CN109978286A - It is a kind of to be diversion thunderstorm Route planner based on the more aircrafts for improving ant group algorithm - Google Patents

It is a kind of to be diversion thunderstorm Route planner based on the more aircrafts for improving ant group algorithm Download PDF

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CN109978286A
CN109978286A CN201910375465.8A CN201910375465A CN109978286A CN 109978286 A CN109978286 A CN 109978286A CN 201910375465 A CN201910375465 A CN 201910375465A CN 109978286 A CN109978286 A CN 109978286A
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thunderstorm
aircraft
aircrafts
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向征
张文奇
何德暘
张文军
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Civil Aviation Flight University of China
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Abstract

It is diversion thunderstorm Route planner the invention discloses a kind of based on the more aircrafts for improving ant group algorithm, comprising the following steps: step 1: describing thunderstorm belt institute overlay area and other regions in airspace with the mode of rasterizing, obtain gridding airspace；Step 2: the thunderstorm routeing model that is diversion of single aircraft is established based on the gridding airspace；Step 3: changing bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal；Step 4: describing the aircraft with the mode of rasterizing and change the occupied airspace of bit path, and it is superimposed with the thunderstorm belt of rasterizing, superimposed region is the region for needing to avoid for other aircrafts；Step 5: for other single aircrafts, repeating step 2- step 4, change bit path until search for all list the optimal of aircraft, complete more aircrafts and be diversion thunderstorm routeing.The present invention realizes the conflict avoiding between more aircrafts while the more aircrafts of planning are diversion thunderstorm air route.

Description

It is a kind of to be diversion thunderstorm Route planner based on the more aircrafts for improving ant group algorithm
Technical field
The present invention relates to a kind of air lane planing method more particularly to a kind of more aircrafts based on improvement ant group algorithm Be diversion thunderstorm Route planner.
Background technique
With the fast development of Civil Aviation Industry, airflight flow rapidly increases, how under the flight flow of rapid growth Persistently keeping flight safety is an important topic.Many external conditions can cause serious influence to flight safety, wherein one A extreme conditions is exactly Thunderstorm Weather.Thunderstorm region be frequently accompanied by jolt, accumulated ice, hail are hit, thunder and lightning, downburst etc. pair Aircraft has safely the meteorological condition of the serious danger side of body, therefore allows aircraft to fly into thunderstorm belt in no instance Among domain.At present during practical flight, when there is large area thunderstorm region overlay in the flight route of aircraft, ground Air traffic controller generally according to related civil aviation regulations CAR and the working experience of itself and ability, for aircraft plan one around Fly thunderstorm changes boat air route, guides aircraft to be diversion thunderstorm region using radar vectoring mode.
Artificial planning changes boat air route and aircraft is guided to carry out changing boat, increases the work difficulty of controller.Especially when Multi rack aircraft need to be diversion simultaneously thunderstorm when, will substantially increase for the be diversion complexity in air route of these aircrafts planning Lothrus apterus Add, therefore the workload of controller and pressure can also increase in geometric multiple at heart.At this moment, if controller's attention point With improper, the flight dynamic of aircraft in regulatory area can not be monitored in time, it will cause to clash between each frame aircraft A possibility that increased dramatically, or even occur civil aviaton's unsafe incidents.Therefore, it is necessary to be controller in air traffic control automation system It provides real-time aid decision to support, the thunderstorm air route of being diversion of Lothrus apterus is planned for more aircrafts, to reduce the work of controller Make load, improves flight safety.
Changing boat routeing is substantially an optimization problem, changes boat objective function by foundation and accordingly constrains item Part searches for one from an optimal path for changing boat origin-to-destination for aircraft.Tradition changes in boat routeing, changes boat target Include: distance is most short, oil consumption is minimum, number of turns is minimum, airspace use scope at least etc., constraint condition includes: and thunderstorm belt Between interval, interval, angle of turn, airspace range between aircraft etc., heuristic algorithm includes: genetic algorithm, ant Ant algorithm, simulated annealing, A* algorithm etc..
Above-mentioned tradition changes boat Route planner when carrying out changing boat routeing, seldom considers to change boat mesh to more aircrafts Scalar functions are modeled, so be difficult in more aircrafts while changing endurance progress routeing；Even if there is certain methods to consider The case where more aircrafts change boat, but in its constraint condition there is no comprising in civil aviaton's regulations to the regulation being spaced between aircraft, It is likely to cause aircraft and generates flight collision between each aircraft when being diversion thunderstorm；The convergence of conventional search algorithm simultaneously Speed is relatively slow, calculates that the outcome quality that the time is longer, solves is poor, so real-time is not high, the air route that obtains does not meet reality It flies the requirement of operation, therefore, it is difficult to meet the work requirements of controller under the conditions of big flight flow, complicated thunderstorm distribution.
Summary of the invention
The object of the invention is that provide a kind of based on the more aviations for improving ant group algorithm to solve the above-mentioned problems Device is diversion thunderstorm Route planner.
The present invention through the following technical solutions to achieve the above objectives:
It is a kind of to be diversion thunderstorm Route planner based on the more aircrafts for improving ant group algorithm, comprising the following steps:
Step 1: describing thunderstorm belt institute overlay area and other regions in airspace with the mode of rasterizing, obtain gridding sky Domain；
Step 2: the thunderstorm routeing model that is diversion of single aircraft is established based on the gridding airspace；
Step 3: changing bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal；
Step 4: describing the aircraft with the mode of rasterizing and change the occupied airspace of bit path, and by itself and rasterizing Thunderstorm belt is superimposed, and superimposed region is the region for needing to avoid for other aircrafts；
Step 5: for other single aircrafts, repeating step 2- step 4, change until having searched for all single the optimal of aircraft Bit path completes more aircrafts and is diversion thunderstorm routeing.
Preferably, the gridding airspace is indicated with following matrix in the step 1:
Wherein, i, j=1,2 ..., n；In grid map, if a grid is discontented in thunderstorm region, it is extended for one Grid, the side length of a grid are preferably 20km.
Preferably, the thunderstorm routeing model that is diversion of single aircraft is described with following formula in the step 2:
minΔSk=Lk-Ck
In formula, LkAir route length after changing boat for single aircraft k, CkIt is long the air route in the case of navigating is not changed for single aircraft k Degree；
Single aircraft k is changing endurance, should be by following constraint:
First, single aircraft, which changes boat air route apart from thunderstorm belt, to be Dan Hang in rasterizing grid representation less than 10 kms Distance of the pocket away from thunderstorm belt grid central point must not be less than 10 kms, it may be assumed that
In formula,Change the two-dimensional coordinate of each radar points on air route after boat for single aircraft k, m=1,2 ..., n, cijFor the center point coordinate of thunderstorm belt grid, i.e. i, i, j j corresponding when being Airspace (i, j)=1；
Second, single aircraft changes endurance and is limited by performance, and the angle of turn on air route is not greater than 90 degree, it may be assumed that
In formula,It is single aircraft k in two-dimensional coordinateOn course, m=1,2 ..., n,0 degree to 360 degree it Between.
Preferably, changing bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal in the step 3 Search formula it is as follows:
Wherein, μ 'i,j(t)=exp (Ftcosθ)μi,j(t)
In formula, τi,j(t) information prime function of the grid i to the path grid j when being the t times cyclic search, μ 'i,j(t) being should By the improved ant group algorithm heuristic information saturation of Artificial Potential Field Method on path, α is pheromones heuristic greedy method, and β is Expected heuristic value, allowedmIndicate grid i next accessibility grid set；μi,jIt (t) is the tradition on the path Ant group algorithm heuristic information saturation, the inverse of value distance between optional grid and target gridding, i.e. μi,j(t)=1/d (j, GOAL), FtResultant force after being added for repulsion of the barrier to aircraft with gravitation of the target point to aircraft, θ are optional Path and resultant force FtThe angle in direction.
The beneficial effects of the present invention are:
The present invention changes bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal, is planning more aircrafts Be diversion thunderstorm air route while, realize the conflict avoiding between more aircrafts, meet big flight flow, complicated thunderstorm distribution item The work requirements of controller under part；More specific advantage is as follows:
1, change boat objective function for more aircrafts to be modeled, change endurance simultaneously for more aircrafts and carry out routeing；
2, it contains in civil aviaton's regulations to the regulation being spaced between aircraft, can be avoided more in the constraint condition of model The flight collision that frame aircraft generates between them when being diversion thunderstorm；
3, it can quickly be cooked up high-quality in heuristic algorithm using the improved ant group algorithm of Artificial Potential Field is based on Amount changes boat air route, to meet the work requirements of controller under the conditions of big flight flow, complicated thunderstorm distribution.
Detailed description of the invention
Fig. 1 is of the present invention to be diversion the specific side of thunderstorm Route planner based on the more aircrafts for improving ant group algorithm Method flow chart.
Specific embodiment
The present invention will be further explained below with reference to the attached drawings:
The thunderstorm Route planner as shown in Figure 1, more aircrafts of the present invention based on improvement ant group algorithm are diversion, packet Include following steps:
Step 1: describing thunderstorm belt institute overlay area and other regions in airspace with the mode of rasterizing, obtain gridding sky Domain；
In this step, the gridding airspace is indicated with following matrix:
Wherein, i, j=1,2 ..., n；In grid map, if a grid is discontented in thunderstorm region, it is extended for one Grid, the side length of a grid are 20km.
Step 2: the thunderstorm routeing model that is diversion of single aircraft is established based on the gridding airspace；
In this step, the thunderstorm routeing model that is diversion of single aircraft is described with following formula:
minΔSk=Lk-Ck
In formula, LkAir route length after changing boat for single aircraft k, CkIt is long the air route in the case of navigating is not changed for single aircraft k Degree；
Single aircraft k is changing endurance, should be by following constraint:
First, single aircraft, which changes boat air route apart from thunderstorm belt, to be Dan Hang in rasterizing grid representation less than 10 kms Distance of the pocket away from thunderstorm belt grid central point must not be less than 10 kms, it may be assumed that
In formula,Change the two-dimensional coordinate of each radar points on air route after boat for single aircraft k, m=1,2 ..., n, cijFor the center point coordinate of thunderstorm belt grid, i.e. i, i, j j corresponding when being Airspace (i, j)=1；
Second, single aircraft changes endurance and is limited by performance, and the angle of turn on air route is not greater than 90 degree, it may be assumed that
In formula,It is single aircraft k in two-dimensional coordinateOn course, m=1,2 ..., n,0 degree to 360 degree it Between.
Step 3: changing bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal；
In this step, search formula is as follows:
Wherein, μ 'i,j(t)=exp (Ftcosθ)μi,j(t)
In formula, τi,j(t) information prime function of the grid i to the path grid j when being the t times cyclic search, μ 'i,j(t) being should By the improved ant group algorithm heuristic information saturation of Artificial Potential Field Method on path, α is pheromones heuristic greedy method, and β is Expected heuristic value, allowedmIndicate grid i next accessibility grid set；μi,jIt (t) is the tradition on the path Ant group algorithm heuristic information saturation, the inverse of value distance between optional grid and target gridding, i.e. μi,j(t)=1/d (j, GOAL), FtResultant force after being added for repulsion of the barrier to aircraft with gravitation of the target point to aircraft, θ are optional Path and resultant force FtThe angle in direction；
In above-mentioned ant group algorithm, ant is dense with the pheromones that front ant leaves in the path passed by when selecting path Degree obtains optimal result finally by ant colony collaboratively searching path as foundation；Ant is searched for since initial position, according to Location point transition probability is ceaselessly selected from a location point to another location point, until searching target point, epicycle Route searching terminates.After epicycle route searching, the pheromones on path are updated, are prepared for next round search； Ant needs to consider aircraft and target point and aircraft and thunderstorm belt (i.e. barrier) during finding optimal path The distance between, to select optimal path；Therefore, aircraft and the distance between barrier or target point relationship can be turned Turn to the relationship between aircraft and gravitation and the resultant force of repulsion, i.e., it is in place with the resultant force size of gravitation and repulsion reflection aircraft institute Set a little with the distance between barrier or target point；If resultant force is larger, then it represents that should be under the inspiration of potential field power far from obstacle Object or close to target point；Barrier is inversely proportional to the repulsion of aircraft with aircraft with a distance from barrier, i.e., aircraft is from barrier Hinder the more close then repulsion of the distance of object bigger, it is on the contrary then repulsion is smaller；The characteristics of gravitation, is identical as repulsion, i.e., aircraft is from target point Nearlyr gravitation is bigger, and the remoter gravitation of distance is smaller；FtThe selected possibility of biggish grid is larger, or and FtAngular separation compared with The possibility that small grid is chosen as path is larger；Moving direction and gesture based on aircraft in the improved ant group algorithm of Artificial Potential Field The direction of field force is identical, is able to achieve aircraft and avoids thunderstorm belt and move to target point.
Step 4: describing the aircraft with the mode of rasterizing and change the occupied airspace of bit path, and by itself and rasterizing Thunderstorm belt is superimposed, and superimposed region is the region for needing to avoid for other aircrafts；
Step 5: for other single aircrafts, repeating step 2- step 4, change until having searched for all single the optimal of aircraft Bit path completes more aircrafts and is diversion thunderstorm routeing.
It shows and of the present invention is diversion thunderstorm Route planner in Fig. 1 based on the more aircrafts for improving ant group algorithm Specific method process, the process include the part innovated of the present invention, also comprising conventional ant group algorithm process, content with it is above-mentioned Each step is totally corresponding but does not correspond, and provides the view and is conducive to understand all processes of this method, conventional method stream Journey is not illustrated in this specification.
Above-described embodiment is presently preferred embodiments of the present invention, is not a limitation on the technical scheme of the present invention, as long as Without the technical solution that creative work can be realized on the basis of the above embodiments, it is regarded as falling into the invention patent Rights protection scope in.

Claims (5)

  1. The thunderstorm Route planner 1. a kind of more aircrafts based on improvement ant group algorithm are diversion, it is characterised in that: including following Step:
    Step 1: describing thunderstorm belt institute overlay area and other regions in airspace with the mode of rasterizing, obtain gridding airspace；
    Step 2: the thunderstorm routeing model that is diversion of single aircraft is established based on the gridding airspace；
    Step 3: changing bit path using the improvement ant group algorithm search based on Artificial Potential Field Method is optimal；
    Step 4: describing the aircraft with the mode of rasterizing and change the occupied airspace of bit path, and by the thunderstorm of itself and rasterizing Area is superimposed, and superimposed region is the region for needing to avoid for other aircrafts；
    Step 5: for other single aircrafts, repeating step 2- step 4, change air route until having searched for all single the optimal of aircraft Diameter completes more aircrafts and is diversion thunderstorm routeing.
  2. The thunderstorm Route planner 2. more aircrafts according to claim 1 based on improvement ant group algorithm are diversion, it is special Sign is: in the step 1, the gridding airspace is indicated with following matrix:
    Wherein, i, j=1,2 ..., n；In grid map, if a grid is discontented in thunderstorm region, it is extended for a grid Lattice.
  3. The thunderstorm Route planner 3. more aircrafts according to claim 2 based on improvement ant group algorithm are diversion, it is special Sign is: in the step 1, the side length of a grid is 20km.
  4. The thunderstorm Route planner 4. more aircrafts according to claim 2 based on improvement ant group algorithm are diversion, it is special Sign is: in the step 2, the thunderstorm routeing model that is diversion of single aircraft is described with following formula:
    minΔSk=Lk-Ck
    In formula, LkAir route length after changing boat for single aircraft k, CkThe air route length in the case of boat is not changed for single aircraft k；
    Single aircraft k is changing endurance, should be by following constraint:
    First, single aircraft, which changes boat air route apart from thunderstorm belt, to be single aircraft in rasterizing grid representation less than 10 kms Distance away from thunderstorm belt grid central point must not be less than 10 kms, it may be assumed that
    In formula,Change the two-dimensional coordinate of each radar points after navigating on air route, m=1,2 ..., n, c for single aircraft kijFor The center point coordinate of thunderstorm belt grid, i.e. i, i, j j corresponding when being Airspace (i, j)=1；
    Second, single aircraft changes endurance and is limited by performance, and the angle of turn on air route is not greater than 90 degree, it may be assumed that
    In formula,It is single aircraft k in two-dimensional coordinateOn course, m=1,2 ..., n,Between 0 degree to 360 degree.
  5. The thunderstorm Route planner 5. more aircrafts based on improvement ant group algorithm according to claim 2,3 or 4 are diversion, It is characterized by: searching for the optimal search for changing bit path using the improvement ant group algorithm based on Artificial Potential Field Method in the step 3 Formula is as follows:
    Wherein, μ 'i,j(t)=exp (Ftcosθ)μi,j(t)
    In formula, τi,j(t) information prime function of the grid i to the path grid j when being the t times cyclic search, μ 'i,jIt (t) is the path Upper by Artificial Potential Field Method improved ant group algorithm heuristic information saturation, α is pheromones heuristic greedy method, and β is expectation Heuristic greedy method, allowedmIndicate grid i next accessibility grid set；μi,jIt (t) is traditional ant colony on the path Algorithm heuristic information saturation, the inverse of value distance between optional grid and target gridding, i.e. μi,j(t)=1/d (j, GOAL), FtResultant force after being added for repulsion of the barrier to aircraft with gravitation of the target point to aircraft, θ is can routing Diameter and resultant force FtThe angle in direction.
CN201910375465.8A 2019-05-07 2019-05-07 It is a kind of to be diversion thunderstorm Route planner based on the more aircrafts for improving ant group algorithm Pending CN109978286A (en)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108803660A (en) * 2018-06-22 2018-11-13 苏州得尔达国际物流有限公司 Shipping unmanned aerial vehicle group paths planning method
CN110319829A (en) * 2019-07-08 2019-10-11 河北科技大学 Unmanned aerial vehicle flight path planing method based on adaptive polymorphic fusion ant colony algorithm
CN110530384A (en) * 2019-07-17 2019-12-03 江苏大学 A kind of vehicle path planning method using mixing potential field ant group algorithm
CN110647162A (en) * 2019-10-16 2020-01-03 厦门理工学院 Route planning method for tour guide unmanned aerial vehicle, terminal equipment and storage medium
CN113012478A (en) * 2021-02-23 2021-06-22 中国民用航空华东地区空中交通管理局 Rapid and simple method for changing sails in thunderstorm dangerous weather
CN113593306A (en) * 2021-08-13 2021-11-02 中国民航大学 Scattered-point thunderstorm dangerous weather re-voyage method based on safety

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101136080A (en) * 2007-09-13 2008-03-05 北京航空航天大学 Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making
CN101136081A (en) * 2007-09-13 2008-03-05 北京航空航天大学 Unmanned operational aircraft multiple plains synergic tasks distributing method based on ant colony intelligent
CN106323295A (en) * 2016-08-29 2017-01-11 中国船舶重工集团公司第七0九研究所 Method for diverting aircraft under dangerous weather conditions on basis of weather radar data
CN106595663A (en) * 2016-11-28 2017-04-26 四川航天系统工程研究所 Aircraft auto-route planning method with combination of searching and optimization
CN107544553A (en) * 2017-10-11 2018-01-05 湖北工业大学 A kind of Path Planning for UAV based on hybrid ant colony
CN108413959A (en) * 2017-12-13 2018-08-17 南京航空航天大学 Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101136080A (en) * 2007-09-13 2008-03-05 北京航空航天大学 Intelligent unmanned operational aircraft self-adapting fairway planning method based on ant colony satisfactory decision-making
CN101136081A (en) * 2007-09-13 2008-03-05 北京航空航天大学 Unmanned operational aircraft multiple plains synergic tasks distributing method based on ant colony intelligent
CN106323295A (en) * 2016-08-29 2017-01-11 中国船舶重工集团公司第七0九研究所 Method for diverting aircraft under dangerous weather conditions on basis of weather radar data
CN106595663A (en) * 2016-11-28 2017-04-26 四川航天系统工程研究所 Aircraft auto-route planning method with combination of searching and optimization
CN107544553A (en) * 2017-10-11 2018-01-05 湖北工业大学 A kind of Path Planning for UAV based on hybrid ant colony
CN108413959A (en) * 2017-12-13 2018-08-17 南京航空航天大学 Based on the Path Planning for UAV for improving Chaos Ant Colony Optimization

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
张文军: ""基于复杂天气下冲突避让的航路改航研究"", 《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》, 15 November 2018 (2018-11-15), pages 031 - 156 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108803660A (en) * 2018-06-22 2018-11-13 苏州得尔达国际物流有限公司 Shipping unmanned aerial vehicle group paths planning method
CN108803660B (en) * 2018-06-22 2021-06-18 苏州得尔达国际物流有限公司 Freight transport unmanned aerial vehicle group path planning method
CN110319829A (en) * 2019-07-08 2019-10-11 河北科技大学 Unmanned aerial vehicle flight path planing method based on adaptive polymorphic fusion ant colony algorithm
CN110319829B (en) * 2019-07-08 2022-11-18 河北科技大学 Unmanned aerial vehicle flight path planning method based on self-adaptive polymorphic fusion ant colony algorithm
CN110530384A (en) * 2019-07-17 2019-12-03 江苏大学 A kind of vehicle path planning method using mixing potential field ant group algorithm
CN110647162A (en) * 2019-10-16 2020-01-03 厦门理工学院 Route planning method for tour guide unmanned aerial vehicle, terminal equipment and storage medium
CN110647162B (en) * 2019-10-16 2022-10-14 厦门理工学院 Route planning method for tour guide unmanned aerial vehicle, terminal equipment and storage medium
CN113012478A (en) * 2021-02-23 2021-06-22 中国民用航空华东地区空中交通管理局 Rapid and simple method for changing sails in thunderstorm dangerous weather
CN113012478B (en) * 2021-02-23 2022-02-11 中国民用航空华东地区空中交通管理局 Rapid and simple method for changing sails in thunderstorm dangerous weather
CN113593306A (en) * 2021-08-13 2021-11-02 中国民航大学 Scattered-point thunderstorm dangerous weather re-voyage method based on safety

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Application publication date: 20190705