Multi-target path planning method and device based on advantage test factor, and medium

Abstract

The invention provides a multi-target path planning method and device based on advantage test factors and a medium, and relates to the field of geographic information, and the method comprises the steps: obtaining slope data and land surface coverage data, carrying out the quantification of the data into a traffic capacity value, and constructing a multi-target cross-country traffic model; in combination with an improved advantage test strategy, introducing an advantage test factor in a path expansion stage of the multi-target cross-country traffic model, and performing dominance check among cost vectors by using the advantage test factor; designing a multi-objective heuristic function of the multi-objective cross-country traffic model; and obtaining to-be-planned multi-target cross-country path data, inputting the data to the multi-target cross-country traffic model, obtaining a plurality of Pareto optimal path sets, and completing multi-target cross-country path planning. The multi-target cross-country traffic model can reduce the number of times of comparison between cost vectors in the multi-target cross-country path expansion process, can efficiently expand the path, and improves the path planning efficiency.

Description

Technical Field

[0001] The present application relates to the field of geographic information, and in particular to a multi-objective path planning method, device and medium based on advantage verification factors. Background Art

[0002] Since the off-road environment involves a variety of complex traffic constraints, off-road path planning may require the optimization of multiple conflicting objectives. For example, in a rescue mission, it is necessary to comprehensively consider multiple factors such as traffic risk, rescue distance, and arrival time. Different from the traditional single-objective path planning, there may not be a unique optimal path in this multi-objective scenario. The path planning result is usually a set of Pareto optimal paths that are difficult to distinguish. The increase in the optimization target dimension leads to the need for a large number of pros and cons comparisons to exhaust all Pareto optimal paths, which greatly limits the search efficiency of the algorithm and restricts the effect of the multi-objective off-road path planning algorithm in practical applications. Therefore, it is necessary to design an efficient multi-objective off-road path planning method to meet the needs of multiple objectives and multiple standards in different fields.

[0003] The traditional method to solve the multi-objective off-road path planning problem is to convert the multi-objective problem into a single-objective problem through weighted method, constraint method and other methods. However, this method does not solve the conflict between objectives and does not provide multiple optimal solutions to assist decision-making.

[0004] Economists proposed the Pareto optimality principle to solve multi-objective optimization problems when studying economic equilibrium. This method truly optimizes multiple objectives and avoids the loss of feasible solutions. Multi-objective path planning algorithms based on this principle are mainly meta-heuristic algorithms, but such algorithms are highly complex and computationally inefficient; and path expansion requires a large number of repeated dominance tests, which is far from meeting the needs of some application scenarios with high timeliness. Summary of the invention

[0005] The purpose of the present invention is to provide a multi-objective path planning method, device and medium based on advantage test factors in order to solve the problem that the off-road path planning in large-scale off-road scenarios currently has a single target and low efficiency.

[0006] The above-mentioned purpose of the present application is achieved through the following technical solutions:

[0007] S1: Obtain slope data and surface cover data and quantify them into traffic capacity values ​​to build a multi-objective off-road traffic model;

[0008] S2: Combined with the improved dominance test strategy, the dominance test factor is introduced in the path extension stage of the multi-objective off-road traffic model, and the dominance test factor is used to perform dominance checks between cost vectors;

[0009] S3: Design multi-objective heuristic functions for multi-objective off-road traffic models;

[0010] S4: Obtain the multi-objective off-road path data to be planned and input it into the multi-objective off-road traffic model to obtain a set of multiple Pareto optimal paths, and complete the multi-objective off-road path planning.

[0011] Optionally, step S1 includes:

[0012] Taking the hexagonal grid as the basic unit of grid division, the area proportion method is used to quantify the grid around the terrain factors of the slope data and the surface cover factors of the surface cover data, and the capacity value of the hexagonal grid is assigned; the capacity value includes: slope capacity value and slope capacity value;

[0013] By calculating the maximum change rate of the elevation of adjacent grids, the slope capacity value is obtained as follows:

[0014]

[0015] Slope capacity value = slope × λ slope

[0016] Among them, I m is the elevation value of grid m; I n is the elevation value of grid n; dist(m,n) is the distance between grid m and grid n;

[0017] The single factor analysis and multi-factor analysis were combined to calculate the capacity value of the hexagonal grid.

[0018] The single factor analysis method is used to determine whether the grid is passable. When the proportion of water systems and buildings in the grid exceeds the threshold, the grid is defined as impassable and the grid capacity value is defined as 0. If the grid capacity value is not 0, the multi-factor analysis method is used to linearly weight each influencing factor to calculate the grid capacity value, as follows

[0019]

[0020] Among them, s i Indicates the area occupied by the influencing factor in the grid, s water and building represents the area occupied by water systems and buildings in the grid, S represents the total area of ​​the grid, p is the threshold of the proportion of inaccessible factors in the grid, and λ i is the weight of the influencing factor on traffic flow.

[0021] Optionally, the improved advantage test strategy is as follows:

[0022] Given a vector and vector set X, Indicates not included The vector of the first dimension components (v2,…v m ), T(X) represents the set of all vectors that do not contain the first-dimensional component;

[0023] If the non-dominated vector in T(X) satisfy Then you only need to judge and The dominant relationship can be judged Whether it is dominated by X;

[0024] If the set X dominates Then the following formula is satisfied:

[0025]

[0026]

[0027] The specific steps of introducing the advantage test factor in the path expansion stage are as follows:

[0028] Let OPEN be the triple queue maintained by the execution process, and perform lexicographic sorting on the OPEN queue. The definition of lexicographic order is as follows:

[0029] Use < L Indicates dictionary order, when When satisfied:

[0030]

[0031] Let G cl is the expanded grid set, COSTS is the found Pareto path;

[0032] G cl And the minimum value of the grid capacity value in COSTS is defined as the advantage test factor;

[0033] The advantage test factor is used to determine whether the grid can be expanded, specifically including: during the expansion process, the advantage test factor of the current grid is compared with the grid travel cost of the current path. When the advantage test factor is less than the grid travel cost, the grid is not expanded. When the advantage test factor is greater than or equal to the grid travel cost, the grid is expanded.

[0034] Step S3 includes:

[0035] The multi-objective heuristic function is as follows:

[0036] The multi-objective heuristic function H(n) satisfies the following formula:

[0037]

[0038] Among them, P sn Refers to the path from the starting grid s to the grid n, Refers to the path P sn The actual cost vector, is the heuristic cost vector of the heuristic function H(n) at node n, Refers to the comprehensive cost vector from grid s through grid n to the target grid;

[0039] The calculation formula of the multi-objective heuristic function is as follows:

[0040]

[0041]

[0042]

[0043] Among them, h slope Represents the estimated slope cost between grid n and target grid goal, h traff Indicates the estimated grid travel cost between grid n and target grid goal, slope max is the global maximum value of slope, traff max It is the global maximum value of the grid capacity.

[0044] Optionally, the path P sn The actual cost vector The calculation is as follows:

[0045]

[0046]

[0047]

[0048]

[0049] Among them, n and n′ are adjacent grids, dist(n′,n) is the distance between adjacent grids, and g slope represents the slope cost between n and n′; slope avg Represents the average of the slope travel costs of adjacent grids; avg represents the average value of the grid travel cost of adjacent grids; g traff represents the grid travel cost between n and n′; Represents the travel cost of adjacent grids.

[0050] An electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a multi-objective path planning method based on a dominance verification factor.

[0051] A computer-readable storage medium stores instructions, and when the instructions are executed, a multi-objective path planning method based on advantage verification factors is executed.

[0052] The beneficial effects of the technical solution provided by this application are:

[0053] A multi-objective off-road traffic model based on an improved advantage check strategy is designed. The model is based on the multi-objective A* algorithm, designs a multi-objective heuristic function adapted to off-road scenarios, and proposes an advantage check factor to accelerate the algorithm's advantage check, so that it can efficiently complete multi-objective path planning tasks in complex field scenarios. It can reduce the number of comparisons between cost vectors during the multi-objective off-road path expansion process, can efficiently expand the path, and improve the efficiency of path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The present application will be further described below with reference to the accompanying drawings and embodiments, in which:

[0055] Figure 1 is a flow chart of a multi-objective path planning method based on advantage test factors in an embodiment of the present application;

[0056] Figure 2 is a schematic diagram of the quantification of the traffic capacity of the multi-objective path planning method based on the advantage test factor in the embodiment of the present application;

[0057] Figure 3 Schematic diagram of a multi-objective off-road traffic model of a multi-objective path planning method based on advantage test factors in an embodiment of the present application;

[0058] Figure 4 Schematic diagram of an improved advantage test strategy of a multi-objective path planning method based on an advantage test factor in an embodiment of the present application;

[0059] Figure 5 Schematic diagram of a multi-objective heuristic function of a multi-objective path planning method based on a dominance test factor in an embodiment of the present application;

[0060] Figure 6 is a schematic diagram of a multi-objective off-road path planning result of a multi-objective path planning method based on advantage test factors in an embodiment of the present application;

[0061] Figure 7is a spatial efficiency analysis diagram of a multi-objective path planning method based on a dominance test factor in an embodiment of the present application;

[0062] Figure 8 is a time efficiency analysis diagram of a multi-objective path planning method based on advantage test factors in an embodiment of the present application;

[0063] Fig. 9 It is a schematic diagram of the electronic device structure of the multi-objective path planning method based on the advantage test factor in the embodiment of the present application. DETAILED DESCRIPTION

[0064] In order to have a clearer understanding of the technical features, purposes and effects of the present application, the specific implementation methods of the present application are now described in detail with reference to the accompanying drawings.

[0065] The embodiment of the present application provides a multi-objective path planning method based on advantage test factors.

[0066] Please refer to Figure 1 , Figure 1 : is a flowchart of a multi-objective path planning method based on advantage test factors in an embodiment of the present application, including:

[0067] S1: Obtain slope data and surface cover data and quantify them into traffic capacity values ​​to build a multi-objective off-road traffic model;

[0068] Step S1 includes:

[0069] The hexagonal grid is used as the basic unit of grid division, taking into account the complex factors affecting off-road traffic. Please refer to Figure 2 , using the area ratio method to perform grid quantification around the terrain factors of the slope data and the surface cover factors of the surface cover data, and assigning the capacity value of the hexagonal grid; the capacity value includes: slope capacity value and slope capacity value;

[0070] By calculating the maximum change rate of the elevation of adjacent grids, the slope capacity value is obtained as follows:

[0071]

[0072] Slope capacity value = slope × λ slope

[0073] Among them, I m is the elevation value of grid m; I n is the elevation value of grid n; dist(m,n) is the distance between grid m and grid n;

[0074] The single factor analysis and multi-factor analysis were combined to calculate the capacity value of the hexagonal grid.

[0075] The single factor analysis method is used to determine whether the grid is passable. When the proportion of water systems and buildings in the grid exceeds the threshold, the grid is defined as impassable and the grid capacity value is defined as 0. If the grid capacity value is not 0, the multi-factor analysis method is used to linearly weight each influencing factor to calculate the grid capacity value, as follows

[0076]

[0077] Among them, s i Indicates the area occupied by the influencing factor in the grid, s water and building represents the area occupied by water systems and buildings in the grid, S represents the total area of ​​the grid, p is the threshold of the proportion of inaccessible factors in the grid, and λ i is the weight of the influencing factor on traffic flow.

[0078] Specifically, as shown in Table 1. The buildings and water systems use a single factor quantification method to judge the accessibility of the grid, so there is no need to assign weights. The threshold of the single factor analysis method is defined as 0.5. The multi-objective off-road access model finally constructed is as follows: Figure 3 shown.

[0079] Table 1 Index system of traffic influencing factors

[0080]

[0081]

[0082] S2: Combined with the improved dominance test strategy, the dominance test factor is introduced in the path extension stage of the multi-objective off-road traffic model, and the dominance test factor is used to perform dominance checks between cost vectors;

[0083] Please refer to Figure 4 , the improved advantage test strategy is as follows:

[0084] Given a vector and vector set X, Indicates not included The vector of the first dimension components (v2,…v n ), T(X) represents the set of all vectors that do not contain the first-dimensional component;

[0085] If the non-dominated vector in T(X) satisfy Then you only need to judge and The dominant relationship can be judged Whether it is dominated by X;

[0086] If the set X dominates Then the following formula is satisfied:

[0087]

[0088]

[0089] The specific steps of introducing the advantage test factor in the path expansion stage are as follows:

[0090] Let OPEN be the triple queue maintained by the execution process, and perform lexicographic sorting on the OPEN queue. The definition of lexicographic order is as follows:

[0091] Use < L Indicates dictionary order, when When satisfied:

[0092]

[0093] Let G cl is the expanded grid set, COSTS is the found Pareto path;

[0094] G cl And the minimum value of the grid capacity value in COSTS is defined as the advantage test factor;

[0095] The advantage test factor is used to determine whether the grid can be expanded, specifically including: during the expansion process, the advantage test factor of the current grid is compared with the grid travel cost of the current path. When the advantage test factor is less than the grid travel cost, the grid is not expanded. When the advantage test factor is greater than or equal to the grid travel cost, the grid is expanded.

[0096] Specifically, since the path extension operation of multi-objective A* involves a large number of cost vectors With G op ∪G cl , inspiration vector The dominance check between COSTS significantly affects the path extension efficiency. Therefore, this application introduces an improved dominance check strategy into the path extension stage of the multi-objective A* algorithm, and designs a dominance check factor for off-road scenarios to improve the path extension efficiency.

[0097] S3: Design multi-objective heuristic functions for multi-objective off-road traffic models;

[0098] Step S3 includes:

[0099] The multi-objective heuristic function is as follows:

[0100] The multi-objective heuristic function H(n) satisfies the following formula:

[0101]

[0102] Among them, P sn Refers to the path from the starting grid s to the grid n, Refers to the path P sn The actual cost vector, is the heuristic cost vector of the heuristic function H(n) at node n, Refers to the comprehensive cost vector from grid s through grid n to the target grid;

[0103] The calculation formula of the multi-objective heuristic function is as follows:

[0104]

[0105]

[0106]

[0107] Among them, h slope Represents the estimated slope cost between grid n and target grid goal, h traff Indicates the estimated grid travel cost between grid n and target grid goal, slope max is the global maximum value of slope, traff max It is the global maximum value of the grid capacity.

[0108] The path P sm The actual cost vector The calculation is as follows:

[0109]

[0110]

[0111]

[0112]

[0113] Among them, n and n′ are adjacent grids, dist(n′,n) is the distance between adjacent grids, and g slope represents the slope cost between n and n′; slope avg Represents the average of the slope travel costs of adjacent grids; avg represents the average value of the grid travel cost of adjacent grids; g traff represents the grid travel cost between n and n′; Represents the travel cost of adjacent grids.

[0114] Specifically, refer to Figure 5:The multi-objective A* algorithm uses the heuristic function H(n) to guide the expansion of the path. When the heuristic function H(n) satisfies the consistency definition, the algorithm can guarantee to find the path with the minimum cost vector. Therefore, how to design a heuristic function that meets the consistency definition for slope and capacity is the key to algorithm optimization.

[0115] S4: Obtain the multi-objective off-road path data to be planned and input it into the multi-objective off-road traffic model to obtain a set of multiple Pareto optimal paths, and complete the multi-objective off-road path planning.

[0116] Specifically, the factors affecting off-road traffic are quantified and a multi-objective off-road traffic model is constructed; the advantage test factor is introduced in the path expansion stage, and the dominance check of the cost vector is performed using the advantage test factor; a multi-objective heuristic function adapted to the multi-objective off-road traffic model is designed to guide the path expansion and obtain multiple Pareto optimal paths. For the path planning results, please refer to Figure 6 .

[0117] like Figure 7 As shown in the figure, the depth of the list to be expanded between the path expansion method based on the advantage test factor and the traditional path expansion method is compared. The results show that the list to be expanded based on the lexicographic order makes the grid expansion more orderly and its depth is reduced accordingly.

[0118] like Figure 8 As shown in FIG, the path extension method based on the advantage test factor is compared with the traditional path extension method, and the results show that the time efficiency is improved by an average of 93.7%.

[0119] The present application also discloses an electronic device. Fig. 9 , Fig. 9 The electronic device 500 may include: at least one processor 501 , at least one network interface 504 , a user interface 503 , a memory 505 , and at least one communication bus 502 .

[0120] The communication bus 502 is used to realize the connection and communication between these components.

[0121] The user interface 503 may include a display screen, and the optional user interface 503 may also include a standard wired interface or a wireless interface.

[0122] The network interface 504 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0123] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0124] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A multi-objective path planning method based on advantage test factors, characterized in that: The method comprises the following steps: S1: Obtain slope data and surface cover data and quantify them into traffic capacity values ​​to build a multi-objective off-road traffic model; S2: Combined with the improved advantage test strategy, the advantage test factor is introduced in the path extension stage of the multi-objective off-road traffic model, and the dominance check between cost vectors is performed using the advantage test factor; S3: Design multi-objective heuristic functions for multi-objective off-road traffic models; S4: Obtain the multi-objective off-road path data to be planned and input it into the multi-objective off-road traffic model to obtain a set of multiple Pareto optimal paths, and complete the multi-objective off-road path planning.

2. A multi-objective path planning method based on advantage test factors as claimed in claim 1, characterized in that: Step S1 includes: Taking the hexagonal grid as the basic unit of grid division, the area proportion method is used to quantify the grid around the terrain factors of the slope data and the surface cover factors of the surface cover data, and the capacity value of the hexagonal grid is assigned; the capacity value includes: slope capacity value and slope capacity value; By calculating the maximum change rate of the elevation of adjacent grids, the slope capacity value is obtained as follows: Slope capacity value = slope × λ slope Among them, I m is the elevation value of grid m; I n is the elevation value of grid n; dist(m,n) is the distance between grid m and grid n; The single factor analysis and multi-factor analysis were combined to calculate the capacity value of the hexagonal grid. The single factor analysis method is used to determine whether the grid is passable. When the proportion of water systems and buildings in the grid exceeds the threshold, the grid is defined as impassable and the grid capacity value is defined as 0. If the grid capacity value is not 0, the multi-factor analysis method is used to linearly weight each influencing factor to calculate the grid capacity value, as follows Among them, s i Indicates the area occupied by the influencing factor in the grid, s water and building represents the area occupied by water systems and buildings in the grid, S represents the total area of ​​the grid, p is the threshold of the proportion of inaccessible factors in the grid, and λ i is the weight of the influencing factor on traffic flow.

3. A multi-objective path planning method based on advantage test factors as claimed in claim 1, characterized in that: The improved advantage test strategy is as follows: Given a vector and vector set X, Indicates not included The vector of the first dimension components (v2,…v n ), T(X) represents the set of all vectors that do not contain the first-dimensional component; If the non-dominated vector in T(X) satisfy Then you only need to judge and The dominant relationship can be judged Whether it is dominated by X; If the set X dominates Then the following formula is satisfied:

4. A multi-objective path planning method based on advantage test factors as claimed in claim 1, characterized in that: The specific steps of introducing the advantage test factor in the path expansion stage are as follows: Let OPEN be the triple queue maintained by the execution process, and perform lexicographic sorting on the OPEN queue. The definition of lexicographic order is as follows: use Indicates dictionary order, when When satisfied: Let G cl is the expanded grid set, COSTS is the found Pareto path; G cl And the minimum value of the grid capacity value in COSTS is defined as the advantage test factor; The advantage test factor is used to determine whether the grid can be expanded, specifically including: during the expansion process, the advantage test factor of the current grid is compared with the grid travel cost of the current path. When the advantage test factor is less than the grid travel cost, the grid is not expanded. When the advantage test factor is greater than or equal to the grid travel cost, the grid is expanded.

5. A multi-objective path planning method based on advantage test factors as claimed in claim 1, characterized in that: Step S3 includes: The multi-objective heuristic function is as follows: The multi-objective heuristic function H(n) satisfies the following formula: Among them, P sn Refers to the path from the starting grid s to the grid n, Refers to the path P sn The actual cost vector, is the heuristic cost vector of the heuristic function H(n) at node n, Refers to the comprehensive cost vector from grid s through grid n to the target grid; The calculation formula of the multi-objective heuristic function is as follows: Among them, h slope Represents the estimated slope cost between grid n and target grid goal, h traff Indicates the estimated grid travel cost between grid n and target grid goal, slope max is the global maximum value of slope, traff max It is the global maximum value of the grid capacity.

6. A multi-objective path planning method based on advantage test factors as claimed in claim 5, characterized in that: The path P sn The actual cost vector The calculation is as follows: Among them, n and n′ are adjacent grids, dist(n′,n) is the distance between adjacent grids, and g slope represents the slope cost between n and n′; slope avg Represents the average of the slope travel costs of adjacent grids; avg represents the average value of the grid travel cost of adjacent grids; g traff represents the grid travel cost between n and n′; Represents the travel cost of adjacent grids.

7. An electronic device, characterized in that: The electronic device comprises a processor (501), a memory (505), a user interface (503) and a network interface (504), wherein the memory (505) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (505) so that the electronic device executes the method according to any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed by a computer, the method steps according to any one of claims 1 to 6 are performed.

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