CN115617068A - Fixed-wing unmanned aerial vehicle cluster obstacle avoidance method and system based on artificial potential field method - Google Patents
Fixed-wing unmanned aerial vehicle cluster obstacle avoidance method and system based on artificial potential field method Download PDFInfo
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Abstract
The invention discloses a fixed wing unmanned aerial vehicle cluster obstacle avoidance method and system based on an artificial potential field method, wherein the method comprises the following steps: defining an area where the fixed-wing unmanned aerial vehicle is blocked by the obstacle to be an obstacle area, and fitting the obstacle area into an ellipse; setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode; calculating the attractive force borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and the target point, and calculating the total repulsive force borne by the piloting unmanned aerial vehicle based on the repulsive force formed by the elliptic obstacle region to the piloting unmanned aerial vehicle and the repulsive force formed by the adjacent following unmanned aerial vehicle to the piloting unmanned aerial vehicle; controlling the piloting unmanned aerial vehicle based on the attractive force and the total repulsive force borne by the piloting unmanned aerial vehicle; based on the repulsion that the unmanned aerial vehicle formed and adjacent other following unmanned aerial vehicles formed to this following unmanned aerial vehicle in the oval obstacle region, calculate this following unmanned aerial vehicle and receive total repulsion to control this following unmanned aerial vehicle. The invention ensures that the obstacle avoidance path is smoother and can avoid collision of unmanned aerial vehicles.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a fixed-wing unmanned aerial vehicle cluster obstacle avoidance method and system based on an artificial potential field method.
Background
In recent years, along with the continuous development of the unmanned aerial vehicle industry, the unmanned aerial vehicle is widely applied, but a single individual is limited by the visual field, load, endurance and the like, so that the work of large-area operation, reconnaissance and the like is difficult to complete. Therefore, unmanned aerial vehicle clusters get important attention in the fields of scientific research and engineering. The unmanned cluster enables a plurality of unmanned aerial vehicles with limited autonomous ability to communicate with each other through an ad hoc mechanism, share information such as position, speed and obstacle, and complete mutual cooperation under a high autonomous degree, so that a preset task is completed or a designated area is reached to complete flight action under the unmanned control condition.
Due to the complementary action among all unmanned aerial vehicles in the cluster, the unmanned aerial vehicles have the advantages of survivability, cooperativity and expansibility, play great advantages in practical application, and can be used for tasks such as large-area reconnaissance and striking, biological control, rescue and search and capture and the like. Common unmanned aerial vehicle is mostly rotor unmanned aerial vehicle on the existing market, can be used to commercial flight performance and take photo by plane etc.. And research about fixed wing unmanned aerial vehicle is then less, compares in rotor unmanned aerial vehicle, and fixed wing unmanned aerial vehicle flying speed is faster, and turning radius needs be bigger, so requires more to the algorithm of cluster.
In the existing clustering method, due to the complexity and variability of the working environment and the relatively large cluster volume, cluster obstacle avoidance becomes one of the key problems which cannot be avoided. Aiming at the problem of obstacle avoidance of an unmanned aerial vehicle cluster, the current commonly used obstacle avoidance algorithm comprises an artificial potential field method, a neural network method and an optimization theory. The artificial potential field method is simple in principle, easy to understand and small in calculated amount, so that the calculation speed is high, the real-time performance is high, and the method becomes a preferred method in an obstacle avoidance control strategy. However, there still exists a certain common problem, such as local minima exists among the unmanned aerial vehicle, the obstacle and the target, that is, the unmanned aerial vehicle easily falls into the equilibrium point of the attractive force and the repulsive force, and thus cannot move forward. In addition, the navigation guiding acting force of the pilot is increased along with the increase of the distance between the unmanned aerial vehicle and the pilot, so that the unmanned aerial vehicle approaches the pilot continuously, although the repulsive force borne by the unmanned aerial vehicle is increased along with the decrease of the distance between the obstacles, the adopted repulsive force potential energy function is bounded, namely the repulsive acting force is also bounded, so that the phenomenon that the distance between the unmanned aerial vehicle and the obstacles is too close to be smaller than the minimum safe distance or even a collision phenomenon occurs may occur.
Disclosure of Invention
The embodiment of the invention provides a fixed wing unmanned aerial vehicle cluster obstacle avoidance method and system based on an artificial potential field method, which are used for solving the problem that the unmanned aerial vehicle cluster obstacle avoidance method in the prior art is not suitable for a fixed wing unmanned aerial vehicle cluster.
The fixed wing unmanned aerial vehicle cluster obstacle avoidance method based on the artificial potential field method comprises the following steps:
defining an area in which a barrier obstructs the flight of the fixed-wing unmanned aerial vehicle as a barrier area, and fitting the barrier area into an ellipse;
setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode;
calculating attractive force borne by a piloted unmanned aerial vehicle based on the distance between the piloted unmanned aerial vehicle and a target point, and calculating total repulsive force borne by the piloted unmanned aerial vehicle based on repulsive force formed by an oval obstacle region to the piloted unmanned aerial vehicle and repulsive force formed by adjacent following unmanned aerial vehicles to the piloted unmanned aerial vehicle;
controlling the piloted unmanned aerial vehicle based on the attractive force borne by the piloted unmanned aerial vehicle and the total repulsive force borne by the piloted unmanned aerial vehicle;
to every unmanned aerial vehicle that follows, based on the repulsion that this followed unmanned aerial vehicle and adjacent other following unmanned aerial vehicle that the oval obstacle region formed to this follow unmanned aerial vehicle formed the repulsion, calculate this following unmanned aerial vehicle and receive total repulsion to based on this following unmanned aerial vehicle and receive total repulsion, control should follow unmanned aerial vehicle.
According to some embodiments of the invention, the calculating the gravitation borne by the piloted drone based on the distance between the piloted drone and the target point comprises:
judging whether the distance between the piloting unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and the target point.
According to some embodiments of the present invention, the calculating a total repulsive force borne by the following unmanned aerial vehicle based on a repulsive force formed by the elliptical obstacle region to the following unmanned aerial vehicle and a repulsive force formed by another adjacent following unmanned aerial vehicle to the following unmanned aerial vehicle includes:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points with the distance smaller than the detection distance from the following unmanned aerial vehicle from all the obstacle points;
calculating repulsion R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the location of the ith target obstacle point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
According to some embodiments of the invention, the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Represents the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
according to some embodiments of the invention, the method further comprises:
in the control process of a piloting unmanned aerial vehicle or a following unmanned aerial vehicle, fixed disturbance is added.
The fixed wing unmanned aerial vehicle cluster obstacle avoidance system based on the artificial potential field method comprises the following steps:
the obstacle processing module is used for defining an area where an obstacle obstructs the flight of the fixed-wing unmanned aerial vehicle as an obstacle area and fitting the obstacle area into an ellipse;
the device comprises a setting module, a control module and a control module, wherein the setting module is used for setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode;
the control module is used for calculating attractive force borne by the piloted unmanned aerial vehicle based on the distance between the piloted unmanned aerial vehicle and a target point, and calculating total repulsive force borne by the piloted unmanned aerial vehicle based on repulsive force formed by the oval obstacle region to the piloted unmanned aerial vehicle and repulsive force formed by adjacent following unmanned aerial vehicles to the piloted unmanned aerial vehicle; controlling the piloted unmanned aerial vehicle based on the attractive force borne by the piloted unmanned aerial vehicle and the total repulsive force borne by the piloted unmanned aerial vehicle; to every unmanned aerial vehicle that follows, based on the repulsion that the unmanned aerial vehicle formed and adjacent other unmanned aerial vehicles that follow were followed to the repulsion that this unmanned aerial vehicle formed in the oval obstacle region, calculate this unmanned aerial vehicle that follows and receive total repulsion to based on this unmanned aerial vehicle that follows receives, control this unmanned aerial vehicle that follows.
According to some embodiments of the invention, the control module is to:
and judging whether the distance between the piloted unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloted unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloted unmanned aerial vehicle based on the distance between the piloted unmanned aerial vehicle and the target point.
According to some embodiments of the invention, the control module is to:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points with the distance smaller than the detection distance from the following unmanned aerial vehicle from all the obstacle points;
calculating repulsive force R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the location of the ith target obstacle point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
According to some embodiments of the invention, the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Represents the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
according to some embodiments of the invention, the control module is further configured to:
in the control process of a piloting unmanned aerial vehicle or a following unmanned aerial vehicle, fixed disturbance is added.
By adopting the embodiment of the invention, the obstacle area is fitted into the ellipse, which is equivalent to adding extra virtual obstacle points, so that the obstacle avoiding path is smoother, and meanwhile, the unmanned aerial vehicle is also calculated as the obstacle points, thereby avoiding the mutual collision of the unmanned aerial vehicles.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. In the drawings:
FIG. 1 is a schematic diagram of an artificial potential field method;
FIG. 2 is a schematic diagram of a cluster formation of fixed wing drones in an embodiment of the present invention;
FIG. 3 is a schematic view of an original obstacle area;
FIG. 4 is a schematic view of an embodiment of the present invention fitted to an elliptical obstruction area;
fig. 5 is a schematic diagram of obstacle avoidance of a fixed-wing drone cluster in an embodiment of the present invention;
FIG. 6 is a schematic diagram of an obstacle avoidance path based on an original obstacle area;
FIG. 7 is a schematic diagram of an obstacle avoidance path based on an elliptical obstacle area according to an embodiment of the present invention;
fig. 8 is a flowchart of a fixed-wing unmanned aerial vehicle cluster obstacle avoidance method based on an artificial potential field method in the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
The fixed wing unmanned aerial vehicle cluster obstacle avoidance method based on the artificial potential field method comprises the following steps:
defining an area where a barrier obstructs the flight of the fixed-wing unmanned aerial vehicle as a barrier area, fitting the barrier area into an ellipse, comparing fig. 3 with fig. 4, wherein two black points in fig. 3 are two barrier areas, and two ellipses in fig. 4 are ellipse areas after the two barrier areas are fitted. The purpose of fitting the obstacle area to an oval shape is to make the path of the fixed wing drone around the obstacle smoother.
Setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode; it can be understood that one of the fixed wing unmanned aerial vehicle cluster is the piloting unmanned aerial vehicle, and other then for following the unmanned aerial vehicle, the piloting unmanned aerial vehicle is compared and is close to the target point in following the unmanned aerial vehicle. The formation of clusters of fixed wing drones may be referred to as triangular formation, circular formation, herringbone formation, or other formations of various shapes. After the formation is determined, the initial positions of all the follower unmanned planes and the pilot unmanned plane are correspondingly determined.
Calculating attractive force borne by a piloted unmanned aerial vehicle based on the distance between the piloted unmanned aerial vehicle and a target point, and calculating total repulsive force borne by the piloted unmanned aerial vehicle based on repulsive force formed by an oval obstacle region to the piloted unmanned aerial vehicle and repulsive force formed by adjacent following unmanned aerial vehicles to the piloted unmanned aerial vehicle;
controlling the piloted unmanned aerial vehicle based on the attractive force borne by the piloted unmanned aerial vehicle and the total repulsive force borne by the piloted unmanned aerial vehicle;
to every unmanned aerial vehicle that follows, based on the repulsion that this followed unmanned aerial vehicle and adjacent other following unmanned aerial vehicle that the oval obstacle region formed to this follow unmanned aerial vehicle formed the repulsion, calculate this following unmanned aerial vehicle and receive total repulsion to based on this following unmanned aerial vehicle and receive total repulsion, control should follow unmanned aerial vehicle.
Referring to fig. 1, the core principle of the artificial potential field method is to regard a target point and an obstacle as objects having attraction and repulsion to an unmanned aerial vehicle, respectively, and the resultant force of the attraction and the repulsion guides movement.
This application is based on artifical potential field method to control the motion of piloting unmanned aerial vehicle, and the improvement lies in, and this application still calculates repulsion with unmanned aerial vehicle as the barrier.
By adopting the embodiment of the invention, the obstacle area is fitted into the ellipse, which is equivalent to adding extra virtual obstacle points, so that the obstacle avoiding path is smoother, and meanwhile, the unmanned aerial vehicle is also calculated as the obstacle points, thereby avoiding the mutual collision of the unmanned aerial vehicles.
On the basis of the above-described embodiment, modified embodiments are further proposed, and it is to be noted here that, in order to make the description brief, only the differences from the above-described embodiment are described in each modified embodiment.
According to some embodiments of the invention, the major axis of the ellipse is less than 90 ° from the direction of the fixed-wing drone towards the target point.
According to some embodiments of the invention, the calculating the gravitation borne by the piloted drone based on the distance between the piloted drone and the target point comprises:
judging whether the distance between the piloting unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and the target point. Therefore, the situation that when the piloting unmanned aerial vehicle is far away from the target point, the gravity of the target point is too large, and movement abnormity is caused can be avoided.
According to some embodiments of the present invention, the calculating a total repulsive force borne by the following unmanned aerial vehicle based on a repulsive force formed by the elliptical obstacle region to the following unmanned aerial vehicle and a repulsive force formed by another adjacent following unmanned aerial vehicle to the following unmanned aerial vehicle includes:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points with the distance smaller than the detection distance from the following unmanned aerial vehicle from all the obstacle points;
calculating repulsion R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the ith target obstaclePosition of object point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
Adopt above-mentioned processing mode to consider following unmanned aerial vehicle many at flight in-process barrier point, if every barrier point all participates in the repulsion calculation, then can very big increase calculation burden, so set up a "detection distance" here, only when following the distance between unmanned aerial vehicle and the barrier point and being less than when detecting the distance, just can participate in the repulsion calculation. The method is also suitable for calculating the repulsion force of the piloted unmanned aerial vehicle.
According to some embodiments of the invention, the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Representing the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
according to some embodiments of the invention, the method further comprises:
in the control process of the piloting unmanned aerial vehicle or the following unmanned aerial vehicle, fixed disturbance is added. From this, can solve the pilotage unmanned aerial vehicle or follow unmanned aerial vehicle under artifical potential field method, if the balanced area of the power of sinking in, to the inapplicability of fixed wing unmanned aerial vehicle under current random disturbance, so change random disturbance into directional disturbance to prevent that unmanned aerial vehicle from turning to suddenly at will.
The following describes in detail a fixed-wing drone cluster obstacle avoidance method based on an artificial potential field method according to an embodiment of the present invention. It is to be understood that the following description is illustrative only and is not intended as a specific limitation of the invention. All similar structures and similar variations thereof adopted by the invention are included in the scope of the invention.
The fixed wing unmanned aerial vehicle cluster obstacle avoidance method based on the artificial potential field method comprises the following steps:
step 1: setting unmanned aerial vehicles, total number and starting positions of all unmanned aerial vehicles, setting terminal positions, setting simulation step numbers, setting positions and sizes of obstacles.
Step 2: in order to solve the problems that the steering angle of the unmanned aerial vehicle is too large, the unmanned aerial vehicle is not smooth in path and the like, the obstacle is refitted into an oval shape, namely the outer contour of the obstacle is artificially changed, and a virtual obstacle point is added to ensure that the path of the unmanned aerial vehicle is smooth.
And 3, step 3: setting a cluster communication topology matrix as
The last row of the matrix is the pilot information stream. As shown in fig. 2, the outbound degree is the maximum, and the inbound degree is 0, so that the node is a key node and can be used as a pilot of the whole cluster.
And 4, step 4: and setting the initial speed and the initial acceleration of each unmanned aerial vehicle. The setting follows the position error matrix between unmanned aerial vehicle and the piloting unmanned aerial vehicle, and it is 0 to be worth noting pilot and self error. The formation is set to be triangular, or circular, etc.
And 5: and calculating the distance of the piloting unmanned aerial vehicle relative to the terminal point and the azimuth angle relative to the terminal point. And if the distance is greater than the set maximum gravitation distance, taking the set gravitation distance as the distance between the unmanned aerial vehicle and the terminal. This measure is mainly to avoid under the current situation unmanned aerial vehicle to apart from the terminal when far away, the gravitation of terminal too big, cause the motion unusual.
Step 6: and calculating the magnitude of the repulsive force according to the barrier data returned by the sensor. Because there are many obstacles in the flight process of the unmanned aerial vehicle, if each obstacle participates in the calculation of the repulsive force, the calculation load is greatly increased, so that a detection distance is set at the position, and only when the distance between the unmanned aerial vehicle and the obstacle is smaller than the detection distance, the unmanned aerial vehicle participates in the calculation of the repulsive forceTo repulsive force calculation. According to the formula:
or
And summing all the repulsive forces to obtain the magnitude of the repulsive force of the unmanned aerial vehicle at the current position. The invention here optimizes the summation coefficient thereof, where d m For detecting the distance, the summation parameter which is originally constant becomes a variable value, so that the summation of the repulsive force is more reasonable, namely, the obstacle with a longer distance has smaller influence on the unmanned aerial vehicle.
And 7: and according to the magnitude of the resultant force, the speed of the unmanned aerial vehicle in the next step length is obtained, and the next position of the unmanned aerial vehicle is calculated according to the speed and the current position of the unmanned aerial vehicle. It is worth noting that: because the artificial potential field method has the position where the attractive force and the repulsive force are balanced, the calculated speed is very small, so that disturbance needs to be added to push the unmanned aerial vehicle to break the unreasonable balance, and because the unmanned aerial vehicle with the fixed wings cannot be suddenly and randomly steered, the method specially adds a reasonable azimuth angle and a speed value to the previous azimuth angle to break through the balance point.
And 8: in order to make the cluster unmanned aerial vehicles keep the formation, the position deviation between the unmanned aerial vehicles is added, and the position of the unmanned aerial vehicle of the pilot is calculated. That is, the ideal position matrix calculated in the previous step is subtracted from the position error matrix set in step 4 to obtain the ideal position of the maintaining formation.
And step 9: in order to solve the obstacle avoidance problem among multiple unmanned aerial vehicles, the unmanned aerial vehicles except the unmanned aerial vehicles are used as obstacles, the repulsive force is calculated, the speed of the unmanned aerial vehicles is calculated, and the collision problem of the unmanned aerial vehicles can be avoided.
Step 10: and comparing the obtained ideal speed with the maximum flying speed set by the unmanned aerial vehicle, and if the maximum flying speed is exceeded, moving according to the maximum speed.
Step 11: the actual movement speed of the unmanned aerial vehicle can be obtained according to the steps, and the next movement position of the unmanned aerial vehicle can be known by combining the last position of the unmanned aerial vehicle.
Step 12: and (5) circulating the steps from the step 5 to the step 11 until the positions of the unmanned aerial vehicle of the pilot and the terminal point are less than the set value, namely representing that the unmanned aerial vehicle formation completes the whole motion process.
The invention selects a distributed control structure in pilot-follower formation to realize formation control. The leader-follower formation is a common clustering mode, mainly comes from a consistent first-order formation algorithm idea, and is characterized in that how to control the state quantities such as the relative position, the speed and the like between the leader unmanned aerial vehicle and the follower unmanned aerial vehicle, when the position, the speed and the like reach certain stable states, the maintenance of the whole clustering formation is realized, and the leader unmanned aerial vehicle is the control core of a cluster. The first order continuous system model of the formation system is as follows:
in the formula
u i Respectively represent the state quantity and the input quantity under the i node (the state represents the information of the speed, the acceleration, the position and the like of the unmanned aerial vehicle calculated next time, and the input quantity is the information of the speed, the acceleration, the position and the like of the unmanned aerial vehicle calculated last time), and
u i ∈R n and n represents the dimension of the state quantity.
Calculation of input under general conditions
Wherein a is ij To form the adjacency matrix elements, N i Is member iA set of neighbors.
In the setting, the scene leads other robots to the pilot from the starting point and reaches the set end point after a certain distance, so that the algorithm of the followers in the formation under the ideal condition adopts the following form:
wherein, χ i (k) Represents the position of the ith drone calculated for the kth iteration, ∈ being a constant greater than 0, r ij Indicating the relative position between robot i and robot j, in relation to the distance between them.
The navigator algorithm can be written as:
p is a self-adjustable constant, k is a self-adjustable constant, D (k) is the distance between the k iterative pilots and the target point, aN i As coefficient of repulsion, rN i (k) The relative position of the pilot and other robots.
And the repulsion coefficient is increased after the problem is refined, and other factors such as the uniqueness of the obstacle are increased.
The unmanned aerial vehicle of the pilot scans the environment around according to the self-sensor to obtain the position of the surrounding obstacle according to the formula
Calculating repulsive force to move away from the obstacle, wherein R j Representing the sum of the repulsive forces to which the drone is subjected,
and the coordinate of the barrier is represented, m is the number of the barriers, and alpha is a repulsion coefficient and is used for adjusting the magnitude of the repulsion.
The α -repulsion coefficient may be according to the formula:
the result of the calculation is that,
XR is the current drone location.
d is the distance between the unmanned aerial vehicle and the obstacle, delta d, so that the repulsive force can be adjusted according to the requirement.
d m For the detection distance, the detection distance can be adjusted according to actual needs, and the delta is a constant, and influences are generated on the speed control of the unmanned aerial vehicle according to the formula.
The atress condition of follower unmanned aerial vehicle in the team need be taken into account to pilot unmanned aerial vehicle, and the atress calculates according to artifical potential field method and reachs, so pilot's control model as follows:
wherein beta is a constant, follower unmanned aerial vehicle's control model as follows:
for example, the starting point is set to (0, 0), the end point is set to (1500 ), and the simulation operation is performed by adding the obstacle.
Fig. 4 and 5 are simulation diagrams before and after unmanned aerial vehicle formation obstacle avoidance, respectively.
The obstacle in fig. 4 is obtained by performing original position refitting on the obstacle according to the algorithm in the invention, adding a virtual obstacle edge manually, and optimizing the calculation of the repulsive force coefficient.
As is apparent from comparison between fig. 6 and fig. 7, fig. 6 shows the flight path of the unmanned aerial vehicle without optimization, which has a sharp corner and is not smooth, and fig. 8 is a block diagram of the implementation process of the whole invention. Compared with the traditional artificial potential field method, the method is more suitable for the fixed-wing unmanned aerial vehicle cluster, and has obvious advantages and innovativeness.
The method can solve the problem that the unmanned aerial vehicle is inapplicable to the fixed wing unmanned aerial vehicle under the existing random disturbance if the unmanned aerial vehicle falls into a force balance area under the artificial potential field method, so that the random disturbance is changed into directional disturbance, and the unmanned aerial vehicle is prevented from steering suddenly and randomly. The problem that unmanned aerial vehicles collide with each other when the unmanned aerial vehicle cluster avoids the obstacle can be solved, so an obstacle avoidance model between the unmanned aerial vehicles is introduced. The problem that the unmanned aerial vehicle cluster keeps away the route of barrier is not smooth, and maneuverability is not strong to the fixed wing can be solved. Additional virtual obstacles are added to smooth the path.
As can be seen from fig. 5, the invention enables the unmanned aerial vehicle to keep the formation as much as possible in the obstacle avoidance process, so that the working range of the unmanned aerial vehicle cluster can be maintained.
Because the additional virtual barrier is added, the obstacle avoidance path is smoother, and meanwhile, the unmanned aerial vehicles are counted as the barrier, so that the unmanned aerial vehicles are prevented from colliding with each other.
It should be noted that the above-mentioned embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention, and those skilled in the art can make various modifications and changes. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The fixed wing unmanned aerial vehicle cluster obstacle avoidance system based on the artificial potential field method comprises the following steps:
the obstacle processing module is used for defining an area where an obstacle obstructs the flight of the fixed-wing unmanned aerial vehicle as an obstacle area and fitting the obstacle area into an ellipse;
the setting module is used for setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode;
the control module is used for calculating attractive force borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and a target point, and calculating total repulsive force borne by the piloting unmanned aerial vehicle based on repulsive force formed by an elliptical obstacle region to the piloting unmanned aerial vehicle and repulsive force formed by adjacent following unmanned aerial vehicles to the piloting unmanned aerial vehicle; controlling the piloting unmanned aerial vehicle based on the attractive force borne by the piloting unmanned aerial vehicle and the total repulsive force borne by the piloting unmanned aerial vehicle; to every unmanned aerial vehicle that follows, based on the repulsion that this followed unmanned aerial vehicle and adjacent other following unmanned aerial vehicle that the oval obstacle region formed to this follow unmanned aerial vehicle formed the repulsion, calculate this following unmanned aerial vehicle and receive total repulsion to based on this following unmanned aerial vehicle and receive total repulsion, control should follow unmanned aerial vehicle.
According to some embodiments of the invention, the control module is to:
judging whether the distance between the piloting unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and the target point.
According to some embodiments of the invention, the control module is to:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points of which the distance from the following unmanned aerial vehicle is smaller than the detection distance from all the obstacle points;
calculating repulsive force R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the location of the ith target obstacle point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
According to some embodiments of the invention, the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Representing the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
according to some embodiments of the invention, the control module is further configured to:
in the control process of a piloting unmanned aerial vehicle or a following unmanned aerial vehicle, fixed disturbance is added.
It should be noted that, in the description of the present specification, the numbers of the embodiments of the present invention are only for description, and do not represent the merits of the embodiments.
Suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the description of the present invention, and have no specific meaning in themselves. Thus, "module", "component" or "unit" may be used mixedly.
Reference to the description of the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," or the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Although some embodiments described herein include some features included in other embodiments instead of others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. The particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. For example, in the claims, any of the claimed embodiments may be used in any combination.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one of 8230, and" comprising 8230does not exclude the presence of additional like elements in a process, method, article, or apparatus comprising the element.
Any reference signs placed between parentheses shall not be construed as limiting the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The use of the words first, second, third and the like are used for distinguishing between similar objects and not necessarily for describing any order. These words may be interpreted as names.
"and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
Claims (10)
1. A fixed wing unmanned aerial vehicle cluster obstacle avoidance method based on an artificial potential field method is characterized by comprising the following steps:
defining an area in which a barrier obstructs the flight of the fixed-wing unmanned aerial vehicle as a barrier area, and fitting the barrier area into an ellipse;
setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode;
calculating the attractive force borne by a piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and a target point, and calculating the total repulsive force borne by the piloting unmanned aerial vehicle based on the repulsive force formed by an elliptical obstacle region to the piloting unmanned aerial vehicle and the repulsive force formed by adjacent following unmanned aerial vehicles to the piloting unmanned aerial vehicle;
controlling the piloting unmanned aerial vehicle based on the attractive force borne by the piloting unmanned aerial vehicle and the total repulsive force borne by the piloting unmanned aerial vehicle;
to every unmanned aerial vehicle that follows, based on the repulsion that the unmanned aerial vehicle formed and adjacent other unmanned aerial vehicles that follow were followed to the repulsion that this unmanned aerial vehicle formed in the oval obstacle region, calculate this unmanned aerial vehicle that follows and receive total repulsion to based on this unmanned aerial vehicle that follows receives, control this unmanned aerial vehicle that follows.
2. The method of claim 1, wherein calculating the gravitational force experienced by the piloted drone based on a distance between the piloted drone and a target point comprises:
judging whether the distance between the piloting unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and the target point.
3. The method of claim 1, wherein calculating the total repulsion force experienced by the following drone based on the repulsion force of the elliptical obstacle region against the following drone and the repulsion force of adjacent other following drones against the following drone comprises:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points with the distance smaller than the detection distance from the following unmanned aerial vehicle from all the obstacle points;
calculating repulsion R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the location of the ith target obstacle point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
4. A method according to claim 3, wherein the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Representing the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
5. the method of claim 1, wherein the method further comprises:
in the control process of a piloting unmanned aerial vehicle or a following unmanned aerial vehicle, fixed disturbance is added.
6. The utility model provides a fixed wing unmanned aerial vehicle cluster keeps away barrier system based on artifical potential field method which characterized in that includes:
the obstacle processing module is used for defining an area where an obstacle obstructs the flight of the fixed-wing unmanned aerial vehicle as an obstacle area and fitting the obstacle area into an ellipse;
the device comprises a setting module, a control module and a control module, wherein the setting module is used for setting fixed-wing unmanned aerial vehicle cluster formation in a piloting unmanned aerial vehicle following unmanned aerial vehicle mode;
the control module is used for calculating attractive force borne by the piloting unmanned aerial vehicle based on the distance between the piloting unmanned aerial vehicle and a target point, and calculating total repulsive force borne by the piloting unmanned aerial vehicle based on repulsive force formed by an elliptical obstacle region to the piloting unmanned aerial vehicle and repulsive force formed by adjacent following unmanned aerial vehicles to the piloting unmanned aerial vehicle; controlling the piloting unmanned aerial vehicle based on the attractive force borne by the piloting unmanned aerial vehicle and the total repulsive force borne by the piloting unmanned aerial vehicle; to every unmanned aerial vehicle that follows, based on the repulsion that the unmanned aerial vehicle formed and adjacent other unmanned aerial vehicles that follow were followed to the repulsion that this unmanned aerial vehicle formed in the oval obstacle region, calculate this unmanned aerial vehicle that follows and receive total repulsion to based on this unmanned aerial vehicle that follows receives, control this unmanned aerial vehicle that follows.
7. The system of claim 6, wherein the control module is to:
and judging whether the distance between the piloted unmanned aerial vehicle and the target point is larger than the maximum gravitation distance, if so, calculating the gravitation borne by the piloted unmanned aerial vehicle based on the maximum gravitation distance, otherwise, calculating the gravitation borne by the piloted unmanned aerial vehicle based on the distance between the piloted unmanned aerial vehicle and the target point.
8. The system of claim 6, wherein the control module is to:
determining a plurality of obstacle points based on the elliptical obstacle area;
screening all target obstacle points with the distance smaller than the detection distance from the following unmanned aerial vehicle from all the obstacle points;
calculating repulsion R formed by all target obstacle points to the following unmanned aerial vehicle j based on formula 1 j ：
Wherein,
indicating the location of the ith target obstacle point, x j Representing the target position of the following drone, m representing the total number of target obstacle points, and α representing the repulsion coefficient.
9. The system according to claim 8, wherein the repulsive force coefficient α is calculated according to formula 2 or formula 3:
wherein, χ R Representing the current actual position of the following drone, d m Representing the detection distance, delta being a constant, d representing the distance between the following unmanned aerial vehicle and the target obstacle point;
10. the system of claim 6, wherein the control module is further configured to:
in the control process of a piloting unmanned aerial vehicle or a following unmanned aerial vehicle, fixed disturbance is added.
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| CN116627181B (en) * | 2023-07-25 | 2023-10-13 | 吉林农业大学 | Intelligent obstacle avoidance method for plant protection unmanned aerial vehicle based on spatial reasoning |
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