CN112684807A - Unmanned aerial vehicle cluster three-dimensional formation method - Google Patents
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Abstract
The invention discloses a cluster three-dimensional formation method for unmanned aerial vehicles, and aims to provide a formation method which can realize collision avoidance operation in a dynamic environment and solve the problem that unmanned aerial vehicles can not move due to local minimum points. The invention is realized by the following technical scheme: setting target point coordinates of each unmanned aerial vehicle on a ground station based on an improved artificial potential field method, acquiring the initial position of each unmanned aerial vehicle and the distance between the unmanned aerial vehicles, and planning an optimal barrier-free path of each unmanned aerial vehicle unit; adding dynamic adjustment factors into the potential field function, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle unit by using the potential field function, and calculating the position of the unmanned aerial vehicle unit in the next step according to the resultant force; then judging whether the unmanned aerial vehicle enters into a local minimum point or not to obtain an optimal path planned by the main path of the unmanned aerial vehicle formation; and generating a collaborative and efficient execution pre-planned path under the condition of dynamic change of the target and the obstacle, writing the planned barrier-free optimal path into the unmanned aerial vehicle flight control system, and flying to the target point according to the instruction.
Description
Technical Field
The invention relates to the technical field of formation flight of unmanned aerial vehicles, in particular to a three-dimensional formation method of multiple unmanned aerial vehicles based on an improved artificial potential field method.
Background
The Artificial Potential Field method (APF) is a virtual force method. The basic idea of the method is to introduce a time-varying artificial potential field, acquire information by using a visual sensor, a sonar sensor and the like, and then apply an artificial potential field function to realize collision avoidance of the mobile robot on a static Obstacle (Obstacle). The robot motion in the surrounding environment is designed into an abstract man-made motionGravitational fieldMotion in (1), target point (Goal) pairMobile robotProduce 'gravity', obstacleThe object generates repulsion force to the mobile robot, and finally the movement of the mobile robot is controlled by solving the resultant force. The idea of the artificial potential field method is similar to the movement of electrons in an electric field created by positive and negative charges. The obstacle generates repulsion to the mobile robot, the target point generates attraction, and the resultant force of the attraction and the repulsion is used as the acceleration force of the mobile robot to control the motion direction of the robot and calculate the position of the robot. The method constructs a scalar function of the castration function such that the target point corresponds to its minimum value and the obstacle region corresponds to some larger value of the castration function. At any other position the potential function is monotonically decreasing towards the target point. Therefore, no matter where the starting point is in the free space, as long as the path exists, the mobile robot can find the target point through the direction of the negative gradient of the potential energy value. Because the mobile robot has limitation to the perception of the surrounding environment information, the problem that the robot falls into a local minimum point easily occurs. When the robot is at a local extreme point, the robot is easy to generate a locking phenomenon, is easy to fall into the local minimum point, has the problems that a target near an obstacle cannot reach, a path is not optimal and the like, and cannot move. The conventional artificial potential field method therefore results in a planning failure due to the local minima problem.
The artificial potential field method has a great number of applications in a path planning algorithm of a robot, and is often used for solving a plane path planning problem. Because the unmanned aerial vehicle cluster is widely applied to search rescue and traffic monitoring, the unmanned aerial vehicle must be capable of successfully perceiving and avoiding obstacles and avoiding collision with the obstacles or other unmanned aerial vehicles. Therefore, in recent years, researchers at home and abroad propose a plurality of methods for planning tracks, mainly including a unit decomposition method, a method based on mathematical programming and a method based on artificial potential field. The unit decomposition method is to replace a planning area with a new space synthesized by a plurality of units, and although the unit decomposition method can accurately plan the optimal flight path, the algorithm complexity is high, and the real-time performance is difficult to ensure; the method based on mathematical programming mainly uses functions to simulate conditions influencing path programming, and the method integrates track distance, obstacle position and other factors in track programming, but the algorithm is too complex, and the selection of the functions also needs to be considered emphatically; the traditional manual potential field method has poor control precision in the formation control. When the traditional artificial potential field method is used for generating the covering airway, some modes which are good to use in reality cannot be generated.
Compared with other three-dimensional route planning algorithms, the improved artificial potential field method has the remarkable advantages that: firstly, obstacle avoidance planning is carried out by combining the current unmanned aerial vehicle motion state, when an airway is planned by an artificial potential field method, the resultant force received by the current position is calculated according to a potential force field, an object moving in the environment is regarded as a mass point in a virtual force field, the virtual force field is composed of an attraction force field of a target and a repulsion force field of an obstacle, and the airway for collision avoidance is planned by searching a route descending along a potential function. The most remarkable characteristics are that the calculation amount is small and the calculation speed is high. Secondly, a smooth and safe airway can be obtained by utilizing an artificial potential field method, and other airway planning algorithms not only need to carry out smooth operation on the airway, but also possibly need to carry out flight safety performance detection such as minimum direct flight distance, maximum climbing angle and the like again. At present, the traditional artificial potential field method is mainly used for two-dimensional ground robot formation. In order to effectively realize three-dimensional formation flying of the unmanned aerial vehicles, the three-dimensional coordinates of the unmanned aerial vehicles must be accurately positioned, and the height positioning of the general unmanned aerial vehicles is inaccurate. The traditional air route planning algorithm is provided on the assumption that a fixed task target and a stable and unchangeable flight environment are adopted, static planning is adopted, the task target and the flight environment of the unmanned aerial vehicle are possibly changed and uncertain in actual exploration, business and other applications, and the traditional air route planning algorithm is insufficient in online planning capability. Therefore, the traditional route planning algorithm cannot meet the requirement of the unmanned aerial vehicle on route change during rapid operation in a dynamically-changed task execution environment. Aiming at the problems that targets near an obstacle cannot be reached and local minimum points and oscillation exist in the traditional artificial potential field method, the potential field function is improved in the prior art, and a target point is guaranteed to be a global minimum point of a potential field. In a dynamic potential field, a judgment mechanism for judging whether the unmanned aerial vehicles get into local minimum points is introduced, and the local minimum points are jumped out by combining a method of moving 90 degrees along a target direction, so that path planning, cooperative obstacle avoidance and collision avoidance of formation of multiple unmanned aerial vehicles are realized. The improved method effectively makes up the defects of the traditional artificial potential field method and improves the practicability of the artificial potential field method. However, when the requirement on the accuracy of the terrain in the flight environment is high, the number of the terrain grids is increased sharply, so that the search space is enlarged, the planning time of the algorithms is greatly increased, the reaction speed of the unmanned aerial vehicle is greatly reduced, and the dynamic planning capability of the unmanned aerial vehicle is reduced. Secondly, the actual flight path and flight performance of the unmanned aerial vehicle are not fully considered in the paths planned by the methods, most paths are unsmooth paths formed by straight line connection between key coordinate points, and safety indexes of the unmanned aerial vehicle, such as the maximum turning radius, the minimum straight flight distance and the like, must be considered in the connection mode. Therefore, these algorithms commonly used for global routing still have certain deficiencies in online real-time routing for dynamic environments. Therefore, the invention provides a regression search algorithm to carry out global optimization on the final path.
Disclosure of Invention
Aiming at the defects that the traditional Artificial Potential Field (APF) method cannot adapt to a complex environment, so that the unmanned aerial vehicle is trapped in a local stagnation state and the path is not smooth enough, the invention provides an unmanned aerial vehicle cluster three-dimensional formation method based on an improved artificial potential field method according to the improved artificial potential field method.
The above object of the present invention can be achieved by the following measures: an unmanned aerial vehicle cluster three-dimensional formation method has the following technical characteristics: based on an improved artificial potential field method, setting the coordinates of a target point of each unmanned aerial vehicle on a ground station, and acquiring the initial position of each unmanned aerial vehicle and the distance between the unmanned aerial vehicles through a GPS (global positioning system), a barometer, laser ranging and ultrasonic ranging; adding dynamic adjustment factors into the potential field function, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle unit by using the potential field function, and calculating the position of the unmanned aerial vehicle unit in the next step according to the resultant force; judging whether the local minimum point is trapped, if so, jumping out of the local minimum point by adopting a local minimum point escape strategy, otherwise, calculating the position of the next step according to the magnitude and the direction of the calculated resultant force; optimizing a path based on a regression search method to obtain an optimal or suboptimal flight trajectory from an initial position to a task target completion position, and appointing to complete an autonomous path planning airspace from the initial position to a target point to obtain an optimal path planned by a main path of unmanned aerial vehicle formation; generating a pre-planned path with cooperativity and high execution efficiency under the condition of dynamic change of a target and an obstacle, writing the planned barrier-free optimal path into an unmanned aerial vehicle flight control system, and flying the unmanned aerial vehicle formation unit to the coordinates of the target point according to instructions.
Compared with the prior art, the invention has the following beneficial effects:
the method is based on an improved artificial potential field method, the target point coordinates of each unmanned aerial vehicle are arranged on a ground station, the accurate height and distance are calculated by combining a GPS, a barometer, laser ranging and ultrasonic ranging, the optimal barrier-free path of each unmanned aerial vehicle is planned, a pre-planned path is generated, the attraction effect of the target point on the unmanned aerial vehicle is weakened, and the path continuity is improved; the dynamic adjustment factors are added into the potential field function to plan a global optimal path, autonomous path planning from a starting point to a target point of an unmanned aerial vehicle formation is achieved in a designated airspace, the cluster has good cooperativity and high execution efficiency in the process, the formation efficiency is improved, the path planning capability is strong, the safety, the real-time performance and the accessibility of the path planning of the unmanned aerial vehicle under the condition of dynamic change of the target and the obstacle are met, and the tracking and obstacle avoidance speed of the unmanned aerial vehicle in a dynamic environment is improved. Through the improved version of the artificial potential field method, the collision problem of unmanned aerial vehicle formation is avoided, and the problem that the unmanned aerial vehicle cannot move due to being trapped in local minimum points is solved.
The invention solves the problem of unreachable target fundamentally by improving the potential field function; and a local minimum point escaping strategy is adopted to jump out the local minimum point, so that the defect that a feasible path cannot be found due to the fact that an artificial potential field method is easy to fall into the local minimum is overcome. The problem of local minimum point of traditional artifical potential field method, near the barrier target can not reach is solved, the collision problem of unmanned aerial vehicle formation has been avoided to the global optimum route of every unmanned aerial vehicle has been planned, the efficiency of formation has been improved.
The invention uses the thought of an artificial potential field for reference, takes the planning result of the artificial potential field method as prior knowledge, adopts a regression-based search method to optimize the path aiming at the track planning problem of the aircraft, and searches an optimal path from the starting position to the task target completion position under the current specific condition, thereby not only ensuring the perfect completion of the flight task, but also saving the flight time and energy consumption and reducing the overload of the aircraft. Overcomes the defects of the traditional artificial potential field method and has stronger practicability.
The invention adopts an appointed autonomous path planning airspace from a starting point to a target point to obtain an optimal path planned by a main path of an unmanned aerial vehicle formation, generates a preplanned path with cooperativity and high execution efficiency under the condition of dynamic change of a target and an obstacle, writes the planned barrier-free optimal path into an unmanned aerial vehicle flight control system, and enables the unmanned aerial vehicle to fly to the coordinates of the target point according to instructions. Can divide into groups many unmanned aerial vehicles through central control system, carry out unmanned aerial vehicle's position in the group, height, speed, information real-time sharing such as declination, unmanned aerial vehicle quantity can be adjusted according to the intensive degree of unmanned aerial vehicle in the group, constitute the cooperative mode of many unmanned aerial vehicle global behaviors with single unmanned aerial vehicle local behavior, each unmanned aerial vehicle unit in the cluster gives central control system with positional information transfer, can adjust according to the real-time positional information of unmanned aerial vehicle cluster, make and carry out abundant communication between the unmanned aerial vehicle, thereby reduce gain coefficient's adverse effect, solve the problem of anticollision between the machine. Simulation results show that: the invention can keep the stable formation of the unmanned aerial vehicles, and simultaneously can avoid obstacles in the flight process and avoid collision among the airplanes.
The invention is mainly used for stably forming the unmanned aerial vehicle three-dimensional forming flying.
Drawings
Fig. 1 is a flow chart of a process for cluster three-dimensional formation of unmanned aerial vehicles according to the present invention;
FIG. 2 is a schematic diagram of an artificial potential field method;
FIG. 3 is a schematic view of a local minimum point of escape;
FIG. 4 is a schematic diagram of a regression search method;
FIG. 5 is a diagram of simulation results of the method of the present invention.
The technical scheme of the invention is further described in detail in the following with reference to the attached drawings.
Detailed Description
See fig. 1. According to the invention, based on an improved artificial potential field method, a target point (Goal) coordinate of each unmanned aerial vehicle is set on a ground station, the distance between the initial position of each unmanned aerial vehicle and the unmanned aerial vehicles is obtained through a GPS, a barometer, laser ranging and ultrasonic ranging, the accurate height and distance are calculated by combining a corresponding signal analysis processing algorithm and a filtering algorithm, and the optimal barrier-free path of each unmanned aerial vehicle unit is drawn by using the improved artificial potential field law; adding dynamic adjustment factors into the potential field function, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle unit by using the potential field function, and calculating the position of the unmanned aerial vehicle unit in the next step according to the resultant force; then judging whether the unmanned aerial vehicle enters into a local minimum point, searching an optimal or suboptimal flight track from an initial position to a task target completion position, and appointing to complete an autonomous path planning airspace from the initial position to a target point to obtain a main path planning optimal path for unmanned aerial vehicle formation; generating a collaborative and efficient pre-planned path under the condition of dynamic change of a target and an Obstacle (Obstacle), writing the planned barrier-free optimal path into an unmanned aerial vehicle flight control system, and flying the unmanned aerial vehicle formation unit to a target point according to an instruction.
Based on an improved artificial potential field method, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle by a formation algorithm according to obstacles and target points, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle according to an improved potential field function, and calculating the position of the unmanned aerial vehicle flying to the next step; judging whether the unmanned aerial vehicle sinks into the local minimum point, if so, executing a strategy of escaping from the local minimum point to jump out the local minimum point, and if not, calculating the position of the next step according to resultant force; finally, judging whether the target point is reached, if not, repeating the steps, and continuously calculating the magnitude and the direction of the resultant force; if the target point is reached, the next step is carried out, and path optimization is carried out based on a regression search method to obtain an optimal path; and finally, the unmanned aerial vehicle flies and controls to send command signals according to the planned barrier-free optimal path, so that the unmanned aerial vehicle flies to the target.
The hardware system of the invention comprises: including ground station system, unmanned aerial vehicle machine carries flight control system, GPS positioning system, ultrasonic ranging, barometer, laser rangefinder etc.. The unmanned aerial vehicle unit further obtains whether obstacle information exists or not through obtaining environmental information by the sensing system, formation control, obstacle avoidance control and collision avoidance control are carried out according to the obtained information, corresponding control quantity is obtained, the obtained control quantity is transmitted to the flight control system, namely an autopilot, and then the unmanned aerial vehicle is controlled through the flight control system.
See fig. 2. Constructing a virtual potential field according to an artificial potential field method, constructing a path rectangular coordinate system XYZ of the position of an Unmanned Aerial Vehicle (UAV) by improving an artificial potential field function and introducing a local minimum point judgment mechanism, wherein the attraction F of a target point (Goal) on the UAV isattiWhile being subjected to the repulsive force F of the Obstacle (Obstacle)repiUnmanned aerial vehicle UAV in resultant force FiMoves to a target point under the action of the unmanned aerial vehicle, and utilizes the resultant force F of the unmanned aerial vehicleiDecomposing the formed force diagram, optimizing a potential field function by adopting a local minimum point escape method and a regression search method, and introducing the relative distance l between each unmanned aerial vehicle and a corresponding target thereof on the basis of the traditional artificial potential field functiongi(i is 1,2, 3.. n), calculating a repulsive force field U generated by the jth obstacle of the unmanned plane to the ith unmanned planerep1ijAnd a repulsion field U generated by the k frame unmanned aerial vehicle UAV to the i frame unmanned aerial vehiclerep2ik(i ≠ k), the calculation formula is as follows:
(i＝1,2,3,...,n；j＝1,2,3,...,m；k＝1,2,3,...,n)
in the formula, k1、k2I ≠ k, which is a repulsive force gain coefficient,
representing the distance from the ith unmanned aerial vehicle to the corresponding target point, wherein p is a real number greater than zero; loijRepresenting the distance from the ith unmanned aerial vehicle to the jth obstacle; lmaxIs an artificially set obstacle influence distance,/ikRepresenting the distance between the ith unmanned aerial vehicle and the kth unmanned aerial vehicle; n represents the number of unmanned stands; m represents the number of obstacles.
According to the repulsion field U generated by the jth barrier to the ith unmanned aerial vehiclerep1ijRepulsion field U generated by kth unmanned aerial vehicle for ith unmanned aerial vehiclerep2ikAnd the gravity gain coefficient eta to obtain a gravity field U between the ith unmanned aerial vehicle and a corresponding target point thereofatti，
When the distance between the unmanned aerial vehicle and the obstacle is greater than the influence distance, the repulsion of the obstacle borne by the unmanned aerial vehicle is zero. With an arbitrary real number p greater than 0
Multiplying the original potential field function, so that the repulsive force borne by the unmanned aerial vehicle at the target point is 0, and the target point is ensured to be a global minimum point of the whole potential field, so that the problem that the target nearby the obstacle cannot be reached is solved.
See fig. 3. According to the Obstacle (Obstacle) suffered by the ith unmanned aerial vehicle and the repulsive force F of other unmanned aerial vehiclesrepiPosition coordinate P of ith unmanned aerial vehicleiAnd the position coordinate P of the jth obstacleojThe repulsion force can be found as a negative gradient as a function of the gravitational potential field:
in the formula (I), the compound is shown in the specification,
and
the unit vector of the repulsive force direction of the jth obstacle and the unit vector of the repulsive force direction of the kth unmanned aerial vehicle are respectively; frepi1And Frepi2Is FrepiTwo force components. Then Frepi1Directional unmanned aerial vehicle, Frepi2Is directed to the target point (Goal),
in the formula, k1、k2P is an arbitrary real number greater than 0, l is the repulsive gain factoroijIs the distance between the ith unmanned aerial vehicle and the jth obstacle; lmaxIs an artificially set obstacle influence distance; lgi(i ═ 1,2, 3.., n) is the relative distance between each Unmanned Aerial Vehicle (UAV) and its corresponding target, which is introduced on the basis of the conventional artificial potential field function.
After the unmanned aerial vehicle unit judges that the unmanned aerial vehicle unit sinks into the local minimum point, the unmanned aerial vehicle unit moves along any direction on the normal plane of the target direction, the local minimum point jumps out, if the distance between the position of the unmanned aerial vehicle unit at the previous moment and the current position is smaller than a certain set value, the unmanned aerial vehicle is judged to sink into the local minimum point, at the moment, an additional potential field is added, the unmanned aerial vehicle moves along any direction on the normal plane vertical to the target direction for a certain distance, and the unmanned aerial vehicle moves along any direction on the normal plane verticalAnd escape the local minima. When F is presentrepi＝FattiThat is, the attraction force applied to the unmanned aerial vehicle is equal to the repulsion force, and when the directions are opposite, the unmanned aerial vehicle is in a local minimum point and cannot move. The additional potential field function is:
wherein a is a constant greater than zero, PlocalRepresenting the position of a local minimum,/amaxIs the maximum influence distance of the additional potential field. The unmanned aerial vehicle trapped in the local minimum point escapes from the local minimum point under the action of the additional potential field force.
See fig. 4. After the barrier-free path is planned by using the improved artificial potential field method, the path is globally optimized by adopting a regression search method. In the embodiment, the path optimization by the regression search method is selected, and is a global optimization scheme, and r target points { P ] are selected on the path planned by the improved artificial potential field method1,P2,...,Pt,...,Pr} (if the number of selected points is more, the iteration time is longer, but the precision is higher, so compromise is needed), connecting the target point P1、P2If line segment
Safety distance l from Obstacle (Obstacle)0If the boundary circle of the range has no intersection point, the target point P is connected1、P3This step is repeated in sequence. If it is
Safety distance l from obstacle0The boundary circles of the range do not have intersections, but
Safety distance l from obstacle0The boundary circles of the range have intersection points, then
Selection optimizationThe latter path segments. Then from PkAnd (5) starting points, repeating the process until the coordinates of the corresponding target point (Goal) are reached, and ending the operation. The research performed in this embodiment is in a dynamic potential field with time variable as reference, and if the step length is set to be constant, the motion of the unmanned aerial vehicle is uniform, and the target point { P }1,P2,...,Pt,...,PrThe selection of the unmanned aerial vehicle is actually determined according to different coordinates of the unmanned aerial vehicle at equal time intervals.
From the simulation effect diagram of fig. 5, it can be seen that the path finally planned by the regression search method is globally optimal.
The foregoing is directed to the preferred embodiment of the present invention and it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. An unmanned aerial vehicle cluster three-dimensional formation method has the following technical characteristics: setting a target point (Goal) coordinate of each unmanned aerial vehicle on a ground station based on an improved artificial potential field method, acquiring an initial position of each unmanned aerial vehicle and a distance between the unmanned aerial vehicles through a GPS (global positioning system), a barometer, laser ranging and ultrasonic ranging, and calculating accurate height and distance by combining the GPS, the barometer, the laser ranging and the ultrasonic ranging to plan an optimal barrier-free path of each unmanned aerial vehicle unit; adding dynamic adjustment factors into the potential field function, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle unit by using the potential field function, and calculating the position of the unmanned aerial vehicle unit in the next step according to the resultant force; then judging whether the unmanned aerial vehicle enters into a local minimum point, searching an optimal or suboptimal flight track from an initial position to a task target completion position, and appointing to complete an autonomous path planning airspace from the initial position to a target point to obtain a main path planning optimal path for unmanned aerial vehicle formation; generating a collaborative and efficient pre-planned path under the condition of dynamic change of a target and an Obstacle (Obstacle), writing the planned barrier-free optimal path into an unmanned aerial vehicle flight control system, and flying the unmanned aerial vehicle formation unit to a target point according to an instruction.
2. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: based on an improved artificial potential field method, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle by a formation algorithm according to obstacles and target points, calculating the magnitude and direction of resultant force borne by each unmanned aerial vehicle according to an improved potential field function, and calculating the position of the unmanned aerial vehicle flying to the next step; judging whether the unmanned aerial vehicle sinks into the local minimum point, if so, executing a strategy of escaping from the local minimum point to jump out the local minimum point, and if not, calculating the position of the next step according to resultant force; finally, judging whether the target point is reached, if not, repeating the steps, and continuously calculating the magnitude and the direction of the resultant force; if the target point is reached, the next step is carried out, and path optimization is carried out based on a regression search method to obtain an optimal path; and finally, the unmanned aerial vehicle flies and controls to send command signals according to the planned barrier-free optimal path, so that the unmanned aerial vehicle flies to the target.
3. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: the unmanned aerial vehicle unit further obtains whether obstacle information exists or not through obtaining environmental information by the sensing system, formation control, obstacle avoidance control and collision avoidance control are carried out according to the obtained information, corresponding control quantity is obtained, the obtained control quantity is transmitted to the flight control system, namely an autopilot, and then the unmanned aerial vehicle is controlled through the flight control system.
4. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: the method comprises the steps of constructing a virtual potential field according to an artificial potential field method, and constructing a path rectangular coordinate system XYZ of the position of an Unmanned Aerial Vehicle (UAV) by improving an artificial potential field function and introducing a local minimum point judgment mechanism.
5. The unmanned aerial vehicle cluster three-dimensional formation method of claim 1The method is characterized in that: unmanned Aerial Vehicle (UAV) in-process force FiMoves to a target point under the action of the unmanned aerial vehicle, and utilizes the resultant force F of the unmanned aerial vehicleiDecomposing the formed force diagram, optimizing a potential field function by adopting a local minimum point escape method and a regression search method, and introducing the relative distance l between each unmanned aerial vehicle and a corresponding target thereof on the basis of the traditional artificial potential field functiongi(i is 1,2, 3.. n), calculating a repulsive force field U generated by the jth obstacle of the unmanned plane to the ith unmanned planerep1ijAnd a repulsion field U generated by the k frame unmanned aerial vehicle UAV to the i frame unmanned aerial vehiclerep2ik(i ≠ k), the calculation formula is as follows:
in the formula, k1、k2I ≠ k, which is a repulsive force gain coefficient,
representing the distance from the ith unmanned aerial vehicle to the corresponding target point, wherein p is a real number greater than zero; loijRepresenting the distance from the ith unmanned aerial vehicle to the jth obstacle; lmaxIs an artificially set obstacle influence distance,/ikRepresenting the distance between the ith unmanned aerial vehicle and the kth unmanned aerial vehicle; n represents the number of unmanned stands; m represents the number of obstacles.
6. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 5, wherein: according to the repulsion field U generated by the jth barrier to the ith unmanned aerial vehiclerep1ijRepulsion field U generated by kth unmanned aerial vehicle for ith unmanned aerial vehiclerep2ikAnd the gravity gain coefficient eta to obtain a gravity field U between the ith unmanned aerial vehicle and a corresponding target point thereofatti，
7. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: according to the Obstacle (Obstacle) suffered by the ith unmanned aerial vehicle and the repulsive force F of other unmanned aerial vehiclesrepiPosition coordinate P of ith unmanned aerial vehicleiAnd the position coordinate P of the jth obstacleojThe repulsion force can be found as a negative gradient as a function of the gravitational potential field:
in the formula (I), the compound is shown in the specification,
and
the unit vector of the repulsive force direction of the jth obstacle and the unit vector of the repulsive force direction of the kth unmanned aerial vehicle are respectively; frepi1And Frepi2Is FrepiTwo force components.
8. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 7, wherein: frepi1Directional unmanned aerial vehicle, Frepi2Is directed to a target point (Goal), wherein,
in the formula, k1、k2P is an arbitrary real number greater than 0, l is the repulsive gain factoroijIs the distance between the ith unmanned aerial vehicle and the jth obstacle; lmaxIs an artificially set obstacle influence distance; lgi(i ═ 1,2, 3.., n) is the relative distance between each Unmanned Aerial Vehicle (UAV) and its corresponding target, which is introduced on the basis of the conventional artificial potential field function.
9. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: after the unmanned aerial vehicle unit judges to get into local minimum, remove along arbitrary direction on the normal plane of target direction, jump out local minimum, if the distance of the position of unmanned aerial vehicle unit at the previous moment and current position is less than certain setting value, then judge that unmanned aerial vehicle has got into local minimum, at this moment, add an additional potential field, unmanned aerial vehicle removes one section distance along arbitrary direction on the normal plane perpendicular with the target direction to flee from local minimum.
10. The three-dimensional formation method for unmanned aerial vehicle clusters as claimed in claim 1, wherein: when F is presentrepi＝FattiThat is, the attractive force that unmanned aerial vehicle received is equal to repulsion exactly, and when the direction is opposite, unmanned aerial vehicle is in local minimum point, can't remove, and additional potential field function is:
wherein a is a constant greater than zero, PlocalRepresenting the position of a local minimum,/amaxIs the maximum influence distance of the additional potential field. The unmanned aerial vehicle trapped in the local minimum point escapes from the local minimum point under the action of the additional potential field force.
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