CN108459616B - Unmanned aerial vehicle group collaborative coverage route planning method based on artificial bee colony algorithm - Google Patents
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Abstract
The invention belongs to the field of unmanned early warning machine route planning, and discloses an unmanned aerial vehicle group collaborative coverage route planning method based on an artificial bee colony algorithm, which comprises the following steps: setting a warning area and a flyable area of the unmanned aerial vehicle group and parameters of the unmanned aerial vehicle group, setting parameters of an artificial bee colony algorithm and an initial honey source group, determining the state of the unmanned aerial vehicle group at the next moment by the artificial bee colony algorithm, and updating the speed deflection angle of the unmanned aerial vehicle group; the monitoring area can be effectively covered in real time, and a foundation is laid for using the unmanned aerial vehicle group for early warning.
Description
Technical Field
The invention belongs to the field of unmanned early warning machine route planning, and particularly relates to an unmanned plane group collaborative coverage route planning method based on an artificial bee colony algorithm, which is suitable for the collaborative coverage route planning problem of an unmanned early warning plane group.
Background
The advantages of drone applications in modern warfare have been increasingly applied to the performance of many dangerous and complex tasks. The unmanned early warning machine for early warning detection is mainly used for automatically covering a specified area and early warning potential dangers.
Under the complicated and changeable informationized battlefield environment, a single unmanned aerial vehicle faces the limitations of detection angles, detection ranges and the like when executing a coverage task, the exertion of the fighting effect is restricted, and the difficulty of the single unmanned aerial vehicle for satisfactorily completing the task is more and more large. The cooperative detection of multiple unmanned aerial vehicles means that two or more unmanned aerial vehicles cooperate with each other and cooperatively execute a coverage task. For single unmanned early warning machine, the angle restriction that can overcome radar detecter when many unmanned early warning machines carry out the task in coordination observes the target area from a plurality of different positions, when facing regional search task on a large scale, many unmanned aerial vehicles can realize the effective coverage to whole reconnaissance region, consequently has better reconnaissance efficiency and stronger task fault-tolerant ability.
Unmanned aerial vehicle route planning is the core of many unmanned aerial vehicle cooperative control, is a key technology that realizes unmanned aerial vehicle autonomous flight, can provide one or many safe feasible routes for unmanned aerial vehicle to different task demands, plays the key role to unmanned aerial vehicle accomplishes the task smoothly. In order to successfully complete the cooperative coverage task, the route planning of the unmanned aerial vehicle for early warning detection mainly aims to establish a flyable spatial path, and in a given warning area, a plurality of unmanned aerial vehicles are searched from respective starting points, so that the optimal or feasible route which completely covers the area and meets given constraint conditions and performance indexes is achieved at a higher speed, and after the unmanned aerial vehicle group is completely covered, the unmanned aerial vehicle group needs to continue to move and keep the state.
For the problem of cooperative coverage, few research reports are reported at home and abroad. In 2007, the penghs of the university of defense science and technology, Shenlingcheng and the like reduce the complexity of problem solving, the problem solving method is decomposed into two subproblems of multi-unmanned aerial vehicle task area allocation and complete coverage path planning, a scanning line mode-based area coverage path searching method is provided, and a certain effect is achieved. However, the method is rigid and not intelligent enough. The problem of the maximum area coverage of the mobile station, which is said in hundredths and google, is substantially different from the problem to be solved by the present invention. The final result of the maximum area coverage problem of the mobile base station is static, and the motion state of the unmanned early warning aircraft cannot become static after flying for a period of time in the air, namely, the unmanned aircraft still needs to continue moving and maintain the trend of complete coverage after completely covering a specified area.
Disclosure of Invention
Aiming at the problems, the invention aims to provide an unmanned aerial vehicle group cooperative coverage route planning method based on an artificial bee colony algorithm.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme.
An unmanned aerial vehicle group collaborative coverage route planning method based on an artificial bee colony algorithm comprises the following steps:
step 1, setting a warning area and a flyable area of an unmanned aerial vehicle cluster, wherein the unmanned aerial vehicle cluster comprises N unmanned aerial vehicles, and setting the maximum speed deflection angle of each unmanned aerial vehicle and the maximum acting distance of each airborne radar; n is a positive integer greater than zero;
step 2, setting the initial speed of each unmanned aerial vehicle to form an initial speed vector of the unmanned aerial vehicle cluster, and setting the initial position of each unmanned aerial vehicle to form an initial position matrix of the unmanned aerial vehicle cluster;
step 3, setting an initial honey source group of an artificial bee colony algorithm, wherein the initial honey source group comprises CS honey sources, the dimension of each honey source is Dim dimension, each honey source represents a speed deflection angle scheme of the unmanned aerial vehicle cluster, and a one-dimensional numerical value of each honey source corresponds to the speed deflection angle of an unmanned aerial vehicle in the unmanned aerial vehicle cluster; setting the maximum iteration times MaxCycles of the artificial bee colony algorithm for one time, and allowing the maximum times Limit that the honey sources are not updated;
step 4, randomly determining a speed deflection angle vector for each honey source in the initial honey source group, and obtaining a feasible position coordinate matrix of each honey source at the next moment according to the speed vector of the current moment of the unmanned aerial vehicle group, the position matrix of the current moment and the speed deflection angle vector corresponding to each honey source; calculating to obtain a fitness value corresponding to each honey source according to a feasible position coordinate matrix of each honey source at the next moment, selecting a honey source corresponding to the maximum fitness value from fitness values corresponding to CS honey sources as an optimal honey source, and recording the optimal honey source Best, a feasible position matrix BestLoc of the unmanned aerial vehicle cluster corresponding to the optimal honey source and the fitness value BestFit of the optimal honey source;
step 5, setting a recording matrix, initializing the recording matrix to be an all-zero matrix, wherein the dimension of the recording matrix is CS, and the recording matrix is used for recording the times that CS honey sources are not updated;
step 6, performing neighborhood search on each honey source in the current Better honey source group by using CS employment bees to obtain CS searched new honey sources, calculating the fitness value of the CS searched new honey sources, selecting the honey sources with large fitness values to form a Better honey source group Better according to the fitness value of each new honey source searched and the fitness value of the corresponding honey source in the current Better honey source group, recording the position matrix BetterLoc and the fitness matrix Betterfit of each honey source in the Better honey source group, and updating the recording matrix; the initial state of the current better honey source group is an initial honey source group;
step 7, selecting honey sources in the current Better honey source group Better by adopting CS observation bees to respectively adopt a roulette mode to perform neighborhood search to obtain CS new honey sources which are searched, calculating the fitness value of the CS new honey sources which are searched, selecting the honey source with a large fitness value to update the Better honey source group Better, the position matrix BetterLoc and the fitness matrix Betterfit according to the fitness value of each new honey source which is searched and the fitness value of each honey source in the current Better honey source group Better, and updating the recording matrix; selecting a honey source with the largest fitness value from the updated Better honey source group Better, comparing the largest fitness value with the fitness value of the optimal honey source Best, and if the largest fitness value is larger than the fitness value of the optimal honey source Best, updating the optimal honey source Best, and a feasible position matrix BestLoc and the fitness value BestFit of the optimal honey source;
removing the honey sources with the non-updated times more than or equal to the maximum times Limit of allowing the honey sources to be not updated in the updated Better honey source group setter, and randomly generating new honey sources to replace the removed honey sources;
step 8, taking the Better honey source group Better obtained in the step 7 as the current Better honey source group, repeatedly executing the step 6 and the step 7 until the maximum iteration times MaxCycles of the artificial bee colony algorithm is reached, and determining the speed deflection angle of the unmanned aerial vehicle group from the current moment to the next moment according to the optimal honey source obtained in the last iteration;
step 9, updating a position matrix and a speed vector of the unmanned aerial vehicle cluster at the next moment according to the speed deflection angle of the unmanned aerial vehicle cluster from the current moment to the next moment;
and Step 10, setting the searching Step number Step of the unmanned aerial vehicle cluster, repeatedly executing Step 4 and Step 9 for Step times, and finishing the collaborative coverage route planning process of the unmanned aerial vehicle cluster.
The invention provides a flight path planning method for unmanned aerial vehicle group collaborative coverage by using an artificial bee colony algorithm on the basis of angle search, and the aim of optimizing the real-time coverage area of the unmanned aerial vehicle group is achieved. And guiding the next track point of the unmanned aerial vehicle cluster by using an artificial bee colony algorithm. When the coverage area is reduced, the flight attitude of the unmanned aerial vehicle cluster can be quickly adjusted, so that the effect of optimal coverage area in real time can be achieved. Under the parameters set by the simulation experiment of the invention, the real-time coverage area is not less than 99.5 percent, and a foundation is laid for the early warning of the unmanned aerial vehicle group.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an unmanned aerial vehicle group collaborative coverage route planning method based on an artificial bee colony algorithm according to an embodiment of the present invention;
fig. 2 is a schematic view of a flight model of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a real-time coverage area corresponding to a time when the method of the present invention is used for unmanned aerial vehicle fleet route planning;
FIG. 4 is a schematic diagram of a final track route obtained by planning a flight path of an unmanned aerial vehicle using the method of the present invention;
fig. 5 is a schematic diagram of the final real-time coverage percentage obtained by using the method of the present invention to perform unmanned aerial vehicle track planning.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An unmanned aerial vehicle group collaborative coverage route planning method based on an artificial bee colony algorithm is disclosed, as shown in fig. 1, the method comprises the following steps:
step 1, setting a warning area and a flyable area of an unmanned aerial vehicle cluster, wherein the unmanned aerial vehicle cluster comprises N unmanned aerial vehicles, and setting the maximum speed deflection angle of each unmanned aerial vehicle and the maximum acting distance of each airborne radar; n is a positive integer greater than zero.
Specifically, firstly, a warning area S and a feasible flight area A of the unmanned aerial vehicle are set, the unmanned aerial vehicle cluster needs to maximally cooperatively cover the warning area in real time, and meanwhile, the unmanned aerial vehicle cluster is guaranteed not to fly out of the feasible flight area. Then, according to the principle that the centripetal force is provided by the component force of the lifting force by utilizing the difference of the ailerons when the unmanned aerial vehicle turnsTurning, and obtaining the maximum speed deflection angle theta when the unmanned aerial vehicle is overloaded maximally in the processm. Finally, setting the working parameters of the airborne radar system, and determining the maximum working distance R of the single airborne radar according to the specific system parameters of the radarmax。
Step 1 can be divided into the following substeps:
1.1 setting an alert area S and a feasible flight area A of the unmanned aerial vehicle. The warning area refers to an area to be covered by the unmanned aerial vehicle group airborne distributed radar system. The feasible flight area comprises all warning areas, the unmanned aerial vehicle has to fly in the feasible flight area, and if the unmanned aerial vehicle flies out of the feasible flight area, the effective detection flight area is reduced and is vulnerable to threats such as potential ground air defense missiles. The final goal of the flight path planning is to maximize coverage of the surveillance area in real time so that the radar system can obtain maximum ground information. The alert zone S is represented as:
1.2 setting specific system parameters of the unmanned aerial vehicle, here mainly the maximum speed deflection angle theta of the unmanned aerial vehiclem. When the unmanned aerial vehicle turns, the unmanned aerial vehicle is subjected to differential motion by virtue of the ailerons, so that the body inclines, and the unmanned aerial vehicle turns by utilizing centripetal component force of lift force. Carrying out stress analysis on the unmanned aerial vehicle:
Lcosγ＝mg
mVp 2/R＝Lsinγ
in the formula, L represents lift force, gamma represents roll angle, namely turning inclination angle of the airframe, m represents dead weight of the airframe of the unmanned aerial vehicle, R represents turning radius, and VpRepresenting unmanned aerial vehicle cruise speed, then there are:
R＝Vp 2/(g·tanγ)
tan γ is known in some literature as overload. Obviously transship the more, turning radius is less, and unmanned aerial vehicle turns and receives the restraint less. However, there is an upper limit to the overload of the drone, and when the overload is maximum, the roll angle is maximized, at which time the minimum turning radius R can be obtainedmin. By geometric relationship, from the minimum turning radius RminFlying speed V of carrierpAnd the time interval of flight DeltaT can obtain the maximum turning angle theta (namely the maximum speed deflection angle)m. Maximum turning angle thetamThe maximum included angle generated by the change of the speed and the direction of the unmanned aerial vehicle at two adjacent moments is indicated.
1.3 setting the parameters of the airborne radar system, and the final purpose of unmanned aerial vehicle group flight path planning is to maximally cover a monitoring area in real time, so that the action range of the single unmanned aerial vehicle airborne radar system needs to be determined. The detection area is simplified into a circle, and the maximum action distance of the radar system is set as RmaxThe radar maximum range can be calculated according to the radar equation:
in the above formula, P2Denotes the peak power of the radar system, G denotes the antenna gain, λ denotes the electromagnetic wave wavelength, σ denotes the scattering cross-sectional area of the target, k denotes the Boltzmann constant, T0Indicating standard room temperature, B indicating receiver bandwidth, F indicating noise figure, L indicating radar self-loss, (S/N)0minRepresenting a minimum detectable threshold.
And 2, setting the initial speed of each unmanned aerial vehicle to form an initial speed vector of the unmanned aerial vehicle cluster, and setting the initial position of each unmanned aerial vehicle to form an initial position matrix of the unmanned aerial vehicle cluster.
Setting the initial speed and position of the unmanned aerial vehicle, namely the speed and the position of the unmanned aerial vehicle when the unmanned aerial vehicle enters a feasible flight area: recording initial velocity vector of unmanned aerial vehicle group
Initial position matrix of unmanned aerial vehicle group
Wherein,
indicating the initial velocity of the ith drone,
represents the initial position of the ith unmanned aerial vehicle, an
And
respectively representing the x coordinate and the y coordinate of the ith unmanned aerial vehicle at the initial moment; i-1, 2, …, N denotes the total number of drones in the drone swarm.
Step 3, setting an initial honey source group of an artificial bee colony algorithm, wherein the initial honey source group comprises CS honey sources, the dimension of each honey source is Dim dimension, each honey source represents a speed deflection angle scheme of the unmanned aerial vehicle cluster, and a one-dimensional numerical value of each honey source corresponds to the speed deflection angle of an unmanned aerial vehicle in the unmanned aerial vehicle cluster; and setting the maximum iteration times MaxCycles of the artificial bee colony algorithm for one time, and allowing the maximum times Limit that the bee resources are not updated.
Step 3 can be divided into the following substeps:
3.1 artificial bee colony algorithm coding. And setting the initial honey source number of the artificial bee colony algorithm as CS and the dimension of each honey source as Dim. And (3) encoding by taking the flight deflection angle theta of the unmanned aerial vehicle as a parameter, wherein each dimension in the honey source represents the speed deflection angle generated by a single unmanned aerial vehicle, and one honey source represents a planning scheme. The characteristic of one-step planning is combined, namely, the artificial bee colony algorithm can only obtain the next unmanned aerial vehicle flying target point, so that the whole process can be regarded as that the unmanned aerial vehicle group generates a plurality of schemes in advance, and the scheme with the largest current coverage area is selected.
3.2 setting the value range of the parameters. Boundary conditions include the maximum velocity yaw angle θ of the dronemThe maximum number of times that the honey source is allowed to be not updated is Limit, the maximum iteration number of the algorithm is MaxCycles, the algorithm termination condition (the number of steps taken by the unmanned aerial vehicle) and the feasible flight area A of the unmanned aerial vehicle. When the algorithm generates the speed of the droneWhen the deflection angle exceeds the maximum deflection angle of the unmanned aerial vehicle, the following formula is used for correction:
additionally, flight path modeling is required. Firstly, the position deflection angle of the unmanned aerial vehicle is determined
The schematic view of the flight model of the unmanned aerial vehicle is shown in fig. 2, and the position deflection angle of the unmanned aerial vehicle can be obtained according to simple geometric knowledge
Is half the speed yaw angle theta. Next, a feasible location is determined, the feasible location being deflected by a maximum velocity angle θmAnd determining that the feasible position is a circular arc line.
The method specifically comprises the following steps:
(1) a position deflection angle is determined. When the speed deflection angle of the unmanned aerial vehicle is used as information for coding, if the unmanned aerial vehicle is considered to turn at a constant speed and circumference, the position deflection angle generated by adjacent moment positions can be obtained according to simple geometric knowledge
Is to produce half of the velocity yaw angle theta, i.e.
(2) And determining the feasible positions. When the speed deflection angle theta of the unmanned aerial vehicle does not exceed the maximum speed deflection angle thetamAnd the generated flight path is feasible. When the unmanned aerial vehicle flies at a constant speed, an arc line can be determined according to the deflection angle of the unmanned aerial vehicle, and each point on the arc line can be regarded as a feasible flight path. To simplify the process, this arc may be approximated as a circular arc. This approximation is reasonable because the distance travelled by the drone along the arc and the chord length is approximately equal. At the same time, the center of the circle corresponding to the arc can be determinedThe angle is the velocity yaw angle.
Step 4, randomly determining a speed deflection angle vector for each honey source in the initial honey source group, and obtaining a feasible position coordinate matrix of each honey source at the next moment according to the speed vector of the current moment of the unmanned aerial vehicle group, the position matrix of the current moment and the speed deflection angle vector corresponding to each honey source; calculating to obtain a fitness value corresponding to each honey source according to the feasible position coordinate matrix of each honey source at the next moment, selecting the honey source corresponding to the maximum fitness value from the fitness values corresponding to the CS honey sources as an optimal honey source, and recording the optimal honey source Best, the feasible position matrix BestLoc of the unmanned aerial vehicle cluster corresponding to the optimal honey source and the fitness value BestFit of the optimal honey source.
The invention is based on angle search, i.e. the solution space is the deflection angle of the Dim-dimensional unmanned aerial vehicle, i.e. the solution space of the problem is equivalent to the honey source of the artificial bee colony algorithm. The method is characterized in that the method imitates a basic artificial bee colony algorithm, firstly, the algorithm is initialized, the population number of the initialized bee resources is CS, namely, CS group solution is initialized. The initialization process is equivalent to randomly determining a feasible flight plan without the constraint of maximum coverage area.
The step 4 specifically comprises the following steps:
(4a) randomly determining a velocity deflection angle vector theta for the kth honey source in the initial honey source groupk：
θk＝(θk1,θk2…,θki,…θkDim)
θki＝(θmax-θmin)ξ+θmin
Wherein, thetakiDenotes the i-th dimension of the yaw angle of the kth honey source in the initial honey source group, and k is 1kDimThe Dim-dimension speed deflection angle of the kth honey source in the initial honey source group is shown, CS represents the total number of the honey sources contained in the initial honey source group, and thetamaxRepresenting the maximum speed yaw angle, theta, of the droneminMinimum speed yaw angle, ξ for [0,1 ], for unmanned aerial vehicles]A random number of intervals;
(4b) root of herbaceous plantAccording to the speed t of the ith unmanned aerial vehicle in the unmanned aerial vehicle group at the current moment
Position of current time t
And ith dimension velocity deflection angle theta of kth honey sourcekiObtaining feasible position coordinates of the ith unmanned aerial vehicle corresponding to the kth honey source at the next moment t +1
Wherein t represents the current time, the initial value of t is zero, VpThe speed of the unmanned aerial vehicle flying at a constant speed is shown,
i-dimension velocity deflection angle theta representing k-dimension honey sourcekiThe corresponding unmanned aerial vehicle deflects from the current moment to the next moment;
sequentially taking 1, Dim from i to obtain a feasible position coordinate matrix corresponding to the kth honey source at the next moment;
enabling k to sequentially take 1,1 and CS to respectively obtain feasible position coordinate matrixes corresponding to CS honey sources at the next moment;
(4c) the fitness value may be calculated by a fitness function to cover an area SkAnd penalty Lim for flying out of warning areakThe weighting is as a fitness function. If the unmanned plane is at the same timeCovering an area, it is only remembered once. Calculating the fitness value f of the kth honey sourcek：
fk＝Sk-ω*Limk
Sk＝Sk1∪Sk2∪...Ski...∪SkDim
Wherein S iskRepresenting the total coverage area S of the unmanned aerial vehicle flight scheme corresponding to the kth honey source to the warning areakiThe coverage area of the warning area by the ith unmanned aerial vehicle of the kth honey source is represented, i is 1kThe penalty that the unmanned plane corresponding to the kth honey source flies out of the warning area is represented, and U represents the union set;
sequentially taking 1,1 and CS from k to respectively obtain fitness values f corresponding to CS honey sources1,f2…,fk,…fCS；
Specifically, the substep (4c) is punished Lim of the penalty that the flight scheme of the unmanned aerial vehicle corresponding to the kth honey source flies out of the warning areakThe method specifically comprises the following steps:
wherein, LimkiThe penalty, x, of flying out of the warning area of the unmanned aerial vehicle corresponding to the ith dimension value of the kth honey sourceedgeAnd yedgeFollowed by
And change when
Between the minimum and maximum, the penalty is 0, xminAnd xmaxRespectively representing the minimum and maximum values of the abscissa of the warning zone, yminAnd ymaxRespectively representing the minimum and maximum values of the ordinate of the surveillance zone.
(4d) Selecting the honey source corresponding to the maximum fitness value from the fitness values corresponding to the CS honey sources as the optimal honey sourceAnd recording the optimal honey source Best, the feasible position BestLoc of the unmanned aerial vehicle group corresponding to the optimal honey source, and the fitness value BestFit ═ max (f) of the optimal honey source1,f2…,fk,…fCS)。
And 5, setting a recording matrix, initializing the recording matrix into an all-zero matrix, wherein the dimension of the recording matrix is CS, and the recording matrix is used for recording the times that CS honey sources are not updated.
The step 5 specifically comprises the following steps: setting recording matrix Record ═ r1 r2…rk…rCS]，r k0, k-1, 2, …, CS; CS represents the total number of honey sources contained in the honey source group.
Step 6, performing neighborhood search on each honey source in the current Better honey source group by using CS employment bees to obtain CS searched new honey sources, calculating the fitness value of the CS searched new honey sources, selecting the honey sources with large fitness values to form a Better honey source group Better according to the fitness value of each new honey source searched and the fitness value of the corresponding honey source in the current Better honey source group, recording the position matrix BetterLoc and the fitness matrix Betterfit of each honey source in the Better honey source group, and updating the recording matrix; the initial state of the current better honey source group is an initial honey source group.
The step 6 specifically comprises the following steps:
(6a) randomly generating a first parameter Param belonging to [1 and Dim ] and a second parameter Neighbour belonging to [1 and CS ] by the kth employing bee, wherein Neighbour is not equal to k;
(6b) carrying out neighborhood search on the kth honey source in the current better honey source group by the kth employment bee to obtain a speed deflection angle vector theta 'of the kth new honey source'kVelocity yaw Angle vector θ 'of kth New Honey Source'kMiddle i-th dimension speed deflection angle theta'kiComprises the following steps:
where φ is a random number, and φ ∈ (-1,1), θkiThe ith dimension velocity deflection corresponding to the kth honey source in the current better honey source groupAngle thetakparamA param dimensional speed deflection angle corresponding to the kth honey source in the current better honey source group;
and enabling k to sequentially take 1,. and CS, and respectively obtaining CS speed deflection angle vectors theta corresponding to new honey sources'1,θ'2,…,θ'k,…θ'CS；
In sub-step (6b), the velocity yaw angle vector θ 'of the kth new honey source'kMiddle i-th dimension speed deflection angle theta'kiExceeding the maximum speed deflection angle theta of the dronemaxAnd minimum velocity yaw angle thetaminThen, the following formula is adopted for correction:
(6c) acquiring feasible position coordinate matrixes and fitness values corresponding to CS new honey sources at the next moment, comparing the fitness values of the CS new honey sources with the fitness values of the CS honey sources in a current superior honey source group in a one-to-one correspondence manner, and reserving the honey sources with larger fitness values to form a superior honey source group setter;
(6d) update the recording matrix Record ═ r'1 r′2…r′k…r′CS]；
Wherein k is 1,2, …, CS is the total number of honey sources in the honey source group, r'kRepresenting updated values of the k-th dimension of data in the record matrix, rkIndicates the number of times that the kth honey source has not been updated, f (θ)k) Representing the fitness value, f (theta'k) Representing the fitness value of the kth new honey source in the current better honey source group.
Step 7, selecting honey sources in the current Better honey source group Better by adopting CS observation bees to respectively adopt a roulette mode to perform neighborhood search to obtain CS new honey sources which are searched, calculating the fitness value of the CS new honey sources which are searched, selecting the honey source with a large fitness value to update the Better honey source group Better, the position matrix BetterLoc and the fitness matrix Betterfit according to the fitness value of each new honey source which is searched and the fitness value of each honey source in the current Better honey source group Better, and updating the recording matrix; selecting a honey source with the largest fitness value from the updated Better honey source group Better, comparing the largest fitness value with the fitness value of the optimal honey source Best, and if the largest fitness value is larger than the fitness value of the optimal honey source Best, updating the optimal honey source Best, and a feasible position matrix BestLoc and the fitness value BestFit of the optimal honey source;
and eliminating the honey sources with the non-updated times more than or equal to the maximum times Limit of allowing the honey sources to be not updated in the updated Better honey source group setter, and randomly generating new honey sources to replace the eliminated honey sources.
The step 7 specifically comprises the following steps:
(7a) normalizing the fitness values of the CS honey sources in the current Better honey source group Better to obtain normalized fitness values (f)s1,fs2,…,fsk,…,fsCS)；fskDenotes the fitness value of the kth honey source after normalization, k is 1.
(7b) Selecting a honey source for neighborhood search by the kth observation bee according to a roulette mode, and randomly generating a third parameter rand belonging to (0, 1); then there is
Wherein a is an integer and a is ∈ [1, CS ]]，fsnDenotes the fitness value of the nth honey source after normalization, n is 1. Namely, the kth observer selects the a th honey source to perform neighborhood search;
(7c) randomly generating a fourth parameter Param' ∈ [1, Dim by the kth observer]And a fifth parameter Neighbour ∈ [1, CS ]]Neighbour' ≠ k; the kth observation bee carries out neighborhood search on the a-th honey source in the current Better honey source group setter to obtain a speed deflection angle vector theta of the kth new honey sourcekVelocity yaw angle vector θ ″' of the kth new honey sourcekMiddle ith dimension velocity deflection angle theta ″)kiComprises the following steps:
wherein φ is a random number, and φ is epsilon (-1,1), theta'kiIs the ith dimension speed deflection angle, theta ', corresponding to the kth honey source in the current better honey source group'kparamA param dimensional speed deflection angle corresponding to the kth honey source in the current better honey source group;
and (5) sequentially taking 1,1 and CS from k to respectively obtain speed deflection angle vectors theta' corresponding to CS new honey sources1,θ″2,…,θ″k,…θ″CS；
(7d) Acquiring feasible position coordinate matrixes and fitness values corresponding to the CS new honey sources at the next moment, which are obtained in the substep (7c), respectively comparing the fitness values of the CS new honey sources with the fitness values of the CS honey sources in the current Better honey source group Better in a one-to-one correspondence manner, and selecting the honey source with a large fitness value to correspondingly update the honey sources, the position matrixes Betterloc and the fitness matrixes Betterfit in the Better honey source group Better;
(7e) updating the recording matrix Record ═ r ″1 r″2…r″k…r″CS]；
Wherein k is 1,2, …, CS represents the total number of honey sources in the updated better honey source group, r ″kDenotes the value of f (θ ″') after the k-th dimensional data in the record matrix is updated againk) Denotes a fitness value f (theta ') of the kth new honey source in the updated set of more optimal honey sources'k) Representing the fitness value of the kth honey source in the current better honey source group;
(7f) selecting a honey source with the largest fitness value from the updated Better honey source group Better, comparing the largest fitness value with the fitness value of the optimal honey source Best, and if the largest fitness value is larger than the fitness value of the optimal honey source Best, updating the optimal honey source Best, and the feasible position coordinate BestLoc and the fitness value BestFit of the optimal honey source;
(7g) traverse Record matrix Record ═ r ″)1 r″2…r″k…r″CS]Find out r ″)indexAnd (3) removing the index honey source in the updated better honey source group when the index belongs to (1,2, …, CS) of the corresponding index honey source when the Limit is larger than or equal to the Limit, and randomly generating a new honey source to replace the removed honey source.
And 8, taking the Better honey source group Better obtained in the step 7 as the current Better honey source group, repeatedly executing the step 6 and the step 7 until the maximum iteration times MaxCycles of the artificial bee colony algorithm is reached, and determining the speed deflection angle of the unmanned aerial vehicle group from the current moment to the next moment according to the optimal honey source obtained in the last iteration.
And 9, updating the position matrix and the speed vector of the unmanned aerial vehicle cluster at the next moment according to the speed deflection angle of the unmanned aerial vehicle cluster from the current moment to the next moment.
First, by the position deflection angle
In relation to the velocity yaw angle theta, from the position yaw angle
The velocity yaw angle θ is obtained. Then, the yaw angle θ and the current-time velocity v are calculated based on the velocityoldObtaining the speed v of the algorithm at the next moment after iterationnew. And finally, finishing updating the speed information.
The method can be divided into the following sub-steps:
9.1 calculate the velocity yaw angle. Position deflection angle when unmanned aerial vehicle at uniform velocity circumference turns
For twice the velocity deflection angle theta, the position deflection angle is actually obtained when a one-step optimization is performed
Further, the velocity yaw angle θ can be obtained.
And 9.2, speed correction. According to the velocity deflection angle theta and the current moment velocity voldThe speed v of the next moment after the algorithm iteration can be obtainednewThis can be represented by the following formula: v. ofnew＝mod((vold+θ)，2π)
Taking the speed direction as the speed direction of the next moment, a feasible position circular arc formed by stretching by taking the speed direction as a symmetry axis can be obtained, and the next artificial bee colony algorithm optimizing processing is carried out.
And Step 10, setting the searching Step number Step of the unmanned aerial vehicle cluster, repeatedly executing Step 4 and Step 9 for Step times, and finishing the collaborative coverage route planning process of the unmanned aerial vehicle cluster.
After the unmanned aerial vehicle flies for a period of time, the flight path of the next unmanned aerial vehicle group is calculated, and the planning work of the flight path of the unmanned aerial vehicle group is completed by using serial processing on the time.
The effect of the present invention can be further illustrated by the following simulation experiments:
1. simulation conditions are as follows:
1) and setting environmental information and self system parameters of the unmanned aerial vehicle to-be-flown area. The monitoring area S of the unmanned aerial vehicle is set to be a rectangular area of 200km multiplied by 200km, and the feasible flight area A of the unmanned aerial vehicle is set to be a rectangular area of 340km multiplied by 340 km. Utilize 6 unmanned aerial vehicles to fly in this region, each unmanned aerial vehicle flight initial position is arbitrary a little on the frame of rectangular region, and each unmanned aerial vehicle's initial velocity is arbitrary. The cruising speed of the unmanned aerial vehicle is set to be 150m/s, the maximum roll angle of the carrier is set to be 30 degrees, the flight interval delta T is set to be 20s, and the maximum speed deflection angle theta of the unmanned aerial vehicle flying can be calculatedmIs 45 degrees. The area coverage area R of a single unmanned aerial vehicle can be obtained by radar equationmax (maximum range of range), where the area coverage is set to a circle with a radius of 70 km.
And (3) assuming that the unmanned aerial vehicle group flight path of 100 steps is predicted, carrying out unmanned aerial vehicle group flight path planning by using an artificial bee group algorithm:
2) the specific algorithm parameters are shown in the following table:
2. simulation content and result analysis
The artificial bee colony algorithm is adopted to plan the unmanned aerial vehicle cluster flight path, and the coverage condition of the unmanned aerial vehicle on the whole area at a certain moment is shown in figure 3. The rectangular area represents the monitoring area S,
the position and the speed direction of the unmanned aerial vehicle are shown, and the circle shows the coverage area of a single unmanned aerial vehicle;
fig. 4 shows the unmanned aerial vehicle fleet route finally obtained by this route planning, the dashed boxes indicate the warning areas S of the unmanned aerial vehicles, and each curve indicates the planned route of one unmanned aerial vehicle. By the drawing, when one unmanned aerial vehicle approaches another unmanned aerial vehicle, the algorithm can automatically force the unmanned aerial vehicle to leave the original position and fly towards other directions. Since the flight is dynamic, when 100% coverage is reached, coverage will necessarily drop, and the algorithm will try to compensate for the coverage as it drops. If no other unmanned aerial vehicle enters the monitoring area of a certain unmanned aerial vehicle, the unmanned aerial vehicle can turn quickly to make up the lost coverage rate and cause the phenomenon of circle flight. Therefore, the unmanned aerial vehicle group flight path planning has strict requirements on initial conditions.
Fig. 5 shows a real-time coverage map using the method of the present invention, where the abscissa is the number of search steps and the ordinate is the coverage. It can be derived from the graph that the real-time coverage of the drone swarm is effectively improved using an artificial swarm algorithm. As can be seen from fig. 3, when the positions of the unmanned aerial vehicles are distributed uniformly, the coverage rate is high; when the position of the unmanned aerial vehicle flies away from the monitoring area S at a certain moment, the real-time coverage area of the unmanned aerial vehicle cluster is reduced.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (7)
1. An unmanned aerial vehicle group collaborative coverage route planning method based on an artificial bee colony algorithm is characterized by comprising the following steps:
step 1, setting a warning area and a flyable area of an unmanned aerial vehicle cluster, wherein the unmanned aerial vehicle cluster comprises N unmanned aerial vehicles, and setting the maximum speed deflection angle of each unmanned aerial vehicle and the maximum acting distance of each airborne radar; n is a positive integer greater than zero;
step 2, setting the initial speed of each unmanned aerial vehicle to form an initial speed vector of the unmanned aerial vehicle cluster, and setting the initial position of each unmanned aerial vehicle to form an initial position matrix of the unmanned aerial vehicle cluster;
step 3, setting an initial honey source group of an artificial bee colony algorithm, wherein the initial honey source group comprises CS honey sources, the dimension of each honey source is Dim dimension, each honey source represents a speed deflection angle scheme of the unmanned aerial vehicle cluster, and a one-dimensional numerical value of each honey source corresponds to the speed deflection angle of an unmanned aerial vehicle in the unmanned aerial vehicle cluster; setting the maximum iteration times MaxCycles of the artificial bee colony algorithm for one time, and allowing the maximum times Limit that the honey sources are not updated;
step 4, randomly determining a speed deflection angle vector for each honey source in the initial honey source group, and obtaining a feasible position coordinate matrix of each honey source at the next moment according to the speed vector of the current moment of the unmanned aerial vehicle group, the position matrix of the current moment and the speed deflection angle vector corresponding to each honey source; calculating to obtain a fitness value corresponding to each honey source according to a feasible position coordinate matrix of each honey source at the next moment, selecting a honey source corresponding to the maximum fitness value from fitness values corresponding to CS honey sources as an optimal honey source, and recording the optimal honey source Best, a feasible position matrix BestLoc of the unmanned aerial vehicle cluster corresponding to the optimal honey source and the fitness value BestFit of the optimal honey source;
step 5, setting a recording matrix, initializing the recording matrix to be an all-zero matrix, wherein the dimension of the recording matrix is CS, and the recording matrix is used for recording the times that CS honey sources are not updated;
step 6, performing neighborhood search on each honey source in the current Better honey source group by using CS employment bees to obtain CS searched new honey sources, calculating the fitness value of the CS searched new honey sources, selecting the honey sources with large fitness values to form a Better honey source group Better according to the fitness value of each new honey source searched and the fitness value of the corresponding honey source in the current Better honey source group, recording the position matrix BetterLoc and the fitness matrix Betterfit of each honey source in the Better honey source group, and updating the recording matrix; the initial state of the current better honey source group is an initial honey source group;
step 7, selecting honey sources in the current Better honey source group Better by adopting CS observation bees to respectively adopt a roulette mode to perform neighborhood search to obtain CS new honey sources which are searched, calculating the fitness value of the CS new honey sources which are searched, selecting the honey source with a large fitness value to update the Better honey source group Better, the position matrix BetterLoc and the fitness matrix Betterfit according to the fitness value of each new honey source which is searched and the fitness value of each honey source in the current Better honey source group Better, and updating the recording matrix; selecting a honey source with the largest fitness value from the updated Better honey source group Better, comparing the largest fitness value with the fitness value of the optimal honey source Best, and if the largest fitness value is larger than the fitness value of the optimal honey source Best, updating the optimal honey source Best, and a feasible position matrix BestLoc and the fitness value BestFit of the optimal honey source;
removing the honey sources with the non-updated times more than or equal to the maximum times Limit of allowing the honey sources to be not updated in the updated Better honey source group setter, and randomly generating new honey sources to replace the removed honey sources;
step 8, taking the Better honey source group Better obtained in the step 7 as the current Better honey source group, repeatedly executing the step 6 and the step 7 until the maximum iteration times MaxCycles of the artificial bee colony algorithm is reached, and determining the speed deflection angle of the unmanned aerial vehicle group from the current moment to the next moment according to the optimal honey source obtained in the last iteration;
step 9, updating a position matrix and a speed vector of the unmanned aerial vehicle cluster at the next moment according to the speed deflection angle of the unmanned aerial vehicle cluster from the current moment to the next moment;
and Step 10, setting the searching Step number Step of the unmanned aerial vehicle cluster, repeatedly executing Step 4 and Step 9 for Step times, and finishing the collaborative coverage route planning process of the unmanned aerial vehicle cluster.
2. The unmanned aerial vehicle group collaborative coverage route planning method based on the artificial bee colony algorithm according to claim 1, wherein the step 2 specifically comprises:
recording initial velocity vector of unmanned aerial vehicle group
Initial position matrix of unmanned aerial vehicle group
Wherein,
indicating the initial velocity of the ith drone,
represents the initial position of the ith unmanned aerial vehicle, an
And
respectively representing the x coordinate and the y coordinate of the ith unmanned aerial vehicle at the initial moment; 1,2, …And N, N represents the total number of drones contained in the drone swarm.
3. The unmanned aerial vehicle group collaborative coverage route planning method based on the artificial bee colony algorithm according to claim 1, wherein the step 4 specifically comprises:
(4a) randomly determining a velocity deflection angle vector theta for the kth honey source in the initial honey source groupk：
θk＝(θk1,θk2…,θki,…θkDim)
θki＝(θmax-θmin)ξ+θmin
Wherein, thetakiDenotes the i-th dimension of the yaw angle of the kth honey source in the initial honey source group, and k is 1kDimThe Dim-dimension speed deflection angle of the kth honey source in the initial honey source group is shown, CS represents the total number of the honey sources contained in the initial honey source group, and thetamaxRepresenting the maximum speed yaw angle, theta, of the droneminMinimum speed yaw angle, ξ for [0,1 ], for unmanned aerial vehicles]A random number of intervals;
(4b) according to the speed t of the ith unmanned aerial vehicle in the unmanned aerial vehicle group at the current moment
Position of current time t
And ith dimension velocity deflection angle theta of kth honey sourcekiObtaining feasible position coordinates of the ith unmanned aerial vehicle corresponding to the kth honey source at the next moment t +1
Wherein t represents the current time, the initial value of t is zero, VpThe speed of the unmanned aerial vehicle flying at a constant speed is shown,
i-dimension velocity deflection angle theta representing k-dimension honey sourcekiThe corresponding unmanned aerial vehicle deflects from the current moment to the next moment;
sequentially taking 1, Dim from i to obtain a feasible position coordinate matrix corresponding to the kth honey source at the next moment;
enabling k to sequentially take 1,1 and CS to respectively obtain feasible position coordinate matrixes corresponding to CS honey sources at the next moment;
(4c) calculating the fitness value f of the kth honey sourcek：
fk＝Sk-ω*Limk
Sk＝Sk1∪Sk2∪...Ski...∪SkDim
Wherein S iskRepresenting the total coverage area S of the unmanned aerial vehicle flight scheme corresponding to the kth honey source to the warning areakiThe coverage area of the warning area by the ith unmanned aerial vehicle of the kth honey source is represented, i is 1kThe penalty that the unmanned plane corresponding to the kth honey source flies out of the warning area is represented, and U represents the union set;
sequentially taking 1,1 and CS from k to respectively obtain fitness values f corresponding to CS honey sources1,f2…,fk,…fCS；
(4d) Selecting the honey source corresponding to the maximum fitness value from the fitness values corresponding to the CS honey sources as the optimal honey source, and recording the optimal honey sourceRecording the optimal honey source Best, the feasible position BestLoc of the unmanned aerial vehicle group corresponding to the optimal honey source, and the fitness value BestFit ═ max (f) of the optimal honey source1,f2…,fk,…fCS)。
4. The unmanned aerial vehicle group collaborative coverage route planning method based on the artificial bee colony algorithm according to claim 1, wherein the step 5 specifically comprises:
setting recording matrix Record ═ r1 r2 … rk … rCS]，rk＝0，k＝1,2,…,CS；rkThe number of times that the kth honey source is not updated is shown, and CS shows the total number of the honey sources contained in the honey source group.
5. The unmanned aerial vehicle group collaborative coverage route planning method based on the artificial bee colony algorithm according to claim 1, wherein the step 6 specifically comprises:
(6a) randomly generating a first parameter Param belonging to [1 and Dim ] and a second parameter Neighbour belonging to [1 and CS ] by the kth employing bee, wherein Neighbour is not equal to k;
(6b) carrying out neighborhood search on the kth honey source in the current better honey source group by the kth employment bee to obtain a speed deflection angle vector theta 'of the kth new honey source'kVelocity yaw Angle vector θ 'of kth New Honey Source'kMiddle i-th dimension speed deflection angle theta'kiComprises the following steps:
where φ is a random number, and φ ∈ (-1,1), θkiThe ith dimension speed deflection angle theta corresponding to the kth honey source in the current better honey source groupkparamA param dimensional speed deflection angle corresponding to the kth honey source in the current better honey source group;
and enabling k to sequentially take 1,. and CS, and respectively obtaining CS speed deflection angle vectors theta corresponding to new honey sources'1,θ'2,…,θ'k,…θ'CS；
(6c) Acquiring feasible position coordinate matrixes and fitness values corresponding to CS new honey sources at the next moment, comparing the fitness values of the CS new honey sources with the fitness values of the CS honey sources in a current superior honey source group in a one-to-one correspondence manner, and reserving the honey sources with larger fitness values to form a superior honey source group setter;
(6d) update the recording matrix Record ═ r'1 r′2 … r′k … r′CS]；
Wherein k is 1,2, …, CS is the total number of honey sources in the honey source group, r'kRepresenting updated values of the k-th dimension of data in the record matrix, rkA value indicating that the kth-dimensional data is not updated in the recording matrix, f (θ)k) Representing the fitness value, f (theta'k) Representing the fitness value of the kth new honey source in the current better honey source group.
6. The unmanned aerial vehicle fleet cooperative coverage route planning method based on the artificial bee colony algorithm according to claim 5, wherein in the sub-step (6b),
velocity yaw angle vector θ 'of kth new honey source'kMiddle i-th dimension speed deflection angle theta'kiExceeding the maximum speed deflection angle theta of the dronemaxAnd minimum velocity yaw angle thetaminThen, the following formula is adopted for correction:
7. the unmanned aerial vehicle group collaborative coverage route planning method based on the artificial bee colony algorithm according to claim 1, wherein the step 7 specifically comprises:
(7a) for the current better honey source groupNormalizing the fitness values of the CS honey sources in the Better to obtain normalized fitness values (f)s1,fs2,…,fsk,…,fsCS)；fskDenotes the fitness value of the kth honey source after normalization, k is 1.
(7b) Selecting a honey source for neighborhood search by the kth observation bee according to a roulette mode, and randomly generating a third parameter rand belonging to (0, 1); then there is
Wherein a is an integer and a is ∈ [1, CS ]]，fsnDenotes the fitness value of the nth honey source after normalization, n is 1.
(7c) Randomly generating a fourth parameter Param' ∈ [1, Dim by the kth observer]And a fifth parameter Neighbour ∈ [1, CS ]]Neighbour' ≠ k; the kth observation bee carries out neighborhood search on the a-th honey source in the current Better honey source group setter to obtain a speed deflection angle vector theta of the kth new honey sourcekVelocity yaw angle vector θ ″' of the kth new honey sourcekMiddle ith dimension velocity deflection angle theta ″)kiComprises the following steps:
wherein φ is a random number, and φ is epsilon (-1,1), theta'kiIs the ith dimension speed deflection angle, theta ', corresponding to the kth honey source in the current better honey source group'kparamA param dimensional speed deflection angle corresponding to the kth honey source in the current better honey source group;
and (5) sequentially taking 1,1 and CS from k to respectively obtain speed deflection angle vectors theta' corresponding to CS new honey sources1,θ″2,…,θ″k,…θ″CS；
(7d) Acquiring feasible position coordinate matrixes and fitness values corresponding to the CS new honey sources at the next moment, which are obtained in the substep (7c), respectively comparing the fitness values of the CS new honey sources with the fitness values of the CS honey sources in the current Better honey source group Better in a one-to-one correspondence manner, and selecting the honey source with a large fitness value to correspondingly update the honey sources, the position matrixes Betterloc and the fitness matrixes Betterfit in the Better honey source group Better;
(7e) updating the recording matrix Record ═ r ″1 r″2 … r″k … r″CS]；
Wherein k is 1,2, …, CS represents the total number of honey sources in the updated better honey source group, r ″kDenotes the value of f (θ ″') after the k-th dimensional data in the record matrix is updated againk) Denotes a fitness value f (theta ') of the kth new honey source in the updated set of more optimal honey sources'k) Representing the fitness value of the kth honey source in the current better honey source group;
(7f) selecting a honey source with the largest fitness value from the updated Better honey source group Better, comparing the largest fitness value with the fitness value of the optimal honey source Best, and if the largest fitness value is larger than the fitness value of the optimal honey source Best, updating the optimal honey source Best, and the feasible position coordinate BestLoc and the fitness value BestFit of the optimal honey source;
(7g) traverse Record matrix Record ═ r ″)1 r″2 … r″k … r″CS]Find out r ″)indexAnd (3) removing the index honey source in the updated better honey source group when the index belongs to (1,2, …, CS) of the corresponding index honey source when the Limit is larger than or equal to the Limit, and randomly generating a new honey source to replace the removed honey source.
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