CN106705970B - Multi-unmanned aerial vehicle collaborative path planning method based on ant colony algorithm - Google Patents
Multi-unmanned aerial vehicle collaborative path planning method based on ant colony algorithm Download PDFInfo
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Abstract
The invention discloses an ant colony algorithm-based multi-unmanned aerial vehicle collaborative path planning method, which comprises the following steps: (1) analyzing the flight environment of the unmanned aerial vehicle, and establishing an environment modeling based on a Voronoi diagram; (2) calculating the cost of the edges in the environment modeling based on the Voronoi diagram; (3) planning an initial path for the unmanned aerial vehicle by using an ant colony algorithm; (4) whether the cooperation can be achieved is judged by smoothing the initial path of each unmanned aerial vehicle, and corresponding operation is executed according to the result. The invention realizes the mutual cooperation among a plurality of different unmanned aerial vehicles, intelligently and autonomously adapts to complex and variable war environment factors, dynamically adjusts the strategy of the unmanned aerial vehicles, and cooperatively finishes the battle task.
Description
The technical field is as follows:
the method is applied to the fields of unmanned aerial vehicle flight control, obstacle avoidance, shortest path search, path smoothing processing, load balancing and the like, and particularly relates to technologies of voronoi diagram construction, edge weight setting, ant colony algorithm shortest path search, trajectory smoothing to achieve collaboration and the like.
Background art:
the unmanned aerial vehicle technology is a hot research field of military and civil aircrafts in recent years, and is widely applied to military applications such as battlefield reconnaissance and monitoring, positioning and correcting, damage assessment and the like, and civil applications such as border patrol, environment detection, aerial photography, exploration resources, disaster monitoring, public security monitoring, logistics transportation and the like. Compared with manned aircrafts, the unmanned aerial vehicle can execute low-altitude flight with low visibility and low cloud layer, thereby obviously increasing the time of flying every day, accelerating the operation progress, and also being capable of executing high-precision high-resolution remote sensing flight in real time and the like. Due to the characteristics of the military unmanned aerial vehicle such as speed advantage and good maneuvering performance, the military unmanned aerial vehicle is used for executing tasks with high difficulty, sometimes even multiple unmanned aerial vehicles are needed to execute one or more tasks, a proper task allocation strategy needs to be provided online or offline, and a high-safety path planning scheme is used for arranging the unmanned aerial vehicles (group) to execute the tasks.
For different flight environments, it is reasonable to select a proper countermeasure environment to simulate a real battlefield. These environment models include a three-dimensional environment based on a B-Spline curve, a two-dimensional undirected graph based on a probabilistic road map and a voronoi map, and the like. The Voronoi diagram is a classical polygon diagram based on plane division regions, which is composed of an unequal number of generation points and perpendicular bisectors of the generation point connecting lines, each generation point is surrounded by a specific polygon, and the distances from the points in the polygons to the generation point are smaller than the distances from the other generation points. The sides of the voronoi diagram are used as the trimmable sides of the unmanned aerial vehicle, so that the threat suffered by the aircraft can be reduced to the minimum. The ant colony algorithm is an intelligent algorithm for simulating biological behaviors, has strong robustness and solvability, and can lead the solution of the algorithm to be optimal by adding heuristic information of the side weight.
Considering the flyability of the unmanned aerial vehicle, the flying angle cannot be a sharp angle, the aircraft needs to be smoothed when turning, so that the aircraft turns in a circular arc, and the rotation angle of the unmanned aerial vehicle cannot be larger than the maximum rotation angle. The flexibility of this smoothing can be used to coordinate multiple drones by making the path length change slightly by trajectory smoothing. Because the single unmanned aerial vehicle has single performance and limited capacity, and the cluster integrating multiple unmanned aerial vehicles has to arrive and execute tasks in a certain sequence when executing tasks, the problem of cooperation of multiple unmanned aerial vehicles draws extensive attention. The research of the efficient distributed sensing and comprehensive path planning method by effectively utilizing the team capacity and the capability of the whole aircraft unit is the main content of the whole cooperative control. Cooperative flight among unmanned aerial vehicles is defined as different contents by different requirements, but mainly takes the whole task as a target to distribute tasks and plan paths for all unmanned aerial vehicles so as to complete the cooperative safety. In addition, the factors to be considered in collaborative path planning need to dynamically avoid obstacles of the other party besides the kinematic constraint limit of the collaborative path planning, reduce the risk of collision of the aircraft, and adapt to the factors such as the change of the threat environment of the enemy and the like.
The unmanned aerial vehicle collaborative path planning realizes the feasibility of multiple unmanned aerial vehicles for executing tasks, can also detect the possibility of whether the current unmanned aerial vehicle can realize collaborative flight, and enables multiple unmanned aerial vehicles to realize collaborative flight by changing the flight state or re-planning for unmanned aerial vehicles which cannot realize collaborative flight.
Disclosure of Invention
The invention provides an ant colony algorithm-based multi-unmanned aerial vehicle collaborative path planning method aiming at the defects of the prior art, which solves the difficulty of collaborative combat in the flight process of the existing multi-unmanned aerial vehicle, such as the need that a plurality of unmanned aerial vehicles start from respective starting points and reach corresponding target points simultaneously so as to complete a certain task together. In addition, this demand also is higher to the real-time nature requirement of algorithm, and the computational rate will keep up with unmanned aerial vehicle's airspeed, just so can make its better application in many unmanned aerial vehicle cooperative combat.
In order to achieve the purpose, the invention adopts the technical scheme that:
a multi-unmanned aerial vehicle collaborative path planning method based on an ant colony algorithm comprises the following steps:
analyzing the flight environment of the unmanned aerial vehicle, and establishing an environment modeling based on a Voronoi diagram;
calculating the cost of edges in the environment modeling based on the Voronoi diagram;
planning an initial path for the unmanned aerial vehicle by using an ant colony algorithm;
and (4) judging whether the cooperation can be achieved or not by smoothing the initial path of each unmanned aerial vehicle, and executing corresponding operation according to the result.
Preferably, the step (1) comprises the steps of:
(1-1) determining the flight altitude of the unmanned aerial vehicle, intercepting two-dimensional plane terrain information of the flight altitude, and projecting the ground threat to the two-dimensional plane terrain information to obtain the ground threat plane terrain;
(1-2) abstracting ground threat plane terrain and other threat sources into a threat point set { xi}；
(1-3) establishing a coordinate system in a plane to obtain a coordinate set { (x) of the threat sourcei，yi) And generating a Voronoi diagram.
And (1-4) inputting the starting point and the end point of the unmanned aerial vehicle, and completing the environment modeling of the Voronoi diagram.
Preferably, the step (2) comprises the steps of:
(2-1) calculating the cost of the threat opposite sides of the terrain factors:
wherein the content of the first and second substances,
representing the cost of the ith edge by a threat source j causing a threat by a fixed obstacle; k is the threat level of the fixed obstacle; k is a human commitment coefficient; r isijIs the distance from the fixed barrier to the ith side;
(2-2) calculating the cost of the threat opposite side of the investigation capability but no attack capability:
wherein the content of the first and second substances,
is the cost of the ith edge by the threat source j of the threat caused by the radar; l isiIs the length of edge i; d1/8,i,jDistance to radar at 1/8 on the ith edge; qjIs the transmission power, Q, of the radarjThe calculation formula is as follows:
wherein, PtIs the transmitter power;g is the gain of the radio; a. theeIs the effective area of the transmitter, δ is the cross-sectional area of the radar; r is the length of the range radar;
(2-3) calculating the cost of the threat opposite side with both detection capability and attack capability:
wherein the content of the first and second substances,
threat source j causing threat by the missile to the ith path, attack ability of the missile (1- α) as missile hit rate, pijProbability of detecting a missile on the ith edge for the unmanned aerial vehicle;
(2-4) calculating the length cost of the edge:
Pi-L＝λLi
wherein, Pi-LThe cost of length-to-edge i; λ is a coefficient; l isiIs the length of the ith side.
(2-5) total edge cost calculation formula:
wherein a, b, c, d are constants such that a + b + c + d is 1; m is the number of fixed obstacles, n is the number of radars, and r is the number of missiles.
Preferably, the ant colony algorithm in the step (3) is:
(3-1) ants start from the initial node according to a transition probability formula
Selecting a transfer node and adding the initial node to a tabu table, wherein
ηij(t) represents the time of t<i,j>Heuristic information on the path;
τij(t) represents the time t<i,j>Pheromones on the path;
α denotes respectively τij(t)、ηij(t) a weight coefficient;
ηir(t): indicates the time t<i,j>Heuristic information on the path;
τir(t): indicates the time t<i,j>Pheromone concentration on the pathway;
(3-2) the ants select transfer nodes according to the transfer probability, and add the selected transfer nodes into a taboo table; judging whether the transfer node reaches the end point, if not, continuously repeating the step (3-2) until the end point is reached; if the terminal is reached, turning to (3-3);
(3-3) judging whether the iteration times reach a fixed value, if not, turning to (3-4) updating the pheromone, and if so, turning to (3-5) updating the pheromone;
(3-4) updating the path of the loop according to the pheromone updating formula, wherein the iteration times are +1, and the step is switched to (3-6)
(3-5) updating the paths in the last cycles according to the pheromone updating formula, wherein the iteration times are +1, and the operation is switched to (3-6);
(3-6) if the iteration times are larger than the maximum algebra, finishing searching to obtain the shortest path, and otherwise, turning to (3-1); wherein, the pheromone updating formula is as follows:
rho represents the pheromone volatilization coefficient;
q is a constant representing pheromone concentration;
Lkthe total length of the path traveled by ant k in this cycle.
Preferably, the step (4) comprises the steps of:
(4-1) smoothing the angle, which does not meet a certain preset angle, at the turning position of the initial path to obtain a smoothed path length interval;
(4-2) taking the maximum value of the lower limit of the corresponding path length interval of each unmanned aerial vehicle as A, and taking the minimum value of the upper limit of the corresponding path length interval of each unmanned aerial vehicle as B;
(4-3) judging the value of A-B;
(4-4) if A-B is less than or equal to 0, completing the synergy;
(4-5) if A-B >0, no synergy can be achieved.
Compared with the prior art, the invention has the advantages that:
1) carrying out environment modeling through a Voronoi diagram, and ensuring the flight safety of the unmanned aerial vehicle to the maximum extent at the initial point of path planning by utilizing the construction principle of the Voronoi diagram;
2) factors influencing the total cost of unmanned aerial vehicle flight in the environment are researched, new relevant factors such as radar detection probability, missile attack probability, enemy plane detection probability and the like are added, and the influence of fixed obstacles on unmanned aerial vehicle flight and the total path of unmanned aerial vehicle flight are not only considered, so that the environment modeling is closer to the reality;
3) optimizing an ant colony algorithm, optimizing a heuristic function in the heuristic ant colony algorithm, increasing the maximum and minimum values of pheromones to prevent the algorithm from becoming premature, and accelerating the convergence speed of the algorithm by two different pheromone updating mechanisms;
4) the method comprises the steps of performing track smoothing on angles which do not accord with the flight constraint of the unmanned aerial vehicle in a path planned for the first time, limiting the range of the angles to be smoothed, and saving computing resources;
5) the cooperation of multiple unmanned aerial vehicles is finished by utilizing track smoothing, the path length interval of the unmanned aerial vehicle is obtained by smoothing the initial path, the cooperation is achieved by operating the interval, the cooperation purpose is achieved by changing the speed of the unmanned aerial vehicle instead of meeting the requirement of reaching the destination at the same time.
Drawings
FIG. 1 is a schematic flow diagram of the structure of the present invention;
FIG. 2 is a schematic overall flow diagram of the present invention;
FIG. 3 is a schematic view of Voronoi environment modeling of the present invention;
FIG. 4 is a flowchart of an initial path based on the ant colony algorithm of the present invention;
FIG. 5 is a collaborative flow diagram of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples.
The unmanned aerial vehicle has the advantages of high confidentiality, small error, strong execution force, high efficiency and low cost in the execution process, can be used for replacing soldiers to complete tasks with great threat to lives, and has become a research hotspot technology in the military field in recent years. All countries around the world strive to invest a lot of manpower, material resources and financial resources to carry out all-round research and deployment on the artificial wetland. A large number of different types and capabilities of drones have been developed and deployed to the battlefield to perform various combat missions. However, during the process of performing some complex tasks (such as reconnaissance and percussion integration), the capability of a single drone is limited, which is not enough to complete the task. The unmanned aerial vehicles of different types need to cooperate with each other, intelligently and autonomously adapt to complex and variable war environment factors, dynamically adjust own strategies, and cooperate to complete the battle mission. The invention researches and solves the problems of path planning of multiple unmanned aerial vehicles and the like by using a related theory of computational intelligence, and provides technical support for the confrontation of the unmanned aerial vehicles in the future.
Referring to fig. 1, at the beginning of algorithm design, threats possibly suffered by an unmanned aerial vehicle in a flight environment need to be considered, a Voronoi model is established according to the threats, then the influence of various threats on edges in a graph is calculated, an initial path is searched by improving a classical ant colony algorithm, then a path length interval corresponding to the unmanned aerial vehicle is obtained according to a smoothing principle, whether synergy can be achieved or not is judged according to the path length interval, if synergy can be achieved, the unmanned aerial vehicles cooperate, and if the synergy cannot be achieved, the unmanned aerial vehicles cannot meet task requirements. As shown in fig. 2, the specific design process is as follows:
a multi-unmanned aerial vehicle collaborative path planning method based on an improved ant colony algorithm comprises the following steps:
(1) analyzing the flight environment of the unmanned aerial vehicle, and establishing an environment model based on a Voronoi diagram as follows:
(1-1) determining the flying height of the unmanned aerial vehicle, intercepting two-dimensional plane terrain information of the flying height, and projecting the ground threat to the height plane to obtain the ground threat plane terrain;
(1-2) abstracting ground threat plane terrain and other threat sources into a threat point set { xi}；
(1-3) establishing a coordinate system in a plane to obtain a coordinate set { (x) of the threat sourcei，yi) And generating a Voronoi diagram.
(1-4) inputting the starting point and the end point of the unmanned aerial vehicle, and completing the environment modeling of the Voronoi diagram, as shown in FIG. 3. Where the starting point is represented by a triangle and the target point by a star. R represents an enemy radar, M represents an enemy missile, and O represents a terrain obstacle. And all vertexes in the generated Voronoi graph are labeled in sequence, so that the representation of the shortest path obtained by a subsequent path planning algorithm is facilitated.
(2) The specific steps of weighting the edges in the established Voronoi diagram are as follows:
(2-1) the following four factors are mainly considered to have an influence on the cost of the side: landform factor threats (fixed) such as mountains, threat sources with detection capability but no attack capability such as radars (radars), threat sources with detection capability and attack capability such as missiles (Guided missiles), and Length (Length) cost of edges per se;
(2-2) mountain and other terrain factor threats (fixed):
wherein the content of the first and second substances,
representing the cost of the ith edge by a threat source j causing a threat by a fixed obstacle; k is the threat level of the fixed obstacle; k is a human commitment coefficient; r isijIs the distance from the fixed barrier to the ith edge. For convenience of calculation, the length of a connecting line from the fixed barrier to the midpoint of the ith side is taken as the length of the connecting line;
(2-3) Radar (Radar) and other threats with detection capability but no attack capability:
wherein the content of the first and second substances,
is the cost of the ith edge by the threat source j of the threat caused by the radar; l isiIs the length of edge i; qjIs the transmit power of the radar;
Qjthe calculation formula is as follows:
wherein P is the transmit power of the radar; ptIs the transmitter power; g is the gain of the radio; a. theeIs the effective area of the transmitter, δ is the cross-sectional area of the radar; r is the length from the radar, provided that R ≦ Rmax(RmaxIs the radar maximum detection radius);
(2-4) threat of both detection and attack ability of Guided missiles and the like:
wherein the content of the first and second substances,
threat source j causing threat by the missile to the ith path, attack ability of the missile (1- α) as missile hit rate, pijIs the probability that the drone detected on the ith edge.
(2-5) Length of edge (Length) cost: pi-L＝λLi
Wherein, Pi-LThe cost of length-to-edge i; λ is a coefficient; l isiIs the length of the ith side.
(2-6) total cost calculation formula:
wherein a, b, c, d are constants, and a + b + c + d is 1. m is the number of fixed obstacles, n is the number of radars, and r is the number of missiles.
(3) A flow chart for planning an initial path for an unmanned aerial vehicle by using an improved ant colony algorithm is detailed in fig. 4, and the specific steps are as follows:
(3-1) ants start from the initial node according to a transition probability formula
Selecting a transfer node and adding the initial node to a tabu table, wherein
ηij(t) represents the time of t<i,j>Heuristic information on the path;
τij(t) represents the time t<i,j>Pheromones on the path;
α denotes respectively τij(t)、ηij(t) a weight coefficient;
ηir(t): indicates the time t<i,j>Heuristic information on the path;
τir(t): indicates the time t<i,j>Route of travelPheromone concentration of (a);
(3-2) the ants select transfer nodes according to the transfer probability, and add the selected transfer nodes into a taboo table; judging whether the transfer node reaches the end point, if not, continuously repeating the step (3-2) until the end point is reached; if the terminal is reached, turning to (3-3);
(3-3) judging whether the iteration times reach a fixed value, if not, turning to (3-4) updating the pheromone, and if so, turning to (3-5) updating the pheromone;
(3-4) updating the path of the loop according to the pheromone updating formula, wherein the iteration times are +1, and the step is switched to (3-6)
(3-5) updating the paths in the last cycles according to the pheromone updating formula, wherein the iteration times are +1, and the operation is switched to (3-6);
(3-6) if the iteration times are larger than the maximum algebra, finishing searching to obtain the shortest path, and otherwise, turning to (3-1); wherein, the pheromone updating formula is as follows:
rho represents the pheromone volatilization coefficient;
q is a constant representing pheromone concentration;
Lkthe total length of the path traveled by ant k in this cycle.
(4) Whether the cooperation can be achieved is judged by smoothing each unmanned aerial vehicle path, corresponding operation is executed according to the result, and a cooperation flow chart is shown in fig. 5 and comprises the following specific steps:
(4-1) smoothing the angle, which does not meet a certain preset angle, at the turning position of the initial path to obtain a smoothed path length interval;
(4-2) taking the maximum value of the lower limit of the corresponding path length interval of each unmanned aerial vehicle as A, and taking the minimum value of the upper limit of the corresponding path length interval of each unmanned aerial vehicle as B;
(4-3) judging the value of A-B;
(4-4) if A-B is less than or equal to 0, synergy can be completed;
after the cooperation is completed, any value can be taken from the intersection of the unmanned aerial vehicle path length intervals as the unmanned aerial vehicle path length reference; calculating the difference value between each unmanned aerial vehicle and the original path value according to the reference; and calculating the length which needs to be increased or decreased corresponding to each angle by using a greedy algorithm according to the path length difference, and determining the position of the inscribed circle according to the length until the requirement is met.
(4-5) if A-B >0, no synergy can be achieved.
The present invention has been illustrated by the above embodiments, but it should be understood that the above embodiments are for illustrative and descriptive purposes only and are not intended to limit the invention to the scope of the described embodiments. Furthermore, it will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that many variations and modifications may be made in accordance with the teachings of the present invention, which variations and modifications are within the scope of the present invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A multi-unmanned aerial vehicle collaborative path planning method based on an ant colony algorithm comprises the following steps:
analyzing the flight environment of the unmanned aerial vehicle, and establishing an environment modeling based on a Voronoi diagram;
step (2) calculating the cost of the edge in the environment modeling based on the Voronoi diagram, comprising the following steps:
(2-1) calculating the cost of the threat opposite sides of the terrain factors:
wherein the content of the first and second substances,
threat source j pair ith representing threat caused by fixed obstacleCost of edges; k is the threat level of the fixed obstacle; k is a human commitment coefficient; r isijIs the distance from the fixed barrier to the ith side;
(2-2) calculating the cost of the threat opposite side of the investigation capability but no attack capability:
wherein the content of the first and second substances,
is the cost of the ith edge by the threat source j of the threat caused by the radar; l isiIs the length of edge i; d1/8，i，jDistance to radar at 1/8 on the ith edge; qjIs the transmission power, Q, of the radarjThe calculation formula is as follows:
wherein, PtIs the transmitter power; g is the gain of the radio; a. theeIs the effective area of the transmitter, δ is the cross-sectional area of the radar; r is the length of the range radar;
(2-3) calculating the cost of the threat opposite side with both detection capability and attack capability:
wherein the content of the first and second substances,
threat source j causing threat by the missile to the ith path, attack ability of the missile (1- α) as missile hit rate, pijThe probability of detection of the unmanned aerial vehicle on the ith side is obtained;
(2-4) calculating the length cost of the edge:
Pi-L＝λLi
wherein the content of the first and second substances,Pi-Lthe cost of length-to-edge i; λ is a coefficient; l isiIs the length of the ith side;
(2-5) total edge cost calculation formula:
wherein a, b, c, d are constants such that a + b + c + d is 1; m is the number of fixed obstacles, n is the number of radars, and r is the number of missiles;
and (3) planning an initial path for the unmanned aerial vehicle by using an ant colony algorithm, wherein the ant colony algorithm comprises the following steps:
(3-1) ants start from the initial node according to a transition probability formula
Selecting a transfer node and adding the initial node to a tabu table, wherein
ηij(t) represents the time of t<i,j>Heuristic information on the path;
τij(t) represents the time t<i,j>Pheromones on the path;
α denotes respectively τij(t)、ηij(t) a weight coefficient;
ηir(t): indicates the time t<i,j>Heuristic information on the path;
τir(t): indicates the time t<i,j>Pheromone concentration on the pathway;
(3-2) the ants select transfer nodes according to the transfer probability, and add the selected transfer nodes into a taboo table; judging whether the transfer node reaches the end point, if not, continuously repeating the step (3-2) until the end point is reached; if the terminal is reached, turning to (3-3);
(3-3) judging whether the iteration times reach a fixed value, if not, turning to (3-4) updating the pheromone, and if so, turning to (3-5) updating the pheromone;
(3-4) updating the path of the loop according to the pheromone updating formula, wherein the iteration times are +1, and then turning to (3-6);
(3-5) updating the paths in the last cycles according to the pheromone updating formula, wherein the iteration times are +1, and the operation is switched to (3-6);
(3-6) if the iteration times are larger than the maximum algebra, finishing searching to obtain the shortest path, and otherwise, turning to (3-1); wherein, the pheromone updating formula is as follows:
ρ: representing a pheromone volatility coefficient;
q: a constant representing a pheromone concentration;
Lk: the total length of the path passed by the ant k in the cycle;
and (4) judging whether the cooperation can be achieved or not by smoothing the initial path of each unmanned aerial vehicle, and executing corresponding operation according to the result.
2. The ant colony algorithm-based multi-unmanned aerial vehicle collaborative path planning method according to claim 1, wherein the step (1) comprises the steps of:
(1-1) determining the flight altitude of the unmanned aerial vehicle, intercepting two-dimensional plane terrain information of the flight altitude, and projecting the ground threat to the two-dimensional plane terrain information to obtain the ground threat plane terrain;
(1-2) placing the groundThreat plane terrain and other threat sources are abstracted into a set of threat points { x }i}；
(1-3) establishing a coordinate system in a plane to obtain a coordinate set { (x) of the threat sourcei，yi) And generating a Voronoi diagram;
and (1-4) inputting the starting point and the end point of the unmanned aerial vehicle, and completing the environment modeling of the Voronoi diagram.
3. The ant colony algorithm-based multi-unmanned aerial vehicle collaborative path planning method according to claim 1, wherein the step (4) comprises the steps of:
(4-1) smoothing the angle, which does not meet a certain preset angle, at the turning position of the initial path to obtain a smoothed path length interval;
(4-2) taking the maximum value of the lower limit of the corresponding path length interval of each unmanned aerial vehicle as A, and taking the minimum value of the upper limit of the corresponding path length interval of each unmanned aerial vehicle as B;
(4-3) judging the value of A-B;
(4-4) if A-B is less than or equal to 0, completing the synergy;
(4-5) if A-B >0, no synergy can be achieved.
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| CN108981714B (en) * | 2018-08-21 | 2020-08-18 | 北航（四川）西部国际创新港科技有限公司 | Unmanned aerial vehicle path planning and feasibility analysis method |
| CN109387214A (en) * | 2018-09-05 | 2019-02-26 | 南京理工大学 | A kind of Robot Path Planning Algorithm based on virtual wall |
| CN109282815A (en) * | 2018-09-13 | 2019-01-29 | 天津西青区瑞博生物科技有限公司 | Method for planning path for mobile robot based on ant group algorithm under a kind of dynamic environment |
| CN109919391B (en) * | 2019-03-19 | 2021-01-08 | 北京航空航天大学 | Solar airplane autonomous optimization flight path planning method and system |
| CN110058613B (en) * | 2019-05-13 | 2022-05-13 | 大连海事大学 | Multi-unmanned-aerial-vehicle multi-ant-colony collaborative target searching method |
| CN110262542B (en) * | 2019-05-21 | 2020-12-04 | 西北大学 | Rotor unmanned aerial vehicle energy-saving path optimization method combining rotation angle and distance |
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| CN110608743B (en) * | 2019-10-18 | 2020-11-06 | 南京航空航天大学 | Multi-unmanned aerial vehicle collaborative route planning method based on multi-population chaotic grayling algorithm |
| CN111273686B (en) * | 2020-02-15 | 2021-02-09 | 北京理工大学 | Path planning method for multiple unmanned aerial vehicles to simultaneously reach designated place in three-dimensional environment |
| CN111414006B (en) * | 2020-03-27 | 2023-09-08 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster reconnaissance task planning method based on distributed sequential allocation |
| CN111639811B (en) * | 2020-06-01 | 2023-10-31 | 中国农业大学 | Multi-agricultural-machine collaborative operation remote management scheduling method based on improved ant colony algorithm |
| CN111750869A (en) * | 2020-08-14 | 2020-10-09 | 中国电子科技集团公司第五十四研究所 | Unmanned aerial vehicle path planning method for reconstructing Voronoi diagram in real time |
| CN112346422B (en) * | 2020-11-12 | 2023-10-20 | 内蒙古民族大学 | Method for realizing unit operation scheduling by double ant colony intelligent anti-competition |
| CN112801540B (en) * | 2021-02-24 | 2023-12-05 | 中国人民解放军国防科技大学 | Intelligent collaborative architecture design method based on unmanned clusters |
| CN113552897B (en) * | 2021-06-30 | 2023-01-03 | 南京理工大学 | Task priority based air-ground unmanned system reconnaissance collaborative path planning method |
| CN114166228A (en) * | 2021-12-07 | 2022-03-11 | 武汉科技大学 | Unmanned aerial vehicle continuous monitoring path planning method |
| CN114397911B (en) * | 2022-01-18 | 2024-04-09 | 中科视拓（北京）科技有限公司 | Unmanned aerial vehicle cluster countermeasure decision-making method based on multiple intelligent agents |
| CN115118724A (en) * | 2022-06-23 | 2022-09-27 | 福州大学 | Ant colony algorithm-based multi-unmanned aerial vehicle-assisted edge computing system deployment optimization method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122974A (en) * | 2007-09-13 | 2008-02-13 | 北京航空航天大学 | Un-manned plane fairway layout method based on Voronoi graph and ant colony optimization algorithm |
| CN104359473A (en) * | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
-
2016
- 2016-11-21 CN CN201611023386.3A patent/CN106705970B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101122974A (en) * | 2007-09-13 | 2008-02-13 | 北京航空航天大学 | Un-manned plane fairway layout method based on Voronoi graph and ant colony optimization algorithm |
| CN104359473A (en) * | 2014-10-24 | 2015-02-18 | 南京航空航天大学 | Collaborative flight path intelligent planning method for formation flying of unmanned planes under dynamic environment |
Non-Patent Citations (3)
| Title |
|---|
| "3D Smooth Path Planning for a UAV in Cluttered Natural Environments";Kwangjin Yang;《2008 IEEE/RSJ International Conference on Intelligent Robots and Systems》;20080926;正文第794-800页 * |
| "基于改进蚁群算法的多无人机航路规划研究";孟祥恒等;《计算机仿真》;20081130;第25卷(第11期);正文第56-59页 * |
| "雷达威胁环境下的多无人机协同航迹规划";郜晨等;《应用科学学报》;20140531;第32卷(第3期);正文第287-291页 * |
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