CN113805609A - Unmanned aerial vehicle group target searching method based on chaos lost pigeon group optimization mechanism - Google Patents
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Abstract
本发明公开一种混沌迷失鸽群优化机制的无人机群目标搜索方法，包括：(1)环境地图初始化：使用栅格化实现搜索环境初始化；(2)采用混沌迷失鸽群优化机制对无人机进行协同路径优化，针对全局最优位置来更新无人机的下一时刻航迹点坐标，引导无人机飞往最高效的搜索区域；(3)广播无人机状态信息：采用通信机制实现多无人机之间信息共享，来更新无人机运动状态；(4)目标分配：选择匹配度最高的无人机搜索目标，得到目标搜索最优方案。基于混沌迷失机制的鸽群算法在求解质量上有着明显的优势，其混沌初始化策略使算法拥有快速的收敛速度和较优的收敛精度；迷失机制则让算法拥有很强的跳出局部最优的能力。
The invention discloses an unmanned aerial vehicle swarm target search method with a chaotic lost pigeon swarm optimization mechanism, including: (1) environment map initialization: using grid to realize search environment initialization; The UAV performs coordinated path optimization, updates the UAV's track point coordinates at the next moment according to the global optimal position, and guides the UAV to fly to the most efficient search area; (3) Broadcast UAV status information: use a communication mechanism Realize information sharing among multiple UAVs to update the motion state of UAVs; (4) Target allocation: select the UAV search target with the highest matching degree, and obtain the optimal target search plan. The pigeon flock algorithm based on the chaotic loss mechanism has obvious advantages in solving quality. Its chaotic initialization strategy enables the algorithm to have fast convergence speed and better convergence accuracy; the lost mechanism allows the algorithm to have a strong ability to jump out of the local optimum. .
Description
Technical Field
The invention relates to a method for cooperatively searching multiple dynamic targets by multiple unmanned aerial vehicles based on chaos lost pigeon swarm optimization mechanism, belonging to the technical field of unmanned aerial vehicle cooperation
Background
In recent years, with the development of the related technologies of unmanned aerial vehicles, the application of unmanned aerial vehicles has penetrated into the social aspect. Due to the characteristics of strong adaptability, small risk, low cost and no casualties of people, the multifunctional robot is an indispensable tool for key and time-sensitive task search and rescue. However, as the task environment is more and more complex, the unmanned aerial vehicle has the characteristics of omnibearing performance and large range, the single unmanned aerial vehicle is more and more difficult to search a target area, and all aerial search tasks can not be completed in a short time. Therefore, research on a cooperation mechanism among multiple unmanned aerial vehicles helps to solve more complex and valuable application problems. One or a group of ordered tasks are distributed for each unmanned aerial vehicle, so that the overall operational efficiency of the unmanned aerial vehicle is optimal. Obviously, multi-unmanned aerial vehicle collaborative target search can be converted into a multi-target optimization problem. In order to solve the multi-objective optimization problem, in recent years, a bionic intelligent algorithm has become one of the main subjects of research.
The bionic intelligent algorithm is an advanced technology which simulates a simple social system by utilizing mutual cooperation of information interaction mechanisms among individuals according to the biological structure and functional working principle, and common bionic intelligent algorithms comprise a genetic algorithm, a particle swarm algorithm, a bat algorithm and the like. The pigeon optimization algorithm is a new group intelligent optimization algorithm proposed in recent years. Fewer parameters need to be adjusted. And because of the advantages of high convergence rate, easy realization and the like, the method is widely applied to the fields of neural networks, path planning and the like. The pigeon swarm optimization algorithm can be divided into a map and compass operator and a landmark operator. In the map and compass part, the pigeon updates its position and velocity according to the global optimal position in each iteration. While in the landmark section, the pigeons update their positions with the best position for each iteration. With these two partial updates, the pigeon will quickly find the global optimal position. Although the pigeon swarm optimization algorithm has the advantages over other intelligent optimization algorithms such as a particle swarm algorithm, a differential evolution algorithm and the like, the pigeon swarm optimization algorithm still has the common premature convergence problem.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defect of cooperative search of multiple unmanned aerial vehicles in the background art, a cooperative target search method of an unmanned aerial vehicle cluster based on a chaos lost pigeon swarm optimization mechanism is provided, and the purpose is to provide an online cooperative search method of multiple unmanned aerial vehicles, aiming at overcoming the defects of overlong search time and low efficiency, and further effectively improving the unmanned aerial vehicle autonomous control level in a complex search environment.
The technical scheme is as follows: an unmanned aerial vehicle group target searching method based on a chaos lost pigeon group optimization mechanism comprises the following steps:
s1: and initializing the environment map. Discretizing the two-dimensional search space in a rasterization mode, and constructing an environment map by using a grid method; regarding the whole search area as a two-dimensional rectangular space omega-R2The length and width of the whole search area are respectively LxAnd Ly(wherein LxAnd LyAll are natural numbers); dividing the whole search area into M unit square grids with the same area size, wherein M is Lx×LyEach grid position is regarded as a unit c ═ x, y]TWhere x and y are the coordinates of the center point of the unit within the task area.
S2: and (3) unmanned plane path optimization based on the chaotic lost pigeon swarm optimization mechanism. Carrying out cooperative path optimization on the unmanned aerial vehicle cluster by comprehensively using a pigeon flock optimization algorithm of a chaos initialization mechanism and a loss mechanism; the specific implementation content is as follows:
s21: initializing pigeon group parameter sets and chaotic initializing pigeon group distribution. Number of initialization pigeons NpMap and compass operators and maximum number of iterations N of landmark operatorsC1，NC2(ii) a In an initialization stage, Tent chaotic mapping is used for optimizing the distribution of the pigeon groups, so that the generated initial sequence is more random, the solution space coverage is wider, and better pigeon group distribution is obtained;
s22: and initializing the state of the unmanned aerial vehicle system. Giving the initial position X of each unmanned aerial vehicleiAnd an initial velocity ViSimultaneously, the initialization of the position and the speed of the pigeon group corresponding to each unmanned aerial vehicle is completed; calculating the fitness value of each pigeon;
s23: map and compass operator stages based on a loss mechanism. The traditional pigeon flock algorithm comprises two stages: a map and compass operator stage and a landmark operator stage. In the map and compass operator stage, the main principle of pigeons finding destinations is to form maps in their brains by perceiving the earth's magnetic field. Meanwhile, the direction of the user can be adjusted through the compass according to the height of the sun.
The invention introduces a loss exploration mechanism to improve the map and compass operators to enhance the global search capability of the algorithm. In each iteration updating process, a loss mechanism is adopted to optimize the problem that the traditional pigeon swarm is easy to fall into local optimum, and a loss factor m 1E [0, 1] is added on the basis of the original pigeon swarm algorithm to represent the loss probability of the pigeons. Each iteration of the pigeon flock algorithm generates a random number, and if the random number is less than m1, the lost search operation is performed. When the pigeon enters the lost state, the pigeon can be freely explored. Firstly, randomly generating the speed direction of the next moment, after a certain period of exploration flight, if the target direction information can not be obtained in the period of time, changing the flight direction, and then repeatedly iterating for a period of time, recovering the state of the compass which can be detected.
S24: and a landmark operator phase. When approaching the destination, the pigeons can use the landmarks as a tool for optimizing the position of the pigeons. The number of pigeons is reduced by half during each iteration. The current position of each pigeon is ranked according to the fitness function, the ranked pigeons are considered as unfamiliar landmarks and are discarded, and the central positions of the rest pigeons are used as the current global optimal position and are used as the flying target direction.
S25: and updating the pigeon group position and the grid information. And updating the speed and the position of the pigeon group, and updating the information of the grid where the pigeon i is located.
S26: and executing S23, S24 and S25 until the iteration times meet the maximum iteration times, finishing position optimization of the whole pigeon group and outputting a global optimal position.
S3: and updating the state of the unmanned aerial vehicle. And taking the optimal solution obtained in the step S2 as a global optimal point coordinate, and regarding the optimal solution as a track point coordinate of the unmanned aerial vehicle at the next moment, tracking the track point coordinate by the unmanned aerial vehicle, updating the motion state of the unmanned aerial vehicle, and guiding the unmanned aerial vehicle to move to an area which needs to be searched most. The global optimal position generated when the position optimization is completed by the whole pigeon group is the optimal solution.
S4: and broadcasting the unmanned plane state information. The unmanned aerial vehicle motion state is updated, and meanwhile data are sent to other unmanned aerial vehicles in the communication constraint; the data content transmission mainly comprises the following steps:
1) the target position detected by the unmanned aerial vehicle and whether the target is tracked by the unmanned aerial vehicle with the highest matching degree;
2) grid labels where there may be targets or where there may not be targets;
3) the latest reconnaissance time of the reconnaissance grid and whether a target exists at the time;
wherein use the distance between the unmanned aerial vehicle as communication constraint, if the distance between two unmanned aerial vehicles is less than unmanned aerial vehicle's communication radius promptly, then will become communication neighbor to share above-mentioned information with it.
S5: and (4) target distribution. When any unmanned aerial vehicle i finds a target in the detection radius, selecting the unmanned aerial vehicle with the highest matching degree to search the target; the matching degree is measured by the distance from the unmanned aerial vehicle j to the target k, the unmanned aerial vehicle with the minimum distance is selected as the unmanned aerial vehicle with the highest matching degree to execute the tracking task of the target k, and other unmanned aerial vehicles continuously execute the previous tasks unchanged.
S6: s2, S3, S4 and S5 are repeated until all targets are completely tracked.
A system for realizing the unmanned aerial vehicle cluster target search of the chaos lost pigeon group optimization mechanism comprises:
the environment map initialization module: the method is used for discretizing the two-dimensional search space in a rasterization mode and constructing an environment map by using a grid method;
an unmanned aerial vehicle path optimizing module of a chaos lost pigeon group optimizing mechanism: carrying out cooperative path optimization on the unmanned aerial vehicle cluster by comprehensively using a pigeon flock optimization algorithm of a chaos initialization mechanism and a loss mechanism;
unmanned aerial vehicle state update module: the unmanned aerial vehicle tracking system is used for regarding the global optimal point coordinates as track point coordinates of the unmanned aerial vehicle at the next moment, tracking the track point coordinates by the unmanned aerial vehicle, updating the motion state of the unmanned aerial vehicle and guiding the unmanned aerial vehicle to move to an area which is most required to be searched;
broadcast unmanned aerial vehicle state information module: when the motion state of the unmanned aerial vehicle is updated, data are sent to other unmanned aerial vehicles in the communication constraint;
a target assignment module: when any unmanned aerial vehicle i finds a target in the detection radius, selecting the unmanned aerial vehicle with the highest matching degree to search the target;
a coordination module: s6: and repeatedly calling an unmanned aerial vehicle path optimizing module, an unmanned aerial vehicle state updating module, a broadcasting unmanned aerial vehicle state information module and a target distribution module which execute the chaotic lost pigeon group optimizing mechanism until all targets are completely tracked.
A computer device comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the computer program, the unmanned aerial vehicle cluster cooperative target searching method based on the chaos lost pigeon swarm optimization mechanism is realized.
A computer readable storage medium storing a computer program for executing the method for searching for a cooperative target of a fleet of unmanned aerial vehicles based on a chaotic lost pigeon swarm optimization mechanism as described above.
Has the advantages that: the invention discloses a multi-unmanned aerial vehicle cooperative target searching method based on a chaos lost pigeon group optimization mechanism aiming at the problem of multi-unmanned aerial vehicle cooperative searching, and the method comprises the steps of firstly carrying out environment description on a task area to be searched, and describing a searching space by using a rasterized map; and the next optimal waypoint of the unmanned aerial vehicle is optimized by combining information such as the position of the improved pigeon swarm algorithm, the current unmanned aerial vehicle state and the like, which can appear in the target, so that the optimized search of the unmanned aerial vehicle is realized. By using the chaotic lost pigeon swarm optimization mechanism, the target search efficiency of the unmanned aerial vehicle can be effectively improved, so that the unmanned aerial vehicle has better global search capability. On the other hand, an unmanned aerial vehicle communication mechanism is used, so that the unmanned aerial vehicle can realize cooperation and information sharing.
Drawings
Fig. 1 is a diagram of a collaborative target search model of an unmanned aerial vehicle in an embodiment of the present invention;
fig. 2 is a flow chart of an algorithm for optimizing a pigeon group based on chaos and loss.
Detailed Description
The present invention is further illustrated by the following examples, which are intended to be purely exemplary and are not intended to limit the scope of the invention, as various equivalent modifications of the invention will occur to those skilled in the art upon reading the present disclosure and fall within the scope of the appended claims.
As shown in fig. 1, the method for searching an unmanned aerial vehicle group target by using a chaos lost pigeon swarm optimization mechanism disclosed in the embodiment of the present invention specifically comprises the following steps:
step (1): and initializing the environment map. Regarding the whole search area as a two-dimensional rectangular space omega-R2The length and width of the whole search area are respectively LxAnd Ly(wherein LxAnd LyAll are natural numbers); dividing the whole search area into M unit square grids with the same area size, wherein M is Lx×Ly. Each grid location is considered as a unit c ═ x, y]TWhere x and y are the coordinates of the center point of the unit within the task area.
Step (2): and (3) unmanned plane path optimization based on the chaotic lost pigeon swarm optimization mechanism. Carrying out cooperative path optimization on the unmanned aerial vehicle cluster by comprehensively using a pigeon flock optimization algorithm of a chaos initialization mechanism and a loss mechanism; optimizing the next optimal track point of the unmanned aerial vehicle, so as to guide the unmanned aerial vehicle to realize the optimal search;
as shown in fig. 2, the chaos lost pigeon flock-based optimization algorithm disclosed in the embodiment of the present invention specifically includes the following steps:
and (2.1) initializing a pigeon group parameter set and chaotic initialization pigeon group distribution. Number of initialization pigeons NpMap and compass operators and maximum number of iterations N of landmark operatorsC1，NC2(ii) a In the initialization stage, the Tent chaotic mapping is used for optimizing the pigeon flock distribution, so that the generated initial pointThe initial sequence is more random, the solution space coverage is wider, and better pigeon group distribution is obtained;
and (2.2) initializing the state of the unmanned aerial vehicle system. Giving the initial position X of each unmanned aerial vehicleiAnd an initial velocity ViSimultaneously, the initialization of the position and the speed of the pigeon group corresponding to each unmanned aerial vehicle is completed; calculating the fitness value of each pigeon;
the fitness value function of the pigeon i corresponding to the unmanned plane k is as follows:
wherein N is the total number of targets, and i is the pigeon number; j is a target number; k is the unmanned aerial vehicle number; omega1Is a tracking factor; omega2Is an exploration factor; dikThe distance between the unmanned plane k and the pigeon i; pijRepresenting the probability of the target j existing at the position of the pigeon i; cjkRepresenting the matching degree of the unmanned plane k and the existing target j; t isjIndicating whether target j is not tracked by the drone with which it is more matched; siRepresenting the urgency of the grid where the pigeon i is to be explored;
probability P of target j existing at position of pigeon iij
Wherein τ is a memory attenuation factor; t is the number of times the grid is searched; r is the total number of grids; y is the number of the searched grids;
matching degree C of unmanned aerial vehicle k and existing target jjkExpressed as:
wherein d isjkIs the distance between the target and the drone. Wherein the closer the distance, the higher the matching degree; the target will assign the highest match corresponding to itAnd tracking the unmanned aerial vehicle with the matching degree.
Whether target j is tracked by the unmanned aerial vehicle T matched with target jjExpressed as:
the urgency of the pigeon i on the grid to be explored can be illustrated by whether other grids within the probing radius of the grid are detected. When the grid where the pigeon i is located is not detected at the latest time, the more the total number of the grids which are not detected in the detection radius is, the lower the urgency of waiting for the detection of the grid where the pigeon i is located is. Otherwise, the higher. When the grid where the pigeon i is located at the latest time is detected, the urgency level is described by the total number of the detected grids in the detection radius. The higher the total number of grids already detected within the detection radius, the lower the urgency of the grid on which the pigeon i is to wait for detection.
Urgency S to be explored for grid of pigeon iiExpressed as:
where ω is an exploratory factor, N1The total number of undetected grids in the detection radius is obtained; n is a radical of2Is the total number of grids that have been detected within the detection radius.
And (2.3) based on the iterative update phase of the loss mechanism. The basic pigeon flock optimization consists of two independent iterative cycles, and two operators act on different cycles respectively.
(a) Map and compass operator stages
Position X of each pigeoniAnd velocity ViThe update formula is as follows:
Vi(n)＝Vi(n-1)e-Rn+rand(Xg(n)-Xi(n-1))
Xi(n)＝Vi(n)+Xi(n-1)
wherein Xg(n) is the global optimal position for the nth iteration, which can obtain the optimal position in the whole search space, and R is the map and compass operator determined by the practical situation and experience, wherein rand belongs to [0, 1]]。
In the later iteration stage of the map and compass part of the traditional pigeon swarm optimization algorithm, all pigeons are gradually gathered, and the optimal position of the pigeon swarm is only slightly changed in a long time. In this case, the position of the pigeons is updated particularly slowly, and each pigeon will slowly stop moving until finally converging to a certain position in the search space, which may be a local optimum. Therefore, a pigeon flock optimization method based on the loss mechanism is provided. Firstly, a loss factor m 1E [0, 1] is added on the basis of the original operator to represent the loss probability of the pigeons. The algorithm generates a random number for each iteration, and if the random number is less than m1, the lost search operation is performed. When the pigeon enters the lost state, the pigeon can be freely explored. Firstly, randomly generating the speed direction of the next moment, after a certain period of exploration flight, if the target direction information can not be obtained in the period of time, changing the flight direction, and then repeatedly iterating for a period of time, recovering the state of the compass which can be detected.
The formula of updating the position and the speed in the lost state is as follows:
diffitnessi(t)＝fitnessi(t)-fitnessi(t-1)
wherein Vi(t) is the current time speed; vi(t-1) is the speed at the previous moment. Vimax is the individual maximum allowable speed; diffitnessiIs a fitness difference value; biThe fitness change is marked. Each time updated toThereafter, if the fitness is not reduced, the label biIncreasing 1, continuously changing the flight direction if the fitness of the direction is not reduced for 3 times, keeping the flight direction if the fitness of the direction is reduced, and marking biIs set to 0.
(b) Landmark operator phase
When approaching the destination, the pigeons can use the landmarks as a tool for optimizing the position of the pigeons. Number N of pigeons in each iterationpWill be reduced by half. The current position of each pigeon is ranked according to the fitness function, the ranked pigeons are considered as unfamiliar landmarks and are discarded, and the central positions of the rest pigeons are used as the landmarks to serve as the reference direction of flight. The update equation set is as follows:
Xi(n)＝Xi(n-1)+rand(Xc(n)-Xi(n-1))
wherein N isp(n) represents the number of pigeon lots in the nth iteration. XcFor the mean center of the pigeon in each iteration, fitness (X)i(n)) is the fitness. For the maximum optimization problem we have:
Fitness(Xi(n))＝fmax(Xi(n))
fmax() Representing a function for maximum evaluation.
For the minimization optimization problem we have:
where epsilon represents a very small number greater than 0.
Finally, we can get the global maximum after n iterationsPreferred position Xp：
Xp＝max(Xg(1)，Xg(2)，...，Xg(n))
(c) The information of the grid where the pigeon i is located is updated according to the following formula:
wherein, Vi(t)，Xi(t) respectively represents the velocity and position of the t-th iteration of the pigeon i in the N-dimensional space, omega is the inertial weight, c1，c2Respectively representing the cognitive coefficient and social coefficient of the pigeon, r1，r2Is a random number with a variation range of (0, 1); pi＝{Pi1，Pi2，…PinAnd Pg＝{Pg1，Pg2，…PgnRespectively representing the optimal experience position of the pigeon i and the optimal experience positions of all pigeons in the pigeon group;
individual historical optimum PiThe update formula of (2):
fitness () represents a Fitness value function. Global history optimal PgThe update formula of (2) is:
and (3): and updating the state of the unmanned aerial vehicle. Coordinate X of global optimum pointpThe unmanned aerial vehicle tracks the track point coordinates as the track point coordinates of the unmanned aerial vehicle at the next moment, updates the motion state of the unmanned aerial vehicle and guides the unmanned aerial vehicle to move towards the area which needs to be searched most;
and (4): and broadcasting the unmanned plane state information. The unmanned aerial vehicle motion state is updated, and meanwhile data are sent to other unmanned aerial vehicles in the communication constraint; transmitted to other drones within communication constraintsData string P(j，t)＝(Uj，Tk，Jk j) In the formula: u shapejThe unmanned aerial vehicle serial number and the current position information of the unmanned aerial vehicle are used for finding a target; t iskThe found object serial number and the coordinate information of the object; j. the design is a squarek jSetting J for the cost of unmanned plane J flying to target kk jIs the distance between drone i and target k.
And (5): and (4) target distribution. When any unmanned aerial vehicle i finds a target in the detection radius, each unmanned aerial vehicle calculates the matching degree of the unmanned aerial vehicle with the target, and the unmanned aerial vehicle with the highest matching degree tracks the target, so that a cooperative cooperation sharing mechanism among the populations is achieved; the matching degree is measured by the cost of flying the unmanned aerial vehicle j to the target k, the unmanned aerial vehicle with the minimum cost is selected to execute the tracking task of the target k, and other unmanned aerial vehicles continuously execute the previous tasks unchanged;
and (6): and (5) repeating the steps (2), (3), (4) and (5) until the plurality of targets are completely tracked.
It should be apparent to those skilled in the art that the above-mentioned steps of the unmanned aerial vehicle fleet target search method using the chaos lost pigeon swarm optimization mechanism or the modules of the unmanned aerial vehicle fleet target search system using the chaos lost pigeon swarm optimization mechanism according to the embodiments of the present invention can be implemented by a general-purpose computing device, they can be centralized on a single computing device or distributed on a network formed by a plurality of computing devices, alternatively, they can be implemented by program codes executable by the computing devices, so that they can be stored in a storage device and executed by the computing devices, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be respectively fabricated into each integrated circuit module, or multiple modules or steps in them can be fabricated into a single integrated circuit module. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
Claims (10)
1. A target searching method of an unmanned aerial vehicle group based on a chaos lost pigeon group optimization mechanism is characterized by comprising the following steps:
s1: initializing an environment map: discretizing the two-dimensional search space in a rasterization mode, and constructing an environment map by using a grid method;
s2: unmanned aerial vehicle path optimization based on chaos lost pigeon swarm optimization mechanism: carrying out cooperative path optimization on the unmanned aerial vehicle cluster by comprehensively using a pigeon flock optimization algorithm of a chaos initialization mechanism and a loss mechanism;
s3: and (3) updating the state of the unmanned aerial vehicle: the global optimal point coordinates are regarded as track point coordinates of the unmanned aerial vehicle at the next moment, the unmanned aerial vehicle tracks the track point coordinates, the motion state of the unmanned aerial vehicle is updated, and the unmanned aerial vehicle is guided to move towards an area which needs to be searched most;
s4: broadcasting the state information of the unmanned aerial vehicle: the unmanned aerial vehicle motion state is updated, and meanwhile data are sent to other unmanned aerial vehicles in the communication constraint;
s5: target allocation: when any unmanned aerial vehicle i finds a target in the detection radius, selecting the unmanned aerial vehicle with the highest matching degree to search the target;
s6: s2, S3, S4 and S5 are repeated until all targets are completely tracked.
2. The method for searching the unmanned aerial vehicle group target based on the chaotic lost pigeon swarm optimization mechanism as claimed in claim 1, wherein in S1, the whole search area is regarded as a two-dimensional rectangular space Ω -R2The length and width of the whole search area are respectively LxAnd Ly(ii) a Dividing the whole search area into M unit square grids with the same area size, wherein M is Lx×LyEach grid position is regarded as a unit c ═ x, y]TWhere x and y are the coordinates of the center point of the unit within the task area.
3. The method for searching the unmanned aerial vehicle group target by chaos lost pigeon group optimization mechanism according to claim 1, wherein in S2, the pigeon group optimization algorithm of chaos initialization and lost mechanism is used comprehensively to optimize the cooperative path of the unmanned aerial vehicle group; the specific implementation content is as follows:
s21: initializing pigeon group ginsengMeasuring set and chaotically initializing pigeon group distribution; number of initialization pigeons NpMap and compass operators and maximum number of iterations N of landmark operatorsC1，NC2；
S22: initializing the state of the unmanned aerial vehicle system; giving the initial position X of each unmanned aerial vehicleiAnd an initial velocity ViSimultaneously, the initialization of the position and the speed of the pigeon group corresponding to each unmanned aerial vehicle is completed; calculating the fitness value of each pigeon;
s23: a map and compass operator stage based on a loss mechanism;
s24: a landmark operator stage;
s25: updating the pigeon group position and the grid information; updating the speed and the position of the pigeon group and the information of the grid where the pigeon i is located;
s26: and executing S23, S24 and S25 until the iteration times meet the maximum iteration times or when a global optimal position is found, the position of the whole pigeon group is optimized.
4. The unmanned aerial vehicle group target searching method based on the chaotic lost pigeon swarm optimization mechanism as claimed in claim 3, wherein in the map and compass operator stage, a lost exploration mechanism is introduced to improve the map and compass operators to enhance the global searching capability of the algorithm; in each iteration updating process, a loss mechanism is adopted to optimize the problem that the traditional pigeon flock is easy to fall into local optimum, a loss factor m1 is added on the basis of a pigeon flock algorithm, and a loss factor m1 represents the loss probability of pigeons; each iteration of the pigeon swarm algorithm generates a random number, and if the random number is less than m1, the lost exploration operation is carried out; when the pigeon enters the lost state, the pigeon can be freely explored; firstly, randomly generating the speed direction of the next moment, after exploration flight for a set time, changing the flight direction if target direction information cannot be obtained in the time, and then repeatedly iterating for a period of time to recover the state of the compass which can be detected.
5. The chaotic lost pigeon flock of claim 3The unmanned aerial vehicle cluster target searching method of the optimization mechanism is characterized in that in the S21, a pigeon cluster parameter set is initialized and pigeon cluster distribution is initialized in a chaotic manner; number of initialization pigeons NpMap and compass operators and maximum number of iterations N of landmark operatorsC1，NC2(ii) a In the initialization stage, the Tent chaotic mapping is used for optimizing the pigeon flock distribution.
6. The unmanned aerial vehicle swarm target searching method based on the chaotic lost pigeon swarm optimization mechanism as claimed in claim 1, wherein the unmanned aerial vehicle motion state is updated and simultaneously sends data to other unmanned aerial vehicles within communication constraints; the data content transmission mainly comprises the following steps:
1) the target position detected by the unmanned aerial vehicle and whether the target is tracked by the unmanned aerial vehicle with the best matching degree;
2) grid labels where there may be targets or where there may not be targets;
3) the latest reconnaissance time of the reconnaissance grid and whether a target exists at the time;
the distance between the unmanned aerial vehicles is used as communication constraint, namely, if the distance between the two unmanned aerial vehicles is smaller than the communication radius of the unmanned aerial vehicles, the unmanned aerial vehicles become communication neighbors and share the sent data information with the communication neighbors.
7. The unmanned aerial vehicle swarm target searching method based on the chaotic lost pigeon swarm optimization mechanism as claimed in claim 1, wherein in S5, when any unmanned aerial vehicle i finds a target within a detection radius, the unmanned aerial vehicle with the highest matching degree is selected to search for the target; the matching degree is measured by the distance from the unmanned aerial vehicle j to the target k, the unmanned aerial vehicle with the minimum distance is selected as the unmanned aerial vehicle with the highest matching degree to execute the tracking task of the target k, and other unmanned aerial vehicles continuously execute the previous tasks unchanged.
8. An unmanned aerial vehicle cluster target search system based on a chaos lost pigeon swarm optimization mechanism is characterized by comprising the following components:
the environment map initialization module: the method is used for discretizing the two-dimensional search space in a rasterization mode and constructing an environment map by using a grid method;
an unmanned aerial vehicle path optimizing module of a chaos lost pigeon group optimizing mechanism: carrying out cooperative path optimization on the unmanned aerial vehicle cluster by comprehensively using a pigeon flock optimization algorithm of a chaos initialization mechanism and a loss mechanism;
unmanned aerial vehicle state update module: the unmanned aerial vehicle tracking system is used for regarding the global optimal point coordinates as track point coordinates of the unmanned aerial vehicle at the next moment, tracking the track point coordinates by the unmanned aerial vehicle, updating the motion state of the unmanned aerial vehicle and guiding the unmanned aerial vehicle to move to an area which is most required to be searched;
broadcast unmanned aerial vehicle state information module: when the motion state of the unmanned aerial vehicle is updated, data are sent to other unmanned aerial vehicles in the communication constraint;
a target assignment module: when any unmanned aerial vehicle i finds a target in the detection radius, selecting the unmanned aerial vehicle with the highest matching degree to search the target;
a coordination module: and S6, repeatedly calling an unmanned aerial vehicle path optimizing module, an unmanned aerial vehicle state updating module, a broadcasting unmanned aerial vehicle state information module and a target distribution module which execute the chaotic lost pigeon group optimizing mechanism until all targets are completely tracked.
9. A computer device, characterized by: the computer device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the unmanned swarm target searching method of the chaotic lost pigeon swarm optimization mechanism according to any one of claims 1-7.
10. A computer-readable storage medium characterized by: the computer readable storage medium stores a computer program for executing the unmanned aerial vehicle fleet target searching method of the chaotic lost pigeon flock optimization mechanism according to any one of claims 1 to 7.
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