CN112327939A - High-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method in urban block environment - Google Patents
High-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method in urban block environment Download PDFInfo
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Abstract
The invention discloses a planning method for a high-rise fire-fighting multi-unmanned aerial vehicle collaborative path in an urban block environment, which comprises the steps of firstly, constructing a three-dimensional environment model of an urban block by using a grid method, and screening the three-dimensional environment model to obtain a flyable area; searching and screening a flyable region by using a three-dimensional A-star algorithm, and planning a global reference path; and then, acquiring environmental information in real time by using a sensor carried by the high-rise fire-fighting unmanned aerial vehicle, updating the three-dimensional environment model in real time, performing online local path planning through a particle swarm optimization algorithm, determining whether an unknown obstacle appears on the current path, and judging whether the high-rise fire-fighting unmanned aerial vehicle needs to dynamically avoid the obstacle. In addition, through setting up minimum safe distance, realize many high-rise fire control unmanned aerial vehicle's collaborative flight. The method realizes the cooperative path planning and autonomous flight of the high-rise fire-fighting unmanned aerial vehicle by using a hybrid algorithm, has reasonable path planning, high response speed, flexible obstacle avoidance in the flight process and higher safety.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicle path planning, in particular to a high-rise fire-fighting multi-unmanned aerial vehicle cooperative path planning method in an urban block environment.
Background
The number of high-rise buildings in China is the front of the world, but fire accidents of the high-rise buildings frequently occur due to the fact that the high-rise buildings are dense in personnel and rich in combustible materials. In order to guarantee the lives and the properties of people, economy and maintenance of the lives and the properties of people, the nation places high importance on the fire fighting work of high-rise buildings and encourages the use of emerging intelligent equipment such as fire fighting unmanned aerial vehicles and the like to play roles in the fields of emergency rescue, disaster relief and the like. When a high-rise building catches fire, the traditional fire-fighting modes and equipment face difficulties in space, time and operation, the fire-fighting unmanned aerial vehicle can rapidly move and arrive at a fire-catching floor in the least time to carry out rescue and relief work, and the path planning of the fire-fighting unmanned aerial vehicle becomes the most important loop. In the path planning of the fire-fighting unmanned aerial vehicle, global reference path planning is required according to prior information, and local path dynamic planning based on unmanned aerial vehicle sensor information is required. Compared with a single algorithm, the hybrid algorithm adopted in the path planning has more obvious advantages in the rapidity and safety of the fire-fighting unmanned aerial vehicle. When facing the fire-fighting task, the working efficiency of the multiple unmanned aerial vehicles in cooperation is higher than that of the single unmanned aerial vehicle.
Chinese patent CN109506654A published in 2019, 3, month and 22 provides a low-altitude route planning method and device and an aircraft. The method comprises the following steps: performing rasterization modeling on the flight area to obtain a space model of the flight area; obtaining the cost value of each grid in the space model, wherein the cost value is used for representing the influence of the aircraft on navigation in the space of the corresponding grid; and planning the three-dimensional air route of the flight area according to the cost value of each grid. The method solves the problem of planning the flight path of the unmanned aerial vehicle, but the method has single algorithm, the planned flight path is not reasonable enough, and the method cannot cope with the complicated and variable flight environment of the unmanned aerial vehicle.
Disclosure of Invention
The invention provides a high-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method in an urban block environment, aiming at overcoming the defects that the path planned by the existing method is unreasonable and cannot cope with the complex and changeable urban block flight environment.
The technical scheme of the invention is as follows:
the invention provides a high-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method in an urban block environment, which comprises the following steps:
s1: the method comprises the steps that a three-dimensional environment model of an urban block is built through a grid method, coordinates of a high-rise fire-fighting unmanned aerial vehicle are set to be D (x, y, z), attitude information is set to be D (theta, psi), theta is a pitch angle of the high-rise fire-fighting unmanned aerial vehicle, and psi is a course angle of the high-rise fire-fighting unmanned aerial vehicle;
s2: screening the three-dimensional environment model to obtain a flyable area comprising a starting point and a terminal point of the high-rise fire-fighting unmanned aerial vehicle;
s3: set up the minimum safe distance between the high-rise fire control unmanned aerial vehicle, record as dsafeTo ensure the distance d between any two high-rise fire-fighting unmanned aerial vehiclesijSatisfy dij＞2*dsafe；
The real-time coordinate of any two high-rise fire-fighting unmanned aerial vehicles is Di＝(xi,yi,zi)、Dj＝(xj,yj,zj) Then, then
S4: making a global reference path plan based on a three-dimensional A-x algorithm, and searching a global reference path between a starting point and an end point;
s5: on the basis of the global reference path, performing dynamic planning on a local path based on a particle swarm optimization algorithm to obtain a global optimal path;
s6: and the high-rise fire-fighting unmanned aerial vehicle finishes flying according to the global optimal path and reaches the destination.
Preferably, the flyable area in S2 does not include a no-fly zone for unmanned aerial vehicles, an open space with dense personnel, and a strong signal interference area.
Preferably, the specific steps of S4 are:
s4.1: searching nodes by using a three-dimensional A-x algorithm, and setting a searching direction;
s4.2: in the node searching process, screening nodes by using flight limiting conditions of the high-rise fire-fighting unmanned aerial vehicle;
s4.3: carrying out weighted evaluation on the nodes screened in the S4.2 by using an evaluation function, storing the node with the minimum cost and taking the node as a target point of the next search;
the evaluation function is: (n) g (n) + h (n) + ωHH (n), where f (n) is the total cost of the current node, g (n) is the Euclidean distance from the starting point to the current node during searching, h (n) is the Euclidean distance from the current node to the target point, ωHH (n) is the height weight coefficient, and is the height difference between the previous node of the current node and the current node;
s4.4: and when h (n) < (g (n)) in the S4.3, obtaining an optimal solution, and forming a complete global reference path for the flight of the high-rise fire-fighting unmanned aerial vehicle by the stored nodes.
Preferably, in S4.1, the three-dimensional a-algorithm searches nodes from the starting point to the end point in the forward search, and searches nodes from the end point to the starting point in the reverse search.
Preferably, in S4.2, the flight limitation condition includes a pitch angle θ and a heading angle ψ of the high-rise fire-fighting unmanned aerial vehicle;
during forward search, an OPEN1 table is established, and nodes screened according to flight limitation conditions are put into the OPEN1 table;
in the reverse search, an OPEN2 table is established, and the nodes screened according to the flight restrictions are put into the OPEN2 table.
Preferably, in S4.3, during forward search, a CLOSE1 table is established, and the evaluation function is used to calculate that the node with the lowest cost in the OPEN1 table is stored in the CLOSE1 table and deleted in the OPEN1 table;
when the search is reversed, a CLOSE2 table is established, and the node with the minimum cost in the OPEN2 table is calculated by using an evaluation function, stored in the CLOSE2 table and deleted in the OPEN2 table.
Preferably, the specific steps of S5 are:
s5.1: acquiring environmental information in real time by using a sensor carried on a high-rise fire-fighting unmanned aerial vehicle and updating a three-dimensional environment model of a city block in real time;
s5.2: initializing a particle swarm in a three-dimensional environment model of the city block by utilizing a particle swarm optimization algorithm, and setting the particle size and the maximum iteration number kmax；
S5.3: giving each particle an initial position and velocity;
s5.4: setting acceleration coefficient c in particle swarm optimization algorithm1And c2；
S5.5: setting an inertia factor omega in a particle swarm optimization algorithm;
s5.6: and the particle swarm optimization algorithm updates the speed and the position of each particle in each iteration until the iteration is finished or the global optimal position is found, so that the dynamic planning of the local path is completed.
Preferably, in S5.5, a linear decreasing weight strategy is adopted to set the inertia factor ω, and the inertia factor ω is dynamically adjusted in an algorithm iteration process, wherein
Wherein ω ismaxRepresenting the maximum value of the inertia factor, ωminThe minimum value of the inertia factor is represented,
representing the ratio of the current iteration to the maximum iteration.
Preferably, the specific steps of S5.6 are:
setting in the D-dimension algorithm dimension, the particle group is composed of N particles, and at the k-th iteration, the position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The velocity of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The individual optimal position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The global optimal position of the whole particle swarm is expressed as
Wherein D is 1,2, …, D, k is 1,2, … kmax(ii) a Then:
when k is equal to 1, the first step is carried out,
namely the initial speed and the initial position of the particles; c. C1And c2Is the acceleration coefficient, omega is the inertia factor, rand1() And rand2() Is a random function;
preferably, in S5.6, if the particle swarm optimization algorithm fails to update the global optimal position in the iterative process, a new random speed and a new random position are given to the particle:
wherein ω is1And ω2For any inertia factor, rand1() And rand2() Is a random function.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the method comprises the steps of searching and screening a three-dimensional environment model of a constructed city block by using a three-dimensional A-x algorithm to obtain a global reference path, and dynamically planning a local path by using a particle swarm optimization algorithm to realize dynamic obstacle avoidance. The high-rise fire-fighting unmanned aerial vehicle flies according to the path planned by the two algorithms together, on the basis of the global reference path, the position of the obstacle can be found in time when the obstacle is encountered, and after the dynamic planning of the local path is carried out, the high-rise fire-fighting unmanned aerial vehicle flies according to the global optimal path, so that the path planning is more reasonable, the response speed is high, and the high-rise fire-fighting unmanned aerial vehicle has higher safety.
Drawings
Fig. 1 is a flowchart of a high-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method in an urban block environment according to embodiment 1;
fig. 2 is a flow chart of collision avoidance between multiple high-rise fire-fighting unmanned aerial vehicles according to embodiment 1;
fig. 3 is a flow chart of the three-dimensional a algorithm described in example 1;
fig. 4 is a flowchart of the particle swarm optimization algorithm described in embodiment 1.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a method for planning a collaborative path of a plurality of high-rise fire-fighting unmanned aerial vehicles in a city block environment, as shown in fig. 1, the method comprises the following steps:
s1: the method comprises the steps that a three-dimensional environment model of an urban block is built through a grid method, coordinates of a high-rise fire-fighting unmanned aerial vehicle are set to be D (x, y, z), attitude information is set to be D (theta, psi), theta is a pitch angle of the high-rise fire-fighting unmanned aerial vehicle, and psi is a heading angle of the high-rise fire-fighting unmanned aerial vehicle; determining three-dimensional coordinates of a starting point and an end point of the high-rise fire-fighting unmanned aerial vehicle in the three-dimensional environment model;
s2: screening the three-dimensional environment model to obtain a flyable area comprising a starting point and a terminal point of the high-rise fire-fighting unmanned aerial vehicle;
s3: setting high-rise fire-fightingMinimum safe distance between drones, denoted dsafeTo ensure the distance d between any two high-rise fire-fighting unmanned aerial vehiclesijSatisfy dij＞2*dsafe；
Determining real-time coordinates of any two high-rise fire-fighting unmanned aerial vehicles as Di＝(xi,yi,zi)、Dj＝(xj,yj,zj) Then, then
S4: making a global reference path plan based on a three-dimensional A-x algorithm, and searching a global reference path between a starting point and an end point;
s5: on the basis of the global reference path, performing dynamic planning on a local path based on a particle swarm optimization algorithm to obtain a global optimal path;
s6: and the high-rise fire-fighting unmanned aerial vehicle finishes flying according to the global optimal path and reaches the destination.
The flyable area in the S2 does not include a no-fly area for unmanned aerial vehicles, an open space with dense personnel, and a strong signal interference area.
The specific steps of S4 are as follows:
s4.1: searching nodes by using a three-dimensional A-x algorithm, and setting a searching direction;
s4.2: in the node searching process, screening nodes by using flight limiting conditions of the high-rise fire-fighting unmanned aerial vehicle;
s4.3: carrying out weighted evaluation on the nodes screened in the S4.2 by using an evaluation function, storing the node with the minimum cost and taking the node as a target point of the next search;
the evaluation function is: (n) g (n) + h (n) + ωHH (n), where f (n) is the total cost of the current node, g (n) is the Euclidean distance from the starting point to the current node during searching, h (n) is the Euclidean distance from the current node to the target point, ωHH (n) is the height weight coefficient, and is the height difference between the previous node of the current node and the current node;
s4.4: and when h (n) < (g (n)) in the S4.3, obtaining an optimal solution, and forming a complete global reference path for the flight of the high-rise fire-fighting unmanned aerial vehicle by the stored nodes.
In S4.1, the three-dimensional a-x algorithm searches nodes from the starting point to the end point during the forward search, searches nodes from the end point to the starting point during the reverse search, and alternately performs the forward search and the reverse search.
In S4.2, the flight limiting conditions comprise a pitch angle theta and a course angle psi of the high-rise fire-fighting unmanned aerial vehicle;
during forward search, an OPEN1 table is established, and nodes screened according to flight limitation conditions are put into the OPEN1 table;
in the reverse search, an OPEN2 table is established, and the nodes screened according to the flight restrictions are put into the OPEN2 table.
In S4.3, during forward searching, a CLOSE1 table is established, the evaluation function is used for calculating that the node with the minimum cost in the OPEN1 table is stored in the CLOSE1 table and is deleted in the OPEN1 table;
when the search is reversed, a CLOSE2 table is established, and the node with the minimum cost in the OPEN2 table is calculated by using an evaluation function, stored in the CLOSE2 table and deleted in the OPEN2 table.
The specific steps of S5 are as follows:
s5.1: acquiring environmental information in real time by using a sensor carried on a high-rise fire-fighting unmanned aerial vehicle and updating a three-dimensional environment model of a city block in real time;
s5.2: initializing a particle swarm in a three-dimensional environment model of the city block by utilizing a particle swarm optimization algorithm, and setting the particle size and the maximum iteration number kmax；
S5.3: giving each particle an initial position and velocity;
s5.4: setting acceleration coefficient c in particle swarm optimization algorithm1And c2；
S5.5: setting an inertia factor omega in a particle swarm optimization algorithm;
s5.6: and the particle swarm optimization algorithm updates the speed and the position of each particle in each iteration until the iteration is finished or the global optimal position is found, so that the dynamic planning of the local path is completed.
In said S5.5Setting an inertia factor omega by adopting a linear decreasing weight strategy and dynamically adjusting the inertia factor omega in the iterative process of the algorithm, wherein
Wherein ω ismaxRepresenting the maximum value of the inertia factor, ωminThe minimum value of the inertia factor is represented,
representing the ratio of the current iteration to the maximum iteration.
The S5.6 comprises the following specific steps:
setting in the D-dimension algorithm dimension, the particle group is composed of N particles, and at the k-th iteration, the position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The velocity of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The individual optimal position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The global optimal position of the whole particle swarm is expressed as
Wherein D is 1,2, …, D, k is 1,2, … kmax(ii) a Then:
when k is equal to 1, the first step is carried out,
namely the initial speed and the initial position of the particles; c. C1And c2Is the acceleration coefficient, omega is the inertia factor, rand1() And rand2() Is a value range of [0, 1]A random function;
in S5.6, if the particle swarm optimization algorithm is stalled in updating the global optimal position during the iteration process, a new random speed and a new random position are given to the particle:
wherein ω is1And ω2For any inertia factor, rand1() And rand2() Is a value range of [0, 1]Is used to generate the random function of (2).
The beneficial effect of this embodiment is:
in the embodiment, a three-dimensional A-x algorithm is used for searching and screening a three-dimensional environment model of a constructed city block to obtain a global reference path, and then a particle swarm optimization algorithm is used for dynamic planning of a local path to achieve dynamic obstacle avoidance. The high-rise fire-fighting unmanned aerial vehicle flies according to the path planned by the two algorithms together, on the basis of the global reference path, the position of the obstacle can be found in time when the obstacle is encountered, and after the dynamic planning of the local path is carried out, the high-rise fire-fighting unmanned aerial vehicle flies according to the global optimal path, so that the path planning is more reasonable, the response speed is high, and the high-rise fire-fighting unmanned aerial vehicle has higher safety.
In addition, the embodiment also has the following beneficial effects: by setting the minimum safety distance between the high-rise fire-fighting unmanned aerial vehicles, the cooperative flight of the multiple high-rise fire-fighting unmanned aerial vehicles is realized, and the fire rescue efficiency is improved; the search direction of the three-dimensional A-x algorithm is changed from one-way to two-way, so that the working efficiency of the algorithm is improved; the flight condition limit of the high-rise fire-fighting unmanned aerial vehicle is added during node searching, the node calculation workload is reduced, and the planned path conforms to the dynamic characteristics of the unmanned aerial vehicle; the particle swarm optimization algorithm is easy to generate update stagnation of the global optimal position, and by endowing the particles with new random speed and random position, the diversity of the particles is increased, so that the position and speed jump of the particles is free from update stagnation.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. 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 claims of the present invention.
Claims (10)
1. A high-rise fire-fighting multi-unmanned aerial vehicle collaborative path planning method under a city block environment is characterized by comprising the following steps:
s1: the method comprises the steps that a three-dimensional environment model of an urban block is built by using a grid method, in the three-dimensional environment model, the three-dimensional coordinates of a high-rise fire-fighting unmanned aerial vehicle are set to be D (x, y, z), the attitude information is set to be D (theta, psi), theta is the pitch angle of the high-rise fire-fighting unmanned aerial vehicle, and psi is the course angle of the high-rise fire-fighting unmanned aerial vehicle;
s2: screening the three-dimensional environment model to obtain a flyable area comprising a starting point and a terminal point of the high-rise fire-fighting unmanned aerial vehicle;
s3: set up the minimum safe distance between the high-rise fire control unmanned aerial vehicle, record as dsafeTo ensure the distance d between any two high-rise fire-fighting unmanned aerial vehiclesijSatisfy dij＞2*dsafe；
S4: performing global reference path planning based on a three-dimensional A-x algorithm, and searching a global reference path between a starting point and an end point;
s5: on the basis of the global reference path, performing dynamic planning on a local path based on a particle swarm optimization algorithm to obtain a global optimal path;
s6: and the high-rise fire-fighting unmanned aerial vehicle finishes flying according to the global optimal path and reaches the destination.
2. The method for planning the cooperative path of the high-rise fire-fighting multi-unmanned aerial vehicle in the city block environment according to claim 1, wherein the flyable area in S2 does not include a no-fly area of unmanned aerial vehicles, an open space with dense personnel and a strong signal interference area.
3. The method for planning the collaborative path of the high-rise fire-fighting multi-unmanned aerial vehicle in the urban block environment according to claim 2, wherein the specific steps of S4 are as follows:
s4.1: searching nodes by using a three-dimensional A-x algorithm, and setting a searching direction;
s4.2: in the node searching process, screening nodes by using flight limiting conditions of the high-rise fire-fighting unmanned aerial vehicle;
s4.3: carrying out weighted evaluation on the nodes screened in the S4.2 by using an evaluation function, storing the node with the minimum cost and taking the node as a target point of the next search;
the evaluation function is: (n) g (n) + h (n) + ωHH (n), where f (n) is the total cost of the current node, g (n) is the Euclidean distance from the starting point to the current node during searching, h (n) is the Euclidean distance from the current node to the target point, ωHH (n) is the height weight coefficient, and is the height difference between the previous node of the current node and the current node;
s4.4: and when h (n) < (g (n)) in the S4.3, obtaining an optimal solution, and forming a complete global reference path for the flight of the high-rise fire-fighting unmanned aerial vehicle by the stored nodes.
4. The method of claim 3, wherein in S4.1, the three-dimensional A-x algorithm searches nodes from a starting point to an end point in a forward search and from the end point to the starting point in a reverse search.
5. The method for planning the collaborative path of the high-rise fire-fighting multi-unmanned aerial vehicle in the urban street environment according to claim 4, wherein in S4.2, the flight limitation conditions comprise a pitch angle theta and a course angle psi of the high-rise fire-fighting unmanned aerial vehicle;
during forward search, an OPEN1 table is established, and nodes screened according to flight limitation conditions are put into the OPEN1 table;
in the reverse search, an OPEN2 table is established, and the nodes screened according to the flight restrictions are put into the OPEN2 table.
6. The method of claim 5, wherein in S4.3, during forward search, a CLOSE1 table is created, and the node with the lowest cost in the OPEN1 table is calculated by using an evaluation function and stored in a CLOSE1 table, and deleted in an OPEN1 table;
when the search is reversed, a CLOSE2 table is established, and the node with the minimum cost in the OPEN2 table is calculated by using an evaluation function, stored in the CLOSE2 table and deleted in the OPEN2 table.
7. The method of claim 6, wherein the step S5 comprises the following steps:
s5.1: acquiring environmental information in real time by using a sensor carried on a high-rise fire-fighting unmanned aerial vehicle and updating a three-dimensional environment model of a city block in real time;
s5.2: initializing a particle swarm in a three-dimensional environment model of the city block by utilizing a particle swarm optimization algorithm, and setting the particle size and the maximum iteration number kmax；
S5.3: giving each particle an initial position and an initial velocity;
s5.4: setting acceleration coefficient c in particle swarm optimization algorithm1And c2；
S5.5: setting an inertia factor omega in a particle swarm optimization algorithm;
s5.6: and the particle swarm optimization algorithm updates the speed and the position of each particle in each iteration until the iteration is finished or the global optimal position is found, so that the dynamic planning of the local path is completed.
8. The method of claim 7, wherein in S5.5, a linear decreasing weight strategy is used to set an inertia factor ω and dynamically adjust it during an algorithm iteration process, wherein the method comprises
Wherein ω ismaxRepresenting the maximum value of the inertia factor, ωminThe minimum value of the inertia factor is represented,
representing the ratio of the current iteration to the maximum iteration.
9. The method for planning the collaborative path of the high-rise fire-fighting multi-unmanned aerial vehicle in the urban block environment according to claim 8, wherein the S5.6 comprises the following specific steps:
setting in the D-dimension algorithm dimension, the particle group is composed of N particles, and at the k-th iteration, the position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The velocity of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The individual optimal position of the ith particle is expressed as
Wherein i is 1,2, …, N, D is 1,2, …, D, k is 1,2, … kmax(ii) a The global optimal position of the whole particle swarm is expressed as
Wherein D is 1,2, …, D, k is 1,2, … kmax(ii) a The velocity and position of each particle is updated according to the following formula:
10. The method of claim 9, wherein in S5.6, if the particle swarm optimization algorithm has a global optimal position update stagnation during the iteration process, a new random speed and a new random position are given to the particle:
wherein ω is1And ω2For any inertia factor, rand1() And rand2() Is a random function.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113253761A (en) * | 2021-06-08 | 2021-08-13 | 北京远度互联科技有限公司 | Real-time path planning method and device, movable carrier and storage medium |
| CN113310493A (en) * | 2021-05-28 | 2021-08-27 | 广东工业大学 | Unmanned aerial vehicle real-time navigation method based on event trigger mechanism |
| CN113985913A (en) * | 2021-09-24 | 2022-01-28 | 大连海事大学 | Collection-division type multi-unmanned-plane rescue system based on urban fire spread prediction |
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| CN114138005A (en) * | 2021-10-26 | 2022-03-04 | 南京航空航天大学 | Urban mass logistics unmanned aerial vehicle track planning method and device based on improved A-star algorithm |
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