CN113985891B - Self-adaptive cluster path planning method in post-earthquake life searching process - Google Patents
Self-adaptive cluster path planning method in post-earthquake life searching process Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The application belongs to the technical field of intelligent cluster navigation, and relates to a self-adaptive cluster path planning method in a life searching process after an earthquake. Allocating search clusters for areas needing life searching after earthquake, classifying the search areas and determining the number of each type of area searching equipment; constructing an optimizing algorithm of three-dimensional path planning, and calculating an optimal path through iteration; and carrying out life searching on each searching cluster according to the established optimal path. The method provided by the application is applied to the field of life searching after earthquake; the method can classify areas aiming at different landforms, and can perform optimized life searching by using various intelligent clusters such as unmanned aerial vehicles and the like, so that the searching efficiency of the unmanned aerial vehicles is improved.
Description
Technical Field
The application belongs to the technical field of intelligent cluster navigation, and relates to a self-adaptive cluster path planning method in a life searching process after an earthquake.
Background
Path planning directly relates to success and failure of an intelligent cluster in searching tasks, and particularly searches of different terrains and different scenes in a complex environment. For life search after earthquake, the path planning of the search cluster can be more significant, because good path planning can greatly improve the success rate of search. However, many factors need to be considered for planning a path for life search by using the intelligent cluster after an earthquake, wherein topography and topography are particularly important factors; planning the search path of the intelligent cluster according to various factors such as topography and the like is the guarantee for efficiently completing the search task.
The path planning of the adaptive cluster is classified according to the dimension of the planning space and can be classified into two-dimensional planning and three-dimensional planning. The two-dimensional planning mainly considers the range and the dangerous intensity of the no-fly zone, and ignores the altitude information, so that the practicability is limited to a certain extent. In the three-dimensional planning, the height and terrain constraints are integrated to be more close to various practical application scenes. At present, scholars at home and abroad propose a plurality of effective path planning algorithms, and the effective path planning algorithms can be mainly divided into two types: 1. the numerical calculation class mainly comprises a dynamic programming algorithm, a gradient method, a graph theory optimizing method and the like; 2. the intelligent planning algorithm mainly comprises a genetic algorithm, an ant colony algorithm, an artificial field method, a particle swarm optimization algorithm and the like. The current intelligent planning algorithm receives great importance of various application fields as an emerging path planning algorithm, and is gradually combined with a plurality of scenes. However, for the optimal path planning of the unmanned aerial vehicle after the earthquake, no effective self-adaptive cluster path planning method exists.
Disclosure of Invention
Aiming at the problem that the optimal path planning of the unmanned aerial vehicle after the earthquake in the prior art does not have an effective self-adaptive cluster path planning method, the application provides the self-adaptive cluster path planning method in the life searching process after the earthquake.
In order to achieve the above purpose, the application is realized by adopting the following technical scheme:
the method provided by the application comprises the steps of firstly allocating search clusters for areas needing life searching after earthquake, wherein the search clusters comprise the steps of classifying the search areas and determining the number of each type of area searching equipment; secondly, constructing an optimizing algorithm of three-dimensional path planning, and calculating an optimal path through iteration; and finally, sending out each search cluster to perform life searching according to the established optimal path.
Dividing search areas of different categories according to the searched range, and determining the number of search devices in each category of areas by using a weight calculation mode, wherein the specific technical implementation mode is as follows:
s1, arbitrarily selecting n position objects from p position objects as initial clustering centers;
s2, respectively calculating a weighted average value or a weighted average value of each position object, and distributing each position object into the most similar clusters;
s3, after the distribution of all the position objects is completed, calculating the weighted average value of the objects in each cluster again to obtain the centers of n clusters;
s4, comparing the obtained cluster centers with n cluster centers obtained by previous calculation, if the cluster centers change, turning to S2, otherwise turning to S5;
s5: outputting a clustering result: search areas of different categories.
The weight value is the reciprocal of the distance from the departure point to the inspection point, and normalization processing is carried out.
According to known conditions, the objective equations are listed as follows:
，
wherein, the method comprises the steps of, wherein,f n (k) Region category classification algorithm for S1 to S5, n is the number of categories, g k And (t) the number of the unmanned aerial vehicle frames required for each type of region.
The three-dimensional path planning optimizing algorithm is designed to iterate and calculate an optimal life searching path, and the specific technical implementation mode is as follows:
s1, initializing parameters: according to the actual condition of the earthquake occurrence area, relevant parameters such as the scale of a search cluster, a pheromone importance factor, a heuristic function importance factor, a pheromone volatilization factor, a pheromone release total amount, the maximum iteration number and the iteration initial number are initialized.
S2, constructing a three-dimensional solution space: each search device is randomly placed at different target points, and the next target point to be accessed is calculated for each search device until all the search devices access all the target points.
S3, updating the pheromone: and calculating the path length of each search device, and recording the optimal solution (shortest path) in the current iteration number. And updating pheromones on the connection paths of the target points.
S4, judging whether to terminate. If not, emptying the record list of the path of the searching equipment, and returning to S2; otherwise, the calculation is terminated, and the optimal life search path is output.
The 3D path planning for the patrol of each intelligent device in each area can be completed through iteration of the three-dimensional path planning optimizing algorithm, and the optimal life searching path is obtained.
Compared with the prior art, the application has the advantages and positive effects that:
the method provided by the application is applied to the field of life searching after earthquake; the method can classify areas aiming at different landforms, and can perform optimized life searching by using various intelligent clusters such as unmanned aerial vehicles and the like, so that the searching efficiency of the unmanned aerial vehicles is improved.
Drawings
FIG. 1 is a flow chart of a method for planning a self-adaptive cluster path in a life search process after an earthquake.
Fig. 2 is a full area bottom view of embodiment 1.
FIG. 3 is a graph showing the results of 30 kinds of aggregation in a region of 3000 m or less.
Fig. 4 is a diagram of the shortest-time unmanned aerial vehicle flight path layout in example 1.
Fig. 5 is a drawing of the longest-used unmanned aerial vehicle track plan.
Detailed Description
In order that the above objects, features and advantages of the application will be more clearly understood, a further description of the application will be provided with reference to specific examples. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, however, the present application may be practiced otherwise than as described herein, and therefore the present application is not limited to the specific embodiments of the disclosure that follow.
Example 1
As shown in fig. 1, the present embodiment provides a method for planning a self-adaptive cluster path in a life search process after an earthquake, and illustrates the effect of the present application through a practical application case. The method includes the steps of firstly allocating search clusters for areas needing life searching after an earthquake, dividing the searched range into different types of search areas, and determining the number of search devices in each type of areas by means of weight calculation.
S1, arbitrarily selecting n position objects from p position objects as initial clustering centers;
s2, respectively calculating a weighted average value or a weighted average value of each position object, and distributing each position object into the most similar clusters;
s3, after the distribution of all the position objects is completed, calculating the weighted average value of the objects in each cluster again to obtain the centers of n clusters;
s4, comparing the clustering centers with the clustering centers obtained by previous calculation, if the clustering centers change, turning to S2, otherwise turning to S5;
s5: outputting a clustering result: search areas of different categories.
The weight value is the reciprocal of the distance from the departure point to the inspection point, and normalization processing is carried out.
According to known conditions, the objective equations are listed as follows:
,
,
wherein f n (k) Region category classification algorithm for S1 to S5, n is the number of categories, g k (t) the number of unmanned aerial vehicles needed by each type of area, t is the distance from the departure point to the inspection point, and the unit is: km.
And secondly, designing a three-dimensional path planning optimizing algorithm, iterating and calculating an optimal life searching path. The method comprises the following steps:
s1, initializing parameters: initializing related parameters, such as the scale of a search cluster, a factor of importance of a pheromone, a factor of importance of a heuristic function, a factor of volatilization of the pheromone, the total release amount of the pheromone, the maximum iteration times and the initial iteration times, according to the actual condition of the earthquake occurrence place.
S2, constructing a three-dimensional solution space: each search device is randomly placed at different target points, and the next target point to be accessed is calculated for each search device until all the search devices access all the target points.
S3, updating the pheromone: and calculating the path length of each search device, and recording the optimal solution (shortest path) in the current iteration number. And updating pheromones on the connection paths of the target points.
S4, judging whether the path is terminated, if not, clearing a record table of the path of the searching equipment, and returning to S2; otherwise, the calculation is terminated, and the optimal life search path is output.
3D path planning for the patrol of each intelligent device in each area can be completed by designing corresponding three-dimensional path planning optimizing algorithm iteration, and the optimal life searching path is obtained.
And finally, carrying out life searching according to the divided search areas of different categories and the number of the search devices in each category of areas and combining the optimal life searching paths calculated by iteration.
The method is mainly applied to the field of life searching after earthquake; the method can classify areas aiming at different landforms, and perform optimized life searching by using various intelligent clusters such as unmanned aerial vehicles.
Application cases:
the method is characterized in that 8 months in 2017 are adopted, and after earthquake occurs in a certain region of Sichuan province in China, life signs are detected as a research background; the unmanned aerial vehicle cluster carried life detector is used for searching for life signs, and the scheme provided by the application can be verified to provide accurate target positioning for post-disaster rescue. In this embodiment, it is assumed that the maximum flying height of the unmanned aerial vehicle is 5000 meters, the average flying speed is 60 km/h, the maximum endurance time is 8 hours, the turning radius during flying is not less than 100 meters, and the maximum climbing (diving) angle is 15 degrees. The elevation data of the seismic area is known to have 2913 columns, 2775 rows, with the first column of the first row representing the altitude value (in meters) at the (0, 0) point, and the distance between adjacent cells being 38.2 meters. A total of 30 unmanned aerial vehicles (15 each) are to be dispatched from the base H (110,0), J (110, 55) (unit: km), and returned to their respective departure sites after the completion of the mission. The effective detection distance of the detector is not more than 1000 meters, and the maximum side view angle (the included angle between the connecting line from the detector to the detectable position and the plumb line is 60 degrees, and the time interval from the first unmanned aerial vehicle to the last unmanned aerial vehicle to finish the task to the base is the search task completion time of the whole unmanned aerial vehicle cluster.
Based on the relevant elevation data of the seismic region, a bottom view of the whole region is drawn, the region with the altitude higher than 3000 m is marked red, and the region with the altitude lower than 3000 m is marked blue. The clustering method is used for clustering and dividing the areas, the inspection points of the areas below 3000 m are divided into 30 categories (represented by different colors and symbols), different weights are set according to the distance between the inspection target and the departure point of the unmanned aerial vehicle, the weights are smaller when the distance is longer, namely the dividing area is smaller, so that the unmanned aerial vehicle can return more quickly, and resources are distributed reasonably.
As shown in fig. 2 and 3 (original diagrams of fig. 2 and 3 are colored), each region clustered in fig. 3 is allocated to 30 unmanned aerial vehicles, a target equation is listed and solved according to objectively existing limiting conditions, and a three-dimensional path of each unmanned aerial vehicle is calculated through the self-adaptive cluster path planning method.
According to known conditions, the objective equations are listed as follows:
，
the method is a clustering method, k is the number of classifications, is a self-adaptive cluster path planning method, is a turning radius of an unmanned aerial vehicle in flight, is a climbing (diving) angle of the unmanned aerial vehicle, and is a safe flight distance between the unmanned aerial vehicle and other obstacles (including the ground).
The shortest-time track planning chart (shown in figure 4) and the longest-time track planning chart (shown in figure 5) of 30 unmanned aerial vehicles can be obtained through calculation by the method; and the difference between the longest and shortest time was calculated to be (482.236-250.416)/60= 3.863 (hours). From the calculation result, the searching time and the path which can be saved by the optimal route are very obvious after the scheme provided by the application is planned, so that the method can effectively provide guidance for the unmanned aerial vehicle searching scheme after earthquake disaster.
The present application is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present application without departing from the technical content of the present application still belong to the protection scope of the technical solution of the present application.
Claims (2)
1. The self-adaptive cluster path planning method in the post-earthquake life searching process is characterized by comprising the following steps:
(1) Allocating search clusters for areas needing life searching after earthquake, classifying the search areas and determining the number of each type of area searching equipment;
(2) Constructing an optimizing algorithm of three-dimensional path planning, and calculating an optimal path through iteration;
(3) Performing life searching for each search cluster according to the established optimal path;
the implementation process of the step (1) is as follows:
s1: constructing a search cluster according to the life search range, determining a position object total set P, and randomly selecting n position objects from the position object total set P to serve as an initial clustering center;
s2: respectively calculating a weighted average value or a weighted average value of the distances of the position objects in P, and distributing the position objects into the most similar clusters;
s3: after the distribution is completed, calculating the weighted average value of the position objects in each cluster again to obtain new centers of n clusters;
s4: repeating the steps S2-S3, comparing with n clustering centers obtained in the previous time, turning to S2 if the clustering centers change, otherwise turning to S5;
s5: outputting a clustering result;
the weighted weights in the steps S2 and S3 are obtained by taking the reciprocal of the distance from the searching departure point to the inspection point and carrying out normalization processing; the target formula is:
，
wherein, the method comprises the steps of, wherein,f n (k) Class classification algorithm for classifying search areas, n is the number of classifications, t is the distance mileage, g k And (t) is the number of unmanned racks.
2. The method for planning the adaptive cluster path in the post-earthquake life searching process according to claim 1, wherein the specific implementation steps of the step (2) are as follows:
p1: initializing related parameters according to the actual situation of the earthquake, wherein the related parameters comprise: searching the scale of a cluster, the importance factor of the pheromone, the importance factor of the heuristic function, the volatilization factor of the pheromone, the total release amount of the pheromone, the maximum iteration number and the initial iteration number;
p2: constructing a three-dimensional space: randomly placing each searching device at different target points, and calculating the next target point to be accessed for each searching device until the searching device accesses all the target points;
p3: updating the pheromone: calculating the path length of each search device, recording the shortest path of the current iteration times, and updating the pheromone on each target point connection path;
p4: repeating the steps P2 and P3 until the calculation is stopped, and outputting the optimal path.
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