CN115759499A - Path planning method, device, equipment and medium based on optimization algorithm - Google Patents
Path planning method, device, equipment and medium based on optimization algorithm Download PDFInfo
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Abstract
The invention discloses a path planning method, a path planning device, a path planning equipment and a path planning medium based on an optimization algorithm. Obtaining a target planning area to be subjected to path planning and an original data source; acquiring geographical position information of the obstacle in a pre-constructed line geographical information evaluation model, performing grid segmentation processing in a target planning region, filling original grid data determined according to an original data source, and constructing a grid cost map; initializing parameters of the A-x algorithm, optimizing original grid data corresponding to the grid cost map, and determining a global planning grid cost map; and initializing parameters of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to the initial transition nodes to determine a target planning path. The problems that the on-site exploration and line selection of the power transmission line are complex, the requirement on storage space of an optimization algorithm is high, and the efficiency is low are solved, and the efficiency and the accuracy of path planning of the power transmission line are improved.
Description
Technical Field
The invention relates to the technical field of power transmission path planning, in particular to a path planning method, a path planning device, path planning equipment and a path planning medium based on an optimization algorithm.
Background
The general methods for planning the transmission line include two methods, one is field exploration line selection, and the other is line selection by means of a computer technology and a graph theory intelligent algorithm. The field exploration line selection specifically comprises: carrying out field visit on an area where a planned route passes, determining a starting point and an end point of the route, and acquiring information such as regional long-term planning, local topographic map and landform map and the like; and according to the related data collected in the early stage, presenting the data on the graph according to a larger proportion, marking the starting point of the power transmission line and the end point of the power transmission line on the graph, and connecting the turning points of the line by using distinguishable marks to carry out line selection of the line. Different path trend schemes are designed on the graph, and a more scientific and reasonable scheme is selected by combining the actual situation and the subsequent field survey. The computer technology is combined with a graph theory intelligent algorithm to select the line, and the Dijkstra algorithm and the Floyd algorithm are mainly used at present.
In the process of implementing the invention, the inventor finds that the prior art has the following defects: at present, the steps of on-site exploration and line selection are complex, most of workers are required to obtain geographic information data on the spot, then path correction is carried out, the feasibility of a line is continuously verified, and a large amount of manpower, material resources and time cost are wasted. The Dijkstra algorithm is used for route planning, the requirement on the storage space is high, a large amount of time is spent in the searching process, and a large amount of memory space is occupied. The Floyd algorithm is used for planning the line, the time complexity is high, time is consumed when large-scale data are processed, and the efficiency is poor.
Disclosure of Invention
The invention provides a path planning method, a path planning device and a path planning medium based on an optimization algorithm, which are used for improving the efficiency and the accuracy of power transmission line path planning.
According to an aspect of the present invention, there is provided a path planning method based on an optimization algorithm, including:
acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area;
acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model;
performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map;
initializing parameters of an heuristic function of an A-star algorithm, optimizing original grid data corresponding to the grid cost map through the A-star algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning;
and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path.
According to another aspect of the present invention, there is provided an optimization algorithm-based path planning apparatus, including:
the system comprises a target planning region acquisition module, a path planning region acquisition module and a path planning region acquisition module, wherein the target planning region acquisition module is used for acquiring a target planning region to be subjected to path planning and an original data source corresponding to the target planning region;
the obstacle geographic position information acquisition module is used for acquiring obstacle geographic position information corresponding to the target planning area to be subjected to path planning in a pre-constructed line geographic information evaluation model;
the grid cost map building module is used for carrying out grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, filling the target planning area after the grid segmentation processing through the original grid data, and building a grid cost map;
the global planning grid cost map determining module is used for carrying out parameter initialization processing on the heuristic function of the A-x algorithm, optimizing original grid data corresponding to the grid cost map through the A-x algorithm and determining a global planning grid cost map of the target planning area to be subjected to path planning;
and the target planning path determining module is used for carrying out parameter initialization processing on the heuristic function of the ant colony algorithm, carrying out local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node, and determining a target planning path.
According to another aspect of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the optimization algorithm-based path planning method according to any embodiment of the present invention when executing the computer program.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the optimization algorithm-based path planning method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, a target planning area to be subjected to path planning and an original data source corresponding to the target planning area are obtained; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map; performing parameter initialization processing on the heuristic function of the A-algorithm, optimizing original grid data corresponding to the grid cost map through the A-algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path. The problems that the on-site exploration and line selection of the power transmission line are complex, the requirement on storage space of an optimization algorithm is high, and the efficiency is low are solved, the efficiency and the accuracy of path planning of the power transmission line are improved, and the time cost and the labor cost are reduced.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a path planning method based on an optimization algorithm according to an embodiment of the present invention;
FIG. 2 is a flowchart of another optimization algorithm-based path planning method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a path planning apparatus based on an optimization algorithm according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is to be understood that the terms "target," "current," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a path planning method based on an optimization algorithm according to an embodiment of the present invention, where the present embodiment is applicable to a situation of path planning for a power transmission line, and the method may be executed by a path planning device based on an optimization algorithm, and the path planning device based on an optimization algorithm may be implemented in a form of hardware and/or software.
Accordingly, as shown in fig. 1, the method comprises:
s110, acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area.
The target planning area to be subjected to path planning may be a selected area that needs path planning. The raw data source may be the data source used to describe the target planning region.
In this embodiment, by acquiring a target planning area to be subjected to path planning and needing to acquire an original data source corresponding to the target planning area, assuming that the target planning area is a, an original data source corresponding to the target planning area a needs to be acquired at the same time.
Optionally, before the obtaining of the target planning region to be subjected to path planning and the original data source of the target planning region, the method further includes: acquiring surface feature geographic information, and determining an influence factor and an evaluation grade according to the surface feature geographic information; and constructing a line geographic information evaluation model by a fuzzy analytic hierarchy process according to the geographic information, the influence factors and the evaluation level of the ground objects.
The geographic information of the surface feature may be information of a geographic position and a geographic appearance of each area. The influence factor may be an influence factor that influences the power transmission line planning, and specifically, the influence factor may include a conductor cost, an engineering cost, and a tower cost. The evaluation grade can be standard grade obtained by carrying out quantitative processing on the national power grid transmission line project typical construction cost in a grade mode according to the analysis of the national power grid transmission line project typical construction cost and an expert grade method.
The following table 1 shows a typical evaluation scale description. Specifically, the 1 st level corresponds to the condition that the line construction is very suitable and the cost is very low; the level 2 corresponds to that the line construction is relatively suitable and the cost is very low under the condition; the level 3 corresponds to that the line can be constructed under the condition and the cost is general; the 4 th level corresponds to the condition that the line is difficult to construct and has higher cost; the 5 th level corresponds to the condition that the line is difficult to construct and high in cost.
TABLE 1
| Rating of evaluation | Degree of influence |
| 1 | The method is suitable for line construction and has low cost |
| 2 | Under the condition, the method is more suitable for line construction and has lower cost |
| 3 | Under the condition, the line can be constructed with general cost |
| 4 | Under the condition, the line is difficult to construct and has higher cost |
| 5 | Under the condition, the line is difficult to construct and the cost is very high |
The fuzzy analytic hierarchy process may include a fuzzy comprehensive evaluation process and an analytic hierarchy process. Specifically, the analytic hierarchy process can organically combine the qualitative and quantitative analysis in the conventional analytical method, so that the analytical method has higher scientific logicality, can comprehensively analyze the project content, and provides reliable decision basis for project planners. The analytic hierarchy process firstly proposes problems, then analyzes and solves specific problems step by each layer, carries out detailed decomposition on the problems needing to pass through decision and lists solution schemes of phases. After the layered structure is established, the factors in each layer can be analyzed clearly, the factors are processed by adopting the method with the highest efficiency according to specific conditions, and finally, all the elements are arranged in a matrix, so that the state of each element in the actual engineering can be seen clearly.
The fuzzy comprehensive evaluation is to adopt a fuzzy subset to objectively reflect fuzzy indexes of an object through a fuzzy analysis mode, and finally evaluate the indexes according to a fuzzy transformation principle. In the line planning process in the engineering construction process of the power system, the model constructed by the fuzzy mathematical theory knowledge is used for comprehensively processing the preset influence factors of the engineering line by the method, and finally, the most advantageous line scheme is obtained. The fuzzy comprehensive evaluation is to convert qualitative analysis into quantitative analysis by using a fuzzy mathematical principle, so that an analysis result has objective scientificity, the whole analysis logic is clear, and the problem that quantitative analysis is difficult to process can be effectively solved. In the analysis process, the maximum membership principle is required to be used as a conventional method, so that the processing effect on part of special conditions is poor, the information collection is insufficient, and the evaluation result is influenced to different degrees. A plurality of objects can be ranked by a weighted average method.
The route geographic information evaluation model may be a model for evaluating route geographic information in each area, and specifically, the route geographic information evaluation model may include different target planning areas and obstacle geographic position information corresponding to the different target planning areas.
And S120, obtaining the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning in a pre-constructed route geographical information evaluation model.
The geographical position information of the obstacle may be obstacle information describing whether the current area is capable of crossing for laying the power transmission line.
It can be understood that, when selecting the transmission line path, besides being limited by natural conditions such as natural factors, for example, large rivers and lakes, the route is also limited by social conditions, for example: the method comprises the following steps of (1) military areas, airport lifting areas, natural protection areas and the like, wherein the limitation of the factor conditions on the path of the power transmission line is very strict, the power transmission line cannot cross the areas, and the areas need to be avoided.
In this embodiment, when the target planning region to be subjected to path planning is obtained, the grid segmentation processing of the target planning region cannot be directly performed, and the geographical position information of the obstacle corresponding to the target planning region needs to be obtained in a pre-constructed route geographical information evaluation model.
S130, carrying out grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, filling the target planning area subjected to grid segmentation processing through the original grid data, and constructing a grid cost map.
The original raster data may be raster data determined after data processing is performed according to an original data source. The grid cost map may be a cost map filled in a target planning region after the grid segmentation processing is performed on the original grid data, that is, the grid cost map is used for path planning processing.
In this embodiment, after acquiring the geographical position information of the obstacle, it is necessary to perform mesh segmentation processing on the target planning region according to the geographical position information of the obstacle, and after the mesh segmentation processing, the target planning region is divided into regions formed by a plurality of grids.
Further, the original data source is subjected to data processing, original raster data can be determined, and the target planning area after the grid segmentation processing is filled through the original raster data, that is, each grid needs to be filled with data, so that a grid cost map is constructed.
It can be understood that the simulated grid is divided into two types according to the characteristics of the transmission line and the geographical position information of the obstacle: 1. the transmission line may span regions such as: unrestricted areas, both sides of roads, etc.; 2. the transmission line cannot cross regions, such as: large lakes, large river spans, ecological protection areas, airport lift areas, military security areas, and the like.
In addition, in the process of path selection, in addition to the avoidance of the non-traversable grids, the grids which can be passed by the transmission line are not limited by the non-traversable grids, but the cost of the transmission line for the traversable grids is different under the conditions of different terrains, addresses, ice coatings, temperatures, wind speeds and the like, and the influence of the factors on the line construction cost is mostly only qualitatively analyzed without quantitative calculation. Therefore, the geographic factors are graded, qualitative analysis is converted into quantitative calculation, a fuzzy hierarchical analysis model is constructed through a fuzzy hierarchical analysis method, the influence weight of each basic influence factor on the line crossing grid cost is calculated, a line crossing grid cost value is obtained through a grading result and a weight calculation result, namely a grid cost map is constructed, and therefore the evaluation process is converted into quantitative calculation through qualitative analysis.
Optionally, the determining the original raster data according to the original data source includes: processing the original data source through a terrain image auxiliary data standard to obtain a row-column consistent data source; and performing data preprocessing operation on the row-column consistent data source to determine the original raster data.
The terrain image auxiliary data standard can comprise a terrain data standard, an image data standard and an auxiliary data standard, data processing is carried out on an original data source through the terrain data standard, the image data standard and the auxiliary data standard, and the obtained data have the characteristic of consistent row and column. A rank-consistent data source may be a data source that describes data having rank-consistent characteristics.
Preferably, after the row-column consistent data source is obtained, a data preprocessing operation needs to be performed on the row-column consistent data source, the specific data preprocessing may include operations such as re-projection, re-sampling, clipping, and masking, and after the data preprocessing is completed, the original raster data may be determined.
And S140, carrying out parameter initialization processing on the heuristic function of the A-star algorithm, optimizing the original grid data corresponding to the grid cost map through the A-star algorithm, and determining the global planning grid cost map of the target planning area to be subjected to path planning.
The a-algorithm may be a two-dimensional path planning widely applied to known global environment map information. The algorithm principle is that starting from a starting node, searching and selecting surrounding optimal nodes as a next expansion point through a heuristic function, continuously repeating the operation until reaching a target point, and finally returning from a target point original path to the starting point to generate a final path. Since each node has the smallest cost in the whole process, the obtained path has the smallest cost.
Specifically, the evaluation function of the conventional a-x algorithm is: f (n) = G (n) + H (n). In the merit function: f (n) represents the sum of the costs estimated from the starting point to the target point through the current node n; g (n) represents the actual cost from the starting point to the node n; h (n) represents the estimated cost of node n to the destination point.
The global planning grid cost map may be a grid cost map in which original grid data corresponding to the grid cost map is optimized through an a-x algorithm, and a global planning optimum grid cost map corresponding to the target planning region to be subjected to path planning is determined.
S150, parameter initialization processing is carried out on the heuristic function of the ant colony algorithm, and according to preset initial transition nodes, local path planning is carried out on the global planning grid cost map through the ant colony algorithm, and a target planning path is determined.
The ant colony algorithm may be a probabilistic algorithm for finding the optimized path. Specifically, the ant colony algorithm search flow mainly comprises two rules: a move rule and a pheromone update rule. The moving rule determines the direction of the ant to select the position to go next, the pheromone updating rule is the core of the ant colony algorithm, and the development of the ant colony search result towards the direction of the optimal solution can be promoted through the heuristic guidance of the pheromone.
Specifically, the initial transition node may be a preset transition node, and only when a condition of a next transition node is satisfied, the initial transition node can jump to the next transition node from the initial transition node to perform path planning processing. The target planning path may be an optimal planning path determined by an optimization algorithm in the target planning region, that is, the target planning path is relatively more suitable for laying the power transmission line.
It can be understood that power transmission line planning is divided into global planning and local planning. The algorithm firstly utilizes an A-star algorithm to carry out global planning, and after a global planning grid cost map is obtained through optimization of the A-star algorithm, an ant colony algorithm needs to be adopted, and obstacle avoidance and low-cost lines are selected through an ant colony pheromone searching principle.
Furthermore, transition nodes are set according to the characteristics of the grid graph, and the total cost function of the transition nodes is smaller than the cost of the nodes. The target firstly adopts an A-star algorithm to carry out global planning towards the target node, and when the target reaches the transition node, an ant colony search algorithm is adopted to carry out local planning. In the power transmission line planning, an ant colony algorithm is continuously adopted to carry out local high-density obstacle avoidance, so that the planning capability of the model in a complex environment can be effectively improved, and the model is prevented from falling into local deadlock.
According to the technical scheme of the embodiment of the invention, a target planning area to be subjected to path planning and an original data source corresponding to the target planning area are obtained; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, filling the target planning area after grid segmentation processing through the original grid data, and constructing a grid cost map; performing parameter initialization processing on the heuristic function of the A-algorithm, optimizing original grid data corresponding to the grid cost map through the A-algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path. The problems that the on-site exploration and line selection of the power transmission line are complex, the requirement on storage space of an optimization algorithm is high, and the efficiency is low are solved, the efficiency and the accuracy of path planning of the power transmission line are improved, and the time cost and the labor cost are reduced.
Example two
Fig. 2 is a flowchart of another optimization algorithm-based path planning method according to a second embodiment of the present invention, which is refined based on the above embodiments, in this embodiment, a parameter initialization process is performed on the heuristic function of the ant colony algorithm, and according to a preset initial transition node, a local path planning is performed on the global planning grid cost map through the ant colony algorithm, so as to determine a specific operation process of a target planning path, and further refine the operation process.
Accordingly, as shown in fig. 2, the method comprises:
s210, acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area.
And S220, obtaining the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning in a pre-constructed route geographical information evaluation model.
And S230, carrying out grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, filling the target planning area subjected to grid segmentation processing through the original grid data, and constructing a grid cost map.
And S240, carrying out parameter initialization processing on the heuristic function of the A-x algorithm, optimizing original grid data corresponding to the grid cost map through the A-x algorithm, and determining the global planning grid cost map of the target planning area to be subjected to path planning.
And S250, acquiring an initial transition node and an initial pheromone concentration.
Wherein the initial pheromone concentration may be a fixed concentration value given to pheromones by the ant colony algorithm during the initial period. Specifically, after each iteration is completed, after all the ants that have gone out come back, the route that has gone through is calculated, and then the corresponding initial pheromone concentration is updated, that is, the pheromone concentration is obtained.
Optionally, the parameters of the heuristic function of the ant colony algorithm include: ant number, pheromone concentration, heuristic function factor, initial pheromone concentration, pheromone volatilization factor, pheromone constant, and maximum iteration number.
In the embodiment of this market, before performing local optimization through the ant colony algorithm, parameter initialization operations need to be performed on the number of ants, pheromone concentration, heuristic function factors, initial pheromone concentration, pheromone volatilization factors, pheromone constants and maximum iteration times in the ant colony algorithm.
And S260, performing local path planning on the initial transition node on the global planning grid cost map through an ant colony algorithm, determining the optimal local planning path point of each target, and updating the initial pheromone concentration to obtain the pheromone concentration.
The target optimal local planning path point may be a locally optimized path point determined by performing local optimization according to an ant colony algorithm.
And S270, judging whether the concentration of the pheromone reaches the condition of transition to the next transition node, if so, executing S280, and if not, executing S290.
The condition of the next transition node may be to judge whether the current pheromone concentration reaches a jump threshold condition corresponding to a jump to the next transition node.
Specifically, if the pheromone concentration meets the condition of transition to the next transition node, the current transition node can be directly jumped to the next transition node; if the pheromone concentration does not reach the condition of transition to the next transition node, the current local path optimization is not finished, and the operation of obtaining the initial transition node and the initial pheromone concentration needs to be returned until the pheromone concentration reaches the condition of transition to the next transition node.
And S280, judging whether the next transition node is a target termination transition node, if so, executing S2100, and if not, executing S2110.
The target termination transition node may be the last transition node corresponding to the current local path plan.
In the previous example, after directly jumping to the next transition node from the current transition node, it needs to determine again whether the next transition node is the target termination transition node, and if the next transition node is the target termination transition node, the last transition node may be determined.
And S290, returning to execute the S250 until the pheromone concentration reaches the condition of transition to the next transition node.
S2100, forming a target optimal local planning path point set according to the target optimal local planning path points, and determining the target planning path.
In the previous example, after the last transition node, that is, the target termination transition node, is determined, the target optimal local planning path points need to form a target optimal local planning path point set, and the target optimal local planning path points are connected according to the target optimal local planning path point set to further determine the target planning path.
And S2110, returning to execute S240 until the next transition node is the target termination transition node.
In the previous example, if the next transition node is not the target termination transition node, the global planning grid cost map needs to be determined by the a-x algorithm, and the setting and other operations of the initial transition node are performed until the next transition node is the target termination transition node.
According to the technical scheme of the embodiment of the invention, a target planning area to be subjected to path planning and an original data source corresponding to the target planning area are obtained; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map; performing parameter initialization processing on the heuristic function of the A-algorithm, optimizing original grid data corresponding to the grid cost map through the A-algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; acquiring an initial transition node and an initial pheromone concentration; performing local path planning on the initial transition node on the global planning grid cost map through an ant colony algorithm to determine the optimal local planning path point of each target, and updating the initial pheromone concentration to obtain the pheromone concentration; judging whether the concentration of the pheromone reaches the condition of transition to the next transition node, if so, judging whether the next transition node is a target termination transition node; and if the next transition node is determined to be a target termination transition node, forming a target optimal local planning path point set according to each target optimal local planning path point, and determining the target planning path. By setting the transition nodes in the ant colony algorithm, the efficiency and accuracy of power transmission line path planning can be improved, and the time cost and the labor cost are reduced.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a path planning apparatus based on an optimization algorithm according to a third embodiment of the present invention. The path planning device based on the optimization algorithm provided by the embodiment of the invention can be realized by software and/or hardware, and can be configured in terminal equipment or a server to realize the path planning method based on the optimization algorithm in the embodiment of the invention. As shown in fig. 3, the apparatus includes: a target planned area acquisition module 310, an obstacle geographic location information acquisition module 320, a grid cost map construction module 330, a global planned grid cost map determination module 340, and a target planned path determination module 350.
The target planning region acquiring module 310 is configured to acquire a target planning region to be subjected to path planning and an original data source corresponding to the target planning region;
the obstacle geographic position information acquisition module 320 is configured to acquire obstacle geographic position information corresponding to the target planning area to be subjected to path planning from a pre-constructed route geographic information evaluation model;
the grid cost map building module 330 is configured to perform grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determine original grid data according to the original data source, fill the target planning region after the grid segmentation processing by using the original grid data, and build a grid cost map;
the global planning grid cost map determining module 340 is configured to perform parameter initialization processing on an heuristic function of an a-x algorithm, optimize original grid data corresponding to the grid cost map through the a-x algorithm, and determine a global planning grid cost map of the target planning area to be subjected to path planning;
and a target planning path determining module 350, configured to perform parameter initialization processing on the heuristic function of the ant colony algorithm, perform local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node, and determine a target planning path.
According to the technical scheme of the embodiment of the invention, a target planning area to be subjected to path planning and an original data source corresponding to the target planning area are obtained; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map; initializing parameters of an heuristic function of an A-star algorithm, optimizing original grid data corresponding to the grid cost map through the A-star algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path. The problems that the on-site exploration and line selection of the power transmission line are complex, the requirement on storage space of an optimization algorithm is high, and the efficiency is low are solved, the efficiency and the accuracy of path planning of the power transmission line are improved, and the time cost and the labor cost are reduced.
Optionally, the method further includes a route geographic information evaluation model building module, which may be specifically configured to: before the target planning area to be subjected to path planning and an original data source of the target planning area are obtained, surface feature geographic information is obtained, and an influence factor and an evaluation grade are determined according to the surface feature geographic information; and constructing a line geographic information evaluation model by a fuzzy analytic hierarchy process according to the geographic information, the influence factors and the evaluation level of the ground objects.
Optionally, the grid cost map building module 330 may be specifically configured to: processing the original data source through a terrain image auxiliary data standard to obtain a row-column consistent data source; and performing data preprocessing operation on the row-column consistent data source to determine the original raster data.
Optionally, the parameters of the heuristic function of the ant colony algorithm may specifically include: ant number, pheromone concentration, heuristic function factor, initial pheromone concentration, pheromone volatilization factor, pheromone constant, and maximum iteration number.
Optionally, the target planned path determining module 350 may specifically include: an initial transition node and initial pheromone concentration acquisition unit for acquiring the initial transition node and the initial pheromone concentration; the pheromone concentration updating unit is used for carrying out local path planning on the initial transition node on the global planning grid cost map through an ant colony algorithm, determining each target optimal local planning path point, and updating the initial pheromone concentration to obtain the pheromone concentration; the condition judging unit is used for judging whether the pheromone concentration reaches the condition of transition to the next transition node or not, and if yes, judging whether the next transition node is a target termination transition node or not; and the target planning path determining unit is used for forming a target optimal local planning path point set according to each target optimal local planning path point and determining the target planning path if the next transition node is determined to be the target termination transition node.
Optionally, the method further includes a return execution unit, which may be specifically configured to: and after judging whether the pheromone concentration reaches the condition of transition to the next transition node, if not, returning to execute the operation of obtaining the initial transition node and the initial pheromone concentration until the pheromone concentration reaches the condition of transition to the next transition node.
Optionally, the grid cost map further includes a target termination transition node determining unit, configured to, after determining whether the next transition node is the target termination transition node, if it is determined that the next transition node is not the target termination transition node, return to perform parameter initialization processing on an heuristic function of an a-algorithm, and perform an operation of optimizing the grid cost map through the a-algorithm until the next transition node is the target termination transition node.
The optimization algorithm-based path planning device provided by the embodiment of the invention can execute the optimization algorithm-based path planning method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 shows a schematic structural diagram of an electronic device 10 that can be used to implement a fourth embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 may also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as a path planning method based on an optimization algorithm.
In some embodiments, the optimization algorithm-based path planning method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the optimization algorithm based path planning method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the optimization algorithm-based path planning method by any other suitable means (e.g., by means of firmware).
The method comprises the following steps: acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, filling the target planning area after grid segmentation processing through the original grid data, and constructing a grid cost map; performing parameter initialization processing on the heuristic function of the A-algorithm, optimizing original grid data corresponding to the grid cost map through the A-algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired result of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, which when executed by a computer processor, is configured to perform a method for optimizing algorithm-based path planning, the method including: acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area; acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model; performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map; performing parameter initialization processing on the heuristic function of the A-algorithm, optimizing original grid data corresponding to the grid cost map through the A-algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning; and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path.
Of course, the embodiment of the present invention provides a storage medium containing computer-readable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the optimization algorithm-based path planning method provided in any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly can be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the path planning apparatus based on the optimization algorithm, each unit and each module included in the embodiment are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A path planning method based on an optimization algorithm is characterized by comprising the following steps:
acquiring a target planning area to be subjected to path planning and an original data source corresponding to the target planning area;
acquiring the geographical position information of the barrier corresponding to the target planning area to be subjected to path planning from a pre-constructed line geographical information evaluation model;
performing grid segmentation processing on the target planning region according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning region after grid segmentation processing through the original grid data to construct a grid cost map;
initializing parameters of an heuristic function of an A-star algorithm, optimizing original grid data corresponding to the grid cost map through the A-star algorithm, and determining a global planning grid cost map of the target planning area to be subjected to path planning;
and performing parameter initialization processing on the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path.
2. The method according to claim 1, before the obtaining a target planning region to be path planned and a raw data source of the target planning region, further comprising:
acquiring surface feature geographic information, and determining an influence factor and an evaluation grade according to the surface feature geographic information;
and constructing a line geographic information evaluation model by a fuzzy analytic hierarchy process according to the geographic information, the influence factors and the evaluation grade of the ground objects.
3. The method of claim 1, wherein determining raw raster data from the raw data source comprises:
processing the original data source through a terrain image auxiliary data standard to obtain a row and column consistent data source;
and performing data preprocessing operation on the row-column consistent data source to determine the original raster data.
4. The method of claim 1, wherein the parameters of the heuristic function of the ant colony algorithm comprise: ant number, pheromone concentration, heuristic function factor, initial pheromone concentration, pheromone volatilization factor, pheromone constant, and maximum iteration number.
5. The method according to claim 4, wherein the initializing a parameter of the heuristic function of the ant colony algorithm, and performing local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node to determine a target planning path comprises:
acquiring an initial transition node and an initial pheromone concentration;
performing local path planning on the initial transition node on the global planning grid cost map through an ant colony algorithm to determine the optimal local planning path point of each target, and updating the initial pheromone concentration to obtain the pheromone concentration;
judging whether the pheromone concentration reaches the condition of transition to the next transition node, if so, judging whether the next transition node is a target termination transition node;
and if the next transition node is determined to be a target termination transition node, forming a target optimal local planning path point set according to each target optimal local planning path point, and determining the target planning path.
6. The method of claim 5, further comprising, after said determining whether the pheromone concentration meets a condition for transitioning to a next transition node:
and if not, returning to execute the operation of obtaining the initial transition node and the initial pheromone concentration until the pheromone concentration reaches the condition of transition to the next transition node.
7. The method of claim 5, further comprising, after said determining whether said next transition node is a target termination transition node:
and if the next transition node is determined not to be the target termination transition node, returning to execute the parameter initialization processing of the heuristic function of the A-x algorithm, and optimizing the grid cost map through the A-x algorithm until the next transition node is the target termination transition node.
8. A path planning device based on an optimization algorithm is characterized by comprising:
the system comprises a target planning region acquisition module, a path planning region acquisition module and a path planning region acquisition module, wherein the target planning region acquisition module is used for acquiring a target planning region to be subjected to path planning and an original data source corresponding to the target planning region;
the obstacle geographic position information acquisition module is used for acquiring obstacle geographic position information corresponding to the target planning area to be subjected to path planning in a pre-constructed line geographic information evaluation model;
the grid cost map building module is used for carrying out grid segmentation processing on the target planning area according to the geographical position information of the obstacle, determining original grid data according to the original data source, and filling the target planning area subjected to grid segmentation processing through the original grid data to build a grid cost map;
the global planning grid cost map determining module is used for carrying out parameter initialization processing on an heuristic function of an A-star algorithm, optimizing original grid data corresponding to the grid cost map through the A-star algorithm and determining a global planning grid cost map of a target planning area to be subjected to path planning;
and the target planning path determining module is used for carrying out parameter initialization processing on the heuristic function of the ant colony algorithm, carrying out local path planning on the global planning grid cost map through the ant colony algorithm according to a preset initial transition node, and determining a target planning path.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the optimization algorithm based path planning method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing computer instructions for causing a processor to perform the optimization algorithm-based path planning method of any one of claims 1-7 when executed.
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