CN111272179B - Building space unmanned aerial vehicle route planning method based on seed search - Google Patents
Building space unmanned aerial vehicle route planning method based on seed search Download PDFInfo
- Publication number
- CN111272179B CN111272179B CN202010200496.2A CN202010200496A CN111272179B CN 111272179 B CN111272179 B CN 111272179B CN 202010200496 A CN202010200496 A CN 202010200496A CN 111272179 B CN111272179 B CN 111272179B
- Authority
- CN
- China
- Prior art keywords
- route
- seed
- building
- seeds
- list
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
Abstract
The invention discloses a building space unmanned aerial vehicle route planning method based on seed search, which aims at the problem of rapid planning of unmanned aerial vehicle flight routes in complex city building spaces. Uniformly sowing a plurality of seeds in a gap of a task space building, then creating an initial route based on a starting point, copying the seeds into a seed list of the route, and adding the route into a planning medium route list; traversing the planning medium route list, judging whether the last route point of each route is visible with the end point, if visible, finishing the route, if invisible, taking n seeds which are in the seed list of the route and are in the same sight with the last route point and have a short distance, constructing n new routes, adding the n new routes into the planning medium route list, judging whether the routes are finished one by one, repeating the steps until no new routes are generated, and finally calculating the route with the shortest length from a series of finished routes as a final planning result. The method has the characteristics of clear logic, strong adaptability, small code amount, high execution efficiency and easy expansion.
Description
Technical Field
The invention provides a seed search-based building space unmanned aerial vehicle route planning method, which is suitable for the problem of fast and automatic planning of unmanned aerial vehicle flight routes in complex urban building spaces.
Background
With the development of unmanned aerial vehicles and artificial intelligence technologies, unmanned aerial vehicles are widely applied in various fields, flight route planning is the basis of task execution of unmanned aerial vehicles, and automatic and efficient unmanned aerial vehicle route planning is an important guarantee for safely and efficiently completing tasks especially in complex urban building spaces.
Common unmanned aerial vehicle route planning methods mainly include the following categories: an intelligent method. The method comprises a particle swarm optimization algorithm, a genetic algorithm, an ant colony algorithm, tabu search, heuristic search and the like. ② geometric method. Such as a Voronoi chart, and the like, and a plurality of graph theory searching methods such as the Dijkstra method are combined to quickly find the optimal path. And thirdly, an artificial potential field method and the like. The genetic algorithm has too low convergence rate and is easy to fall into local optimum, and can be used in combination with a simulated annealing algorithm, but the defects can not be really solved; the heuristic search A algorithm is a classical method for path search and planning, but the exposed defects in the search process are obvious; the artificial potential field method is generally combined with a Voronoi diagram or a genetic algorithm for optimization, and the problem is that the algorithm convergence time is too long, and the artificial potential field method is generally used for post-processing for finishing route planning. The basic idea of the method is to reduce the search space through dimension reduction and improve the search efficiency, the method is still two-dimensional route planning, but terrain following and terrain avoidance are difficult to consider simultaneously in doing so, and especially route planning in urban building space is realized.
Disclosure of Invention
The invention provides an algorithm for planning unmanned aerial vehicle routes aiming at complex and changeable urban building environments. The invention has the characteristics of clear and visible logic, high calculation efficiency, easy realization and debugging, easy expansion, strong compatibility and the like.
The technical problem to be solved by the invention is realized by the following technical scheme:
a building space unmanned aerial vehicle route planning method based on seed search comprises the following steps:
(1) constructing a planning model, comprising a task environment space model: expressing the space range of unmanned aerial vehicle task execution, and defining the space range as a cube; the city building model: expressing the position and the appearance of the building, expressing the models of all urban buildings by using a series of cubic bounding boxes, and splitting the complex building into a plurality of simple cubes spliced together; and (3) seed model: the three-dimensional coordinate points are uniformly distributed in the environment space, and all the seeds are in the idle area and are not overlapped with the building model in a crossing way; a route model: starting from a starting point, passing through a plurality of seeds, and ending at a spatial broken line of a terminal point, wherein the route comprises a seed set and a waypoint set, and in the course search, the seeds meeting the conditions are taken out from the seed set and placed in the waypoint set until the whole route is opened;
(2) object declaration, including building model list: the system is used for storing all city building models in the environment; seed list: for storing all seeds; planning a medium route list: the temporary route is used for storing the temporary route created in the searching process, and the temporary route is not connected from a starting point to a terminal point; completing the route list: storing the completed complete route, which has completed the connection from the starting point to the end point;
(3) acquiring basic information: acquiring task environment space range information, all city building model information, unmanned aerial vehicle task starting points, unmanned aerial vehicle task end points and unmanned aerial vehicle size constraints;
(4) according to the known task environment space range and the coordinate data of the urban building model, seeds are sown into the environment, the seeds are uniformly filled in gaps of the urban building model in the task environment space range, and the interval between the seeds is 2 times of the maximum side length of the unmanned aerial vehicle cube bounding box;
(5) establishing an initial route based on a route starting point, wherein the initial route only comprises one route point of the unmanned aerial vehicle task starting point, copying all seeds to a seed list in the route, and adding the initial route to a planning medium route list;
(6) traversing the planning medium route list, judging whether the last waypoint of each route is visible with the unmanned aerial vehicle task end point, if so, taking the route as a finished route, supplementing the unmanned aerial vehicle task end point into the route, taking the route out of the planning medium route list, adding the route into the finished route list, and turning to the step (8) after the planning medium route list is traversed; if the last route of the route is not in sight of the unmanned aerial vehicle task end point, the step (7) is carried out;
(7) judging the residual seeds, if the number of the residual seeds is zero or the residual seeds are not in sight of the last route point of the route, not constructing a new route, ending the route searching process, and turning to the step (8); otherwise, calculating the distance between all the remaining seeds of the route and the last waypoint, taking n seeds which are in communication with the last waypoint and have the shortest distance to construct n routes including the original route, respectively adding the corresponding seeds to the waypoints of the routes to be used as the last waypoint, removing the corresponding seeds from the seed list of the routes, and returning to the step (6); n is a set value;
(8) if the number of the air routes in the finished air route list is 0, no air route from the starting point to the end point of the unmanned aerial vehicle task exists, and the air route planning is finished; and if the number of the air routes in the finished air route list is not 0, calculating the distance of each air route one by one, and taking the air route with the shortest distance as the final air route planned at this time.
Wherein, the step (4) comprises the following steps:
(401) calculating the seed positions point by point according to the environment SPACE SPACE (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) and the seed interval step of 2 × UAVSIZE, forming a seed position set POS { (x, y, z) | P (x, y, z) }, wherein P (x, y, z) is defined as follows,
for round-down operation:
(402) calculating the spatial relationship between each calculated Seed position and each Building model in the Building model list Building, if the Seed falls into any Building model, discarding the Seed, otherwise, adding the Seed into the Seed list Seed, and aiming at any Building model Building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) in the Building model list Building, meeting the following conditions:
Seed.x≤Building.Xmin|Seed.x≥Building.Xmax|
Seed.y≤Building.Ymin|Seed.y≥Building.Ymax|
Seed.z≤Building.Zmin|Seed.z≥Building.Zmax
the UAVSIZE is the maximum side length of the unmanned plane cube bounding box, Xmin is the minimum horizontal coordinate, Ymin is the minimum vertical coordinate, Zmin is the minimum height coordinate, Xmax is the maximum horizontal coordinate, Ymax is the maximum vertical coordinate, and Zmax is the maximum height coordinate.
The method for judging the two-point visibility in the step (6) comprises the following steps: densely sampling a connecting line between two points pt1 and pt2, if one sampling point intersects with the building model, judging that the two points are invisible, and processing logic is as follows:
calculating the length of a connecting line of the two points:
for intensive sampling of a connecting line of two points Pt1 and Pt2, for an arbitrary sampling point Pt (x, y, z), the following requirements are met:
aiming at any sampling point Pt and any Building model Building, if the following conditions are met, judging that the sampling point is crossed with the Building model:
Building.Xmin≤Pt.x≤Building.Xmax&
Building.Ymin≤Pt.y≤Building.Ymax&
Building.Zmin≤Pt.z≤Building.Zmax
building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) is the coordinate of any Building model.
Calculating the distance between all the remaining seeds of the route and the last waypoint in the step (7), taking n seeds which are in communication with the last waypoint and have the shortest distance to construct n routes including the original route, adding the corresponding seeds into the waypoints of the routes respectively to serve as the last waypoint, and removing the corresponding seeds from a seed list of the routes, wherein the method specifically comprises the following steps:
(701) before constructing a new route based on an original route line, calculating the distance between the remaining seeds in the route and the last route point of the route;
(702) sorting the distances between all the seeds and the last waypoint from small to large, taking out n seeds closest to the last waypoint, constructing n routes including the original route, adding the n seeds to the waypoints of the n routes respectively, removing the corresponding seeds from a seed list of the routes, and constructing a new route based on the seed algorithm as follows:
firstly, constructing a new airline based on an original airline line, wherein line1 is line;
the seed is then added to the waypoint list of line 1:
line1.ListPoint.Add(seed)；
and finally, deleting the seed from the candidate seed list:
line1.ListSeed.Remove(seed)；
ListPoint is a waypoint set, and ListSeed is a candidate seed set for route planning.
The invention has the following advantages:
1. the invention uses evenly distributed space seeds as media to carry out route planning, and the position of the navigation route is predicted and is easy to understand;
2. the invention adopts the iterative search method to plan the route, the code amount is small, and the operation efficiency is high;
3. the invention has the characteristics of easy expansion and strong compatibility;
4. the invention also has the characteristic of easy realization and debugging.
Drawings
FIG. 1 is a schematic diagram of unmanned aerial vehicle route planning in urban building space based on seed search according to the present invention.
FIG. 2 is the overall process of the method of the present invention.
FIG. 3 is a process flow of the present invention for sowing seeds.
FIG. 4 is a flow chart of the seed-based course search according to the present invention.
Detailed Description
The present invention will be further described with reference to fig. 1 to 4.
The invention designs a flight route planning method based on seeds uniformly distributed in space as media by comprehensively considering factors such as task space range, building distribution, unmanned aerial vehicle passing size and the like. Uniformly sowing a plurality of seeds in a gap of a task space building, then creating an initial route based on a starting point, copying the seeds into a seed list of the route, and adding the route into a planning medium route list; traversing the planning medium route list, judging whether the last route point of each route is visible with the end point, if visible, finishing the route, if invisible, taking n seeds which are in the seed list of the route and are in the same sight with the last route point and have a short distance, constructing n new routes, adding the n new routes into the planning medium route list, judging whether the routes are finished one by one, repeating the steps until no new routes are generated, and finally calculating the route with the shortest length from a series of finished routes as a final planning result. As shown in fig. 2, the method specifically includes the following steps:
(1) the method comprises the following steps of constructing models used in the air route planning, wherein the models comprise a task environment space model, a building model, a seed model and an air route model, and are specifically defined as follows:
(101) a task environment space model: the system comprises a SPACE range used for defining a task environment, a cube SPACE is used for expressing and is defined as SPACE (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax), wherein Xmin is the minimum horizontal coordinate of the environment SPACE, Ymin is the minimum vertical coordinate of the environment SPACE, Zmin is the minimum height coordinate of the environment SPACE, Xmax is the maximum horizontal coordinate of the environment SPACE, Ymax is the maximum vertical coordinate of the environment SPACE, and Zmax is the maximum height coordinate of the environment SPACE;
(102) building a model: the position and the appearance of the urban building are expressed by the cubic bounding boxes, the complex building model is split into a plurality of cubic bounding boxes, and the building model is defined as follows: building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax), wherein Xmin is the minimum horizontal coordinate of the Building model, Ymin is the minimum vertical coordinate of the Building model, Zmin is the minimum height coordinate of the Building model, Xmax is the maximum horizontal coordinate of the Building model, Ymax is the maximum vertical coordinate of the Building model, and Zmax is the maximum height coordinate of the Building model;
(103) and (3) seed model: three-dimensional coordinate points uniformly distributed in an environment space are defined as Seed (X, Y, Z), wherein X is an abscissa of the Seed, Y is an ordinate of the Seed, and Z is a height coordinate of the Seed;
(104) a route model: starting from a starting point, passing through a plurality of seeds and ending at a space broken Line of a terminal point, the route is defined as Line (ListSeed, ListPoint, isOK), wherein ListSeed is a candidate seed set for route planning, ListPoint is a set of waypoints, all waypoints are from the seed set, isOK is a route planning completion mark, and the route from the starting point to the terminal point is established.
(2) The object declares: variables of various objects are stored in the declared airline plan, and mainly comprise a building model list, a seed list, a planning medium airline list and a finishing airline list, and the variables are as follows:
(201) building model list, storing all Building models, defined as Listbuilding (Building1, Building2, … …);
(202) seed list: all seeds, defined as ListSeed (Seed1, Seed2, … …), are stored.
(203) Planning a medium route list: storing all the routes that are not finalized, defined as ListTempLine (Line1, Line2, … …, Linen);
(204) completing the route list: all completed lanes are stored, defined as ListFullLine (Line1, Line2, … …, Linen).
(3) Acquiring basic information: acquiring the SPACE range information of the task environment, and storing the SPACE range information to the SPACE object; acquiring all city building model information, and storing the information to a List building object; acquiring the coordinates of a task starting point of the unmanned aerial vehicle, and storing the coordinates to a START (X, Y, Z) object; acquiring the task END point coordinate of the unmanned aerial vehicle, and storing the task END point coordinate to an END (X, Y, Z) object; and acquiring the maximum size UAVSIZE of the unmanned aerial vehicle.
(4) According to known environment space range, building model coordinate data, broadcast the seed to the environment, the building model clearance of environment is evenly filled to the seed, and in order to guarantee unmanned aerial vehicle flight safety, the interval between the seed adopts 2 times of unmanned aerial vehicle maximum dimension UAVSIZE, as shown in figure 3, concrete step is as follows:
(401) calculating the seed positions point by point according to an environment SPACE SPACE (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) and a seed interval step of 2 × UAVSIZE to form a seed position set POS { (x, y, z) | P (x, y, z) },
wherein P (x, y, z) is defined as follows,
for round-down operation:
(402) calculating the spatial relationship between each calculated Seed position and each Building model in the ListBuilding, if the Seed falls into any Building model, discarding the Seed, otherwise, adding the Seed into the set ListSeed, and if any Seed (x, y, z) in the set ListSeed is directed to any Building model Building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) in the set ListBuilding, the following conditions must be satisfied:
Seed.x≤Building.Xmin|Seed.x≥Building.Xmax|
Seed.y≤Building.Ymin|Seed.y≥Building.Ymax|
Seed.z≤Building.Zmin|Seed.z≥Building.Zmax
referring to FIG. 4, the seed-based course search process is as follows:
(5) an initial route is created based on a route starting point, the initial route only comprises one route point (starting point), all seeds are copied to a seed list in the route, and the initial route is added to a planned central route list, wherein the method specifically comprises the following steps:
line＝new Line()；
line.ListSeed＝ListSeed；
line.ListPoint.Add(START)；
ListTempLine.Add(line)；
(6) traversing the planning medium route list, judging whether the last waypoint of each route is visible with the unmanned aerial vehicle task end point, if so, taking the route as a finished route, supplementing the unmanned aerial vehicle task end point into the route, taking the route out of the planning medium route list, adding the route into the finished route list, and turning to the step (8) after the planning medium route list is traversed; if the last route of the route is not in sight of the unmanned aerial vehicle task end point, the step (7) is carried out;
the method for judging two-point perspective comprises the following steps: densely sampling a connecting line between two points pt1 and pt2, if one sampling point intersects with the building model, judging that the two points are invisible, and processing logic is as follows:
calculating the length of a connecting line of the two points:
for intensive sampling of a connecting line of two points Pt1 and Pt2, for an arbitrary sampling point Pt (x, y, z), the following requirements are met:
aiming at any sampling point Pt and any Building model Building, if the following conditions are met, judging that the sampling point is crossed with the Building model:
Building.Xmin≤Pt.x≤Building.Xmax&
Building.Ymin≤Pt.y≤Building.Ymax&
Building.Zmin≤Pt.z≤Building.Zmax
(7) judging the residual seeds, if the number of the residual seeds is zero or the residual seeds are not in sight of the last route point of the route, not constructing a new route, ending the route searching process, and turning to the step (8); otherwise, calculating the distance between all the remaining seeds of the route and the last waypoint, taking n seeds which are in communication with the last waypoint and have the shortest distance to construct n routes including the original route, respectively adding the corresponding seeds to the waypoints of the routes to be used as the last waypoint, removing the corresponding seeds from the seed list of the routes, and returning to the step (6); n is a set value;
calculating the distance between all the remaining seeds of the route and the last waypoint, taking n seeds which are close to the last waypoint and are in sight with the last waypoint, constructing n routes including the original route, adding the n seeds to the waypoints of the n routes respectively, and removing the corresponding seeds from a seed list of the routes, wherein the specific steps are as follows:
(701) before constructing a new airline based on an original airline line, calculating the distance between the remaining seeds in the airline and the last route point pt of the airline, wherein the specific calculation method comprises the following steps:
(702) sorting the distances between all the seeds and pt from small to large, taking out n seeds closest to pt, constructing n routes including the original route, adding the n seeds to waypoints of the n routes respectively, removing the corresponding seeds from a seed list of the routes, and constructing a new route based on seed algorithm as follows:
firstly, constructing a new airline based on an original airline line, wherein line1 is line;
the seed is then added to the waypoint list of line 1:
line1.ListPoint.Add(seed)；
and finally, deleting the seed from the candidate seed list:
line1.ListSeed.Remove(seed)；
because the seeds are uniformly filled in the task space, if the last waypoint is not located at the boundary of the task space, the last waypoint is always located at the center of a cube formed by 26 nearest seeds, when a new flight path is constructed, all directions and program operation efficiency are considered, and the maximum value of n is suggested to be 26.
(8) If the number of the routes in the finished route list is 0, no route from the starting point to the end point exists, and the route planning is finished; and if the number of the air routes in the finished air route list is not 0, calculating the distance of each air route one by one, and taking the air route with the shortest distance as the final air route planned at this time.
Claims (2)
1. A building space unmanned aerial vehicle route planning method based on seed search is characterized by comprising the following steps:
(1) constructing a planning model, comprising a task environment space model: expressing the space range of unmanned aerial vehicle task execution, and defining the space range as a cube; the city building model: expressing the position and the appearance of the building, expressing the models of all urban buildings by using a series of cubic bounding boxes, and splitting the complex building into a plurality of simple cubes spliced together; and (3) seed model: the three-dimensional coordinate points are uniformly distributed in the environment space, and all the seeds are in the idle area and are not overlapped with the building model in a crossing way; a route model: starting from a starting point, passing through a plurality of seeds, and ending at a spatial broken line of a terminal point, wherein the route comprises a seed set and a waypoint set, and in the course search, the seeds meeting the conditions are taken out from the seed set and placed in the waypoint set until the whole route is opened;
(2) object declaration, including building model list: the system is used for storing all city building models in the environment; seed list: for storing all seeds; planning a medium route list: the temporary route is used for storing the temporary route created in the searching process, and the temporary route is not connected from a starting point to a terminal point; completing the route list: storing the completed complete route, which has completed the connection from the starting point to the end point;
(3) acquiring basic information: acquiring task environment space range information, all city building model information, unmanned aerial vehicle task starting points, unmanned aerial vehicle task end points and unmanned aerial vehicle size constraints;
(4) according to the known task environment space range and the coordinate data of the urban building model, seeds are sown into the environment, the seeds are uniformly filled in gaps of the urban building model in the task environment space range, and the interval between the seeds is 2 times of the maximum side length of the unmanned aerial vehicle cube bounding box;
(5) establishing an initial route based on a route starting point, wherein the initial route only comprises one route point of the unmanned aerial vehicle task starting point, copying all seeds to a seed list in the route, and adding the initial route to a planning medium route list;
(6) traversing the planning medium route list, judging whether the last waypoint of each route is visible with the unmanned aerial vehicle task end point, if so, taking the route as a finished route, supplementing the unmanned aerial vehicle task end point into the route, taking the route out of the planning medium route list, adding the route into the finished route list, and turning to the step (8) after the planning medium route list is traversed; if the last route of the route is not in sight of the unmanned aerial vehicle task end point, the step (7) is carried out;
(7) judging the residual seeds, if the number of the residual seeds is zero or the residual seeds are not in sight of the last route point of the route, not constructing a new route, ending the route searching process, and turning to the step (8); otherwise, calculating the distance between all the remaining seeds of the route and the last waypoint, taking n seeds which are in communication with the last waypoint and have the shortest distance to construct n routes including the original route, respectively adding the corresponding seeds to the waypoints of the routes to be used as the last waypoint, removing the corresponding seeds from the seed list of the routes, and returning to the step (6); n is a set value;
(8) if the number of the air routes in the finished air route list is 0, no air route from the starting point to the end point of the unmanned aerial vehicle task exists, and the air route planning is finished; if the number of the air routes in the finished air route list is not 0, calculating the distance of each air route one by one, and taking the air route with the shortest distance as the final air route planned at this time;
wherein, the step (4) comprises the following steps:
(401) calculating the seed positions point by point according to the environment SPACE SPACE (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) and the seed interval step of 2 × UAVSIZE, forming a seed position set POS { (x, y, z) | P (x, y, z) }, wherein P (x, y, z) is defined as follows,
for round-down operation:
(402) calculating the spatial relationship between each calculated Seed position and each Building model in the Building model list Building, if the Seed falls into any Building model, discarding the Seed, otherwise, adding the Seed into the Seed list Seed, and aiming at any Building model Building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) in the Building model list Building, meeting the following conditions:
Seed.x≤Building.Xmin|Seed.x≥Building.Xmax|
Seed.y≤Building.Ymin|Seed.y≥Building.Ymax|
Seed.z≤Building.Zmin|Seed.z≥Building.Zmax
the UAVSIZE is the maximum side length of the unmanned plane cube bounding box, Xmin is the minimum horizontal coordinate, Ymin is the minimum vertical coordinate, Zmin is the minimum height coordinate, Xmax is the maximum horizontal coordinate, Ymax is the maximum vertical coordinate, and Zmax is the maximum height coordinate;
the method for judging the two-point visibility in the step (6) comprises the following steps: densely sampling a connecting line between two points pt1 and pt2, if one sampling point intersects with the building model, judging that the two points are invisible, and processing logic is as follows:
calculating the length of a connecting line of the two points:
for intensive sampling of a connecting line of two points Pt1 and Pt2, for an arbitrary sampling point Pt (x, y, z), the following requirements are met:
aiming at any sampling point Pt and any Building model Building, if the following conditions are met, judging that the sampling point is crossed with the Building model:
Building.Xmin≤Pt.x≤Building.Xmax&
Building.Ymin≤Pt.y≤Building.Ymax&
Building.Zmin≤Pt.z≤Building.Zmax
building (Xmin, Ymin, Zmin, Xmax, Ymax, Zmax) is the coordinate of any Building model.
2. The method for unmanned aerial vehicle route planning based on seed search of claim 1, wherein the step (7) of calculating the distance between all the remaining seeds of the route and the last waypoint, taking the n seeds which are in sight of and have the closest distance to the last waypoint, constructing n routes including the original route, adding the corresponding seeds to the waypoints of the routes respectively to serve as the last waypoint, and removing the corresponding seeds from the seed list of the routes specifically comprises the following steps:
(701) before constructing a new route based on an original route line, calculating the distance between the remaining seeds in the route and the last route point of the route;
(702) sorting the distances between all the seeds and the last waypoint from small to large, taking out n seeds closest to the last waypoint, constructing n routes including the original route, adding the n seeds to the waypoints of the n routes respectively, removing the corresponding seeds from a seed list of the routes, and constructing a new route based on the seed algorithm as follows:
firstly, constructing a new airline based on an original airline line, wherein line1 is line;
the seed is then added to the waypoint list of line 1:
line1.ListPoint.Add(seed)；
and finally, deleting the seed from the candidate seed list:
line1.ListSeed.Remove(seed)；
ListPoint is a waypoint set, and ListSeed is a candidate seed set for route planning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010200496.2A CN111272179B (en) | 2020-03-20 | 2020-03-20 | Building space unmanned aerial vehicle route planning method based on seed search |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010200496.2A CN111272179B (en) | 2020-03-20 | 2020-03-20 | Building space unmanned aerial vehicle route planning method based on seed search |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111272179A CN111272179A (en) | 2020-06-12 |
| CN111272179B true CN111272179B (en) | 2021-08-31 |
Family
ID=70998373
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010200496.2A Active CN111272179B (en) | 2020-03-20 | 2020-03-20 | Building space unmanned aerial vehicle route planning method based on seed search |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111272179B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112004237B (en) * | 2020-07-14 | 2023-06-16 | 西安爱生无人机技术有限公司 | Unmanned aerial vehicle data link vision evaluation method, readable storage medium and terminal equipment |
| CN112729312A (en) * | 2020-12-25 | 2021-04-30 | 云南电网有限责任公司昆明供电局 | Unmanned aerial vehicle inspection method for high-voltage chamber of transformer substation |
| CN113325867B (en) * | 2021-05-21 | 2022-04-22 | 华中科技大学 | Path planning method and device for searching of unmanned aircraft and unmanned aircraft |
| CN113239864A (en) * | 2021-05-31 | 2021-08-10 | 南通大学 | Route planning method of unmanned aerial vehicle suitable for agricultural investigation |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393747A (en) * | 2011-08-17 | 2012-03-28 | 清华大学 | Collaborative interaction method for unmanned plane cluster and visual navigation system of unmanned plane |
| CN107943072A (en) * | 2017-11-13 | 2018-04-20 | 深圳大学 | Unmanned plane during flying path generating method, device, storage medium and equipment |
| CN108764080A (en) * | 2018-05-17 | 2018-11-06 | 中国电子科技集团公司第五十四研究所 | A kind of unmanned plane vision barrier-avoiding method based on cloud space binaryzation |
| CN109598341A (en) * | 2018-11-21 | 2019-04-09 | 济南浪潮高新科技投资发展有限公司 | A kind of detection of convolutional neural networks training result and method for visualizing based on genetic algorithm |
| CN109683630A (en) * | 2019-01-25 | 2019-04-26 | 南京邮电大学 | Unmanned aerial vehicle flight path planing method based on population and PRM algorithm |
| CN110077598A (en) * | 2019-04-22 | 2019-08-02 | 四川省农业科学院作物研究所 | A kind of sowing rice type unmanned plane and its control system |
| CN110239719A (en) * | 2019-07-01 | 2019-09-17 | 南京林业大学 | Unmanned plane Precise sowing system and method |
| CN110320933A (en) * | 2019-07-29 | 2019-10-11 | 南京航空航天大学 | Unmanned plane avoidance motion planning method under a kind of cruise task |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9675001B2 (en) * | 2014-08-29 | 2017-06-13 | KSi Conveyor, Inc. | Seed distributor for even dispersion at varying flow rates |
| US20160071420A1 (en) * | 2014-09-10 | 2016-03-10 | Appareo Systems, Llc | Do-not-fly and opt-out privacy management system for unmanned aerial vehicles |
| WO2017003538A2 (en) * | 2015-04-14 | 2017-01-05 | Tobin Fisher | System for authoring, executing, and distributing unmanned aerial vehicle flight-behavior profiles |
| US11304380B2 (en) * | 2017-07-20 | 2022-04-19 | Brett Evan Patrick | Method and apparatus for airborne dissemination and implantation of seeds |
| CN107368600A (en) * | 2017-07-26 | 2017-11-21 | 兰州交通大学 | The adjoint point of Random Graph and Edge Coloring algorithm can be distinguished |
-
2020
- 2020-03-20 CN CN202010200496.2A patent/CN111272179B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393747A (en) * | 2011-08-17 | 2012-03-28 | 清华大学 | Collaborative interaction method for unmanned plane cluster and visual navigation system of unmanned plane |
| CN107943072A (en) * | 2017-11-13 | 2018-04-20 | 深圳大学 | Unmanned plane during flying path generating method, device, storage medium and equipment |
| CN108764080A (en) * | 2018-05-17 | 2018-11-06 | 中国电子科技集团公司第五十四研究所 | A kind of unmanned plane vision barrier-avoiding method based on cloud space binaryzation |
| CN109598341A (en) * | 2018-11-21 | 2019-04-09 | 济南浪潮高新科技投资发展有限公司 | A kind of detection of convolutional neural networks training result and method for visualizing based on genetic algorithm |
| CN109683630A (en) * | 2019-01-25 | 2019-04-26 | 南京邮电大学 | Unmanned aerial vehicle flight path planing method based on population and PRM algorithm |
| CN110077598A (en) * | 2019-04-22 | 2019-08-02 | 四川省农业科学院作物研究所 | A kind of sowing rice type unmanned plane and its control system |
| CN110239719A (en) * | 2019-07-01 | 2019-09-17 | 南京林业大学 | Unmanned plane Precise sowing system and method |
| CN110320933A (en) * | 2019-07-29 | 2019-10-11 | 南京航空航天大学 | Unmanned plane avoidance motion planning method under a kind of cruise task |
Non-Patent Citations (4)
| Title |
|---|
| "A Seed-Based Diffusion Route to Monodisperse Intermetallic CuAu Nanocrystal";Chen, Wei等;《ANGEWANDTE CHEMIE-INTERNATIONAL EDITION》;20101231;第49卷(第16期);2917-2921 * |
| "无人机智能测控技术研究综述";柴兴华等;《无线电工程》;20191231;第49卷(第10期);855－860 * |
| "无人机覆盖航迹规划中子区域合并方法设计";王文旭等;《电子设计工程》;20150630;第23卷(第11期);115-118 * |
| 王璐."固定翼无人机自动着舰飞控系统设计".《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》.2019,(第8期),C031-112. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111272179A (en) | 2020-06-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111272179B (en) | Building space unmanned aerial vehicle route planning method based on seed search | |
| CN110220521B (en) | High-precision map generation method and device | |
| Sun et al. | AGV path planning based on improved Dijkstra algorithm | |
| Harrie et al. | Simultaneous graphic generalization of vector data sets | |
| CN109724612A (en) | A kind of AGV paths planning method and equipment based on topological map | |
| CN109685286B (en) | Improved ant colony optimization-based collision avoidance planning method for USV (Universal Serial bus) in unknown static obstacle environment | |
| CN110021072B (en) | Holographic mapping-oriented multi-platform point cloud intelligent processing method | |
| CN108829105A (en) | It is a kind of to dispatch avoidance optimization method based on the warehouse logistics of KM algorithm and Artificial Potential Field Method | |
| CN108334080A (en) | A kind of virtual wall automatic generation method for robot navigation | |
| Li et al. | AUV 3D path planning based on A* algorithm | |
| CN111539112B (en) | Scene modeling method for automatically driving vehicle to quickly search traffic object | |
| CN111080786A (en) | BIM-based indoor map model construction method and device | |
| CN110909961A (en) | BIM-based indoor path query method and device | |
| CN116129066A (en) | Automatic driving high-precision map model based on digital twin and high-precision static map manufacturing method | |
| CN104217580A (en) | Vehicle crowd animation oriented method and vehicle crowd animation oriented system for semantically modeling road networks | |
| CN117191017A (en) | High-precision safety path planning method for inspection robot in chemical scene | |
| KR100844452B1 (en) | The method for generating 3-dimensional apartment complex image | |
| CN114442629B (en) | Mobile robot path planning method based on image processing | |
| Lee et al. | Automating Generalization–Tools and Models | |
| CN109977455A (en) | It is a kind of suitable for the ant group optimization path construction method with terrain obstruction three-dimensional space | |
| CN115657725A (en) | Primary-secondary unmanned aerial vehicle release decision and path planning integrated method and system | |
| CN115143980A (en) | Incremental topological map construction method based on pruning Voronoi diagram | |
| CN101131755A (en) | Route planning method for remaining CGF team formation based on real landform | |
| CN114037798A (en) | Method and device for generating three-dimensional intersection model | |
| Xiao-Dong et al. | New method for UAV online path planning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |