface field, using the most natural human thinking and spatial analysis technology to solve the problem is a specific practice of exploring problem constraints in natural science to accelerate problem solving;

[0061] 2. The invention uses proximity search, where proximity means two objects, so its implementation efficiency is not less than the exponential level of 2, and the overall amount of calculation is not higher than log2N, thus achieving a dimensionality reduction solution to the problem;

[0062] 3. The invention is accomplished through the logical operation of spatial proximity query, which reduces the dimension of line connections to surface adjacency, that is, logically processing spatial multidimensional problems, which is a one-dimensional operation of two-dimensional problems and a specific implementation of dimensionality reduction operations, achieving the goal of simplifying and quickly solving problems.

[0063] 4. The invention uses the mean distance between target points as the basis for grouping and the outer rectangle as a constraint. According to the normal distribution law of things, the mean distance grouping is scientific and reliable, and the outer rectangle ensures that there are no omissions in the grouping. Then, through the proximity constraint, the target grouping is achieved in an efficient, scientific, rigorous and feasible manner.

[0064] It can be seen that the planning method of a parallel multi-target simultaneous delivery path of the present invention groups the delivery points by adopting the strip grouping method and connects the delivery points in the strip, so that the initial delivery path can be quickly obtained. At the same time, based on the ratio of the standard deviation to the mean of the initial delivery path, the initial delivery path is further optimized to obtain multiple delivery paths with delivery distances that meet the requirements, so as to achieve simultaneous delivery of multiple delivery points, thereby improving the planning efficiency and planning accuracy of parallel multi-target simultaneous delivery paths that exist in real life.

[0065] In addition, it should be noted that the path planning method of the present application is not only applicable to automatic planning by computer programming, but also to planar path planning by human working drawings.

[0066] The above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Any modification or equivalent substitution that does not depart from the spirit and scope of the present invention shall be included in the scope of the technical solution of the present invention.

Claims

1. A method for planning a parallel multi-target delivery path, characterized in that: The following steps are involved: S1. Obtain sample data of the starting point and the delivery point, and distribute the starting point and the delivery point in the same two-dimensional plane according to the relative position relationship between the starting point and the delivery point; S2. Determine the number of delivery paths, and group the delivery points into stripes according to the number of delivery paths; S3, connecting the delivery points in each belt and then connecting them with the starting point respectively to obtain several initial delivery paths; S4. Obtain the length of each initial delivery path, calculate the ratio of the standard deviation to the mean of the initial delivery path, and set a limit. If the ratio is less than the limit, the path planning is completed. If the ratio is greater than the limit, the initial delivery path is optimized, and then the ratio of the standard deviation to the mean of the initial delivery path is recalculated until the ratio is less than the limit, and the path planning is completed.

2. The method for planning a parallel multi-target delivery path according to claim 1, characterized in that: In step S3, the delivery points in each strip are connected in sequence along the length direction of the strip, and then the end of the connected path close to the starting point is connected to the starting point to obtain an initial delivery path.

3. The method for planning a parallel multi-target delivery path according to claim 1, characterized in that: When the step S4 optimizes the initial delivery path, each delivery point on the initial delivery path is filtered, and the filtering method includes: S41, obtaining the delivery point to be filtered as the reference delivery point, and constructing a reference triangle through the reference delivery point and two adjacent delivery points on the same initial delivery path as the reference delivery point; S42, constructing a Thiessen polygon network through the delivery points, obtaining delivery points in two other Thiessen polygons adjacent to the Thiessen polygon where the reference delivery point is located as comparison delivery points, and the two comparison delivery points are two adjacent delivery points on another initial delivery path, and constructing a comparison triangle through the reference delivery point and the two comparison delivery points; S43. Calculate the sum of the two adjacent sides of the reference delivery point in the reference triangle minus the length of the opposite side, and obtain the result L1, and calculate the sum of the two adjacent sides of the comparison delivery point in the comparison triangle minus the length of the opposite side, and obtain the result L2; if L1<L2, retain the two adjacent sides in the reference triangle and the opposite side in the comparison triangle, and delete the opposite side in the reference triangle and the two adjacent sides in the comparison triangle; if L1>L2, retain the opposite side in the reference triangle and the two adjacent sides in the comparison triangle, and delete the two adjacent sides in the reference triangle and the opposite side in the comparison triangle.

4. The method for planning a parallel multi-target delivery path according to claim 1, characterized in that: The stripe grouping in step S2 comprises the following steps: S211, constructing a triangular network through the delivery points, and determining the average distance between the delivery points; S212, merging the triangular networks to obtain a merged polygon, and extracting the center point of the merged polygon; S213, connecting the starting point and the center point as the center line, constructing several equidistant lines in parallel on both sides of the center line with a distance from the mean, and making all the delivery points located in two adjacent equidistant lines, or in a strip formed between the center line and the adjacent equidistant lines.

5. The method for planning a parallel multi-target delivery path according to claim 1, characterized in that: The stripe grouping in step S2 comprises the following steps: S221, constructing a minimum outer bounding rectangle through the delivery points, so that all the delivery points are located within the minimum outer bounding rectangle; S222. Evenly arrange a plurality of mutually parallel dividing lines in the minimum outer bounding rectangle to divide the minimum outer bounding rectangle into the strips.

6. The method for planning a parallel multi-target delivery path according to claim 1, characterized in that: The path planning method is used for planar path planning.

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