CN110633857B - Autonomous defense clustering algorithm for heterogeneous unmanned aerial vehicle cluster - Google Patents
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Abstract
Drones can perform transportation tasks in complex battlefield environments. However, a single drone cannot meet mission requirements due to limited power and functionality. To overcome this problem, this document is directed to cooperative control of the drone swarm and autonomous defense. In order to solve the defense problem of the unmanned aerial vehicle group in the process of executing material transportation, an autonomous defense clustering algorithm of the heterogeneous unmanned aerial vehicle group is provided, a motion control equation of the defense unmanned aerial vehicle is provided, and the defense unmanned aerial vehicle is deployed at the periphery of a common transport unmanned aerial vehicle in the aggregation process to form a new heterogeneous unmanned aerial vehicle group. Secondly, a cost function including position and angle is provided, so that the defensive unmanned aerial vehicles are deployed on the periphery of the common transport unmanned aerial vehicle as uniformly as possible. Finally, the effectiveness of the algorithm under the condition of different unmanned aerial vehicle quantities is verified through theoretical analysis and experiments. The defensive unmanned aerial vehicles can be uniformly distributed on the periphery of the common transport unmanned aerial vehicle to form a group of unmanned aerial vehicle clusters with autonomous defensive ability.
Description
Technical Field
The technology belongs to the technical field of intelligent cooperation of unmanned aerial vehicles, and mainly provides an autonomous defense clustering algorithm for a heterogeneous unmanned aerial vehicle cluster, which is used for protecting a transport unmanned aerial vehicle at an unmanned aerial vehicle cluster center by a defense unmanned aerial vehicle when the unmanned aerial vehicle cluster transports substances, so that an unmanned aerial vehicle cluster with autonomous defense capability is formed.
Background
At present, unmanned aerial vehicles are widely used in military and civil fields. Unmanned aerial vehicles can be used in fields such as electric power seek inspection, agricultural insurance and environmental assessment. However, a single drone is limited by its single function and energy. The unmanned aerial vehicle cluster formed by multiple unmanned aerial vehicles can contain multiple unmanned aerial vehicles with different functions. At present, the research on the unmanned aerial vehicle aggregation algorithm mostly stays in the bionic angle, such as wolf swarm, pigeon swarm, bird swarm and the like. These algorithms are all based on a single kind of particle node. Division of labor is not made. However, in practical conditions, unmanned aerial vehicles created by human beings are not a node, and for example, bombers and fighters are used for bombing tasks performed by human beings in war. Under a complex battlefield environment, the unmanned aerial vehicle cluster can meet the requirements of battlefield transportation material tasks. When the unmanned aerial vehicle group transports materials, armed unmanned aerial vehicles are needed to carry out defense protection on the common transport unmanned aerial vehicle group.
Therefore, a heterogeneous autonomous defense unmanned aerial vehicle clustering algorithm is designed from the point, and the algorithm can be aggregated into the heterogeneous autonomous defense unmanned aerial vehicle clustering algorithm in the process that unmanned aerial vehicles are aggregated into an unmanned aerial vehicle cluster.
Disclosure of Invention
Therefore, the technical key point to be innovated in the invention is to provide a heterogeneous autonomous defense unmanned aerial vehicle cluster algorithm for the unmanned aerial vehicle of the transportation task. The two types of unmanned aerial vehicles are gathered into the unmanned aerial vehicle cluster which is armed to defend the unmanned aerial vehicles to be deployed at the periphery of the common transport unmanned aerial vehicle by controlling the cluster forming process of the unmanned aerial vehicles. And optimizing the algorithm by simulating an annealing algorithm to form the target formation unmanned aerial vehicle cluster. In particular to an autonomous defense clustering algorithm for a heterogeneous unmanned aerial vehicle cluster, which comprises the following steps,
the method comprises the following steps: establishing a cost function model, calculating an initial cost value of the initial unmanned aerial vehicle position coordinate through the cost function model, and initializing a simulated annealing algorithm temperature, a termination temperature e, a cooling factor S and a judgment threshold value m =0.2;
step two: calculating the magnitude of resultant force applied to each unmanned aerial vehicle in the system according to the position information of the initial unmanned aerial vehicle in the first step, calculating the displacement of the unmanned aerial vehicle according to the resultant force applied to the unmanned aerial vehicle, and performing vector addition operation on the displacement and the position of the unmanned aerial vehicle in the initial state to obtain the coordinate of the next position;
step three: substituting the position coordinates obtained in the step two into the cost function model to calculate the cost value of the position;
step four: calculating the difference value of the cost value calculated by the position coordinate of the previous moment minus the cost value calculated by the position coordinate of the next moment, and if the difference value is less than 0, recording the position of the coordinate point of the next moment as the initial position point of the next iteration; if the calculated difference is greater than or equal to 0, generating a probability value between [0,1] by the system at the moment, and when the probability value is less than m, receiving the position coordinate point of the current unmanned aerial vehicle as an initial value of the next iteration; if the generated probability value is larger than or equal to m, randomly generating a motion angle theta of the defense unmanned aerial vehicle obeying Gaussian distribution, calculating a position coordinate of the defense unmanned aerial vehicle moving next step through a defense unmanned aerial vehicle motion control equation, calculating a cost value in the current state according to the position coordinate of the defense unmanned aerial vehicle and the current position of the common transport unmanned aerial vehicle, and if the cost value is smaller than the cost value calculated in the current position, replacing the position information of the defense unmanned aerial vehicle in the current state with the position information of the defense unmanned aerial vehicle, otherwise, not performing any treatment;
step five: when the optimal solution is not found or the temperature is not less than the set termination temperature e, returning to the step two, and multiplying the current temperature by the temperature reduction factor s to obtain a low-temperature value; if the temperature is lower than the termination temperature e, the algorithm exits iteration, and finally an unmanned aerial vehicle cluster with the autonomous defense capability is formed.
Further, the position coordinate of the defense unmanned aerial vehicle for next movement is calculated through the movement equation of the defense unmanned aerial vehicle, and the method is realized through the following processes,
defining the unmanned plane cluster center formed by common transport unmanned planes as follows:
wherein v is org Representing a collection of common transport drones, | v org L represents the number of common transport drones; as the defense unmanned aerial vehicles need to be uniformly distributed on the outer layer of the common transportation unmanned aerial vehicle cluster as far as possible when moving. Cannot move directly away from the center when moving, and requires a certain angle change. This induces a gaussian perturbation randomly generating a gaussian distributionThe angle theta.
The defense unmanned aerial vehicle needs to move to the periphery of the common transport unmanned aerial vehicle cluster, and the defense unmanned aerial vehicle is distributed uniformly on the periphery of the common defense unmanned aerial vehicle as far as possible, so that a motion equation of the defense unmanned aerial vehicle is defined as follows.
p org Virtual center, p, for a fleet of unmanned aerial vehicles formed under a common transport drone i The position coordinates of the table type unmanned aerial vehicle i and the angle generated randomly by the theta table type are added to the coordinates of the current defense unmanned aerial vehicle to obtain the coordinates of the defense unmanned aerial vehicle at the next moment.
Further, the cost function model is as follows, the central coordinates of the unmanned aerial vehicle group consisting of the defense type unmanned aerial vehicles consisting of armed unmanned aerial vehicles and the unmanned aerial vehicles consisting of the common defense type unmanned aerial vehicles can be defined as follows:
wherein | v def Number of armed defence unmanned aerial vehicles of the | table type, p i The position of the table-type unmanned aerial vehicle, which is the position of all unmanned aerial vehicles; if n unmanned aerial vehicles exist, n angles exist between every two unmanned aerial vehicles and the center of an unmanned aerial vehicle group formed by all the unmanned aerial vehicles; the calculation of the angle n-1 before a certain unmanned aerial vehicle is taken as a starting point can be obtained by the following formula:
ω i the table connects the drone with a virtual cluster center calculated by the entire drone swarm and has an acute angle with the center as the apex angle.
Since the defending drone is deployed on the periphery of the drone swarm and is a circle, the theorem above can only find the angle between [ -pi, 0) and (0, pi ], the last angle is obtained by subtraction, which is defined as follows:
ω n ＝2π-ω 1 -ω 2 -…-ω n-1
ω n the table is the last angle other than the n-1 angles calculated above; therefore, the final cost function is defined as follows:
wherein λ 1 ，λ 2 And λ 3 Respectively representing the weight for defending the unmanned aerial vehicle from moving, the cost weight of the common transportation unmanned aerial vehicle and the weight of the angle cost; these three weights determine the convergence speed of the algorithm, which is 1 by default.
Further, the resultant force includes an attraction force, a repulsion force, a calibration force, and an external input force, respectively calculated by the following models,
i: when unmanned aerial vehicle i moves to unmanned aerial vehicle j's communication position, unmanned aerial vehicle i receives unmanned aerial vehicle j's appeal effect formula as follows this moment:
wherein p is i Table formula i position of the unmanned plane, and r i The communication radius of the ith unmanned aerial vehicle of table formula. d safe Safe distance between the meter type unmanned aerial vehicles;
ii: and when unmanned aerial vehicle i has got into unmanned aerial vehicle j's safety range within in case, two unmanned aerial vehicles just need keep away from each other, avoid colliding with. And the control force of the remote control is defined as follows:
iii: unmanned aerial vehicle is at the in-process of motion, and after unmanned aerial vehicle i and unmanned aerial vehicle j can communicate, unmanned aerial vehicle's speed need be adjusted, and the purpose of doing so is for letting two unmanned aerial vehicle's the continuous near of speed, reaches synchronous motion's purpose at last, and makes the close control factor of speed as follows:
iv: when the unmanned aerial vehicle is in the initial state, the unmanned aerial vehicle is discretely distributed in a space. The scope in this space is greater than unmanned aerial vehicle's communication range far away, sets up a global aggregation point for every unmanned aerial vehicle through GPS. Such that each drone moves towards the rendezvous point. When there is no rendezvous point, a drone may be taken as the global leader. When other unmanned aerial vehicles move to the point, the distance between the unmanned aerial vehicles can be reduced until the unmanned aerial vehicles can communicate with each other. The external input force generated by the unmanned aerial vehicle gathering point can be defined as:
wherein p is i Table i position of the unmanned plane, and p d Table one GPS generated coordinate. Thus, at a certain moment, the unmanned aerial vehicle can decide how to move in the next state according to the four forces.
The resultant force can be defined as follows:
F＝F i a +F i b +F i c +F i d 。
the scheme of the invention provides a cost function including position and angle, so that the defensive unmanned aerial vehicle is deployed on the periphery of the common transport unmanned aerial vehicle as uniformly as possible. Finally, the effectiveness of DFUG under the condition of different unmanned aerial vehicle numbers is verified through theoretical analysis and experiments. The result shows that the defensive unmanned aerial vehicles can be uniformly distributed on the periphery of the common transport unmanned aerial vehicles to form a group of unmanned aerial vehicle clusters with passive defensive capability.
Drawings
FIG. 1 is a schematic flow chart of an algorithm for forming a cluster of unmanned aerial vehicles with defense capability according to the present invention;
fig. 2 a diagram of a communication mode of the drone;
fig. 3 is a force diagram for each drone;
FIG. 4 is a random angle diagram of the process of defending against unmanned aerial vehicle movement;
FIG. 5 is a schematic diagram of the movement of a defensive drone;
FIG. 6 is a graph of angles between defensive drones;
FIG. 7 is a model of unmanned aerial vehicle movement with random angles;
fig. 8 is a diagram of the process by which drones in the algorithm of the present invention reach the aggregation state;
fig. 9 is a cluster diagram formed by an algorithm of a different number of defensive drones and general defensive drones;
FIG. 10 is a graph of average distance values between drones in a cluster of drones during clustering;
fig. 11 is an angle cost value diagram for ensuring that the defensive drones are evenly distributed on the periphery of the common transport drone swarm;
FIG. 12 is a graph of cost function value variation during aggregation for a fleet of drones;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention 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 invention and are not intended to limit the invention.
Referring to fig. 1-12, the present invention provides an autonomous defense clustering algorithm for heterogeneous unmanned aerial vehicles, comprising the following steps:
the method comprises the following steps: establishing a cost function model, calculating an initial cost value of the initial unmanned aerial vehicle position coordinate through the cost function model, and initializing a simulated annealing algorithm temperature, a termination temperature e, a cooling factor S and a judgment threshold value m =0.2;
the position of the defense type unmanned aerial vehicle means that the defense type unmanned aerial vehicle is far away from the center distance of the unmanned aerial vehicle cluster formed by the defense type unmanned aerial vehicle and the common transportation unmanned aerial vehicle, the farther away the distance is, the lower the cost is, the periphery that the defense type unmanned aerial vehicle moves to the common transportation unmanned aerial vehicle can be guaranteed, and meanwhile, the communication range that the defense type unmanned aerial vehicle completely breaks away from the common unmanned aerial vehicle cluster cannot be moved too far. The cost of the common transport drone is also the distance from the center of the drone swarm, but the closer the distance, the less the cost value, because the inner common transport drone needs to be protected inside the drone swarm. And finally the angle control of the defensive type unmanned aerial vehicles is to ensure that the angles among the unmanned aerial vehicles are more uniform and better. Thereby reach the purpose of protection ordinary transportation unmanned aerial vehicle. And the quick maneuvering is realized. The central coordinates of all the unmanned aerial vehicle groups consisting of defense type unmanned aerial vehicles consisting of armed unmanned aerial vehicles and ordinary defense type unmanned aerial vehicles can be defined as follows: the central coordinate of the unmanned aerial vehicle group that defensive type unmanned aerial vehicle that armed unmanned aerial vehicle constitutes and ordinary defensive type unmanned aerial vehicle constitute can be defined down:
wherein | v def Number of armed defence unmanned aerial vehicles of the | table type, p i The position of the table-type unmanned aerial vehicle, which is the position of all unmanned aerial vehicles; if n unmanned aerial vehicles exist, n angles exist between every two unmanned aerial vehicles and the center of an unmanned aerial vehicle group formed by all the unmanned aerial vehicles; the calculation of the angle n-1 before a certain unmanned aerial vehicle is taken as a starting point can be obtained by the following formula:
ω i the table connects the drone with a virtual cluster center calculated by the entire drone swarm and has an acute angle with the center as the apex angle.
Since the defending drone is deployed on the periphery of the drone swarm and is a circle, the theorem above can only find the angle between [ -pi, 0) and (0, pi ], the last angle is obtained by subtraction, which is defined as follows:
ω n ＝2π-ω 1 -ω 2 -…-ω n-1
ω n the table is the last angle other than the n-1 angles calculated above; so the final definition yields the cost function as follows:
wherein λ is 1 ，λ 2 And λ 3 Respectively representing the weight for defending the unmanned aerial vehicle from moving, the cost weight of the common transport unmanned aerial vehicle and the weight of the angle cost; these three weights determine the convergence speed of the algorithm, which is 1 by default.
In the process of moving each unmanned aerial vehicle in the unmanned aerial vehicle cluster, the cost function needs to be calculated. Accepting the state if the cost function value is less than the current cost. Therefore, in the moving process of the unmanned aerial vehicle group, the armed defense unmanned aerial vehicle moves towards the periphery of the common unmanned aerial vehicle group and can form an even ring outside the unmanned aerial vehicle group, so that the common transport unmanned aerial vehicle is protected. As shown in fig. 4.
Step two: calculating the magnitude of resultant force applied to each unmanned aerial vehicle in the system according to the position information of the initial unmanned aerial vehicle in the first step, calculating the displacement of the unmanned aerial vehicle according to the resultant force applied to the unmanned aerial vehicle, and performing vector addition operation on the displacement and the position of the unmanned aerial vehicle in the initial state to obtain the coordinate of the next position;
i: when many unmanned aerial vehicles form an unmanned aerial vehicle crowd, be located communication range each other when two unmanned aerial vehicles, in order to guarantee that the crowd radius of unmanned aerial vehicle crowd is enough little, need these two unmanned aerial vehicles be close to each other and guarantee that two unmanned aerial vehicles are located outside each other's safe distance. That is, when unmanned aerial vehicle i moves to unmanned aerial vehicle j's communication range, unmanned aerial vehicle i receives an appeal of unmanned aerial vehicle j this moment and can express as following as the formula:
wherein p is i Table formula i position of the unmanned plane, and r i The communication radius of the ith unmanned aerial vehicle of table formula. d safe Safe distance between the meter type unmanned aerial vehicles; through above-mentioned formula like this, can guarantee that unmanned aerial vehicle is close to each other, and this effort can disappear again in unmanned aerial vehicle's safety range.
Ii: at many unmanned aerial vehicles in the in-process of motion, the most important point is exactly how to guarantee not collision each other between the unmanned aerial vehicle, and in the condition that initial random condition and unmanned aerial vehicle can move to within other unmanned aerial vehicle's safety range owing to inertial reason, need guarantee that unmanned aerial vehicle keeps away from each other safe radius through the effect of repulsion this moment. And when unmanned aerial vehicle i has got into unmanned aerial vehicle j's safety range within in case, two unmanned aerial vehicles just need keep away from each other, avoid colliding with. While the control force of the remote control is defined as follows:
as well as the attractive forces between drones. Wherein p is i Table formula i position of the unmanned plane, and r i The communication radius of the ith unmanned aerial vehicle of table formula. d safe Safe distance between table formula unmanned aerial vehicle. When drone i enters the safe radius of drone j, drone j will receive the effect of the repulsion by the expression above. So drone j will move out of the safe range of drone i. Attractive at present can ensure that the unmanned aerial vehicles approach each other to form a group and cannot escape from communication radiuses of each other, and repulsive force can ensure that the unmanned aerial vehicles cannot collide in the moving process. However, when the drone is in the upper state, the unmanned group is not in a stable state because the direction and magnitude of the speed are not consistent, and then the speed calibration force is designed to solve the problem.
Iii: unmanned aerial vehicle is at the in-process of motion, and after unmanned aerial vehicle i and unmanned aerial vehicle j can communicate, unmanned aerial vehicle's speed needs to be adjusted, and the purpose of doing so is in order to let two unmanned aerial vehicle's the continuous being close to of speed, reaches synchronous motion's purpose at last, and makes the close control factor of speed as follows:
wherein p is i Table formula i position of the unmanned aerial vehicle, and v i Table represents the movement speed of the ith unmanned aerial vehicle. When the unmanned aerial vehicle is in the position determined in the step one and the step two, the size and the direction of the speeds are gradually close to each other through the action of the calibration force, and a relatively static state is kept, so that the stability of the unmanned aerial vehicle group is ensured. However, only by these three forces, it cannot be guaranteed that the drones are located in a space randomly distributed in the initial condition, and when the space is large enough, it cannot be guaranteed that the drones can communicate with each other, and cannot communicate with each other and make a decision, so that a global GPS position is required to be designed, and the position guides all the drones to move in this direction, so that the following global external input force is designed.
Iv: when the unmanned aerial vehicle is in the initial state, the unmanned aerial vehicle is discretely distributed in a space. The scope in this space is greater than unmanned aerial vehicle's communication range far away, sets up a global rendezvous point for every unmanned aerial vehicle through GPS. Such that each drone moves towards the rendezvous point. When there is no rendezvous point, a drone may be taken as the global leader. When other unmanned aerial vehicles move to the point, the distance between the unmanned aerial vehicles can be shortened until the unmanned aerial vehicles can communicate with each other. The external input force generated by the unmanned aerial vehicle gathering point can be defined as:
wherein p is i Table i position of the unmanned plane, and p d Table one GPS generated coordinate. Thus, at a certain moment, the unmanned aerial vehicle can come according to the four forcesDecides how to move in the next state.
Each unmanned aerial vehicle can start to move under the combined action of the repulsive force, the attractive force, the calibration force and the external input force at a certain moment, so that each unmanned aerial vehicle can be ensured to keep a certain safety distance in the moving process and can converge to a cluster state, and the resultant force of the unmanned aerial vehicles can be defined as follows:
F＝F i a +F i b +F i c +F i d 。
the force is a vector whose magnitude represents how urgently the drone is moving and whose direction represents the direction in newtons (N) that the drone is expected to move in the next state, and the force diagram for each drone in the system is shown in fig. 3.
Step three: substituting the position coordinates obtained in the step two into the cost function model to calculate the cost value of the position;
step four: calculating the difference value of the cost value calculated by the previous position information minus the cost value calculated by the next position state; if the difference value is less than 0, recording the position of the coordinate point of the latter state as the initial position point of the next iteration; if the calculated difference is greater than or equal to 0, generating a number between [0,1] by the system at the moment, receiving the position coordinate point of the current unmanned aerial vehicle when the number is less than m, and receiving the position of the coordinate point of the current unmanned aerial vehicle as the initial value of the next iteration; if the generated probability value is larger than or equal to m, a motion angle theta of the defense unmanned aerial vehicle obeying Gaussian distribution is randomly generated, the position coordinate of the defense unmanned aerial vehicle moving in the next step is calculated through a defense unmanned aerial vehicle movement equation, the cost value of the defense unmanned aerial vehicle in the current state is calculated according to the position coordinate of the defense unmanned aerial vehicle and the current position of the common transport unmanned aerial vehicle, if the cost value is smaller than the cost value calculated by the current position, the position information of the defense unmanned aerial vehicle is used for replacing the position information of the defense unmanned aerial vehicle in the current state, and if not, any processing is not carried out;
calculating the position coordinate of the defense unmanned aerial vehicle for moving next step through the mobile equation of the defense unmanned aerial vehicle, and defining the unmanned aerial vehicle cluster center consisting of common transport unmanned aerial vehicles as follows:
wherein v is org Representing a collection of common transport drones, | v org L represents the number of common transport drones; as the defense unmanned aerial vehicles need to be uniformly distributed on the outer layer of the common transportation unmanned aerial vehicle cluster as far as possible when moving. When moving, the movable part can not move directly to a position far away from the center, and a certain angle change is needed. This induces a gaussian perturbation randomly generating an angle theta that follows a gaussian distribution, as shown in figure 5. This angle is used to calculate the cost of moving in that direction and if the cost is greater than the current state, then no motion is in that direction. The equations of motion of armed defense drones are defined. The defending unmanned aerial vehicle needs to move to the periphery of the common transporting unmanned aerial vehicle cluster, and the defending unmanned aerial vehicle is distributed uniformly on the periphery of the common defending unmanned aerial vehicle as far as possible, so that a movement equation of the defending unmanned aerial vehicle is defined as follows.
p org Virtual center, p, for a fleet of unmanned aerial vehicles formed under a common transport drone i The position coordinates of the table type unmanned aerial vehicle i and the angle generated randomly by the theta table type are added to the coordinates of the current defense unmanned aerial vehicle to obtain the coordinates of the defense unmanned aerial vehicle at the next moment.
Step five: when the optimal solution is not found or the temperature is not less than the set termination temperature e, returning to the step two, and multiplying the current temperature by the temperature reduction factor s to obtain a low-temperature value; if the temperature is lower than the termination temperature e, the algorithm exits iteration, and finally a unmanned aerial vehicle cluster with autonomous defense capability is formed, and the effect graph is shown in fig. 8 (d).
While the embodiments of the present invention have been described in detail, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.
Claims (4)
1. An autonomous defense clustering algorithm for a heterogeneous unmanned aerial vehicle cluster is characterized in that: comprises the following steps of (a) carrying out,
the method comprises the following steps: establishing a cost function model, calculating an initial cost value of the initial unmanned aerial vehicle position coordinate through the cost function model, and initializing a simulated annealing algorithm temperature, a termination temperature e, a cooling factor s and a judgment threshold value m =0.2;
step two: calculating the magnitude of resultant force applied to each unmanned aerial vehicle in the system according to the position information of the initial unmanned aerial vehicle in the first step, calculating the displacement of the unmanned aerial vehicle according to the resultant force applied to the unmanned aerial vehicle, and performing vector addition operation on the displacement and the position of the unmanned aerial vehicle in the initial state to obtain the coordinate of the position of the unmanned aerial vehicle at the next moment;
step three: substituting the position coordinates obtained in the second step into the cost function model to calculate the cost value of the position;
step four: calculating the difference value of the cost value calculated by the position coordinate of the previous moment minus the cost value calculated by the position coordinate of the next moment, and if the difference value is less than 0, recording the position of the coordinate point of the next moment as the initial position point of the next iteration; if the calculated difference is greater than or equal to 0, generating a probability value between [0,1] by the system at the moment, and when the probability value is smaller than m, receiving the position coordinate point of the current unmanned aerial vehicle as an initial value of the next iteration; if the generated probability value is larger than or equal to m, randomly generating a motion angle theta of the defense unmanned aerial vehicle complying with Gaussian distribution, calculating the position coordinate of the defense unmanned aerial vehicle moving next step through a motion control equation of the defense unmanned aerial vehicle, calculating the cost value in the current state, and if the cost value is smaller than the cost value calculated in the current position, replacing the position information of the defense unmanned aerial vehicle in the current state with the position information of the defense unmanned aerial vehicle, otherwise, not performing any treatment;
step five: when the optimal solution is not found or the temperature is not less than the set termination temperature e, returning to the step two, and then multiplying the current temperature by the cooling factor s to obtain a low-temperature value; if the temperature is lower than the termination temperature e, the algorithm exits iteration, and finally an unmanned aerial vehicle cluster with the autonomous defense capability is formed.
2. The autonomous defense clustering algorithm for heterogeneous unmanned aerial vehicles according to claim 1, wherein: calculating the position coordinate of the defense unmanned aerial vehicle moving next step through a motion control equation of the defense unmanned aerial vehicle, and defining an unmanned aerial vehicle cluster center consisting of common transport unmanned aerial vehicles as follows:
wherein v is org Representing a collection of common transport drones, | v org L represents the number of common transport drones; because the defending unmanned aerial vehicle needs angle change when moving, gaussian disturbance is introduced, an angle theta conforming to Gaussian distribution is randomly generated, and a motion control equation of the defending unmanned aerial vehicle is defined as follows:
wherein p is org Virtual center, p, of unmanned aerial vehicle cluster formed under ordinary transport unmanned aerial vehicle i Denotes the position coordinates of drone i and θ denotes the randomly generated angle.
3. The autonomous defense clustering algorithm for heterogeneous unmanned aerial vehicles according to claim 1, wherein: cost function model as follows, the central coordinate of the unmanned aerial vehicle crowd that the defensive type unmanned aerial vehicle that armed unmanned aerial vehicle constitutes and ordinary defensive type unmanned aerial vehicle constitute can define down:
wherein | v def I denotes the number of defensive drones, p i Indicating the location of the drone; if n unmanned aerial vehicles exist, n angles exist between every two unmanned aerial vehicles and the center of an unmanned aerial vehicle group formed by all the unmanned aerial vehicles; the calculation of the angle n-1 before a certain unmanned aerial vehicle is taken as a starting point can be obtained by the following formula:
ω i an acute angle representing a virtual cluster center connecting the drone and calculated by the entire drone cluster, and having the center as the apex angle;
since the defensive drone is deployed in a circle around the periphery of the drone swarm, and the above theorem can only find the angle between [ -pi, 0) and (0, pi ], the last angle can be obtained by subtraction, which is defined as follows:
ω n ＝2π-ω 1 -ω 2 -…-ω n-1
ω n the representation is the last angle in addition to the n-1 angles calculated above; therefore, the final cost function is defined as follows:
wherein λ is 1 ，λ 2 And λ 3 Respectively represent the weight of defending the unmanned aerial vehicle to remove, the weight of cost weight and the angle cost of ordinary transportation unmanned aerial vehicle.
4. The cluster algorithm for autonomous defense of heterogeneous Unmanned Aerial Vehicles (UAV) as claimed in claim 1, wherein: the resultant force includes an attractive force, a repulsive force, a calibration force and an external input force, which are calculated by the following models,
i: when unmanned aerial vehicle i moves to unmanned aerial vehicle j's communication position, unmanned aerial vehicle i receives unmanned aerial vehicle j's appeal effect formula as follows this moment:
wherein p is i Indicates the location of the ith drone, and r i Indicates the communication radius of the ith unmanned plane, d safe Representing a safe distance between drones;
ii: when the unmanned aerial vehicle i enters the safety range of the unmanned aerial vehicle j, the two unmanned aerial vehicles need to be away from each other to avoid collision; while the repulsive force of the away control is defined as follows:
wherein p is i Indicates the location of the ith drone, and r i Indicates the communication radius of the ith unmanned plane, d safe Representing a safe distance between drones;
iii: unmanned aerial vehicle is at the in-process of motion, and after unmanned aerial vehicle i and unmanned aerial vehicle j can communicate, unmanned aerial vehicle's speed needs to be adjusted, and the purpose of doing so is in order to let two unmanned aerial vehicle's the continuous being close to of speed, reaches synchronous motion's purpose at last, and makes the close control factor of speed as follows:
wherein p is i Indicates the location of the ith drone, and v i Representing the movement speed of the ith drone;
iv: setting a global aggregation point for each unmanned aerial vehicle through a GPS in the state of the unmanned aerial vehicle at the initial moment; when the aggregation point does not exist, one unmanned aerial vehicle is taken as a global leader, and when other unmanned aerial vehicles move to the global leader, the distance between the unmanned aerial vehicles can be reduced until the unmanned aerial vehicles can communicate with each other; the external input force generated by the unmanned aerial vehicle's rendezvous point can be defined as:
wherein p is i Indicates the location of the ith drone, and p d The coordinates generated by the GPS are expressed, and how the unmanned aerial vehicle should move in the next state is determined according to the four forces, and the resultant force can be defined as follows:
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