CN113778128A - Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm - Google Patents
Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm Download PDFInfo
- Publication number
- CN113778128A CN113778128A CN202111109696.8A CN202111109696A CN113778128A CN 113778128 A CN113778128 A CN 113778128A CN 202111109696 A CN202111109696 A CN 202111109696A CN 113778128 A CN113778128 A CN 113778128A
- Authority
- CN
- China
- Prior art keywords
- reconnaissance
- unmanned aerial
- aerial vehicle
- task
- intercepted
- 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.)
- Granted
Links
- 230000002068 genetic effect Effects 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 39
- 239000000203 mixture Substances 0.000 claims abstract description 19
- 230000006870 function Effects 0.000 claims description 68
- 238000005457 optimization Methods 0.000 claims description 15
- 238000004590 computer program Methods 0.000 claims description 14
- 230000035772 mutation Effects 0.000 claims description 12
- 239000011159 matrix material Substances 0.000 claims description 8
- 230000004083 survival effect Effects 0.000 claims description 6
- 230000008439 repair process Effects 0.000 claims description 5
- 238000007621 cluster analysis Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 4
- 230000001360 synchronised effect Effects 0.000 description 2
- 206010064571 Gene mutation Diseases 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 210000000349 chromosome Anatomy 0.000 description 1
- 239000012141 concentrate Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036651 mood Effects 0.000 description 1
- 238000009401 outcrossing Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The application relates to an unmanned aerial vehicle reconnaissance mission planning method and device based on a genetic algorithm. The method comprises the following steps: acquiring a task target to be intercepted from a situation map, and obtaining an initial position of the unmanned aerial vehicle in a sub-region of the task to be intercepted through an expectation maximization algorithm of a Gaussian mixture model; setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be detected as a target function; establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function; optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function; and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function. By adopting the method, the continuous reconnaissance efficiency of the unmanned aerial vehicle can be improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle reconnaissance mission planning method and device based on a genetic algorithm, computer equipment and a storage medium.
Background
With the occurrence of more and more natural or man-made disasters, the disasters such as earthquakes, nuclear leakage, large-scale fires and the like not only cause irrecoverable property loss of people, but also threaten the life safety of people more seriously. With the development of unmanned aerial vehicle technology and the rise of artificial intelligence in recent years, intelligent unmanned aerial vehicle detection system has also gradually become a hotspot of unmanned aerial vehicle field research. However, in the face of continuous reconnaissance in a large-area disaster area, a single unmanned aerial vehicle is not in the mood due to limitations of a flight and self information transmission bandwidth, continuous reconnaissance of an unmanned aerial vehicle swarm becomes a perfect solution, although continuous reconnaissance of the unmanned aerial vehicle swarm is researched by a learner, relevant theories of continuous reconnaissance of the unmanned aerial vehicle swarm are still not mature enough, most of the continuous reconnaissance still stay in a theoretical stage, and a distance is still reserved from practical application.
At present, short boards still exist in the aspect of continuous reconnaissance intelligent optimization and optimization of the swarm unmanned aerial vehicle to a designated area, and most of research on the unmanned aerial vehicle focuses on the function and optimization of a single unmanned aerial vehicle. At present, most of unmanned plane swarms are used for civil night-scene unmanned plane light display, the swarms of unmanned planes in the military field are developed to some extent, and the unmanned planes are still in short supply in higher-level application. How to realize the intellectualization and high efficiency of the continuous reconnaissance of the multiple unmanned aerial vehicles still remains to be solved urgently.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a genetic algorithm-based unmanned aerial vehicle reconnaissance mission planning method, apparatus, computer device, and storage medium, which can improve the continuous reconnaissance efficiency of an unmanned aerial vehicle.
An unmanned aerial vehicle reconnaissance mission planning method based on a genetic algorithm, the method comprising:
acquiring a map of an area to be predicted and the position and the number of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task;
binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map;
acquiring a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter;
establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function;
optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function;
and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
In one embodiment, optimizing the solution model by a genetic algorithm to obtain an optimal solution of the objective function includes: performing preliminary optimization on the solved model according to a repair operator in the genetic algorithm to obtain an initial solution of the objective function meeting the constraint condition; selecting the initial solution according to a selection operator in the genetic algorithm to obtain a first approximate solution with the maximum survival probability in the initial solution; and carrying out cross mutation operation on the first approximate solution according to a cross operator and a mutation operator in the genetic algorithm to obtain the optimal solution of the objective function.
In one embodiment, the step of binding the task urgency parameter and the position of the point to be detected to obtain a situation map includes:
obtaining an information matrix T of the situation map according to the task urgency degree parameters and the position of the point to be detected; establishing a situation map according to an information matrix of the situation map;
wherein, the expression of T is as follows:
T＝{(x1，y1，e1)，(x1，y1，e2)，....,((xi，yi，ei))}
xix-coordinate, y, representing a point to be surveyed with serial number iiY-coordinate, e, representing a point to be investigated with serial number iiAnd representing the task urgency of the to-be-detected point with the sequence number i.
In one embodiment, clustering analysis is carried out on the mission target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model, and a clustering result is obtained; the clustering result comprises the following steps: the unmanned aerial vehicle waits to scout mission target subregion and unmanned aerial vehicle are in waiting the initial position of scout mission subregion, include:
clustering analysis is carried out on the mission target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a mission target sub-region to be intercepted by the unmanned aerial vehicle and the maximum expected mean value of the unmanned aerial vehicle in the mission sub-region, and the maximum expected mean value of the unmanned aerial vehicle in the mission sub-region is used as the initial position of the unmanned aerial vehicle in the mission sub-region to be intercepted.
In one embodiment, a constraint condition for unmanned aerial vehicle reconnaissance is set according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter and comprises the following steps:
according to the initial position, the reconnaissance state vector and the reconnaissance force parameter, setting a constraint condition for reconnaissance of the unmanned aerial vehicle, wherein the constraint condition is as follows:
∑rj≤Lmax，(xj，yj)∈Mi
S(j)＝1，(xj，yj)∈Mi
∑Ri≤Rmax，(xj，yj)∈Mi
wherein M isiRepresenting the sub-area of the target of the mission to be detected of the unmanned aerial vehicle, rjIndicating the distance, L, of the drone from the surrounding points to be surveyedmaxRepresenting Euclidean distance and limit value, R, between the unmanned aerial vehicle and the point location to be reconnaissanceiRepresenting a scout parameter, RmaxRepresents the maximum scout force of the unmanned plane (x)j，yj) The initial position of the unmanned plane in the sub-area of the task to be scout is represented, S (j) represents a scout state vector, and 1 represents that the point to be scout is not scout.
In one embodiment, the objective function of the assignment of the task to be spy by the drone is: maxW ═ Σ e, (x)j，yj)∈MiWhere e represents the mission urgency.
In one embodiment, the scout parameter is determined based on the mission urgency parameter, including:
Ri＝α×e2，α＝0.01
where α represents a proportionality coefficient between the scout parameter and the square of the mission urgency.
An unmanned aerial vehicle reconnaissance mission planning apparatus based on a genetic algorithm, the apparatus comprising:
the acquisition situation map module is used for acquiring the map of the area to be predicted and the position and the number of the points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task; binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map;
the system comprises an initial position acquisition module, a task analysis module and a task analysis module, wherein the initial position acquisition module is used for acquiring a task target to be intercepted from a situation map and carrying out cluster analysis on the task target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a cluster result; the clustering result comprises: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
establishing a solution model module for setting the constraint conditions of unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function;
the optimal reconnaissance point location module is used for optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function; and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring a map of an area to be predicted and the position and the number of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task;
binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map;
acquiring a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter;
establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function;
optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function;
and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a map of an area to be predicted and the position and the number of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task;
binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map;
acquiring a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter;
establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function;
optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function;
and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
According to the unmanned aerial vehicle reconnaissance task planning method and device based on the genetic algorithm, the computer equipment and the storage medium, firstly, the map of the area to be predicted and the positions and the number of the points to be reconnaissance in the map are obtained; acquiring preset task urgency parameters of each reconnaissance task; binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map; acquiring a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted; in practice, unmanned plane bee colonies take off from the same take-off site, and constraint conditions for unmanned plane reconnaissance are set according to the initial position, the reconnaissance state vector and the reconnaissance force parameters; the reconnaissance task of the unmanned aerial vehicle can be optimized through constraint conditions, and the sum of the task urgency parameters of the points to be reconnaissance is set as a target function distributed by the unmanned aerial vehicle to be reconnaissance task; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and is set to be in an undetected state, so that the point to be reconnaissance of the unmanned aerial vehicle is the point to be reconnaissance, the time cost is saved, and the reconnaissance force parameter is determined according to the task urgency degree parameter; when the task urgency degree parameter is increased, the reconnaissance strength of the unmanned aerial vehicle is increased, and a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle is established according to the constraint condition and the objective function; optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function; according to the optimal solution of the objective function, the optimal reconnaissance point position distributed by the unmanned aerial vehicle is obtained, so that the unmanned aerial vehicle can obtain the optimal reconnaissance point position when carrying out the reconnaissance task, the reconnaissance efficiency of a single unmanned aerial vehicle in the unmanned bee colony can be improved, and the efficiency of completing the reconnaissance task by the whole unmanned aerial vehicle bee colony is further improved.
Drawings
FIG. 1 is a schematic flow chart of a genetic algorithm-based unmanned aerial vehicle reconnaissance mission planning method in one embodiment;
fig. 2 is a block diagram of a genetic algorithm-based unmanned aerial vehicle reconnaissance mission planning apparatus according to an embodiment;
FIG. 3 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In one embodiment, as shown in fig. 1, there is provided a genetic algorithm-based unmanned aerial vehicle reconnaissance mission planning method, including the following steps:
102, acquiring a map of an area to be predicted and the position and the number of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task; and binding the task urgency degree parameters and the positions of the points to be detected to obtain a situation map.
The situation map is a map which reflects development trends and provides services for timely analysis and decision making, the continuous reconnaissance tasks of the unmanned aerial vehicles often span multiple geographically dispersed points in a long time, and aiming at the problems, the situation map is established to provide targets to be reconnaissance for the reconnaissance tasks of the unmanned aerial vehicles, including the positions and the number of the targets to be reconnaissance. In the continuous scouting process of the unmanned plane swarm, the situation map is automatically updated according to the time interval required by the continuous scouting of the unmanned plane.
104, acquiring a task target to be reconnaissance from the situation map, and performing cluster analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a cluster result; the clustering result comprises the following steps: the unmanned aerial vehicle is to the initial position of reconnaissance mission target subregion and unmanned aerial vehicle in waiting to reconnaissance mission subregion.
The clustering analysis of the mission targets to be intercepted through the expectation maximization algorithm based on the Gaussian mixture model means that the mission targets to be intercepted are divided according to position characteristics to obtain a plurality of mission target sub-regions to be intercepted, then the expected maximum mean value of the unmanned aerial vehicles in the mission sub-regions to be intercepted is calculated to obtain the initial position of each unmanned aerial vehicle in the mission sub-regions to be intercepted, and the optimal takeoff position of the unmanned aerial vehicle is set based on the initial position of each unmanned aerial vehicle in the mission sub-regions to be intercepted.
106, setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; and establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function.
Unmanned aerial vehicle's battery duration is limited, make the continuous reconnaissance of unmanned aerial vehicle bee colony become difficult, give consideration to unmanned aerial vehicle duration restriction when satisfying the time interval's between twice reconnaissance restriction, take off the position of unmanned aerial vehicle and treat the distance sum between the reconnaissance point location in this application and restrict, simultaneously, treat that the reconnaissance point location must be the point location that has not been reconnaissance, because single unmanned aerial vehicle's reconnaissance ability is limited, then concentrate to the mission point of single unmanned aerial vehicle distribution, the reconnaissance power of its all point locations adds and must have certain restriction, to the problem mentioned above, this application sets up the constraint condition that unmanned aerial vehicle reconnaissance task distribution, set up the mission emergency parameter sum of the point of treating reconnaissance as the objective function that unmanned aerial vehicle treats reconnaissance task distribution. And solving the objective function to obtain a reconnaissance point position set distributed by the unmanned aerial vehicle.
108, optimizing the solved model through a genetic algorithm to obtain an optimal solution of the objective function; and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
The genetic algorithm is widely applied to various optimization problem models as a heuristic intelligent optimization method, and compared with other optimization algorithms, the genetic algorithm has the following advantages: a point search mode is replaced by a face search mode, and the search starts from a group, so that the potential parallelism is realized, and the search efficiency is higher; introducing mechanisms such as gene mutation, chromosome mutation and the like in genetics, greatly increasing randomness in the optimization process, greatly reducing the possibility that the algorithm is trapped into local optimization, having strong searching capability and the like, optimizing the initial solution of the solution model through a repair operator, a selection operator, a crossover operator and a mutation operator in the genetic algorithm to obtain the optimal solution of the objective function, wherein the optimal solution is the optimal reconnaissance point distributed by a single unmanned aerial vehicle. The process of optimizing the solution model by using the genetic algorithm is a continuous optimization process and can be used for planning continuous reconnaissance tasks of the unmanned aerial vehicle.
According to the unmanned aerial vehicle reconnaissance task planning method and device based on the genetic algorithm, the computer equipment and the storage medium, firstly, the map of the area to be predicted and the positions and the number of the points to be reconnaissance in the map are obtained; acquiring preset task urgency parameters of each reconnaissance task; binding the task urgency degree parameter with the position of a point to be detected to obtain a situation map; acquiring a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted; in practice, unmanned plane bee colonies take off from the same take-off site, and constraint conditions for unmanned plane reconnaissance are set according to the initial position, the reconnaissance state vector and the reconnaissance force parameters; the reconnaissance task of the unmanned aerial vehicle can be optimized through constraint conditions, and the sum of the task urgency parameters of the points to be reconnaissance is set as a target function distributed by the unmanned aerial vehicle to be reconnaissance task; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and is set to be in an undetected state, so that the point to be reconnaissance of the unmanned aerial vehicle is the point to be reconnaissance, the time cost is saved, and the reconnaissance force parameter is determined according to the task urgency degree parameter; when the task urgency degree parameter is increased, the reconnaissance strength of the unmanned aerial vehicle is increased, and a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle is established according to the constraint condition and the objective function; optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function; according to the optimal solution of the objective function, the optimal reconnaissance point position distributed by the unmanned aerial vehicle is obtained, so that the unmanned aerial vehicle can obtain the optimal reconnaissance point position when carrying out the reconnaissance task, the reconnaissance efficiency of a single unmanned aerial vehicle in the unmanned bee colony can be improved, and the efficiency of completing the reconnaissance task by the whole unmanned aerial vehicle bee colony is further improved.
In one embodiment, optimizing the solution model by a genetic algorithm to obtain an optimal solution of the objective function includes: performing preliminary optimization on the solved model according to a repair operator in the genetic algorithm to obtain an initial solution of the objective function meeting the constraint condition; selecting the initial solution according to a selection operator in the genetic algorithm to obtain a first approximate solution with the maximum survival probability in the initial solution; and carrying out cross mutation operation on the first approximate solution according to a cross operator and a mutation operator in the genetic algorithm to obtain the optimal solution of the objective function.
And carrying out preliminary optimization on the solution model according to a repairing operator in the genetic algorithm, wherein the repairing operator is used for calculating the ratio of the importance of different attributes corresponding to different point positions to the attributes, multiplying the ratio by the maximum value in the limiting condition, namely the profit density, and then taking out the point with the minimum profit density when the randomly generated combination cannot meet the limiting requirement so that the combination meets the limiting condition. The method comprises the steps of carrying out preliminary optimization on a solving model through a repairing operator in a genetic algorithm to obtain all solutions meeting constraint conditions as initial solutions of an objective function, selecting the initial solutions according to a selection operator introducing a roulette concept in the genetic algorithm, calculating the fitness of the initial solutions, judging the survival probability of the initial solutions according to the fitness, selecting the maximum survival probability in the initial solutions as a first approximate solution, carrying out crossing and mutation operations on the first approximate solution through a crossing operator and a mutation operator in the genetic algorithm, presetting iteration times, and continuously iterating the solving process of the objective function through the genetic algorithm until the optimal solution of the objective function is output.
In one embodiment, the step of binding the task urgency parameter and the position of the point to be detected to obtain a situation map includes:
obtaining an information matrix T of the situation map according to the task urgency degree parameters and the position of the point to be detected; establishing a situation map according to an information matrix of the situation map;
wherein, the expression of T is as follows:
T＝{(x1，y1，e1)，(x1，y1，e2)，....,((xi，yi，ei))}
xix-coordinate, y, representing a point to be surveyed with serial number iiRepresentative serial numberY coordinate of the point to be detected, eiAnd representing the task urgency of the to-be-detected point with the sequence number i.
The unmanned aerial vehicle continuous reconnaissance task usually spans many geographically dispersed points in a long time, and aiming at the problems, a situation map is established in the method for providing the to-be-reconnaissance targets for the unmanned aerial vehicle reconnaissance task, the to-be-reconnaissance targets comprise the positions and the number of the to-be-reconnaissance targets, the urgency degree of each task target is set in the situation map, the urgency degree of each task target is bound with the positions of the to-be-reconnaissance points, and the unmanned aerial vehicle can visit the to-be-reconnaissance points according to the urgency degree of the task targets in the reconnaissance process.
In one embodiment, clustering analysis is carried out on the mission target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model, and a clustering result is obtained; the clustering result comprises the following steps: the unmanned aerial vehicle waits to scout mission target subregion and unmanned aerial vehicle are in waiting the initial position of scout mission subregion, include: clustering analysis is carried out on the mission target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a mission target sub-region to be intercepted by the unmanned aerial vehicle and the maximum expected mean value of the unmanned aerial vehicle in the mission sub-region, and the maximum expected mean value of the unmanned aerial vehicle in the mission sub-region is used as the initial position of the unmanned aerial vehicle in the mission sub-region to be intercepted.
Because unmanned aerial vehicles in the unmanned aerial vehicle bee colony take off from the same position, and the duration of the unmanned aerial vehicle is limited, to the above-mentioned problem, in this embodiment, the average value that the unmanned aerial vehicle expects the biggest in the reconnaissance task subregion is taken as the initial position of unmanned aerial vehicle in the reconnaissance task subregion, its initial position is the optimal take-off position of unmanned aerial vehicle in the reconnaissance task subregion, take off from the optimal take-off position and can improve the work efficiency of unmanned aerial vehicle in limited duration, then limit the sum of the distance between the take-off position of unmanned aerial vehicle and the point of waiting to reconnaissance, as the distance constraint of the solution model of the subsequent unmanned aerial vehicle point of waiting to reconnaissance allocation problem.
In one embodiment, a constraint condition for unmanned aerial vehicle reconnaissance is set according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter and comprises the following steps:
according to the initial position, the reconnaissance state vector and the reconnaissance force parameter, setting a constraint condition for reconnaissance of the unmanned aerial vehicle, wherein the constraint condition is as follows:
∑rj≤Lmax，(xj，yj)∈Mi
S(j)＝1，(xj，yj)∈Mi
∑Ri≤Rmax，(xj，yj)∈Mi
wherein M isiRepresenting the sub-area of the target of the mission to be detected of the unmanned aerial vehicle, rjIndicating the distance, L, of the drone from the surrounding points to be surveyedmaxRepresenting Euclidean distance and limit value, R, between the unmanned aerial vehicle and the point location to be reconnaissanceiRepresenting a scout parameter, RmaxRepresents the maximum scout force of the unmanned plane (x)j，yj) The initial position of the unmanned plane in the sub-area of the task to be scout is represented, S (j) represents a scout state vector, and 1 represents that the point to be scout is not scout.
Set up the constraint condition, restrict the distance sum between the position of taking off and the point location of treating the unmanned aerial vehicle and make unmanned aerial vehicle treat the reconnaissance point location and reconnaissance in the time of endurance and carry out the reconnaissance, treat that the reconnaissance point location must be the point location of not being reconnaissance and restrict, practice thrift the time cost of unmanned aerial vehicle reconnaissance, reconnaissance power to all point locations adds and restricts the maximize utilization that makes single unmanned aerial vehicle's reconnaissance power, carry out the construction of the objective function of unmanned aerial vehicle reconnaissance task allocation according to the mission emergency degree of the point location of treating the reconnaissance and can improve unmanned aerial vehicle's reconnaissance efficiency.
In one embodiment, the objective function of the assignment of the task to be spy by the drone is: maxW ═ Σ e, (x)j，yj)∈MiWhere e represents the mission urgency.
The objective function is used for constructing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle.
In one embodiment, the scout parameter is determined based on the mission urgency parameter, including:
Ri＝α×e2，α＝0.01
where α represents a proportionality coefficient between the scout parameter and the square of the mission urgency.
The scouting force of a single unmanned aerial vehicle is limited, and scouting force parameters are determined according to the emergency degree of the scouting mission of the unmanned aerial vehicle, so that the scouting force of the unmanned aerial vehicle can be utilized to the maximum in a limited interval.
It should be understood that, although the various steps in the flowchart of fig. 1 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 1 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 2, there is provided a genetic algorithm-based unmanned aerial vehicle reconnaissance mission planning apparatus, including: a situation map obtaining module 202, an initial position obtaining module 204, a solution model establishing module 206, and an optimal reconnaissance point location obtaining module 208, wherein:
the situation map obtaining module 202 is configured to obtain a map of an area to be predicted and positions and numbers of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task; and binding the task urgency degree parameters and the positions of the points to be detected to obtain a situation map.
The initial position obtaining module 204 is used for obtaining a task target to be reconnaissance from a situation map, and performing clustering analysis on the task target to be reconnaissance through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises the following steps: the unmanned aerial vehicle is to the initial position of reconnaissance mission target subregion and unmanned aerial vehicle in waiting to reconnaissance mission subregion.
Establishing a solution model module 206 for setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; and establishing a solution model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint conditions and the objective function.
The optimal scout point position obtaining module 208 is used for optimizing the solution model through a genetic algorithm to obtain an optimal solution of the objective function; and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
In one embodiment, the obtain optimal scout point module 208 is further configured to optimize the solution model through a genetic algorithm to obtain an optimal solution of the objective function, where the optimal solution includes: performing preliminary optimization on the solved model according to a repair operator in the genetic algorithm to obtain an initial solution of the objective function meeting the constraint condition; selecting the initial solution according to a selection operator in the genetic algorithm to obtain a first approximate solution with the maximum survival probability in the initial solution; and carrying out cross mutation operation on the first approximate solution according to a cross operator and a mutation operator in the genetic algorithm to obtain the optimal solution of the objective function.
In one embodiment, the obtaining a situation map module 202 is further configured to bind the task urgency parameter and the position of the point to be detected, so as to obtain a situation map, including:
obtaining an information matrix T of the situation map according to the task urgency degree parameters and the position of the point to be detected; establishing a situation map according to an information matrix of the situation map;
wherein, the expression of T is as follows:
T＝{(x1，y1，e1)，(x1，y1，e2)，....,((xi，yi，ei))}
xix-coordinate, y, representing a point to be surveyed with serial number iiY-coordinate, e, representing a point to be investigated with serial number iiAnd representing the task urgency of the to-be-detected point with the sequence number i.
In one embodiment, the initial position obtaining module 204 is further configured to perform clustering analysis on the mission target to be intercepted by the expectation-maximization algorithm based on the gaussian mixture model to obtain a sub-region of the mission target to be intercepted by the unmanned aerial vehicle and a maximum expected mean value of the unmanned aerial vehicle in the sub-region of the mission target to be intercepted, and use the maximum expected mean value of the unmanned aerial vehicle in the sub-region of the mission target to be intercepted as the initial position of the unmanned aerial vehicle in the sub-region of the mission target to be intercepted.
In one embodiment, the model-for-solution module 206 is further configured to set a constraint condition for the reconnaissance of the drone according to the initial position, the reconnaissance state vector, and the reconnaissance parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking the reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter and comprises the following steps:
according to the initial position, the reconnaissance state vector and the reconnaissance force parameter, setting a constraint condition for reconnaissance of the unmanned aerial vehicle, wherein the constraint condition is as follows:
∑rj≤Lmax，(xj，yj)∈Mi
S(j)＝1，(xj，yj)∈Mi
∑Ri≤Rmax，(xj，yj)∈Mi
wherein M isiRepresenting the sub-area of the target of the mission to be detected of the unmanned aerial vehicle, rjIndicating the distance, L, of the drone from the surrounding points to be surveyedmaxRepresenting Euclidean distance and limit value, R, between the unmanned aerial vehicle and the point location to be reconnaissanceiRepresenting a scout parameter, RmaxRepresents the maximum scout force of the unmanned plane (x)j，yj) Indicating that the unmanned aerial vehicle is in the sub-area of the task to be detectedThe initial position in (S), (j) represents a scout state vector, and 1 represents that the point to be scout is not scout.
For specific limitations of the unmanned aerial vehicle reconnaissance mission planning apparatus based on the genetic algorithm, reference may be made to the above limitations on the unmanned aerial vehicle reconnaissance mission planning method based on the genetic algorithm, which are not described herein again. All or part of the modules in the unmanned aerial vehicle reconnaissance mission planning device based on the genetic algorithm can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a genetic algorithm based unmanned aerial vehicle reconnaissance mission planning method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, a computer device is provided, comprising a memory storing a computer program and a processor implementing the steps of the method in the above embodiments when the processor executes the computer program.
In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method in the above-mentioned embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An unmanned aerial vehicle reconnaissance mission planning method based on a genetic algorithm is characterized by comprising the following steps:
acquiring a map of an area to be predicted and positions and numbers of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task;
binding the task urgency degree parameter with the position of the point to be detected to obtain a situation map;
acquiring a task target to be intercepted from the situation map, and carrying out clustering analysis on the task target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
setting a constraint condition for unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking a reconnaissance state of the point to be reconnaissance, and the reconnaissance parameter is determined according to the task urgency parameter;
establishing a solving model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint condition and the objective function;
optimizing the solving model through a genetic algorithm to obtain an optimal solution of the objective function;
and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
2. The method of claim 1, wherein optimizing the solution model by a genetic algorithm to obtain an optimal solution for the objective function comprises:
performing preliminary optimization on the solution model according to a repair operator in the genetic algorithm to obtain an initial solution of the objective function meeting the constraint condition;
selecting the initial solution according to a selection operator in the genetic algorithm to obtain a first approximate solution with the maximum survival probability in the initial solution;
and carrying out cross mutation operation on the first approximate solution according to a cross operator and a mutation operator in the genetic algorithm to obtain the optimal solution of the objective function.
3. The method of claim 1, wherein binding the mission urgency parameter and the location of the point to be detected to obtain a situation map comprises:
obtaining an information matrix T of a situation map according to the task urgency degree parameters and the position of the point to be detected; establishing a situation map according to the information matrix of the situation map;
wherein, the expression of T is as follows:
T＝{(x1，y1，e1)，(x1，y1，e2)，....,((xi，yi，ei))}
xix-coordinate, y, representing a point to be surveyed with serial number iiY-coordinate, e, representing a point to be investigated with serial number iiAnd representing the task urgency of the to-be-detected point with the sequence number i.
4. The method according to claim 1, characterized in that the task object to be scout is subjected to clustering analysis through an expectation-maximization algorithm based on a Gaussian mixture model to obtain a clustering result; the clustering result comprises: the unmanned aerial vehicle waits to scout mission target subregion and unmanned aerial vehicle are in waiting the initial position of scout mission subregion, include:
and performing cluster analysis on the mission target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a sub-region of the mission target to be intercepted of the unmanned aerial vehicle and the expected maximum mean value of the unmanned aerial vehicle in the sub-region of the mission to be intercepted, and taking the expected maximum mean value of the unmanned aerial vehicle in the sub-region of the mission to be intercepted as the initial position of the unmanned aerial vehicle in the sub-region of the mission to be intercepted.
5. The method according to claim 1, characterized in that the constraints of the unmanned aerial vehicle reconnaissance are set according to the initial position, the reconnaissance state vector and the reconnaissance parameters; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; the reconnaissance state vector is used for marking a reconnaissance state of the point to be reconnaissance, the reconnaissance parameter is determined according to the mission urgency parameter, and the reconnaissance state vector comprises the following steps:
and setting the constraint conditions of unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance parameters, wherein the constraint conditions are as follows:
wherein M isiRepresenting the sub-area of the target of the mission to be detected of the unmanned aerial vehicle, rjIndicating the distance, L, of the drone from the surrounding points to be surveyedmaxRepresenting Euclidean distance and limit value, R, between the unmanned aerial vehicle and the point location to be reconnaissanceiRepresenting a scout parameter, RmaxRepresents the maximum scout force of the unmanned plane (x)j，yj) Indicating that the drone is on standbyThe initial position in the scout task sub-area, S (j) represents a scout state vector, and 1 represents that the point to be scout is not scout.
6. The method of claim 4, wherein the objective function of the assignment of the mission to be detected by the drone is: maxW ═ Σ e,
where e represents the mission urgency.
7. The method of claim 4, wherein the scout parameter is determined from the mission urgency parameter, comprising:
Ri＝α×e2，α＝0.01
where α represents a proportionality coefficient between the scout parameter and the square of the mission urgency.
8. An unmanned aerial vehicle reconnaissance mission planning apparatus based on a genetic algorithm, the apparatus comprising:
the system comprises an acquisition situation map module, a prediction module and a data processing module, wherein the acquisition situation map module is used for acquiring a map of an area to be predicted and the position and the number of points to be detected in the map; acquiring preset task urgency parameters of each reconnaissance task; binding the task urgency degree parameter with the position of the point to be detected to obtain a situation map;
the acquisition initial position module is used for acquiring a task target to be intercepted from the situation map and carrying out cluster analysis on the task target to be intercepted through an expectation maximization algorithm based on a Gaussian mixture model to obtain a cluster result; the clustering result comprises: the unmanned aerial vehicle is arranged in the task sub-area to be intercepted, and the unmanned aerial vehicle is arranged in the task sub-area to be intercepted;
establishing a solution model module for setting the constraint conditions of unmanned aerial vehicle reconnaissance according to the initial position, the reconnaissance state vector and the reconnaissance force parameter; setting the sum of the task urgency parameters of the points to be intercepted as a target function distributed by the tasks to be intercepted of the unmanned aerial vehicle; establishing a solving model of the point location distribution problem to be reconnaissance of the unmanned aerial vehicle according to the constraint condition and the objective function;
the optimal reconnaissance point location module is used for optimizing the solving model through a genetic algorithm to obtain an optimal solution of the objective function; and obtaining the optimal reconnaissance point position distributed by the unmanned aerial vehicle according to the optimal solution of the objective function.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111109696.8A CN113778128B (en) | 2021-09-18 | 2021-09-18 | Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111109696.8A CN113778128B (en) | 2021-09-18 | 2021-09-18 | Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113778128A true CN113778128A (en) | 2021-12-10 |
| CN113778128B CN113778128B (en) | 2023-03-31 |
Family
ID=78852566
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111109696.8A Active CN113778128B (en) | 2021-09-18 | 2021-09-18 | Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113778128B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744290A (en) * | 2013-12-30 | 2014-04-23 | 合肥工业大学 | Hierarchical target allocation method for multiple unmanned aerial vehicle formations |
| CN106225797A (en) * | 2016-06-30 | 2016-12-14 | 银江股份有限公司 | A kind of paths planning method |
| WO2019083528A1 (en) * | 2017-10-25 | 2019-05-02 | Ford Global Technologies, Llc | Proactive vehicle positioning determinations |
| CN110288165A (en) * | 2019-07-02 | 2019-09-27 | 南京信息工程大学 | The unmanned monitoring and tracking method of multiple target based on quick clustering and optimization distribution |
| LU102400A1 (en) * | 2019-08-06 | 2021-02-09 | Nanjing Seawolf Ocean Tech Co Ltd | Path planning method and system for unmanned surface vehicle based on improved genetic algorithm |
| WO2021035911A1 (en) * | 2019-08-28 | 2021-03-04 | 青岛蓝海未来海洋科技有限责任公司 | Method and system for planning path of unmanned surface vehicle based on forward/reverse data-driven linear parameter-varying genetic algorithm |
| CN112859912A (en) * | 2021-01-11 | 2021-05-28 | 中国人民解放军国防科技大学 | Adaptive optimization method and system for unmanned aerial vehicle path planning in relay charging mode |
| CN113361765A (en) * | 2021-05-31 | 2021-09-07 | 大连海事大学 | Method for planning atmospheric pollution path of port ship through cooperative monitoring of shipborne unmanned aerial vehicle |
-
2021
- 2021-09-18 CN CN202111109696.8A patent/CN113778128B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744290A (en) * | 2013-12-30 | 2014-04-23 | 合肥工业大学 | Hierarchical target allocation method for multiple unmanned aerial vehicle formations |
| CN106225797A (en) * | 2016-06-30 | 2016-12-14 | 银江股份有限公司 | A kind of paths planning method |
| WO2019083528A1 (en) * | 2017-10-25 | 2019-05-02 | Ford Global Technologies, Llc | Proactive vehicle positioning determinations |
| CN110288165A (en) * | 2019-07-02 | 2019-09-27 | 南京信息工程大学 | The unmanned monitoring and tracking method of multiple target based on quick clustering and optimization distribution |
| LU102400A1 (en) * | 2019-08-06 | 2021-02-09 | Nanjing Seawolf Ocean Tech Co Ltd | Path planning method and system for unmanned surface vehicle based on improved genetic algorithm |
| WO2021035911A1 (en) * | 2019-08-28 | 2021-03-04 | 青岛蓝海未来海洋科技有限责任公司 | Method and system for planning path of unmanned surface vehicle based on forward/reverse data-driven linear parameter-varying genetic algorithm |
| CN112859912A (en) * | 2021-01-11 | 2021-05-28 | 中国人民解放军国防科技大学 | Adaptive optimization method and system for unmanned aerial vehicle path planning in relay charging mode |
| CN113361765A (en) * | 2021-05-31 | 2021-09-07 | 大连海事大学 | Method for planning atmospheric pollution path of port ship through cooperative monitoring of shipborne unmanned aerial vehicle |
Non-Patent Citations (5)
| Title |
|---|
| F. PERNKOPF; D. BOUCHAFFRA: "Genetic-based EM algorithm for learning Gaussian mixture models", 《IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 》 * |
| MEHREZ BOULARES，AHMED BARNAWI: "A novel UAV path planning algorithm to search for floating objects on the ocean surface based on object’s trajectory prediction by regression", 《ROBOTICS AND AUTONOMOUS SYSTEMS》 * |
| Y. VOLKAN PEHLIVANOGLU，PERIHAN PEHLIVANOGLU: "An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problems", 《APPLIED SOFT COMPUTING》 * |
| 井田: "无人机集群持续侦察自适应协同控制研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 刘佳杰: "基于模体的无人机集群任务规划方法", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113778128B (en) | 2023-03-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113778127B (en) | Unmanned aerial vehicle reconnaissance path planning method and device based on genetic algorithm | |
| Gong et al. | An effective memetic algorithm for multi-objective job-shop scheduling | |
| Li et al. | An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem | |
| Gong et al. | A non-dominated ensemble fitness ranking algorithm for multi-objective flexible job-shop scheduling problem considering worker flexibility and green factors | |
| CN113064449B (en) | Unmanned aerial vehicle scheduling method and system | |
| JP2021532501A (en) | Intelligent analysis methods, devices, computer equipment and storage media for data | |
| CN112668880B (en) | Method, device, computer equipment and storage medium for manufacturing and dispatching network grid | |
| Tran et al. | Using Fuzzy Clustering Chaotic-based Differential Evolution to solve multiple resources leveling in the multiple projects scheduling problem | |
| Ghalenoei et al. | Discrete invasive weed optimization algorithm: application to cooperative multiple task assignment of UAVs | |
| CN112669084B (en) | Policy determination method, device and computer readable storage medium | |
| CN112232594B (en) | Multi-star emergency observation task planning method and device and computer equipment | |
| CN114943421A (en) | Multi-type satellite collaborative task planning method and device, electronic equipment and storage medium | |
| Lin et al. | Multiobjective trajectory optimization with a cutting and padding encoding strategy for single-UAV-assisted mobile edge computing system | |
| CN115755954A (en) | Routing inspection path planning method and system, computer equipment and storage medium | |
| CN114818303A (en) | Simulation planning method and system for low-carbon park, electronic equipment and storage medium | |
| CN113778128B (en) | Unmanned aerial vehicle reconnaissance task planning method and device based on genetic algorithm | |
| CN117153260A (en) | Spatial transcriptome data clustering method, device and medium based on contrast learning | |
| CN116561478A (en) | Transformer substation plane layout method based on butterfly algorithm of mixed particle swarm | |
| CN115860425A (en) | Residential area monitoring facility planning method, system, terminal and storage medium | |
| CN112363831B (en) | Wind control processing method and device, computer equipment and storage medium | |
| Su et al. | Three-stage multi-objective optimization approach based on sparsity knowledge for multi-airship earth-observation task scheduling | |
| CN113095963A (en) | Real estate cost data processing method, real estate cost data processing device, computer equipment and storage medium | |
| CN117726050B (en) | Method for determining execution quantity of target flight tasks, electronic device and storage medium | |
| CN117669946A (en) | Unmanned aerial vehicle cluster task distribution system based on contract network agreement | |
| US20240242156A1 (en) | Systems and methods for handling sustainability level in physical infrastructure |
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 |