CN108549407B - Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance - Google Patents
Control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance Download PDFInfo
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Abstract
The invention relates to a control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance, which comprises the following steps: when the unmanned aerial vehicles form a formation to execute a flight task, detecting the position and speed information of the unmanned aerial vehicles and the airspace barriers through an airborne sensor, and abstracting the barriers in a flight space into a sphere; secondly, establishing unmanned aerial vehicle formation communication topology according to the position information of each unmanned aerial vehicle, and realizing information interaction between neighbor unmanned aerial vehicles in formation by adopting a distributed transmission mode; thirdly, establishing an unmanned aerial vehicle dynamic model; and (IV) when the obstacle is detected, determining the safe distance for the unmanned aerial vehicle to avoid the obstacle, judging whether the distance between any unmanned aerial vehicle and the obstacle in the formation of the unmanned aerial vehicles meets the requirement of the safe distance, and if not, adjusting the formation through a virtual force strategy and avoiding the obstacle. The algorithm has operability and simplicity, can realize cooperative formation of multiple unmanned aerial vehicles and avoid obstacles more flexibly, and has very important significance for actual multi-cooperative combat.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to a control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance.
Background
The unmanned aerial vehicle has good performance and obvious advantages in the aspects of military and civil use with complex autonomous execution, such as airspace monitoring, radiation monitoring, target positioning and tracking and the like. Many unmanned aerial vehicle formations have the advantage of high task execution success rate even more, mainly show in: (1) large-view detection, high-precision positioning and multi-angle imaging; (2) the success rate of task execution and the overall hit rate can be improved; (3) the endurance time is prolonged, and the integral flight resistance is reduced.
When the mission is performed, obstacles such as buildings, mountains, bird groups and the like may exist in the actual flight airspace of the formation of the unmanned aerial vehicles, and the existence of the obstacles threatens the flight safety of the unmanned aerial vehicles. In addition to obstacles, each drone in the formation is also to avoid collisions with other drones.
At present, the flight paths of a plurality of unmanned aerial vehicles in formation are mostly realized by adopting a model prediction control algorithm. The relatively mature and relatively universal formation algorithms mainly include the Changji-Liquan laws, the artificial situation laws, the behavioral laws and the virtual structure laws. However, when an obstacle is encountered, the existing control algorithm cannot control multiple unmanned aerial vehicles to keep good formation constraint and flexibly avoid the obstacle, communication among the unmanned aerial vehicles is easily interrupted, and designated tasks cannot be completed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a control algorithm for the obstacle avoidance of the multi-unmanned aerial vehicle collaborative formation, which realizes the control of the obstacle avoidance of the multi-unmanned aerial vehicle collaborative formation and allows the multi-unmanned aerial vehicle to avoid the obstacle more flexibly in the formation form.
The technical problem to be solved by the invention is realized by the following technical scheme. The invention relates to a control algorithm for multi-unmanned aerial vehicle cooperative formation obstacle avoidance, which is characterized by comprising the following steps:
when the unmanned aerial vehicles form a formation to execute a flight task, detecting the position and speed information of the unmanned aerial vehicles and the airspace barriers through an airborne sensor, and abstracting the barriers in a flight space into a sphere;
secondly, establishing unmanned aerial vehicle formation communication topology according to the position information of each unmanned aerial vehicle, and realizing information interaction between neighbor unmanned aerial vehicles in formation by adopting a distributed transmission mode;
thirdly, establishing an unmanned aerial vehicle dynamic model;
(IV) when detecting the barrier, confirm unmanned aerial vehicle and keep away the safe distance of barrier to judge whether the interval between arbitrary unmanned aerial vehicle and the barrier accords with safe distance's requirement in the unmanned aerial vehicle formation, if do not accord with, then through virtual power strategy adjustment formation shape and keep away the barrier, wherein, unmanned aerial vehicle keeps away the safe distance of barrier and shows as formula (7):
wherein R isoAnd RiThe radius of the obstacle and the radius of the unmanned aerial vehicle are respectively used for representing the safety distance required by the uncertainty of the information, and eta represents the flight speed of the unmanned aerial vehicleThe angle between the angle direction and the connecting line between the center of the unmanned aerial vehicle and the center of the barrier, kvAnd kηRespectively for adjusting the relative speed and the control parameters of the included angle,
representing the relative speed of movement, V, of the drone and the obstaclei(t) and Vo(t) represents the speed of the drone and the moving obstacle, respectively, at time t, when the obstacle is stationary,
the technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (ii) of the control algorithm for multi-UAV collaborative formation obstacle avoidance, the communication topology is defined as
Wherein the content of the first and second substances,
is a finite non-empty node; representing each unmanned aerial vehicle in the formation;
the information transmission state between the unmanned aerial vehicles is represented by a set of edges between the nodes;
representing a connection weight matrix, aijRepresenting the weight of the connection from unmanned plane node i to unmanned plane node j, if any, from vjDelivery to viThen vjIs viNeighbor drone of (a) this time ij1, otherwise aij＝0；νiIs a set of neighbors of
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (iv) of the control algorithm for multi-unmanned aerial vehicle collaborative formation obstacle avoidance, the virtual force strategy includes the following steps:
(1) carrying out stress analysis on the unmanned aerial vehicle formation, including solving the problems that each unmanned aerial vehicle is subjected to the acting force of other unmanned aerial vehicles in the formation, the attraction of the target point to each unmanned aerial vehicle in the formation, and the repulsion of the obstacle to each unmanned aerial vehicle in the formation;
(2) calculating resultant force borne by each unmanned aerial vehicle;
(3) defining a speed target quantity, and calculating the expected speed of each unmanned aerial vehicle in the three-dimensional direction of the ground coordinate;
(4) converting the expected speed of each unmanned aerial vehicle in each direction into a target flight control instruction of each unmanned aerial vehicle;
(5) and adjusting the flight state of each unmanned aerial vehicle by inputting a target flight control command of each unmanned aerial vehicle so as to keep the formation of the unmanned aerial vehicle formation and change the flight track of the formation of the unmanned aerial vehicle to avoid obstacles.
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (1) of the control algorithm for obstacle avoidance in cooperative formation of multiple unmanned aerial vehicles, the acting force of each unmanned aerial vehicle on other unmanned aerial vehicles in the formation is as follows:
wherein k is1And k2Respectively, a normal number, P, cooperating with the formation control and the flight speed controli(xi,yi,zi) And Pj(xj,yj,zj) Three-dimensional space positions r of unmanned planes i and j in a ground coordinate system respectivelyijRepresents the set spatial distance, V, between UAVs i and jiAnd VjRepresenting the flight speeds of drones i, j, respectively.
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (1) of the control algorithm for obstacle avoidance in cooperative formation of multiple unmanned aerial vehicles, the attraction force of the target point on each unmanned aerial vehicle in the formation is as follows:
wherein the content of the first and second substances,
is shown in equation (6):
wherein k is3Is a proportional gain coefficient of the gravitational potential field, Pt(xt,yt,zt) Is the three-dimensional space position of the target point in the ground coordinate system, rhoit＝||Pt-Pi| represents the spatial distance between unmanned aerial vehicle i and target point t, and | represents L2And (4) norm.
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the above step (1) of the control algorithm for obstacle avoidance in cooperative formation of multiple unmanned aerial vehicles, the repulsion force generated by the obstacle to each unmanned aerial vehicle in the formation is:
wherein k is4Is a positive proportional gain coefficient of the repulsive potential field, c is an adjustable control parameter, pioRepresents the distance, ρ, between drone i and the obstacleiominIndicating the minimum safe distance, P, between the drone and the obstacleo(xo,yo,zo) Is the three-dimensional space position of the obstacle under the ground coordinate system.
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (2) of the control algorithm for obstacle avoidance in cooperative formation of multiple unmanned aerial vehicles, the resultant force applied to each unmanned aerial vehicle is equal to the resultant force of the acting force generated by other unmanned aerial vehicles in the formation, the attractive force generated by the target point, and the repulsive force generated by the obstacle, as shown in formula (11):
the technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (3) of the control algorithm for multi-unmanned aerial vehicle collaborative formation obstacle avoidance, the speed target quantity is defined as follows:
the total virtual force received by the unmanned aerial vehicle is decomposed to three-dimensional directions, and the speeds of the unmanned aerial vehicles in the three-dimensional directions are respectively shown in formulas (13), (14) and (15):
wherein, Vi x，Vi y，Vi zThe speeds of the unmanned aerial vehicle i in the three-dimensional x, y and z directions,
velocity, x, of drone j in three-dimensional x, y, z directions, respectivelyijFor set distance of unmanned plane i and j in x-axis direction, yijFor a set distance, z, of unmanned planes i and j in the y-axis directionijThe set distance between the unmanned planes i and j in the z-axis direction.
The technical problem to be solved by the present invention can be further achieved by the following technical means. In the step (4) of the control algorithm for multi-unmanned aerial vehicle collaborative formation obstacle avoidance, the target flight control instruction is as follows:
wherein, Vi c，
Respectively, a flight speed instruction, a course angle instruction and a yaw angle instruction.
Compared with the prior art, the invention aims at the task characteristics of unmanned aerial vehicle formation obstacle avoidance, establishes communication topology in a multi-unmanned aerial vehicle system, and each unmanned aerial vehicle sends own information state through a communication network and obtains the state of a neighbor unmanned aerial vehicle, and then integrates internal environment information by using a formation consistency algorithm and updates the local state to ensure that the unmanned aerial vehicles and the neighbor unmanned aerial vehicles form a formation consistency, thereby solving the problem of formation maintaining and avoiding inter-machine collision. The method comprises the steps that a virtual potential field is built outside an unmanned aerial vehicle formation by using an improved artificial potential field method, information such as the distribution condition and the range of obstacles in a target position and a flight environment is reflected in the potential field value of each point of the environment in a form more suitable for the unmanned aerial vehicle to avoid obstacles, and the unmanned aerial vehicle determines the flight direction and speed according to the change of the potential field value, so that the unmanned aerial vehicle can avoid airspace obstacles and fly to a target point under the condition that a flight path is not planned in advance. The algorithm has the advantages of operability and simplicity, is very suitable for the dynamic environment of the unmanned aerial vehicle, shows the characteristics of proximity interactivity, group stability, environmental adaptability and the like, can realize the cooperative formation of a plurality of unmanned aerial vehicles, avoids obstacles more flexibly, and has very important significance for the actual multi-cooperative combat.
Drawings
FIG. 1 is a schematic diagram of a virtual gravitational potential field of an unmanned aerial vehicle at a target point according to the present invention;
FIG. 2 is a schematic illustration of the improved safety distance of the present invention;
fig. 3 is a schematic view of the virtual repulsive potential field of the obstacle to the drone according to the present invention;
fig. 4 is a schematic structural diagram of a communication topology of the present invention, taking formation of three unmanned aerial vehicles as an example;
FIG. 5 shows obstacle avoidance flight trajectories for three unmanned aerial vehicles according to the present invention;
FIG. 6 is a graph of pitch angle response for three drones of the present invention;
fig. 7 shows flight trajectories of three unmanned aerial vehicles forming a formation and avoiding obstacles in a narrow passage.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
The embodiment 1 discloses a control algorithm for multi-unmanned aerial vehicle collaborative formation obstacle avoidance, which mainly comprises the following steps:
when the unmanned aerial vehicle formation carries out a flight task, the position and speed information of the unmanned aerial vehicle and an airspace barrier is detected through an airborne sensor, and the detected information is transmitted to an unmanned aerial vehicle control system, so that each unmanned aerial vehicle can respectively carry out appropriate maneuver according to the information, the formation shape is kept, and collision with the barrier is avoided; meanwhile, for the convenience of calculation, the obstacles in the airspace are simplified and abstracted into a sphere with a certain radius.
The on-board sensor of the present invention may be any one of those disclosed in the art or commercially available that can be used in the present invention, such as a laser range finder, an ultrasonic range finder.
And (II) constructing a multi-unmanned aerial vehicle formation communication topology according to the position information of each unmanned aerial vehicle. The communication topology adopts a directed graph to represent information transmission between neighboring unmanned aerial vehicles, the unmanned aerial vehicle formation achieves the purpose of cooperative formation through neighbor information exchange, and the directed graph is recorded as
Wherein the content of the first and second substances,
the nodes are finite non-empty nodes and represent all unmanned aerial vehicles in the formation;
the information transmission state between the unmanned aerial vehicles is represented by a set of edges between the nodes;
representing a connection weight matrix, aijRepresenting connection weight elements, if any, from vjDelivery to viThen vjIs viNeighbor drone of (a) this time ij1, otherwise aij＝0。νiIs a set of neighbors of
The directed graph contains a cluster of directed spanning trees, and if at least one node has a directed path leading to all other nodes, the communication topological structure can enable the unmanned aerial vehicle which obtains the most information to send the information to more neighbor unmanned aerial vehicles.
In the formation of the unmanned aerial vehicles, at least one unmanned aerial vehicle is guaranteed to know the position of the target point, and other unmanned aerial vehicles can fly to the target point along with the neighbor unmanned aerial vehicles by using a communication network. Moreover, no central piloting unmanned aerial vehicle exists in the unmanned aerial vehicle formation, and an individual unmanned aerial vehicle only has local sensing and communication capacity, and changes the motion of the individual unmanned aerial vehicle in due time to adapt to a dynamic environment through information interaction with adjacent unmanned aerial vehicles.
And (III) establishing an unmanned aerial vehicle dynamic model. For the formation flight of the unmanned aerial vehicles, the modeling of the single-machine kinematics of the unmanned aerial vehicles is required in the aspect of the kinematics of the unmanned aerial vehicles, and the kinetic equation is as follows:
wherein (x)i,yi,zi) Representing the three-dimensional spatial position, V, of the drone in the ground coordinate systemi、ψiAnd thetaiRespectively representing the flight speed, track course angle and track pitch angle, tau, of the unmanned aerial vehicleVRepresenting a time constant of speed, tau, associated with the flight stateψWhich represents the time constant of the heading angle,
and τθTwo pitch angle time constants are represented,
for control input of the system, Vi c、
And
respectively representing a speed command, a course angle command and a pitch angle command.
The unmanned aerial vehicle model is a three-degree-of-freedom unmanned aerial vehicle model with an automatic pilot, and each unmanned aerial vehicle keeps flying by controlling a speed retainer, a track pitch angle retainer and a course angle retainer.
And (IV) when the obstacle is detected, judging whether the distance between any unmanned aerial vehicle and the obstacle in the unmanned aerial vehicle formation meets the safety distance requirement of obstacle avoidance of the unmanned aerial vehicle, and if not, adjusting the formation form through a virtual force strategy and avoiding the obstacle.
Wherein the virtual force strategy in the step (IV) comprises the following steps:
(1) constructing a virtual force field, and carrying out stress analysis on unmanned aerial vehicle formation, wherein the method comprises the following specific steps:
(a) different from single unmanned aerial vehicle obstacle avoidance, in the multi-unmanned aerial vehicle collaborative formation obstacle avoidance task, each unmanned aerial vehicle is required to avoid colliding with a neighbor unmanned aerial vehicle and keep a preset distance as far as possible, and therefore the space motion of the unmanned aerial vehicle in a formation mode is regarded as a motion form which is controlled and input by formation virtual force.
The acting force of other unmanned aerial vehicles received by each unmanned aerial vehicle in the multi-unmanned aerial vehicle formation system is defined as follows:
wherein k is1And k2Respectively, a normal number, P, cooperating with the formation control and the flight speed controli(xi,yi,zi) And Pj(xj,yj,zj) Three-dimensional space positions r of unmanned planes i and j in a ground coordinate system respectivelyijRepresenting the set spatial distance between drones i and j.
When the distance between the unmanned aerial vehicle i and the unmanned aerial vehicle j is greater than the set distance of the formation, the unmanned aerial vehicle i is attracted by the unmanned aerial vehicle j to shorten the distance from the unmanned aerial vehicle i to the unmanned aerial vehicle j until the distance between the unmanned aerial vehicle i and the unmanned aerial vehicle j is equal to the distance required by the formation; when the distance between unmanned aerial vehicle i and the unmanned aerial vehicle is less than the distance that formation required, unmanned aerial vehicle i will receive unmanned aerial vehicle j's repulsion, with unmanned aerial vehicle i to unmanned aerial vehicle j's distance grow, until the distance between unmanned aerial vehicle i and the unmanned aerial vehicle j equals the distance that formation required. When the unmanned aerial vehicle formation reaches a stable flight state, | Pi-Pj-rij|→0，|Vi(t)-Vj(t)|→0。
Dividing the formation virtual force into a formation repulsive force and a formation attractive force according to the relative distance from the neighboring unmanned aerial vehicle, as shown in formula (3):
wherein the content of the first and second substances,
in order to form the gravitation of the formation,
is the formation repulsion.
The formation repulsion force enables the adjacent unmanned aerial vehicles to move in opposite directions, so that collision between the unmanned aerial vehicles is avoided; the formation gravitation makes the adjacent and distant unmanned aerial vehicles move in opposite directions, thereby avoiding the loss of connection.
(b) When the formation unmanned aerial vehicles execute flight tasks, the target points always generate the attraction effect on each unmanned aerial vehicle to generate an attraction potential field. Regarding the formation of drones as a whole, as long as a certain drone in the formation reaches a specified target point and other drones keep a desired distance from the drone that reaches the target point, the entire formation is considered to reach the specified target point.
The classical gravitational potential field function is modified to:
where ρ isit＝||Pt-PiI represents the spatial distance between UAV i and target point t, PtFor the three-dimensional space position of the target point t under the ground coordinate system, | | | · | | represents L2Norm, k3Is a proportional gain coefficient of the gravitational potential field.
The improved gravitational potential field is shown in FIG. 1.
The virtual force field where the unmanned aerial vehicle is located is a conservative field, so that the attraction of the attraction potential field is a negative gradient of a potential function, and the obtained attraction of each unmanned aerial vehicle in the attraction potential field is as follows:
wherein the content of the first and second substances,
is represented by formula (6):
(c) when unmanned aerial vehicle gets into the influence range of barrier, can receive the repulsion effect that the barrier produced.
First, it is considered that if the safety distance for avoiding an obstacle by the unmanned aerial vehicle is simply set to a certain value, when the unmanned aerial vehicle enters the range of influence of the obstacle and the flying speed and acceleration are at high levels, it is likely that an obstacle avoidance failure is caused because there is not enough space to complete the avoidance maneuver. Therefore, improve unmanned aerial vehicle and keep away safe distance of barrier and be:
wherein R isoAnd RiThe radius of the obstacle and the radius of the unmanned aerial vehicle represent the safety distance required by information uncertainty, eta represents the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the obstacle, and k represents the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the obstaclevAnd kηRespectively for adjusting the relative speed and the control parameters of the included angle,
representing the relative speed of movement, V, of the drone and the obstaclei(t) and Vo(t) represents the speed of the drone and the moving obstacle, respectively, at time t, when the obstacle is stationary,
representing the flight speed of the drone itself.
Therefore, the larger the relative speed between the unmanned aerial vehicle and the moving obstacle is, the larger the safety distance for avoiding the obstacle by the unmanned aerial vehicle is. When the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the obstacle is larger than pi/2, the unmanned aerial vehicle is shown to fly through the position near the obstacle, a larger safety distance does not need to be kept, and only the minimum safety distance is needed to be kept between the unmanned aerial vehicle and the obstacle. The improved safety distance is shown in fig. 2.
Secondly, the repulsion force of the obstacle to the unmanned aerial vehicle is analyzed. When unmanned aerial vehicle gets into the influence within range of barrier, receive the repulsion potential field effect of barrier, when being close to the barrier, repulsion potential field is big more. In order for the unmanned aerial vehicle to avoid obstacles in a short time, the repulsive potential field function is improved as follows:
wherein k is4Is a positive proportional gain coefficient of the repulsive potential field, c is an adjustable control parameter, pioRepresenting the distance, p, between the drone and the obstacleiominThe minimum safe distance between the unmanned aerial vehicle and the obstacle is represented, and when the minimum safe distance is smaller than the minimum safe distance, the collision is considered to occur. Obviously, when the distance between the unmanned plane i and the obstacle tends to ρiominThen, the repulsive force potential field is positive infinity, as shown in equation (9):
the improved repulsive force field is shown in fig. 3, the repulsive force field function is set to be a generalized Morse function, and the barrier avoidance reliability of the high-speed unmanned aerial vehicle can be guaranteed due to the exponentially increased speed.
Accordingly, the repulsion force of each unmanned aerial vehicle from the obstacle is as follows:
wherein, Po(xo,yo,zo) Is the three-dimensional space position of the obstacle under the ground coordinate system.
The smaller the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the barrier is, the smaller the barrier repulsion force for changing the movement direction of the unmanned aerial vehicle is.
(2) And calculating the resultant force received by each unmanned aerial vehicle. The resultant force that single unmanned aerial vehicle received when keeping away the barrier equals the resultant force of the effort that other unmanned aerial vehicle produced in the formation, the gravitation that the target point produced and the repulsion that the barrier produced, as shown in equation (11):
(3) a velocity target vector is defined. The gradient of the potential field defines the velocity field acting on each unmanned aerial vehicle, and the artificial potential field obstacle avoidance is realized by adjusting the velocity vector of each unmanned aerial vehicle, so that the velocity target vector is directly defined as:
decomposing resultant force received by the unmanned aerial vehicle into three-dimensional directions (under a ground coordinate system) to obtain the expected speed of each unmanned aerial vehicle in the three-dimensional directions, as shown in formulas (13), (14) and (15):
wherein, Vi x，Vi y，Vi zAre respectively provided withFor the velocity of the drone i in the three-dimensional x, y, z directions,
velocity, x, of drone j in three-dimensional x, y, z directions, respectivelyijFor set distance of unmanned plane i and j in x-axis direction, yijFor a set distance, z, of unmanned planes i and j in the y-axis directionijThe set distance between the unmanned planes i and j in the z-axis direction.
(4) And converting the expected speed of the unmanned aerial vehicle in each direction into a target flight control command of each unmanned aerial vehicle. The target flight control command is as follows:
wherein, Vi c，
Respectively, a flight speed instruction, a course angle instruction and a yaw angle instruction.
(5) Adjust each unmanned aerial vehicle flight state in order to keep unmanned aerial vehicle formation and change the flight track of unmanned aerial vehicle formation and keep away the barrier through the target flight control instruction of each unmanned aerial vehicle of input, above-mentioned condition includes:
(a) if the distance between the neighboring unmanned aerial vehicles is larger than or smaller than the set distance, the flight state of the unmanned aerial vehicles is adjusted by inputting a control command, so that the distance between the unmanned aerial vehicles is reduced or increased until the formation distance requirement is met;
(b) when the obstacle is detected within the safe distance, the flight state of the unmanned aerial vehicle is adjusted by inputting a control command to keep the formation, and the whole flight track of the formation is changed to avoid the obstacle, so that the actual flight requirement is met;
(c) when the formation of the unmanned aerial vehicles leaves the influence range of the obstacles and meets the formation constraint, the formation of the unmanned aerial vehicles can be regarded as a whole, and the unmanned aerial vehicles only receive the gravitation of a target point and continuously fly close to the target point.
Assuming that three unmanned aerial vehicles form a formation to avoid obstacles, a communication topology structure taking the formation of the three unmanned aerial vehicles as an example is shown in fig. 4, and initial states and simulation parameters of the unmanned aerial vehicles are as follows:
TABLE 1 initial states of the unmanned aerial vehicles
TABLE 2. physical limitations of unmanned aerial vehicles
The simulation parameters are as follows:
k1＝1；k2＝0.01；k3＝1；k4＝16；＝5；ρiomin＝3；kv＝1.3；kη＝6。
experiment 1: continuous obstacle avoidance effectiveness simulation verification
The three drones fly to the target point (300,300,300) in the initial state. Three static obstacles with different sizes are arranged on a flight path of the unmanned aerial vehicle flying to a target point. The position of the obstacle 1 is (71,75,70) and the radius is 10m, the position of the obstacle 2 is (143,151,147) and the radius is 15m, and the position of the obstacle 3 is (207,203,211) and the radius is 20 m. The formation of the unmanned aerial vehicles is required to be an isosceles right triangle with the waist length of 10. And obtaining a simulation result by using a flight speed instruction (formula 16), a pitch angle instruction (formula 17) and a course angle instruction (formula 18) converted by the multi-unmanned aerial vehicle cooperative formation obstacle avoidance algorithm, as shown in fig. 5 and 6.
In order to conveniently observe the formation change of the unmanned aerial vehicle formation, the positions of three unmanned aerial vehicles at the same moment are selected. As can be seen from fig. 5, formation of the unmanned aerial vehicles has not been completed when the obstacle 1 is detected, the heading angle of the unmanned aerial vehicle 1 changes first, and after information interaction, the unmanned aerial vehicles 2 and 3 start to change the heading. But form and maintain a short formation after flying over the obstacle 1. As can be seen from fig. 5, the formation of the unmanned aerial vehicle changes when avoiding the obstacle 2, but the formation of the unmanned aerial vehicle rapidly recovers after flying over the obstacle 2. As can be seen from fig. 6, when the obstacle is avoided for the third time, the heading angle responses of the three unmanned aerial vehicles are basically synchronized, and the formation is also maintained when the obstacle is avoided. In conclusion, the unmanned aerial vehicle cluster can realize formation cooperative obstacle avoidance under the control algorithm of the invention, and the control algorithm of the invention is effective.
Experiment 2: simulation verification of formation obstacle avoidance control algorithm under narrow passage condition
Two very close obstacles are arranged near a certain position of a path of a flying target point of the formation of the unmanned planes, the positions are (162.5,188,200) and (188,162.5,200), the radii of the obstacles are both 15m, the maximum distance between the formation of the unmanned planes is 14.14m, therefore, the unmanned planes need to cooperate to avoid obstacles, and the simulation result is shown in fig. 7.
As can be seen from fig. 7, three drones can smoothly pass through a narrow passage between two obstacles, and although the formation of the drones changes when the drones avoid the obstacles, the drones quickly recover the formation of the original formation after flying over the obstacles. Therefore, the control algorithm of the invention can ensure that the formation of the unmanned aerial vehicles has high cooperation, good flexibility and strong adaptability to the environment, and the formation of the unmanned aerial vehicles integrally shows the intelligence of group obstacle avoidance. In the traditional artificial potential field obstacle avoidance control algorithm, when an adjacent and close obstacle appears, the unmanned aerial vehicle formation may stop moving due to the problem of the local minimum value, or the unmanned aerial vehicle formation may select respective obstacle avoidance, and the formation form is recovered after flying through the obstacle.
In a word, the multi-unmanned aerial vehicle collaborative formation obstacle avoidance control algorithm provided by the invention can complete collaborative formation obstacle avoidance control on multiple unmanned aerial vehicles, and the detection result of the method is superior to that of the existing method, and the multi-unmanned aerial vehicle collaborative formation obstacle avoidance control algorithm can avoid obstacles in a formation form under the condition allowed by the environment, so that the method is more flexible and effective. For the autonomous coordination control problem of multi-unmanned aerial vehicle formation obstacle avoidance, the control algorithm coordinates the formation maintenance task inside the unmanned aerial vehicle and the external obstacle avoidance task, shows the characteristics of proximity interactivity, group stability, environmental adaptability and the like, and achieves the autonomous, coordination, intelligence and other control requirements of unmanned aerial vehicle cluster formation.
However, the above description is only exemplary of the present invention, and the scope of the present invention should not be limited thereby, and the replacement of the equivalent components or the equivalent changes and modifications made according to the protection scope of the present invention should be covered by the claims of the present invention.
Claims (3)
1. A control algorithm for multi-unmanned aerial vehicle collaborative formation obstacle avoidance is characterized by comprising the following steps:
when the unmanned aerial vehicles form a formation to execute a flight task, detecting the position and speed information of the unmanned aerial vehicles and the airspace barriers through an airborne sensor, and abstracting the barriers in a flight space into a sphere;
secondly, establishing unmanned aerial vehicle formation communication topology according to the position information of each unmanned aerial vehicle, and realizing information interaction between neighbor unmanned aerial vehicles in formation by adopting a distributed transmission mode;
thirdly, establishing an unmanned aerial vehicle dynamic model;
(IV) when the obstacle is detected, determining the safe distance for the unmanned aerial vehicle to avoid the obstacle, judging whether the distance between any unmanned aerial vehicle and the obstacle in the formation of the unmanned aerial vehicles meets the requirement of the safe distance, if not, adjusting the formation through a virtual force strategy and avoiding the obstacle, wherein the virtual force strategy comprises the following steps:
(1) carrying out stress analysis on the unmanned aerial vehicle formation, including solving the problems that each unmanned aerial vehicle is subjected to the acting force of other unmanned aerial vehicles in the formation, the attraction of the target point to each unmanned aerial vehicle in the formation, and the repulsion of the obstacle to each unmanned aerial vehicle in the formation;
(2) calculating resultant force borne by each unmanned aerial vehicle;
(3) defining a speed target quantity, and calculating the expected speed of each unmanned aerial vehicle in the three-dimensional direction of the ground coordinate;
(4) converting the expected speed of each unmanned aerial vehicle in each direction into a target flight control instruction of each unmanned aerial vehicle;
(5) adjusting the flight state of each unmanned aerial vehicle by inputting a target flight control command of each unmanned aerial vehicle to keep the formation of the unmanned aerial vehicles and change the flight path of the formation of the unmanned aerial vehicles to avoid obstacles;
the communication topology is defined as
Wherein the content of the first and second substances,
is a finite non-empty node; representing each unmanned aerial vehicle in the formation;
the information transmission state between the unmanned aerial vehicles is represented by a set of edges between the nodes;
representing a connection weight matrix, viIs a set of neighbors of
The classical gravitational potential field function is modified to:
where ρ isit＝||Pt-PiI represents the spatial distance between UAV i and target point t, Pt(xt,yt,zt) For the three-dimensional space position of the target point t under the ground coordinate system, | | | · | | represents L2Norm, k3Is the force of gravityA proportional gain coefficient of the potential field;
each unmanned aerial vehicle receives the effort of other unmanned aerial vehicles in the formation does:
wherein k is1And k2Respectively, a normal number, P, cooperating with the formation control and the flight speed controli(xi,yi,zi) And Pj(xj,yj,zj) Three-dimensional space positions r of unmanned planes i and j in a ground coordinate system respectivelyijRepresents the set spatial distance, V, between UAVs i and jiAnd VjRespectively representing the flight speeds of the unmanned planes i and j, aijRepresenting the weight of the connection from unmanned plane node i to unmanned plane node j, if any, from vjDelivery to viThen vjIs viNeighbor drone of (a) this timeij1, otherwise aij＝0；
The attraction force of the target point on each unmanned aerial vehicle in the formation is as follows:
wherein the content of the first and second substances,
is shown in equation (6):
wherein k is3Is a proportional gain coefficient of the gravitational potential field, Pt(xt,yt,zt) Is the three-dimensional space position of the target point in the ground coordinate system, rhoit＝||Pt-Pi||Represents the spatial distance between the unmanned aerial vehicle i and the target point t, and represents L2A norm;
the repulsion that the barrier produced to each unmanned aerial vehicle in the formation is:
wherein k is4Is a positive proportional gain coefficient of the repulsive potential field, c is an adjustable control parameter, pioRepresents the distance, ρ, between drone i and the obstacleiominIndicating the minimum safe distance, P, between the drone and the obstacleo(xo,yo,zo) The three-dimensional space position of the obstacle under the ground coordinate system;
the resultant force of each unmanned aerial vehicle is equal to the resultant force of the acting force generated by other unmanned aerial vehicles in the formation, the attractive force generated by the target point and the repulsive force generated by the obstacle, as shown in formula (11):
wherein, unmanned aerial vehicle keeps away safe distance that the barrier as shown in formula (7):
wherein R isoAnd RiThe radius of the obstacle and the radius of the unmanned aerial vehicle represent the safety distance required by information uncertainty, eta represents the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the obstacle, and k represents the included angle between the flight speed direction of the unmanned aerial vehicle and the connecting line between the center of the unmanned aerial vehicle and the center of the obstaclevAnd kηRespectively for adjusting the relative speed and the control parameters of the included angle,
representing the relative speed of movement, V, of the drone and the obstaclei(t) and Vo(t) represents the speed of the drone and the moving obstacle, respectively, at time t, when the obstacle is stationary,
2. the control algorithm for obstacle avoidance in cooperative formation of multiple drones according to claim 1, wherein in step (3), the speed target amount is defined as follows:
the total virtual force received by the unmanned aerial vehicle is decomposed to three-dimensional directions, and the speeds of the unmanned aerial vehicles in the three-dimensional directions are respectively shown in formulas (13), (14) and (15):
wherein, Vi x，Vi y，Vi zThe speeds of the unmanned aerial vehicle i in the three-dimensional x, y and z directions,
velocity, x, of drone j in three-dimensional x, y, z directions, respectivelyijFor set distance of unmanned plane i and j in x-axis direction, yijFor a set distance, z, of unmanned planes i and j in the y-axis directionijTo set upDistance between unmanned planes i and j in the z-axis direction.
3. The control algorithm for obstacle avoidance in cooperative formation of multiple drones according to claim 2, wherein in step (4), the target flight control command is:
wherein the content of the first and second substances,
respectively, a flight speed instruction, a course angle instruction and a yaw angle instruction.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115903885B (en) * | 2022-10-26 | 2023-09-29 | 中国人民解放军陆军炮兵防空兵学院 | Unmanned aerial vehicle flight control method of swarm Agent model based on task traction |
| CN115686069A (en) * | 2022-11-15 | 2023-02-03 | 杭州国科骏飞光电科技有限公司 | Synchronous coordination control method and system for unmanned aerial vehicle cluster |
| 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 |
| CN117193335B (en) * | 2023-11-08 | 2024-04-12 | 山东大学 | Method and system for avoiding dynamic obstacle by multi-agent system |
| CN117608318B (en) * | 2024-01-23 | 2024-04-09 | 北京航空航天大学 | Unmanned aerial vehicle formation obstacle avoidance control method and system based on bird-like phototaxis |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107015571B (en) * | 2017-05-12 | 2020-06-16 | 南京航空航天大学 | Algorithm for tracking and avoiding moving target of formation unmanned aerial vehicle |
| CN107102650B (en) * | 2017-05-27 | 2020-01-31 | 河南科技大学 | unmanned aerial vehicle dynamic path planning method suitable for high-speed environment |
| CN107632614B (en) * | 2017-08-14 | 2020-09-01 | 广东技术师范大学 | Multi-unmanned aerial vehicle formation self-organization cooperative control method based on rigid graph theory |
| CN108196583B (en) * | 2017-08-21 | 2020-09-01 | 中国人民解放军陆军工程大学 | Unmanned aerial vehicle cluster control method |
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