CN111781948A - Unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance method - Google Patents
Unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance method Download PDFInfo
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Abstract
The invention discloses a formation transformation and online dynamic obstacle avoidance method for unmanned aerial vehicles, and belongs to the technical field of unmanned aerial vehicles. The method specifically comprises the following steps: step one, calculating a repulsive force field and a repulsive force of the unmanned aerial vehicle and resultant force borne by the unmanned aerial vehicle through an artificial potential field algorithm; step two, improving the calculation of the artificial potential field algorithm in the step one; combining with improved artificial potential field algorithm calculation, providing an expected path smoothing strategy based on a Kernel method for solving the problems of excessive fold lines and large inflection point angle of an expected path caused by frequent entering and exiting of an obstacle expansion area in the obstacle avoidance process of the unmanned aerial vehicle; and step four, designing an inter-aircraft obstacle avoidance algorithm for realizing avoidance of the unmanned aerial vehicles. According to the flight characteristics of the fixed-wing unmanned aerial vehicle, the inter-aircraft obstacle avoidance artificial potential field is designed, so that the multiple aircrafts cannot fly into a static obstacle area and collide with other aircrafts when flying in formation.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a formation transformation and online dynamic obstacle avoidance method for unmanned aerial vehicles.
Background
When unmanned aerial vehicle formation flies, must keep certain safe distance between each machine inside the formation, therefore online developments are indispensable. The current mature obstacle avoidance and path planning algorithms include an artificial potential field method, an ant colony algorithm, a fast expansion random number method, a Voronoi diagram method, an A-star algorithm and the like. These methods can be divided into global planning and local planning according to the perception of the surrounding environment. The global information is known in the global planning, the global information is unknown in the local planning, and the local information can be sensed only by combining the sensor. Unmanned aerial vehicle formation is kept away the barrier and is kept away the route re-planning behind the barrier under the dynamic environment is a hotspot and difficult point in the technical field of cluster, and collision between the unmanned aerial vehicle under the motion condition also can cause very big loss, consequently keeps away the barrier in real time and is especially important to formation aircraft in flight.
Disclosure of Invention
The invention provides a method for unmanned aerial vehicle formation shape transformation and on-line dynamic obstacle avoidance, aiming at solving the technical problems in the background technology.
The invention is realized by adopting the following technical scheme: a method for unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance specifically comprises the following steps:
step one, calculating a repulsive force field and a repulsive force of the unmanned aerial vehicle and resultant force borne by the unmanned aerial vehicle through an artificial potential field algorithm;
step two, improving the calculation of the artificial potential field algorithm in the step one;
combining with improved artificial potential field algorithm calculation, providing an expected path smoothing strategy based on a Kernel method for solving the problems of excessive fold lines and large inflection point angle of an expected path caused by frequent entering and exiting of an obstacle expansion area in the obstacle avoidance process of the unmanned aerial vehicle;
designing an inter-aircraft obstacle avoidance algorithm for avoiding the unmanned aerial vehicles;
wherein, the first step specifically comprises:
defining the position of the unmanned aerial vehicle as q, and then defining the gravitational field function U of the unmanned aerial vehicle at the point qatt(q)And gravitational force Fatt(q)Respectively satisfy the following formulas:
where λ represents a gravitational gain, ρα(q) represents the distance between the point q and the target a;
the repulsion field and repulsion at point q of the unmanned aerial vehicle are as follows:
η is the repulsive force gain, ρ0Is the maximum radius of influence of the obstacle,
ρr(q) is the distance from point q to the obstacle r;
the resultant force F experienced by the drone is:
F＝Fatt+Frep
Fattas the attraction of the target to the drone, FrepDefining the repulsion of the obstacle to the unmanned aerial vehicle and the attraction of the target point to the unmanned aerial vehicle as the negative gradients of the corresponding repulsion field and attraction field respectively for the repulsion of the obstacle to the unmanned aerial vehicle, and then the attraction F of the target point is applied to the point qatt(q) and repulsive force F of obstaclerep(q) negative gradients of the gravitational potential function and the repulsive potential function of the point respectively,
the assumption that there are N obstacles simultaneously affecting the drone is that, according to the principle of superposition, the resultant force F of the drone is:
in the formula
Repulsive force generated for the ith obstacle。
In a further embodiment, when it is determined that the magnitude of the attraction force of the drone at a certain point is not zero, and the total resultant force experienced by the drone is zero: then, the unmanned aerial vehicle can be judged to be trapped in the local optimal solution at the moment, and if an equivalent barrier exists, the magnitude and direction of the repulsive force generated by the barrier are the same as those of the repulsive force borne by the unmanned aerial vehicle, and at the moment, the unmanned aerial vehicle, the equivalent barrier and the target are necessarily collinear; the specific position of equivalent barrier is reversely released by the artificial potential field method, and the specified flying direction that the unmanned aerial vehicle jumps out of the local optimum is the tangential direction of the unmanned aerial vehicle position and the barrier expansion area circle, and the unmanned aerial vehicle position and the barrier expansion area circle are arbitrarily taken from left to right.
In a further embodiment, when the magnitudes of the attractive and repulsive forces are not proportional, the gravitational field function at this time is:
in the formula (I), the compound is shown in the specification,
representing the distance threshold between no targets of the drone, ξ is the repulsion coefficient,
the corresponding gravity calculation method comprises the following steps:
in a further embodiment of the method of the invention,
when the target point is in an unreachable condition, superposing the distance between the unmanned aerial vehicle and the target point to the nth power on the basis of the artificial potential field method, and then calculating the new repulsive force field function as follows:
in a further embodiment of the method of the invention,
to increase the smoothing effect, y is defined*(i) For the smoothed data, the bandwidth is h, S (t) represents the weighting function, the window length is n, y (j) represents the windowAnd the calculation method of the fitting value at the time t of the jth data in the mouth is as follows:
weighting function S in the formulaj(t) calculation method:
in the formula
tj represents the time of the current time, and Kernel (x) is the Kernel function, which is the Kernel of Kernel smoothing.
In a further embodiment, the fourth step comprises: assuming that the j-th airplane in the formation generates a repulsive force effect on the position q in the environment, the repulsive force field function on the q point is defined as follows:
where ξ is the coefficient of repulsion, σ0Is the radius of the expansion area of the unmanned aerial vehicle,
representing the distance between the drone and the point q, ∈ (0,1) being the expansion zone separation coefficient;
the repulsion that this unmanned aerial vehicle that corresponds is to q is:
distance between machines greater than σ0In the process, the repulsion force of the two machines is zero, and the two machines do not need to avoid barriers; distance between machines is less than sigma0When the two machines enter respective expansion regions and act as barriers to each other, the repulsive force is divided into two sections
The distance between the repulsion generated between the unmanned aerial vehicle and the unmanned aerial vehicle is in a linear relation, the change amplitude of the repulsion is small, which is equivalent to a buffer time for the unmanned aerial vehicle to execute obstacle avoidance action, and the repulsion and the distance are slowly kept away from each other, so that large-angle maneuvering is not frequently performed; when in use
When, two machines are very near this moment, need keep away the barrier immediately for avoiding bumping, and this is facing to being close of two quick-witted distances, and repulsion becomes exponential increase, forces unmanned aerial vehicle to keep away from the other side fast, is in
At this point, the calculated repulsion force of the two ranges is equal, i.e. the repulsion force is continuous, and the command cannot be suddenly changed.
In a further embodiment, the drone expansion zone radius σ0The calculation steps are as follows:
the first step is as follows: two adjacent unmanned aerial vehicles regard self coordinates as original points to the ray of the flying speed direction, and whether the distance between the two rays is smaller than a small distance or not is judgedminIf not, indicating no possibility of collision, let σ00, the unmanned aerial vehicle on the other side does not generate repulsion force on the unmanned aerial vehicle on the other side; if the distance between two rays is less thanminIf so, the paths of the two machines may intersect, and the second step is carried out; selectingminThe principle is based on factors such as unmanned aerial vehicle sensor error, body size and control precision;
the second step is that: if two unmanned aerial vehicles meet, defining the time threshold value of two-aircraft collision as TmThis value represents how much time is left to process in anticipation of a collision, using q1And q is2Representing the positions of the drone one and the drone two,
representing the velocity vectors of drone one and drone two,vqrepresenting vectors
And
the included angle of the unmanned plane II is analyzed, the unmanned plane I is used as a barrier at the moment, and then sigma is calculated0The calculation method of (2) is as follows:
the invention has the beneficial effects that: the method is based on the artificial potential field method, provides corresponding improvement measures aiming at three defects of the artificial potential field method, and then combines the Kernel method to carry out smoothing processing on the expected path, thereby solving the problem of the expected path oscillation. Finally, on the basis, according to the flight characteristics of the fixed-wing unmanned aerial vehicle, the inter-aircraft obstacle avoidance artificial potential field is designed, so that multiple aircrafts cannot fly into a static obstacle area and collide with other aircrafts when flying in formation.
Drawings
Fig. 1 is a diagram of attraction and repulsion of targets and obstacles to a drone.
Fig. 2 avoids the locally optimal solution motion direction diagram.
Fig. 3 is a graph of random perturbation applied to equivalent obstacles.
FIG. 4 is a schematic diagram of Kernel path smoothing.
Fig. 5 is a schematic diagram of the repulsive force fields of two drones.
Fig. 6 is a diagram of the situation that two drones meet.
Fig. 7 is a diagram for calculating obstacle avoidance determination between two drones.
Fig. 8 is a schematic view of the direction of the repulsive force between two drones.
Concrete real-time mode
The invention is further described with reference to the following description of the drawings and specific embodiments.
First, the inventors considered that: when unmanned aerial vehicle formation flies, must keep certain safe distance between each machine inside the formation, therefore online developments are indispensable. The current mature obstacle avoidance and path planning algorithms include an artificial potential field method, an ant colony algorithm, a fast expansion random number method, a Voronoi diagram method, an A-star algorithm and the like. These methods can be divided into global planning and local planning according to the perception of the surrounding environment. The global information is known in the global planning, the global information is unknown in the local planning, and the local information can be sensed only by combining the sensor. For the problem of online obstacle avoidance, dynamic obstacles comprise other airplanes flying in formation, static obstacles comprise a no-fly area, an electronic geo-fence and the like, and therefore the obstacle avoidance is carried out under the condition that global information is known. Considering the constraint characteristics that the fixed wing has the minimum stall speed and the minimum turning radius, the invention provides an online dynamic obstacle avoidance algorithm combining an improved artificial potential field method, Kernel path smoothing and an introduced artificial potential field between machines.
A method for unmanned aerial vehicle formation shape transformation and online dynamic obstacle avoidance specifically comprises the following steps: step one, calculating a repulsive force field and a repulsive force of the unmanned aerial vehicle and resultant force borne by the unmanned aerial vehicle through an artificial potential field algorithm;
step two, improving the calculation of the artificial potential field algorithm in the step one;
combining with improved artificial potential field algorithm calculation, providing an expected path smoothing strategy based on a Kernel method for solving the problems of excessive fold lines and large inflection point angle of an expected path caused by frequent entering and exiting of an obstacle expansion area in the obstacle avoidance process of the unmanned aerial vehicle;
and step four, designing an inter-aircraft obstacle avoidance algorithm for realizing avoidance of the unmanned aerial vehicles.
The Artificial Potential Field (APF) is a path planning algorithm proposed by Khatib in the last 80 th century. The algorithm considers the target and the obstacle as objects with attraction and repulsion to the unmanned aerial vehicle respectively, and the unmanned aerial vehicle moves along the resultant force of the attraction and the repulsion. Fig. 1 shows a diagram of the attraction and repulsion of targets and obstacles to a drone.
Defining the position of the unmanned aerial vehicle as q, and then defining the gravitational field function U of the unmanned aerial vehicle at the point qatt(q)And gravitational force Fatt(q)Respectively satisfy the following formulas:
where λ represents a gravitational gain, ρα(q) represents the distance between the point q and the target a;
the repulsion field and repulsion at point q of the unmanned aerial vehicle are as follows:
η is the repulsive force gain, ρ0Is the maximum radius of influence of the obstacle,
ρr(q) is the distance from point q to the obstacle r;
the resultant force F experienced by the drone is:
F＝Fatt+Frep
Fattas the attraction of the target to the drone, FrepDefining the repulsion of the obstacle to the unmanned aerial vehicle and the attraction of the target point to the unmanned aerial vehicle as the negative gradients of the corresponding repulsion field and attraction field respectively for the repulsion of the obstacle to the unmanned aerial vehicle, and then the attraction F of the target point is applied to the point qatt(q) and repulsive force F of obstaclerep(q) negative gradients of the gravitational potential function and the repulsive potential function of the point respectively,
the assumption that there are N obstacles simultaneously affecting the drone is that, according to the principle of superposition, the resultant force F of the drone is:
in the formula
Repulsive force generated for the i-th obstacle.
The second step is to improve the artificial potential field method, mainly because the artificial potential field method has the following defects:
(1) at a certain point of the target, if the attraction force and the repulsion force are just equal and opposite at a certain point, the unmanned aerial vehicle is easy to fall into a local optimal solution at the moment, and the expression form is that the unmanned aerial vehicle continuously vibrates near the point.
(2) When the target point is far away, the attraction force becomes extremely large, and the unmanned aerial vehicle path may touch an obstacle with relatively small repulsive force.
(3) When an obstacle exists near the target point, the repulsive force is very large, the attractive force is relatively small, and the unmanned aerial vehicle can hardly reach the target point.
The following measures are proposed in this section for three problems of the APF algorithm, which are described separately below.
This subsection proposes the following solution to problem (1):
when the unmanned aerial vehicle is judged to be not zero at a certain point, the total force borne by the unmanned aerial vehicle is zero. Then it can be determined that the drone is stuck in the locally optimal solution at this time. Assuming that an equivalent obstacle exists, the repulsive force generated by the obstacle and the repulsive force received by the unmanned aerial vehicle are the same in magnitude and direction. Unmanned aerial vehicle, equivalent barrier, target must be on the same line this moment. The specific location of the equivalent obstacle is deduced inversely from the above formula. And the flying direction of the unmanned aerial vehicle jumping out of the local optimal is set as the tangential direction of the unmanned aerial vehicle position and the obstacle expansion area circle, and the unmanned aerial vehicle can be selected from left and right. Fig. 2 gives a schematic view of the direction of its movement.
The method of avoiding getting into a locally optimal solution can also be implemented by adding additional disturbances to the drone. However, directly adding disturbance to the unmanned aerial vehicle is not beneficial to stable flight of the unmanned aerial vehicle, and this section determines to apply random disturbance to the equivalent obstacle on the basis of adding the equivalent obstacle, and the specific method is to add a random position error (q) when the position of the equivalent obstacle is reversely solved, as shown in fig. 3.
In the case that the problem (2) may encounter an obstacle, mainly due to the fact that the attractive force and the repulsive force are not proportional, it can be known from the above formula that the attractive force is linear with the distance, and the phenomenon that the attractive force is too large due to too large distance can be avoided by modifying the linear corresponding relationship. The gravitational field function is modified to:
in the formula (I), the compound is shown in the specification,
representing the distance threshold between no targets of the drone, ξ is the repulsion coefficient,
the corresponding gravity calculation method comprises the following steps:
compared with the formula in the first step, when the distance between the unmanned aerial vehicle and the target is smaller than a set threshold, the calculation results of the unmanned aerial vehicle and the target are the same, when the distance is larger than the threshold, the attraction force applied to the unmanned aerial vehicle is not in a linear relation with the distance, and the farther the distance is, the slower the attraction force increasing speed is. The attractive force is limited so that it is not so large as to mask the influence of the repulsive force.
For the case that the target point of problem (3) is not reachable, the distance between the drone and the target point may be superimposed to the nth power on the basis of the formula in step one. The new repulsive force field function calculation method is:
from the above formula, it can be seen that, for the situation that the target point is close to the obstacle, when the unmanned aerial vehicle is approaching the obstacle again, according to the U in the step oneatt(q)The repulsive force field obtained by the calculation formula is increased due to the improved Uatt(q)The formula adds the influence of the distance between the unmanned aerial vehicle and the target point, and the formula is
In reducing, this factor can play the dragging action to the increase of repulsion field to let unmanned aerial vehicle can reach the target point smoothly.
The paths drawn by the improved APF calculation rules have the problems of too many fold lines of expected paths, large inflection angle and the like due to the fact that the unmanned aerial vehicle frequently enters and exits an obstacle expansion area in the obstacle avoidance process, the risks of flying of the unmanned aerial vehicle formation are increased due to the defects, and the efficiency of task execution of the formation is reduced. The invention provides an expected path smoothing strategy based on a Kernel method, so that an expected planned path processed by the Kernel method is more suitable for the flight of a fixed wing unmanned aerial vehicle.
The Kernel data smoothing method is widely applied to the field of information, the essential principle of the Kernel data smoothing method is also the minimum binomial smoothing of a moving window, and the current point is fitted by utilizing the weighted linear combination of all data in the window, so that the smoothing effect is obtained.
Definition of y*(i) For the smoothed data, the bandwidth is h, s (t) represents a weighting function, the window length is n, y (j) represents the jth data in the window, and the calculation method of the fitting value at the time t is as follows:
weighting function S in the formulaj(t) calculation method:
in the formula
tj represents the time of the current time, and Kernel (x) is the Kernel function, which is the Kernel of Kernel smoothing.
There are three expressions, representing different weighting functions:
a uniformity function:
a quadratic function:
gaussian function:
too large a selection of the window length n may cause the weighting function Sj(t) the delay is severe due to computational difficulties, and under-fitting can be caused by too small n selection. Therefore, the method determines to select n with proper size to smooth the expected path in a segmented mode, keeps the continuity and smoothness of the connection points of each segment, and selects a quadratic function to fit through the weighting function.
Assuming improved APF algorithm at t1,t2,t3,t4,t5,t6,t7,t8The expected point position planned at the moment is P1,P2,P3,P4,P5,P6,P7,P8Will P1,P2,P3,P4As a first stage, P5,P6,P7,P8As a second stage, n is 4. Fig. 4 shows a schematic diagram of path generation after Kernel smoothing.
For the unmanned aerial vehicles flying in the formation, other airplanes are regarded as moving obstacles. Each drone in the formation generates a repulsive field that acts on all other drones in the expansion zone. The obstacle avoidance between the unmanned aerial vehicles in the formation can be understood as a mutual avoidance process, and the obstacle avoidance between the two unmanned aerial vehicles on the horizontal plane is taken as an example for analysis. The unmanned aerial vehicle executing the obstacle avoidance process is also regarded as an obstacle by another unmanned aerial vehicle. Fig. 5 shows a schematic diagram of the respective repulsive force fields of two drones.
Considering that the flight speed of the fixed-wing unmanned aerial vehicle is far higher than that of a robot and a multi-rotor wing, and has the limitation of minimum turning radius, the obstacle avoidance of the fixed-wing unmanned aerial vehicle is different from that of a robot or a rotor wing moving in six degrees of freedom, and the execution of the obstacle avoidance action needs a period of buffering. In view of this feature, the present invention redesigns a new repulsive field function.
Suppose that the j-th aircraft in the formation exerts a repulsive force on the location q in the environment. Its repulsive force field function for point q is defined as follows:
where ξ is the coefficient of repulsion, σ0Is the radius of the expansion area of the unmanned aerial vehicle,
representing the distance between the drone and the point q, ∈ (0,1) is the expansion zone separation factor.
The repulsion that this unmanned aerial vehicle that corresponds is to q is:
distance between machines greater than σ0In the process, the repulsion force of the two machines is zero, and the two machines do not need to avoid barriers; distance between machines is less than sigma0When the two machines enter respective expansion regions and act as barriers to each other, the repulsive force is divided into two sections
The distance between the repulsion generated between the unmanned aerial vehicle and the unmanned aerial vehicle is in a linear relation, the change amplitude of the repulsion is small, which is equivalent to a buffer time for the unmanned aerial vehicle to execute obstacle avoidance action, and the repulsion and the distance are slowly kept away from each other, so that large-angle maneuvering is not frequently performed; when in use
When, two machines are very near this moment, need keep away the barrier immediately for avoiding bumping, and this is facing to being close of two quick-witted distances, and repulsion becomes exponential increase, forces unmanned aerial vehicle to keep away from the other side fast, is in
The calculated repulsion force is equal in the two ranges, i.e. the repulsion force is continuous and does not causeBecomes an instructed mutation.
The traditional APF algorithm judges whether the two machines generate repulsion action, and only the distance between the two machines is used as judgment. This obviously does not correspond to the characteristics of the fixed-wing drone direct flight type, i.e. the fixed wing has the minimum stall speed and cannot back up. Assuming the inflation zone radius σ of all drones in the formation0Both are equal, now analyzing the two-plane situation as in fig. 6, the arrow direction in the figure represents the speed direction of the drone.
Defining the distance between two machines in the graph as the hypothesis
Suppose two machines distance in the figure
Equal and smaller than the radius sigma of the expansion zone of the obstacle0. For the diagram (a) in fig. 6, although the distance between the two machines enters the obstacle avoidance area, the two machines are flying in the opposite direction, and the possibility of collision is theoretically impossible; (b) the flying direction of the two aircrafts forms 90 degrees, and the two aircrafts are far away from each other, so that collision is not possible in theory; (c) the flight paths of the two aircrafts are crossed, and the possibility of collision exists; (d) the two machines fly relatively, and the possibility of collision is highest.
According to the characteristics of fixed-wing flight, the radius sigma of the expansion zone of the obstacle needs to be reset0And (4) defining. The calculation steps are as follows:
the first step is as follows: two adjacent unmanned aerial vehicles regard self coordinates as original points to the ray of the flying speed direction, and whether the distance between the two rays is smaller than a small distance or not is judgedminIf not, indicating no possibility of collision, let σ00, the unmanned aerial vehicle on the other side does not generate repulsion force on the unmanned aerial vehicle on the other side; if the distance between two rays is less thanminIf so, the paths of the two machines may intersect, and the second step is carried out; selectingminThe principle is based on factors such as unmanned aerial vehicle sensor error, body size and control precision;
the second step is that: as shown in fig. 7, in the case of meeting two drones, the time threshold for collision between two drones is defined as TmThe value is indicated in the predictionAt the time of collision, how much time is left for processing at least, using q1And q is2Representing the positions of the drone one and the drone two,
representing the velocity vectors of drone one and drone two,vqrepresenting vectors
And
the included angle of the unmanned plane II is analyzed, the unmanned plane I is used as a barrier at the moment, and then sigma is calculated0The calculation method of (2) is as follows:
considering the position and course of the long plane as the reference points of other wing planes in formation flight, the invention determines that the long plane only generates a repulsive field and does not execute obstacle avoidance in the obstacle avoidance process. When meeting with other bureaucratic planes, the other bureaucratic planes execute the obstacle avoidance command. The invention takes the tangential direction of the moving speed of the obstacle as the direction of the repulsive force. FIG. 8, F1、
Representing the resultant, gravitational and repulsive forces to which the drone is subjected, F2、
And the total force, the attraction force and the repulsion force borne by the unmanned aerial vehicle II are represented.
For the on-line dynamic obstacle avoidance problem of formation, the invention provides corresponding improvement measures aiming at three defects of an artificial potential field method on the basis of the artificial potential field method, and then combines a Kernel method to carry out smoothing processing on an expected path, thereby solving the problem of expected path oscillation. Finally, on the basis, according to the flight characteristics of the fixed-wing unmanned aerial vehicle, the inter-aircraft obstacle avoidance artificial potential field is designed, so that multiple aircrafts cannot fly into a static obstacle area and collide with other aircrafts when flying in formation.
Claims (7)
1. A method for unmanned aerial vehicle formation form change and online dynamic obstacle avoidance is characterized by comprising the following steps:
step one, calculating a repulsive force field and a repulsive force of the unmanned aerial vehicle and resultant force borne by the unmanned aerial vehicle through an artificial potential field algorithm;
step two, improving the calculation of the artificial potential field algorithm in the step one;
combining with improved artificial potential field algorithm calculation, providing an expected path smoothing strategy based on a Kernel method for solving the problems of excessive fold lines and large inflection point angle of an expected path caused by frequent entering and exiting of an obstacle expansion area in the obstacle avoidance process of the unmanned aerial vehicle;
designing an inter-aircraft obstacle avoidance algorithm for avoiding the unmanned aerial vehicles;
wherein, the first step specifically comprises:
defining the position of the unmanned aerial vehicle as q, and then defining the gravitational field function U of the unmanned aerial vehicle at the point qatt(q)And gravitational force Fatt(q)Respectively satisfy the following formulas:
where λ represents a gravitational gain, ρα(q) represents the distance between the point q and the target a;
the repulsion field and repulsion at point q of the unmanned aerial vehicle are as follows:
η is the repulsive force gain, ρ0Is the maximum radius of influence of the obstacle,
ρr(q) is the distance from point q to the obstacle r;
the resultant force F experienced by the drone is:
F＝Fatt+Frep
Fattas the attraction of the target to the drone, FrepDefining the repulsion of the obstacle to the unmanned aerial vehicle and the attraction of the target point to the unmanned aerial vehicle as the negative gradients of the corresponding repulsion field and attraction field respectively for the repulsion of the obstacle to the unmanned aerial vehicle, and then the attraction F of the target point is applied to the point qatt(q) and repulsive force F of obstaclerep(q) negative gradients of the gravitational potential function and the repulsive potential function of the point respectively,
the assumption that there are N obstacles simultaneously affecting the drone is that, according to the principle of superposition, the resultant force F of the drone is:
in the formula
Repulsive force generated for the i-th obstacle.
2. The method of claim 1, wherein when it is determined that the attraction force of the unmanned aerial vehicle at a certain point is not zero and the total force applied to the unmanned aerial vehicle is zero: then, the unmanned aerial vehicle can be judged to be trapped in the local optimal solution at the moment, and if an equivalent barrier exists, the magnitude and direction of the repulsive force generated by the barrier are the same as those of the repulsive force borne by the unmanned aerial vehicle, and at the moment, the unmanned aerial vehicle, the equivalent barrier and the target are necessarily collinear; the specific position of equivalent barrier is reversely released by the artificial potential field method, and the specified flying direction that the unmanned aerial vehicle jumps out of the local optimum is the tangential direction of the unmanned aerial vehicle position and the barrier expansion area circle, and the unmanned aerial vehicle position and the barrier expansion area circle are arbitrarily taken from left to right.
3. The unmanned aerial vehicle formation and online dynamic obstacle avoidance method according to claim 1, wherein when the magnitude of the attractive force and the repulsive force is not proportional, the gravitational field function at the moment is:
in the formula (I), the compound is shown in the specification,
representing the distance threshold between no targets of the drone, ξ is the repulsion coefficient,
the corresponding gravity calculation method comprises the following steps:
4. the unmanned aerial vehicle formation form transformation and online dynamic obstacle avoidance method according to claim 1, wherein when a target point is in an unreachable condition, the n-th power of the distance between the unmanned aerial vehicle and the target point is superimposed on the basis of an artificial potential field method, and then the new repulsive field function calculation method is as follows:
5. the method as claimed in claim 1, wherein y is defined to increase smoothing effect*(i) For the smoothed data, the bandwidth is h, s (t) represents a weighting function, the window length is n, y (j) represents the jth data in the window, and the calculation method of the fitting value at the time t is as follows:
weighting function S in the formulaj(t) calculation method:
in the formula
tj represents the time of the current time, and Kernel (x) is the Kernel function, which is the Kernel of Kernel smoothing.
6. The method for controlling the unmanned aerial vehicle formation cooperative control system based on the distributed architecture as claimed in claim 1, wherein the fourth step includes: assuming that the j-th airplane in the formation generates a repulsive force effect on the position q in the environment, the repulsive force field function on the q point is defined as follows:
where ξ is the coefficient of repulsion, σ0Is the radius of the expansion area of the unmanned aerial vehicle,
representing the distance between the drone and the point q, ∈ (0,1) being the expansion zone separation coefficient;
the repulsion that this unmanned aerial vehicle that corresponds is to q is:
distance between machines greater than σ0In the process, the repulsion force of the two machines is zero, and the two machines do not need to avoid barriers; distance between machines is less than sigma0When the two machines enter respective expansion regions and act as barriers to each other, the repulsive force is divided into two sections
The distance between the repulsion generated between the unmanned aerial vehicle and the unmanned aerial vehicle is in a linear relation, the change amplitude of the repulsion is small, which is equivalent to a buffer time for the unmanned aerial vehicle to execute obstacle avoidance action, and the repulsion and the distance are slowly kept away from each other, so that large-angle maneuvering is not frequently performed; when in use
When, two machines are very near this moment, need keep away the barrier immediately for avoiding bumping, and this is facing to being close of two quick-witted distances, and repulsion becomes exponential increase, forces unmanned aerial vehicle to keep away from the other side fast, is in
At this point, the calculated repulsion force of the two ranges is equal, i.e. the repulsion force is continuous, and the command cannot be suddenly changed.
7. The method for controlling the unmanned aerial vehicle formation cooperative control system based on the distributed architecture of claim 6, wherein the radius σ of the expansion area of the unmanned aerial vehicle is0The calculation steps are as follows:
the first step is as follows: two adjacent unmanned aerial vehicles regard self coordinates as original points to the ray of the flying speed direction, and whether the distance between the two rays is smaller than a small distance or not is judgedminIf not, indicating no possibility of collision, let σ00, the unmanned aerial vehicle on the other side does not generate repulsion force on the unmanned aerial vehicle on the other side; if the distance between two rays is less thanminIf so, the paths of the two machines may intersect, and the second step is carried out; selectingminThe principle is based on factors such as unmanned aerial vehicle sensor error, body size and control precision;
the second step is that: if two unmanned aerial vehicles meet, defining the time threshold value of two-aircraft collision as TmThis value represents how much time is left to process in anticipation of a collision, using q1And q is2Representing the positions of the drone one and the drone two,
representing the velocity vectors of drone one and drone two,vqrepresenting vectors
And
the included angle of the unmanned plane II is analyzed, the unmanned plane I is used as a barrier at the moment, and then sigma is calculated0The calculation method of (2) is as follows:
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