CN109976386B - Method and system for cooperatively tracking target by multiple unmanned aerial vehicles - Google Patents
Method and system for cooperatively tracking target by multiple unmanned aerial vehicles Download PDFInfo
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- CN109976386B CN109976386B CN201910269353.4A CN201910269353A CN109976386B CN 109976386 B CN109976386 B CN 109976386B CN 201910269353 A CN201910269353 A CN 201910269353A CN 109976386 B CN109976386 B CN 109976386B
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/12—Target-seeking control
Abstract
The application discloses a method and a system for cooperatively tracking a target by multiple unmanned aerial vehicles, comprising the following steps: s1, obtaining the speed and position information of the target, broadcasting the speed and position information to other unmanned aerial vehicles, receiving the speed information and position information of the target sent by other unmanned aerial vehicles, and performing information fusion; s2, determining a tracking mode by each unmanned aerial vehicle according to the speed information of the fused target; s3, calculating a final guidance vector by each unmanned aerial vehicle according to the navigation information, the speed and the position information of the target and the tracking modes of the unmanned aerial vehicles; s4, the multiple unmanned aerial vehicles respectively update the speed and position information of the target, broadcast the information to other unmanned aerial vehicles, receive the target information sent by other unmanned aerial vehicles, perform information fusion, and return to S2. The tracking mode is determined by calculating the target speed in real time, so that the tracking capability is enhanced, and the target is prevented from escaping through variable speed; through conflict detection and guidance vector correction, obstacles or other unmanned aerial vehicles in the flight process are avoided, flight collision and conflict are prevented, and safety is improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicle autonomous flight path planning, in particular to a method and a system for cooperatively tracking a target by multiple unmanned aerial vehicles.
Background
A multi-unmanned aerial vehicle collaborative flight path planning technology is characterized in that according to specific tasks of each unmanned aerial vehicle, on the premise of meeting various constraint conditions (platform performance constraint, battlefield environment constraint and task collaborative constraint), a flyable flight path from a starting point to a target point is planned for each unmanned aerial vehicle, so that the unmanned aerial vehicle can reach the optimal or better performance index. Compared with the single unmanned aerial vehicle track planning, the multi-machine collaborative track planning needs to coordinate and process the mutual relations among the unmanned aerial vehicle tracks, including the space coordination relation, the time coordination relation and the task coordination relation, so that the multi-unmanned aerial vehicle system can effectively execute tasks in the same task airspace and guarantee the flight safety. And in the cooperative target tracking of the multiple unmanned aerial vehicles, the cooperative tracking of the multiple unmanned aerial vehicles on a specific target is taken as a specific task, and the reliable tracking of the target on a spatial position is cooperatively realized by planning a proper air route for each unmanned aerial vehicle under the condition of ensuring the flight safety.
The existing multi-unmanned aerial vehicle collaborative track planning research is mainly centralized planning, and the planning method of the type needs a central optimization unit in a multi-unmanned aerial vehicle system, namely the position of a track planning node. The unit can acquire flight state information such as positions, speeds and the like of all unmanned aerial vehicles in the system and task environment conditions through wireless communication, and solve a flight path optimization problem by using an optimization algorithm.
The centralized planning is very effective for solving the small-scale track planning problem with a small number of unmanned aerial vehicles, but with the increase of the number of the unmanned aerial vehicles, the scale of a multi-machine system is increased, track parameters to be planned are increased sharply, and the optimization problem to be solved becomes extremely complex; meanwhile, for a multi-unmanned aerial vehicle system, a centralized planning node does not exist under many conditions, and reliable communication between all unmanned aerial vehicles and the planning node cannot be guaranteed.
The patent granted in china, "method and system for cooperatively tracking target by unmanned aerial vehicle group", has an application publication number CN103197684A, an application publication date 2013.07.10, and a flow of the cooperative target tracking method is shown in fig. 1. According to the scheme, on the basis of establishing a wireless communication network, the position and characteristic information of a target are obtained by using an airborne sensor, and the intra-cluster sharing of the position information of the unmanned aerial vehicle and the characteristic information of the position of the tracked target is realized through the wireless communication network. The key point of the scheme for realizing target tracking is that in steps S104 and S105, the cooperative tracking behavior of the unmanned aerial vehicle is controlled by calculating the potential field resultant force. When the potential field resultant force is smaller than a safety threshold value, controlling the unmanned aerial vehicle to track; when the resultant force of the potential fields is greater than a safety threshold, the tracking is abandoned. The method for realizing unmanned aerial vehicle cluster cooperative tracking by calculating potential field resultant force is an artificial potential field method. An important limitation of this type of approach is that it is only applicable to rotary wing drones (requiring hover action in the event of relinquishing tracking), and no longer applicable to fixed wing drones. And fixed wing unmanned aerial vehicle compares in rotor unmanned aerial vehicle, has showing the advantage on the flying speed. Under the condition that the tracked target speed is high, the rotor unmanned aerial vehicle is likely to be incapable of completing the tracking task due to the speed.
The Chinese granted patent "an unmanned aerial vehicle system and method with continuous locking and tracking target ability", its application publication number is CN105929850A, its application publication date is 2016.09.07, and the patent "an unmanned aerial vehicle positioning and target tracking method based on two-dimensional label", its application publication number is CN106197422A, its application publication date is 2016.12.07, all relate to the tracking method to the target of the single frame unmanned aerial vehicle that is more similar, namely according to the position difference between target and unmanned aerial vehicle, adjust the flight direction and speed of unmanned aerial vehicle through the flight control system loop, realize the tracking of tracking the target of unmanned aerial vehicle. The method has the disadvantages that firstly, the method is only suitable for rotor unmanned aerial vehicles and cannot be applied to fixed-wing unmanned aerial vehicles; secondly, the method only considers the tracking of a single unmanned aerial vehicle to the target, the efficiency of the single unmanned aerial vehicle in executing a large-scale searching and tracking task is low, the reliability of the tracking task is poor, and once the target is lost due to special factors, the tracking target is difficult to find again.
In summary, it is desirable to provide a method and a system for tracking a target cooperatively by multiple drones, which are applicable to multiple drones and a fixed-wing drone at the same time, and can change the tracking mode according to the target speed.
Disclosure of Invention
In order to solve the problems, the application provides a method and a system for cooperatively tracking a target by multiple unmanned aerial vehicles.
On one hand, the application provides a method for cooperatively tracking a target by multiple unmanned aerial vehicles, which comprises the following steps:
s1, the first unmanned aerial vehicle obtains the speed information and the position information of the target, broadcasts the speed information and the position information to other unmanned aerial vehicles, receives the speed information and the position information of the target sent by other unmanned aerial vehicles and carries out information fusion;
s2, determining a tracking mode by each unmanned aerial vehicle according to the speed information of the fused target;
s3, calculating a final guidance vector by each unmanned aerial vehicle according to the navigation information, the speed information and the position information of the target and the tracking modes of the unmanned aerial vehicles;
s4, the unmanned planes respectively update the speed information and the position information of the target, broadcast the speed information and the position information to other unmanned planes, receive the speed information and the position information of the target sent by other unmanned planes, perform information fusion and return to S2.
Preferably, the determining the tracking manner in S2 includes:
if the unmanned aerial vehicle is not in a tracking state currently and the speed of the target is greater than the intermediate speed, the unmanned aerial vehicle adopts a flat flight tracking mode;
if the unmanned aerial vehicle is not in a tracking state currently and the speed of the target is less than or equal to the intermediate speed, the unmanned aerial vehicle uses a hovering tracking mode;
if the unmanned aerial vehicle is in a hovering tracking state currently and the speed of a target is greater than or equal to a maximum threshold value, the unmanned aerial vehicle uses a flat flight tracking mode;
and if the unmanned aerial vehicle is currently in a flat flight tracking state and the speed of the target is less than or equal to a minimum threshold value, the unmanned aerial vehicle uses a hovering tracking mode.
Preferably, when the tracking mode is a level flight tracking mode, calculating the final guidance vector includes:
calculating a reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the reference guidance vector according to the correction angle to obtain a final guidance vector.
Preferably, said calculating the reference guidance vector uses a weighting method.
Preferably, when the tracking mode is a hover tracking mode, calculating the final guidance vector includes:
calculating a reference guidance vector;
calculating guidance vector compensation required by phase adjustment, and compensating a reference guidance vector to obtain a compensated reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
Preferably, the calculation of the reference guidance vector uses the lyapunov vector field guidance method.
Preferably, said detecting a collision and calculating a correction angle using the virtual potential field comprises:
monitoring a safe flight area, and if other objects are detected to enter the safe flight area, calculating potential field force by adopting a virtual potential field;
the correction angle is calculated from the potential field force.
Preferably, the intermediate speed is VmThe maximum threshold is (V)m+δV) The minimum threshold is (V)m-δV)，δVIs the transition interval.
In a second aspect, the present application provides a system for tracking a target in cooperation with multiple drones, including:
the target detection module is used for calculating speed information and position information of a target, sending the speed information and the position information to the communication module, and carrying out information fusion according to the speed information and the position information of the target sent by other unmanned aerial vehicles;
the communication module broadcasts the speed information and the position information of the target to other unmanned aerial vehicles, receives the speed information and the position information of the target sent by other unmanned aerial vehicles and sends the speed information and the position information of the target to the target detection module;
and the tracking module is used for determining a tracking mode according to the fused speed information of the target and calculating a final guidance vector according to the navigation information of the tracking module and other unmanned aerial vehicles, the speed information and the position information of the target and the tracking mode.
Preferably, the tracking module comprises: the device comprises a horizontal flight computing unit, a spiral computing unit and a conflict detection unit;
the plane flight calculation unit is used for calculating a reference guidance vector and sending the reference guidance vector to the conflict detection unit;
the spiral calculation unit is used for calculating a reference guidance vector, calculating guidance vector compensation required by phase adjustment, compensating the reference guidance vector to obtain a compensated reference guidance vector, and sending the compensated reference guidance vector to the collision detection unit;
and the collision detection unit is used for detecting a collision, calculating a correction angle by adopting the virtual potential field, and correcting the received reference guidance vector or the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
The application has the advantages that: by estimating the real-time speed of the tracked target, the unmanned aerial vehicle can be stably and effectively switched between a flat flight tracking mode and a spiral tracking mode, the tracking capability is enhanced, and the tracked target is prevented from escaping through variable speed; compared with the traditional method, the method has better tracking task completion capability due to the speed advantage of the fixed-wing unmanned aerial vehicle and the cooperative tracking of the multiple unmanned aerial vehicles; for a static or low-speed moving target, a hover tracking method based on Lyapunov vector field guidance is designed, and hover tracking of a tracked target by a fixed-wing unmanned aerial vehicle is realized; in the cooperative hovering tracking process of the multiple unmanned aerial vehicles, a method for adjusting the cooperative phase of the multiple unmanned aerial vehicles is used to realize reasonable phase distribution during the cooperative hovering tracking of the multiple unmanned aerial vehicles; for a target moving at a high speed, a plane flight tracking method based on weighting is used to realize effective tracking of the unmanned aerial vehicle on the target moving at the high speed; by means of the collision detection and guidance vector correction method, the unmanned aerial vehicle avoids obstacles or other unmanned aerial vehicles in the flight process, prevents flight collision and collision caused by environmental obstacles or other unmanned aerial vehicles, and improves flight safety; by using distributed track planning, each unmanned aerial vehicle independently carries out the optimal solution of the track, and no special planning node is needed, thereby improving the track planning speed and the reliability of the system. The method of the present application is not limited to fixed wing drones and is equally applicable to rotary wing drones.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating preferred embodiments and are not to be construed as limiting the application. Also, like reference numerals are used to denote like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram illustrating the steps of a conventional method and system for cooperatively tracking a target by an unmanned aerial vehicle fleet;
fig. 2 is a schematic step diagram of a method for tracking a target in cooperation with multiple drones, provided by the present application;
FIG. 3 is a schematic view illustrating hover tracking of a method for tracking a target in cooperation with multiple drones according to the present application;
fig. 4 is a schematic diagram of a relative coordinate vector decomposition of a method for cooperatively tracking a target by multiple drones according to the present application;
fig. 5 is a schematic diagram of collision detection and obstacle avoidance in a method for cooperatively tracking a target by multiple unmanned aerial vehicles according to the present application;
fig. 6 is a schematic diagram of a system for tracking a target in cooperation with multiple drones provided by the present application.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may 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 disclosure to those skilled in the art.
According to an embodiment of the application, a method for tracking a target by multiple unmanned aerial vehicles in a coordinated manner is provided, as shown in fig. 2, the method includes:
s1, the first unmanned aerial vehicle obtains the speed information and the position information of the target, broadcasts the speed information and the position information to other unmanned aerial vehicles, receives the speed information and the position information of the target sent by other unmanned aerial vehicles and carries out information fusion;
s2, determining a tracking mode by each unmanned aerial vehicle according to the speed information of the fused target;
s3, calculating a final guidance vector by each unmanned aerial vehicle according to the navigation information, the speed information and the position information of the target and the tracking modes of the unmanned aerial vehicles;
s4, the unmanned planes respectively update the speed information and the position information of the target, broadcast the speed information and the position information to other unmanned planes, receive the speed information and the position information of the target sent by other unmanned planes, perform information fusion and return to S2.
The first drone is the drone that finds the target first in the queue.
The information fusion is used for making decisions of all unmanned aerial vehicles in the queue consistent.
After any unmanned aerial vehicle finds the target, the speed information and the position information of the target are calculated (estimated) according to the navigation information of the unmanned aerial vehicle, and the data are broadcasted to all other unmanned aerial vehicles.
The navigation information comprises information such as position information, speed information and course of the unmanned aerial vehicle. The navigation information is acquired in real time.
S1 further includes: and broadcasting the tracking decision information to other unmanned aerial vehicles, receiving the tracking decision information sent by other unmanned aerial vehicles, and fusing the tracking decision information of the unmanned aerial vehicles with the tracking decision information of other unmanned aerial vehicles.
S4 further includes: and broadcasting the tracking decision information to other unmanned aerial vehicles, receiving the tracking decision information sent by other unmanned aerial vehicles, and fusing the tracking decision information of the unmanned aerial vehicles with the tracking decision information of other unmanned aerial vehicles.
The determining the tracking method in S2 includes:
if the unmanned aerial vehicle is not in a tracking state currently and the speed of the target is greater than the intermediate speed, the unmanned aerial vehicle adopts a flat flight tracking mode;
if the unmanned aerial vehicle is not in a tracking state currently and the speed of the target is less than or equal to the intermediate speed, the unmanned aerial vehicle uses a hovering tracking mode;
if the unmanned aerial vehicle is in a hovering tracking state currently and the speed of a target is greater than or equal to a maximum threshold value, the unmanned aerial vehicle uses a flat flight tracking mode;
and if the unmanned aerial vehicle is currently in a flat flight tracking state and the speed of the target is less than or equal to a minimum threshold value, the unmanned aerial vehicle uses a hovering tracking mode.
When the unmanned aerial vehicle is currently in a tracking state, selecting a tracking mode according to the speed estimation of the target: at a target speed estimate of less than or equal to a minimum threshold (V)m-δV) When the target is tracked, a spiral tracking mode is adopted; estimating at the target speed greater than a maximum threshold (V)m+δV) And tracking the target by adopting a plane flight tracking mode.
The V ismAt an intermediate speed, thereforDelta. described aboveVIs the transition interval.
When the tracking mode is a plane flight tracking mode, calculating the final guidance vector comprises the following steps:
calculating a reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the reference guidance vector according to the correction angle to obtain a final guidance vector.
The computing reference guidance vector uses a weighting method.
Taking a dynamic weighting method as an example, if a horizontal flight tracking mode is adopted, for each unmanned aerial vehicle in each control period, firstly, a reference guidance vector is calculated by adopting the dynamic weighting method; and then, calculating virtual potential field force by detecting conflict, calculating a correction angle according to the virtual potential field force, and correcting to obtain a final guidance vector. And realizing the cooperative plane flight tracking according to the final guidance vector. And after each control period is finished, updating the position and speed estimation information of the tracking target.
Assume that the position of the moving object is pT＝(XTYTZT)TAt a speed of
Exercise, NUAVErecting the unmanned aerial vehicle from different starting positions (X) respectivelyiYiZi)(i＝1，2，…NUAV) And (5) starting.
The optimal distance keeping tracking strategy under the condition of adopting a plane flight tracking mode is adopted, namely, the unmanned aerial vehicle keeps an expected tracking distance with a target by adjusting the speed and the course of the unmanned aerial vehicle, namely, the unmanned aerial vehicle flies to reach an optimal tracking point calculated according to the position and the speed direction of the target. Unmanned aerial vehicle speed V under this strategy1Heading psi1The calculation method is as follows:
ψ1＝ψ+κψ1(ψd-ψ)
wherein
Representing the target speed magnitude, RtFor optimal tracking distance, d represents the distance between the drone and the target, ψ is the current heading of the drone, κd1And kappaψ1To adjust the coefficients, #dFor a desired heading to the optimal tracking point, the calculation is as follows:
wherein XDAnd YDThe abscissa and ordinate of the optimum tracking position are calculated as follows:
XD＝XT+Rtcos(ψT+ψF)
YD＝YT+Rtsin(ψT+ψF)
wherein psiTFor tracking the target course, psiFAnd determining according to the formation position of the unmanned aerial vehicle.
And (3) adopting a speed closed control tracking strategy under the condition of a flat flight tracking mode, namely, adjusting the speed and the course of the unmanned aerial vehicle to enable the speed and the direction of the unmanned aerial vehicle to be the same as the target speed. Unmanned aerial vehicle speed V under this strategy2Heading psi2The calculation method is as follows:
ψ2＝ψ+κψ2(ψT-ψ)
wherein V represents the horizontal velocity of the unmanned plane, kappad2And kappaψ2To adjust the coefficients.
In order to obtain good tracking effect under different states, the two tracking strategies are dynamically weighted according to the actual tracking state, namely the strategy fusion based on dynamic weighting:
wherein, VDFor the dynamically weighted unmanned aircraft speed, #DThe dynamically weighted heading.
Finally, converting the speed and the course required by realizing the tracking into a guidance vector, and under the coordinate system of the northeast, converting the guidance vector u into the guidance vectorrefReference guiding vector u of the plane flight tracking moderefAs shown in the following formula:
when the tracking mode is a spiral tracking mode, calculating the final guidance vector comprises the following steps:
calculating a reference guidance vector;
calculating guidance vector compensation required by phase adjustment, and compensating a reference guidance vector to obtain a compensated reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
The calculation reference guidance vector uses a Lyapunov vector field guidance method.
If a spiral tracking mode is adopted, in each control period, for each unmanned aerial vehicle, firstly, a Lyapunov (Lyapunov) vector field guidance method is adopted to calculate a reference guidance vector; then calculating the phase adjustment angular velocity to compensate the reference guidance vector to obtain a compensated reference guidance vector, and realizing the resultant force distribution of the phases when the multiple unmanned aerial vehicles perform spiral tracking; and then calculating the virtual potential field force by detecting the conflict, and correcting to obtain a final guidance vector. And the unmanned aerial vehicle flight control system executes the final guidance vector to realize the cooperative hovering tracking. And after each control period is finished, updating the position and speed estimation information of the tracking target.
As shown in fig. 3, a hovering tracking mode is adopted for three unmanned aerial vehicles to cooperatively track a ground target. Position of moving object is pT＝(XTYTZT)TAt a speed of
Exercise, NUAVErecting the unmanned aerial vehicle from different starting positions (X) respectivelyiYiZi)(i＝1，2，…NUAV) Starting and guidance-making strategy uiCan be at a certain phase interval thetacmdij(i＝1，2，…NUAV) Uniformly distributed at a target horizontal position (X)T，YT) As a circle center, with a radius of RcmdHeight from target of HcmdOn the target circle of (c) at the flying speed vcmdThe aircraft flies around the target, so that the airborne sensor can effectively cover the target and can move along with the target to prevent the target from escaping. Wherein, thetacmd12Phase separation of first frame unmanned aerial vehicle (UAV1) and second frame unmanned aerial vehicle (UAV2), θcmd23Phase interval, θ, for the second and third drones (UAVs 3)cmd31The phase interval of the third unmanned aerial vehicle and the first unmanned aerial vehicle.
For any unmanned aerial vehicle, calculating a reference guidance vector u thereofi(i＝1，2，…NUAV) For ease of description, the subscript i is omitted. Setting a relative position vector p of a target (moving target) to a droner＝(XrYrZr)T＝(X-XTY-YTZ-ZT)T. Wherein X, Y, Z is coordinate X of unmanned aerial vehiclei、Yi、Zi。
As shown in fig. 4, is a relative coordinate vector decomposition. To prIs subjected to decomposition, Xr、Yr、ZrRespectively represents prThe components on the X, Y, and Z axes. Reference guidance vector u for hovering tracking of target by unmanned aerial vehiclerefAs follows:
where β is the scaling factor, fx、fy、fzThe components of the guidance vector for the static target on the X axis, the Y axis and the Z axis are calculated as follows:
wherein eta is an adjusting factor, the size of eta determines the relative strength of the contraction action and the rotation action of the guidance vector, and the sign of eta determines the flight direction of the unmanned aerial vehicle around the target.
Taking three unmanned aerial vehicles as an example, a method for coordinating phase adjustment, namely compensating a reference guidance vector, is explained. Let the phase interval between i and j unmanned planes be thetaijWith a desired interval of thetacmdijDeviation Delta thetaij＝θcmdij-θij。
For the first drone, the guidance vector compensation required for phase adjustment is:
wherein Δ u1z＝0，
Wherein, X1r、Y1rRespectively representing the components of the relative position vector of the first unmanned aerial vehicle and the tracked target on the X axis and the Y axis, k1For the angular velocity adjustment coefficient of the first unmanned aerial vehicle, it is necessary to satisfy
For the second drone, the guidance vector compensation required for phase adjustment is:
wherein Δ u2z＝0，
Wherein, X2r、Y2rAnd respectively representing the components of the relative position vector of the second unmanned aerial vehicle and the tracking target on the X axis and the Y axis.
For the third drone, the guidance vector compensation required for phase adjustment is:
wherein Δ u3z＝0，
Wherein, X3r、Y3rRespectively representing the components of the relative position vector of the third unmanned aerial vehicle and the tracked target on the X axis and the Y axis, k3For the angular velocity adjustment coefficient of the third unmanned aerial vehicle, it is necessary to satisfy
After the guidance vector compensation required by the phase adjustment is obtained, the reference guidance vectors of all the unmanned aerial vehicles obtained in the last step are respectively accumulated to obtain the compensated reference guidance vectors, and the phase adjustment during the cooperative spiral tracking is realized.
Because many unmanned aerial vehicles can conflict or encounter obstacles in the tracking and phase adjustment process, in order to guarantee flight safety, conflict detection is required, and the tracking strategy is corrected. And the reference guidance vector of the horizontal flight tracking mode is consistent with the correction calculation method of the compensated reference guidance vector of the spiral tracking mode.
Detecting a collision and calculating a correction angle using the virtual potential field, comprising:
monitoring a safe flight area, and if other objects are detected to enter the safe flight area, calculating potential field force by adopting a virtual potential field;
the correction angle is calculated from the potential field force.
In the tracking and phase adjustment processes of multiple unmanned aerial vehicles, conflicts or obstacles may occur, and in order to ensure flight safety, conflict detection is required, and a tracking strategy is corrected.
As shown in fig. 5, a schematic diagram of collision detection and obstacle avoidance for an unmanned aerial vehicle. Is provided with (X)cYcZc) The position of the intruder relative to the local center of the unmanned aerial vehicle. When other objects enter the security zone, i.e.
A collision alarm will be generated. Wherein R isavoidDistance of action for repulsion, RsafeFor safe distances, d is the distance of the obstacle from the drone, FrepAs a repulsive force, FnAnd u is a component force of the repulsion force in the normal direction of the speed direction of the unmanned aerial vehicle, and is a guidance vector before correction, and u' is a guidance vector after correction.
Adopting the virtual potential field, when collision occurs, calculating and obtaining a potential field force expression as follows:
where ζ is the course adjustment gain, ε is a smaller positive number, RavoidDistance of action for repulsion, RsafeFor a safe distance, FrepAnd d is the distance between the obstacle and the unmanned aerial vehicle.
Under the action of the obstacle potential field, the following course correction angle is calculated:
wherein
Represents the components of the ground speed of the unmanned aerial vehicle on the X axis and the Y axis, | | Fr| | represents the magnitude of the component of the repulsive force in the speed direction of the unmanned aerial vehicle, and the calculation mode is as follows:
reference guide vector u of flat flight tracking mode according to upper complaint course correction anglerefAnd correcting to obtain a final guidance vector.
Compensated reference guidance vector u for hover tracking mode at heading correction anglerefAnd + delta u is corrected to obtain a final guidance vector.
Taking the example of correcting the compensated reference guidance vector in the hover tracking mode, the final guidance vector u' obtained after correction is as follows:
the intermediate speed is VmThe maximum threshold is (V)m+δV) The minimum threshold is (V)m-δV)，δVIs the transition interval.
In order to prevent the target from adopting maneuvering escape, effective and stable switching is carried out between two tracking modes. Specifically, under the condition of hover tracking, that is, the unmanned aerial vehicle is currently in the hover tracking state, if the updated target estimated speed is greater than or equal to (V)m+δV) When the flight tracking method is adopted, the tracking mode is switched from spiral tracking to flat flight tracking; under the condition of level flight tracking, namely that the unmanned plane is currently in a level flight tracking state, if the updated target estimated speed is less than or equal to (V)m-δV) And when the tracking mode is switched from the flat flight tracking to the spiral tracking. Wherein the buffer region (V)m+δV) And (V)m-δV) The arrangement effectively avoids the tracking of the target speed estimation at the intermediate speed (critical speed) VmThe tracking mode caused when nearby is frequently switched. The intermediate speed VmThe value of (c) can be set. Said transition region deltaVThe value of (c) can be set.
According to an embodiment of the present application, a system for tracking a target in cooperation with multiple drones is further provided, as shown in fig. 6, including:
the target detection module 101 is used for calculating speed information and position information of a target, sending the speed information and the position information to the communication module, and performing information fusion according to the speed information and the position information of the target sent by other unmanned aerial vehicles;
the communication module 102 is used for broadcasting the speed information and the position information of the target to other unmanned aerial vehicles, receiving the speed information and the position information of the target sent by other unmanned aerial vehicles and sending the speed information and the position information to the target detection module;
and the tracking module 103 is used for determining a tracking mode according to the fused speed information of the target, and calculating a final guidance vector according to the navigation information of the tracking module and other unmanned aerial vehicles, the speed information and the position information of the target and the tracking mode.
The communication module is further used for broadcasting the tracking decision information to other unmanned aerial vehicles and receiving the tracking decision information sent by other unmanned aerial vehicles.
Communication module still is used for information interaction between the unmanned aerial vehicle, information includes between the unmanned aerial vehicle: navigation information, calculation information of the target position and speed, tracking decision information and the like.
The tracking module is also used for sending tracking decision information to the communication module.
The tracking module includes: the device comprises a horizontal flight computing unit, a spiral computing unit and a conflict detection unit;
the plane flight calculation unit is used for calculating a reference guidance vector and sending the reference guidance vector to the conflict detection unit;
the spiral calculation unit is used for calculating a reference guidance vector, calculating guidance vector compensation required by phase adjustment, compensating the reference guidance vector to obtain a compensated reference guidance vector, and sending the compensated reference guidance vector to the collision detection unit;
and the collision detection unit is used for detecting a collision, calculating a correction angle by adopting the virtual potential field, and correcting the received reference guidance vector or the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
The system also includes a navigation module and a flight control module.
The navigation module acquires navigation information of the unmanned aerial vehicle and sends the navigation information to other unmanned aerial vehicles through the communication module.
The navigation information of the unmanned aerial vehicle comprises position information, speed information, course and other information of the unmanned aerial vehicle.
And the flight control module is used for realizing the guidance control of the unmanned aerial vehicle according to the calculated final guidance vector.
The target detection module, the communication module and the tracking module are carried on each unmanned aerial vehicle, so that the cooperative tracking of the multiple unmanned aerial vehicles on the target is realized. The target detection module is used for acquiring speed information and position information of a tracking target, and the communication module is used for information transmission between the unmanned aerial vehicles. And the tracking module is used for calculating a final guidance vector required by realizing the cooperative tracking.
According to the method, the unmanned aerial vehicle can be stably and effectively switched between the flat flight tracking mode and the spiral tracking mode through real-time speed estimation of the tracked target, so that the tracking capability is enhanced, and the tracked target is prevented from escaping through variable speed; compared with the traditional method, the method has better tracking task completion capability due to the speed advantage of the fixed-wing unmanned aerial vehicle and the cooperative tracking of the multiple unmanned aerial vehicles; for a static or low-speed moving target, a hover tracking method based on Lyapunov vector field guidance is designed, and hover tracking of a tracked target by a fixed-wing unmanned aerial vehicle is realized; in the cooperative hovering tracking process of the multiple unmanned aerial vehicles, a method for adjusting the cooperative phase of the multiple unmanned aerial vehicles is used to realize reasonable phase distribution during the cooperative hovering tracking of the multiple unmanned aerial vehicles; for a target moving at a high speed, a plane flight tracking method based on weighting is used to realize effective tracking of the unmanned aerial vehicle on the target moving at the high speed; by means of the collision detection and guidance vector correction method, the unmanned aerial vehicle avoids obstacles or other unmanned aerial vehicles in the flight process, prevents flight collision and collision caused by environmental obstacles or other unmanned aerial vehicles, and improves flight safety; by using distributed track planning, each unmanned aerial vehicle independently carries out the optimal solution of the track, and no special planning node is needed, thereby improving the track planning speed and the reliability of the system. The method of the present application is not limited to fixed wing drones and is equally applicable to rotary wing drones.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. A method for cooperatively tracking a target by multiple unmanned aerial vehicles is characterized by comprising the following steps:
s1, the first unmanned machine obtains the speed information and the position information of the target, broadcasts the speed information and the position information to other unmanned machines, receives the speed information and the position information of the target sent by other unmanned machines and carries out information fusion,
s2, determining a tracking mode by each unmanned aerial vehicle according to the speed information of the fused target,
s3, the multiple unmanned planes respectively calculate the final guidance vector according to the navigation information, the speed information and the position information of the target and the tracking mode of the other unmanned planes,
s4, the multiple unmanned aerial vehicles respectively update the speed information and the position information of the target, broadcast the speed information and the position information to other unmanned aerial vehicles, receive the speed information and the position information of the target sent by other unmanned aerial vehicles, perform information fusion and return to S2;
the determining the tracking method in S2 includes:
if the unmanned aerial vehicle is not in the tracking state currently and the speed of the target is greater than the intermediate speed, the unmanned aerial vehicle adopts a flat flight tracking mode,
if the unmanned aerial vehicle is not in the tracking state currently and the speed of the target is less than or equal to the intermediate speed, the unmanned aerial vehicle adopts a hovering tracking mode,
if the unmanned aerial vehicle is in the hovering tracking state currently and the speed of the target is greater than or equal to the maximum threshold value, the unmanned aerial vehicle adopts a flat flight tracking mode,
if the unmanned aerial vehicle is currently in a flat flight tracking state and the speed of a target is less than or equal to a minimum threshold value, the unmanned aerial vehicle uses a spiral tracking mode;
wherein the intermediate speed is VmThe maximum threshold is (V)m+δV) The minimum threshold is (V)m-δV)，δVIs the transition interval.
2. The method for the cooperative tracking of the target by the multiple unmanned aerial vehicles according to claim 1, wherein when the tracking mode is a level flight tracking mode, the calculating of the final guidance vector comprises:
calculating a reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the reference guidance vector according to the correction angle to obtain a final guidance vector.
3. The method for the cooperative tracking of the target by the multiple drones as recited in claim 2, wherein the calculating the reference guidance vector uses a weighting method.
4. The method for multi-unmanned aerial vehicle cooperative tracking of the target of claim 1, wherein when the tracking mode is a hover tracking mode, calculating the final guidance vector comprises:
calculating a reference guidance vector;
calculating guidance vector compensation required by phase adjustment, and compensating a reference guidance vector to obtain a compensated reference guidance vector;
detecting a conflict and calculating a correction angle by adopting a virtual potential field;
and correcting the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
5. The method for multi-UAV cooperative tracking of a target according to claim 4, wherein the calculation of the reference guidance vector uses a Lyapunov vector field guidance method.
6. The method for cooperative tracking of targets by multiple drones according to claim 2 or 4, wherein the detecting of the collision and the calculating of the correction angle using the virtual potential field comprise:
monitoring a safe flight area, and if other objects are detected to enter the safe flight area, calculating potential field force by adopting a virtual potential field;
the correction angle is calculated from the potential field force.
7. The utility model provides a system for many unmanned aerial vehicles follow tracks of target in coordination which characterized in that includes:
the target detection module is used for calculating the speed information and the position information of the target, sending the speed information and the position information to the communication module, carrying out information fusion according to the speed information and the position information of the target sent by other unmanned aerial vehicles,
the communication module broadcasts the speed information and the position information of the target to other unmanned aerial vehicles, receives the speed information and the position information of the target sent by other unmanned aerial vehicles, sends the speed information and the position information of the target to the target detection module,
the tracking module is used for determining a tracking mode according to the fused speed information of the target and calculating a final guidance vector according to the navigation information of the tracking module and other unmanned aerial vehicles, the speed information and the position information of the target and the tracking mode;
the tracking mode comprises the following steps:
if the unmanned aerial vehicle is not in the tracking state currently and the speed of the target is greater than the intermediate speed, the unmanned aerial vehicle adopts a flat flight tracking mode,
if the unmanned aerial vehicle is not in the tracking state currently and the speed of the target is less than or equal to the intermediate speed, the unmanned aerial vehicle adopts a hovering tracking mode,
if the unmanned aerial vehicle is in the hovering tracking state currently and the speed of the target is greater than or equal to the maximum threshold value, the unmanned aerial vehicle adopts a flat flight tracking mode,
if the unmanned aerial vehicle is currently in a flat flight tracking state and the speed of a target is less than or equal to a minimum threshold value, the unmanned aerial vehicle uses a spiral tracking mode;
wherein the intermediate speed is VmThe maximum threshold is (V)m+δV) The minimum threshold is (V)m-δV)，δvIs the transition interval.
8. The system of claim 7, wherein the tracking module comprises: the device comprises a horizontal flight computing unit, a spiral computing unit and a conflict detection unit;
the plane flight calculation unit is used for calculating a reference guidance vector and sending the reference guidance vector to the conflict detection unit;
the spiral calculation unit is used for calculating a reference guidance vector, calculating guidance vector compensation required by phase adjustment, compensating the reference guidance vector to obtain a compensated reference guidance vector, and sending the compensated reference guidance vector to the collision detection unit;
and the collision detection unit is used for detecting a collision, calculating a correction angle by adopting the virtual potential field, and correcting the received reference guidance vector or the compensated reference guidance vector according to the correction angle to obtain a final guidance vector.
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| CN110488866B (en) * | 2019-08-20 | 2022-08-05 | 西南石油大学 | Unmanned aerial vehicle formation obstacle avoidance method based on gradient function |
| CN110618701B (en) * | 2019-08-26 | 2022-09-30 | 中国人民解放军国防科技大学 | Multi-unmanned aerial vehicle collaborative optimization method and system for target tracking |
| CN111240365A (en) * | 2020-03-12 | 2020-06-05 | 北京航空航天大学 | Unmanned aerial vehicle formation self-adaptive actuator fault compensation method with designated performance |
| CN111487996B (en) * | 2020-04-30 | 2021-09-21 | 华南理工大学 | Multi-unmanned aerial vehicle cooperative control system based on ADRC control and method thereof |
| CN112363539B (en) * | 2020-11-18 | 2021-09-21 | 中国海洋大学 | Multi-unmanned aerial vehicle cooperative target searching method |
| CN112686178B (en) * | 2020-12-30 | 2024-04-16 | 中国电子科技集团公司信息科学研究院 | Multi-view target track generation method and device and electronic equipment |
| CN112923925B (en) * | 2021-01-07 | 2023-02-21 | 天津大学 | Dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking ground target |
| CN113741519B (en) * | 2021-09-01 | 2023-04-25 | 清华大学 | Unmanned aerial vehicle spiral tracking intelligent control method and device, electronic equipment and storage medium |
| CN114371720B (en) * | 2021-12-29 | 2023-09-29 | 国家电投集团贵州金元威宁能源股份有限公司 | Control method and control device for realizing tracking target of unmanned aerial vehicle |
| CN114371733B (en) * | 2022-01-06 | 2023-11-28 | 中国人民解放军海军航空大学 | Multi-unmanned aerial vehicle cooperative surrounding flight control method and system |
| CN115079723A (en) * | 2022-07-20 | 2022-09-20 | 中国人民解放军国防科技大学 | Fixed-wing unmanned aerial vehicle guidance method achieving at any time |
Family Cites Families (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1361409A (en) * | 2000-12-23 | 2002-07-31 | 林清芳 | Enhancement navigation positioning method and its system |
| CN103197684B (en) * | 2013-04-25 | 2016-09-21 | 清华大学 | Unmanned aerial vehicle group works in coordination with the method and system following the tracks of target |
| US20160357187A1 (en) * | 2015-06-05 | 2016-12-08 | Arafat M.A. ANSARI | Smart vehicle |
| CN105425819B (en) * | 2015-11-25 | 2019-01-11 | 南京航空航天大学 | A kind of unmanned plane automatically tracks the method for guidance of ground target |
| US9977963B1 (en) * | 2017-03-03 | 2018-05-22 | Northrop Grumman Systems Corporation | UAVs for tracking the growth of large-area wildland fires |
| CN106950980B (en) * | 2017-04-18 | 2019-08-13 | 南京航空航天大学 | A kind of small-sized fixed-wing unmanned plane guidance computer and method of guidance |
| CN107422748B (en) * | 2017-06-29 | 2019-08-13 | 南京航空航天大学 | A kind of fixed-wing unmanned plane formation guidance device and collaboration homing guidance method |
| CN107748860A (en) * | 2017-09-01 | 2018-03-02 | 中国科学院深圳先进技术研究院 | Method for tracking target, device, unmanned plane and the storage medium of unmanned plane |
| CN107992065A (en) * | 2017-09-14 | 2018-05-04 | 北京航空航天大学 | A kind of fixed-wing unmanned aerial vehicle group movable object tracking and avoidance collision-proof method |
| CN107678023B (en) * | 2017-10-10 | 2020-08-11 | 芜湖华创光电科技有限公司 | Passive positioning and identification system for civil unmanned aerial vehicle |
| CN107992068A (en) * | 2017-11-29 | 2018-05-04 | 天津聚飞创新科技有限公司 | Method for tracking target, device and aircraft |
| CN108319130A (en) * | 2017-12-27 | 2018-07-24 | 清华大学 | The maneuvering target tracking method of vector sum particle filter is guided based on Liapunov |
| CN109116349B (en) * | 2018-07-26 | 2022-12-13 | 西南电子技术研究所（中国电子科技集团公司第十研究所） | Multi-sensor cooperative tracking joint optimization decision method |
| CN108958297A (en) * | 2018-08-03 | 2018-12-07 | 南京航空航天大学 | A kind of multiple no-manned plane collaboration target following earth station |
| CN109283942A (en) * | 2018-09-27 | 2019-01-29 | 易瓦特科技股份公司 | For controlling the flying method and device that unmanned plane is tracked |
| CN109375643B (en) * | 2018-10-24 | 2021-06-25 | 中北大学 | Multi-four-rotor-confrontation tracking target guidance law based on piloting-following type triangular formation |
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