CN113867411B - Unmanned aerial vehicle cluster positioning method and device and computer equipment - Google Patents
Unmanned aerial vehicle cluster positioning method and device and computer equipment Download PDFInfo
- Publication number
- CN113867411B CN113867411B CN202111368081.7A CN202111368081A CN113867411B CN 113867411 B CN113867411 B CN 113867411B CN 202111368081 A CN202111368081 A CN 202111368081A CN 113867411 B CN113867411 B CN 113867411B
- Authority
- CN
- China
- Prior art keywords
- subgroup
- information
- optimal position
- unmanned aerial
- sub
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention discloses a method, a device and computer equipment for locating unmanned aerial vehicle clusters, wherein the method comprises the steps of dividing the unmanned aerial vehicle clusters into different subgroups according to the quantity information of the unmanned aerial vehicle clusters, obtaining the individual optimal position and the global optimal position of each subgroup according to the parameter information, the long-distance machine position information and the anchor point position information of each subgroup, and accurately calculating the position information of the subgroups according to the position information of three parties to realize the relative location from the long-distance machine of the unmanned aerial vehicle to a slave machine in the subgroup; obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group, wherein the reference optimal position can balance the relation between the individual optimal and the global optimal; updating the parameter information of each sub-group according to the reference optimal position of each sub-group, and iterating based on the updated parameter information until the maximum iteration times are reached to obtain the final reference optimal position, wherein the final reference optimal position is the optimal position of each sub-group.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle cluster positioning, in particular to an unmanned aerial vehicle cluster communication positioning method, an unmanned aerial vehicle cluster communication positioning device and computer equipment.
Background
The unmanned aerial vehicle can be used as a communication base station for scenes such as post-disaster communication, emergency networking and the like. In order to realize autonomous networking of unmanned aerial vehicles, quick and accurate relative positioning of newly accessed unmanned aerial vehicles is required. The existing unmanned aerial vehicle cluster positioning method is mostly based on a GPS satellite positioning system, cannot be applied to a GPS refusing environment, is low in general precision, and is poor in expansibility of a large-scale unmanned aerial vehicle cluster. The particle swarm optimization method is a feasible scheme for realizing relative positioning under the GPS refusing environment.
However, in the conventional particle swarm optimization method, particles may continuously oscillate between individual particle history optimization (personal best) and global particle global optimization (global best) during particle iteration, so that the particles updated at the current moment cannot converge to an optimal position. In addition, the particle position at the next moment is a linear superposition value of global optimum of all particles and historic optimum of individual particles at the last moment, which cannot ensure that the updated position result of the particles is better (more approximate to the real position) than the result at the last moment, so that the overall convergence speed is reduced, the time for positioning the unmanned aerial vehicle clusters is longer, and the positioning accuracy is lower.
Disclosure of Invention
Therefore, the invention aims to overcome the defects that in the particle swarm algorithm in the prior art, particles probably vibrate continuously between the history of individual particles and the global optimum of all particles, so that the unmanned aerial vehicle cluster positioning time is longer and the positioning precision is lower, and further provides an unmanned aerial vehicle cluster positioning method, an unmanned aerial vehicle cluster positioning device and computer equipment.
According to a first aspect, an embodiment of the invention discloses a method for locating a cluster of unmanned aerial vehicles, which comprises the following steps: acquiring the quantity information and the position information of unmanned aerial vehicle clusters; the position information comprises long-distance information and anchor point position information in the unmanned aerial vehicle cluster; dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information, and acquiring parameter information of each subgroup; obtaining an individual optimal position and a global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup; obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group; updating parameter information of each sub-group based on the reference optimal position of each sub-group; returning to execute the steps of obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-machine position information and the anchor point position information of each subgroup based on the updated parameter information to update the parameter information of each subgroup based on the reference optimal position of each subgroup until the maximum iteration number is reached and obtaining the final reference optimal position; and obtaining the optimal position of each subgroup based on the final reference optimal position.
Optionally, the parameter information includes position information, speed information of the subgroups and coverage areas of unmanned aerial vehicles in the subgroups, and the obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup includes: obtaining the particle number and the particle position of each subgroup based on the position information of the subgroup and the coverage range of unmanned aerial vehicles in the subgroup; and obtaining the individual optimal position and the global optimal position of each subgroup based on the particle number and the particle position of each subgroup, the long-distance position information and the anchor point position information.
Optionally, the obtaining the individual optimal position and the global optimal position of each sub-group based on the particle number and the particle position of each sub-group, the long-machine position information and the anchor point position information includes: obtaining a fitness function of particles in each subgroup based on the particle position, the long-machine position information and the anchor point position information of each subgroup; obtaining individual optima of particles of each of the subgroups based on the fitness function; and obtaining the global optimum of the particles of each sub-group based on the particle number of each sub-group and the fitness function of the particles.
Optionally, the obtaining the fitness function of the particles in each subgroup based on the particle position, the long-machine position information and the anchor point position information of each subgroup includes: obtaining a first relative measurement distance based on the long machine position information and the particle position of each subgroup; obtaining a second relative measurement distance based on the anchor point position information and the particle position of each sub-group; and obtaining the fitness function based on the first relative measurement distance and the second relative measurement distance.
Optionally, obtaining the reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group includes: and linearly superposing the individual optimum and the global optimum to obtain the optimum position, wherein the weight coefficient of the linear superposition is determined by an exhaustion method.
Optionally, the updating the parameter information of each sub-group based on the reference optimal position of each sub-group includes: updating the position information, the speed information and the fitness function of the particles of each of the sub-groups based on the optimal position of each of the sub-groups.
Optionally, the position information, the velocity information and the fitness function of the particles of each of the subgroups are updated according to the following formula,
P gi-n up ＝P gi-n +v gi-n up ，
f＝f gi-n (P gi-n up )，
v gi-n up indicating the updated particle velocity, g indicating the subgroup number, gi tableShowing the ith particle in subgroup numbered g, gi-n represents the nth iteration of the ith particle in subgroup numbered g,
to select inertial weights, δ=rand (0, 1) is a gaussian distributed random number, r b-gi-n Reference optimum after the nth iteration representing the ith particle in subgroup numbered g, P gi-n Representing the nth post-iteration position of the ith particle in subgroup numbered g; p (P) gi-n up Representing the position after the nth iteration of the ith particle in the updated subgroup numbered g; f (f) gi-n Representing the fitness function after the nth iteration of the ith particle in the subgroup numbered g.
According to a second aspect, the embodiment of the invention also discloses an unmanned aerial vehicle cluster positioning device, which comprises: the acquisition module is used for acquiring the quantity information and the position information of the unmanned aerial vehicle clusters; the position information comprises long-distance information and anchor point position information in the unmanned aerial vehicle cluster; the dividing module is used for dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information and acquiring parameter information of each subgroup; the first optimizing module is used for obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup; the second optimizing module is used for obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group; a first updating module, configured to update parameter information of each sub-group based on a reference optimal position of each sub-group; the iteration module is used for returning and executing the steps of obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-machine position information and the anchor point position information of each subgroup based on the updated parameter information to the step of updating the parameter information of each subgroup based on the reference optimal position of each subgroup until the final reference optimal position is obtained after the maximum iteration times are reached; and the optimal position determining module is used for obtaining the optimal position of each subgroup based on the last reference optimal position.
According to a third aspect, an embodiment of the present invention further discloses a computer device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the unmanned aerial vehicle cluster locating method according to the first aspect or any of the alternative embodiments of the first aspect.
According to a fourth aspect, the present invention further discloses a computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the unmanned aerial vehicle cluster positioning method according to the first aspect or any of the alternative embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
the invention provides an unmanned aerial vehicle cluster positioning method, a device and computer equipment, which comprise the following steps: dividing the unmanned aerial vehicle group into different subgroups according to the quantity information of the unmanned aerial vehicle group, obtaining an individual optimal position and a global optimal position of each subgroup according to the parameter information, the long-distance machine position information and the anchor point position information of each subgroup, and accurately calculating the position information of the subgroups according to the position information of three parties to realize the relative positioning from the long-distance machine to the slave machine of the unmanned aerial vehicle; obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group, wherein the reference optimal position can balance the relation between the individual optimal and the global optimal; updating the parameter information of each sub-group according to the reference optimal position of each sub-group, and iterating based on the updated parameter information until the maximum iteration times are reached to obtain the final reference optimal position, wherein the final reference optimal position is the optimal position of each sub-group. The particle enhancement scheme that the reference optimal position is introduced into the relative positioning of the slaves in the unmanned plane long-distance to the sub-group is realized, and the reference optimal position obtained by combining the global optimal position and the individual optimal position is avoided from continuously oscillating between the individual optimal position and the global optimal position, so that the relation between the individual optimal position and the global optimal position is balanced, the positioning precision of the unmanned plane cluster is improved, the boundary limitation is realized according to the coverage range of the unmanned plane, the number of particles put in the boundary can be reduced, and the realization complexity is low.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a specific example of a method for locating a cluster of unmanned aerial vehicles according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a specific example of a cluster positioning device for a drone in an embodiment of the present invention;
FIG. 3 is a diagram showing a computer device according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; the two components can be directly connected or indirectly connected through an intermediate medium, or can be communicated inside the two components, or can be connected wirelessly or in a wired way. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, the technical features of the different embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
The embodiment of the invention discloses an unmanned aerial vehicle cluster positioning method, which comprises the following steps as shown in fig. 1:
step 101: acquiring the quantity information and the position information of unmanned aerial vehicle clusters; the location information includes long-range location information and anchor location information in the unmanned aerial vehicle cluster.
Illustratively, the unmanned aerial vehicle cluster includes a long machine for flying with a team, a slave machine for following the flying, and an anchor point for performing unmanned aerial vehicle communication positioning, wherein the anchor point can be one or more unmanned aerial vehicles with known positioning, or can be a charging station with a fixed position, and the number of the anchor points is generally greater than three. The embodiment of the invention does not limit the specific form and number of the anchor points, and can be determined by a person skilled in the art according to actual needs.
Step 102: dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information, and acquiring parameter information of each subgroup.
Illustratively, according to the number of unmanned aerial vehicles, other slaves except for the long unmanned aerial vehicle in the unmanned aerial vehicle cluster are divided into different subgroups, wherein each subgroup only comprises one unmanned aerial vehicle. And acquiring parameter information containing unmanned aerial vehicles in the subgroups according to the divided subgroups, wherein the parameter information comprises relative position information, running speed information and a coverage range of the unmanned aerial vehicles. The specific position determining method of the divided subgroups adopts a particle swarm algorithm for determining. For example, the number of unmanned aerial vehicle slaves to be positioned is K, and g=1, 2, …, K is used as a subgroup for each unmanned aerial vehicle, and the number of the subgroup is g.
Step 103: and obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup.
The parameter information of the subgroup may be specific parameter information of the unmanned aerial vehicle such as position information and speed of the unmanned aerial vehicle to be detected in the subgroup. The positioning method of the unmanned aerial vehicle to be detected adopts a particle swarm algorithm to calculate, in step 102, the number of the unmanned aerial vehicle sub-swarm is also the number of the particle swarm initialized by the corresponding particle swarm, and the initialized particle number in each sub-swarm is N g Then the total particle number N tot ＝N 1 +N 2 +…+N K . The number of iterations required for the particles in each sub-group is N, the total number of iterations is NK, and each particle g in the particle sub-group corresponding to the unmanned aerial vehicle g i Is used to initialize random positions
Column vectors consisting of three-dimensional cartesian coordinates of particles, i=1, …, N g . Initializing random velocity for each particle
A column vector consisting of velocity components of the particles in three coordinate axes xyz.
In this embodiment, the individual optimal position is the individual optimal position of each particle in the particle swarm corresponding to the unmanned aerial vehicle sub-group to be tested. For example, when the iteration number is 1, the individual optimum is the position of each particle in the particle swarm after one iteration, when the iteration number is a, the individual optimum is the corresponding position of each particle obtained after the completion of the iteration number corresponding to the minimum fitness function in all iteration processes after a-time iteration in the particle swarm; the global optimal position is the position corresponding to the particle with the smallest fitness function in the individual optimization of all particles after each iteration of all particles in the particle subgroup corresponding to the unmanned aerial vehicle subgroup to be tested is completed, and the position is the global optimal position. Therefore, the individual optimal position and the global optimal position are the positions of particles in the particle swarm corresponding to the unmanned aerial vehicle to be detected, wherein each particle in the particle swarm has an individual optimal position, and the global optimal position is only one global optimal in one particle swarm.
Step 104: and obtaining the reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group.
For example, when determining the optimal position of the unmanned aerial vehicle to be tested, oscillation occurs between the individual optimal position and the global optimal position, and the individual optimal position and the global optimal position are balanced by introducing the reference optimal position, wherein the reference optimal position can be determined according to a group with optimal effect in the linear combination of the individual optimal position and the global optimal position, for example, the particle g i The individual optima of (1) and the global optima of the subgroup g are subjected to linear weighted superposition to obtain particles g i Reference to the optimum(s)
r b-gi-n ＝a 1 P b-gi-n +a 2 g b-g-n Wherein the weighting parameter a 1 ，a 2 Is constant and satisfies a 1 +a 2 ＝1。a 1 ，a 2 Can be selected according to discretization, e.g. a 1 ∈{0.2,0.4,0.6,0.8}，a 2 E {0.8,0.6,0.4,0.2}. Through exhaustion a 1 ，a 2 Finding out the particle with the smallest corresponding fitness function value, namely +.>
Obtaining the final reference optimal particle->
And a particle enhancement scheme with optimal reference is introduced, and the positioning accuracy of the unmanned aerial vehicle cluster is improved by combining the linear combination of the global optimal position and the individual optimal position.
Step 105: and updating the parameter information of each sub-group based on the reference optimal position of each sub-group.
Exemplary, the parameter information of the particles in the corresponding unmanned aerial vehicle subgroup is updated according to the obtained reference optimal position, and the running speed, position information and fitness function of the particles are updated, for example, according to the reference optimal current particle g i Is the position of (2)
The updated particle velocity, position and fitness function values are obtained using the following formula:
P gi-n up ＝P gi-n +v gi-n up ，
f＝f gi-n (P gi-n up )，
v gi-n up indicating the updated particle velocity, g indicating the subgroup number, gi indicating the ith particle in the subgroup numbered g, gi-n indicating the nth iteration of the ith particle in the subgroup numbered g,
to select inertial weights, δ=rand (0, 1) is a gaussian distributed random number, r b-gi-n Reference optimum after the nth iteration representing the ith particle in subgroup numbered g, P gi-n Representing the nth post-iteration position of the ith particle in subgroup numbered g;P gi-n up representing the position after the nth iteration of the ith particle in the updated subgroup numbered g; f (f) gi-n Representing the fitness function after the nth iteration of the ith particle in the subgroup numbered g. Select inertial weight +.>
The value of the constant can be set to be between 0.4 and 0.9, the value is used for representing the inertia intensity of particle movement, the cognitive weight c is a constant, and the value is used for representing the learning evolution capability of the particle, for example, the value can be set to be 2.
Step 106: and returning to execute the steps 103 to 105 based on the updated parameter information until the maximum iteration number is reached, and obtaining the final reference optimal position. Illustratively, the above-mentioned steps 103 to 105 complete one iteration process for the particle swarm algorithm, and continue to iterate according to the obtained reference optimal position until the iteration number reaches the maximum, so as to obtain the final reference optimal position. According to the computing power of the long machine, steps 103 to 105 in the g=1, 2, … and K times of subgroups are executed in series or in parallel, so that the optimal positions of all unmanned aerial vehicles are obtained, and cluster positioning is completed, wherein the total number of iterations is NK.
Step 107: and obtaining the optimal position of each subgroup based on the final reference optimal position. The last reference optimal position obtained after the maximum iteration times is the optimal position of the unmanned aerial vehicle to be tested. Outputting the updated particle position of the current subgroup g until the last iteration
At all N g Individual output particle positions
Finding out the global optimal solution, wherein the corresponding particle position is the output result of the estimated position of the g-th unmanned aerial vehicle +.>
(here, for convenience of presentation, removed)subscript-N).
The unmanned aerial vehicle cluster positioning method provided by the invention comprises the following steps: dividing the unmanned aerial vehicle into different subgroups according to the quantity information of the unmanned aerial vehicle groups, obtaining the individual optimal position and the global optimal position of each subgroup according to the parameter information, the long-distance position information and the anchor point position information of each subgroup, and accurately calculating the position information of the subgroups according to the position information of three parties to realize the relative positioning from the long-distance unmanned aerial vehicle to the slaves in the subgroups; obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group, wherein the reference optimal position can balance the relation between the individual optimal and the global optimal; updating the parameter information of each sub-group according to the reference optimal position of each sub-group, and iterating based on the updated parameter information until the maximum iteration times are reached to obtain the final reference optimal position, wherein the final reference optimal position is the optimal position of each sub-group. The method has the advantages that the particle enhancement scheme of introducing the reference optimum to the relative positioning of the slaves in the sub-group of the unmanned aerial vehicle is realized, the reference optimum position is obtained by combining the global optimum position and the individual optimum position, the global optimum position and the individual optimum position are balanced by the reference optimum position, the optimum balance point of the global optimum position and the individual optimum position is found by the reference optimum position, and the positioning precision of the unmanned aerial vehicle cluster is improved.
As an optional embodiment of the present invention, the parameter information includes location information, speed information of the subgroup, and coverage of the unmanned aerial vehicle in the subgroup, and the step 103 includes: obtaining the particle number and the particle position of each subgroup based on the position information of the subgroup and the coverage range of unmanned aerial vehicles in the subgroup; and obtaining the individual optimal position and the global optimal position of each subgroup based on the particle number and the particle position of each subgroup, the long-distance position information and the anchor point position information.
Exemplary, the delivery of particles within a particle swarm is limited according to the acquired coverage of the unmanned aerial vehicle to be tested, such that the delivered particles do not exceed the coverage of the unmanned aerial vehicle, e.g., the subgroup particle initialization position P of unmanned aerial vehicle g gi Element x of (2) gi ,y gi ,z gi Can be made ofThe coverage of drone g limits the boundary:
x gi ,y gi ,z gi ∈{(x,y,z)|x lo ≤x≤x hi ,y lo ≤y≤y hi ,z lo ≤z≤z hi }，
wherein, lo and hi are the coverage boundary values of x, y and z respectively, and can be determined according to the specific situation of the unmanned aerial vehicle to be detected. According to the boundary limitation realized by the coverage of the unmanned aerial vehicle, the number of particles put in the boundary can be reduced, so that the method has lower realization complexity.
As an optional embodiment of the present invention, the step 103 includes: obtaining a fitness function of particles in each subgroup based on the particle position, the long-machine position information and the anchor point position information of each subgroup; obtaining individual optima of particles of each of the subgroups based on the fitness function; and obtaining the global optimum of the particles of each sub-group based on the particle number of each sub-group and the fitness function of the particles.
The fitness function in the particle swarm algorithm may be, for example, the square of the error between the measured distance and the actual distance of the particle as the fitness function, where the measured distance is the distance from the unmanned aerial vehicle g to be measured to the corresponding anchor point, and the smaller the fitness function value, the closer the particle is to the actual position of the unmanned aerial vehicle g. The iteration times of the sub-group, the selection of the fitness function, the selection of the inertia weight and the cognitive weight are not limited, and can be determined according to actual needs by a person skilled in the art. The particles in the particle swarm corresponding to the unmanned aerial vehicle swarm to be detected are iterated so as to be capable of accurately positioning the unmanned aerial vehicle to be detected.
Specifically, the specific calculation of the fitness function may be that it is assumed that the unmanned aerial vehicle long machine or the unified calculation unit (it is assumed that the coordinates thereof are known as o= (0, 0)) can acquire the unmanned aerial vehicle g to be measured (it is assumed that the coordinates thereof are unknown (x) g ,y g ,z g ) Distance of relative measurement
Wherein (1)>
Can be derived by means of the received signal strength (Received signal strength indicator, abbreviated RSSI) from the unmanned aerial vehicle communication network, i.e.>
Wherein d 0 And
is the reference distance (e.g., 1 m) and the RSSI constant at the reference distance, respectively, η is the path loss constant. Similarly, the relative measuring distance from the anchor point l to the unmanned aerial vehicle g to be measured can be obtained>
Assume that the known location coordinates of the anchor point are (x l ,y l ,z l ),l＝
Is->
At the nth iteration, then the corner mark may be represented as
After determining the fitness function, the individual optimal position may be calculated by determining the particle g at the nth iteration (n=1, …, N) i Individual optimal solutions of (2)
I.e. the fitness function set in all historic n iterations
Particle g of the iteration number corresponding to the minimum value of (3) i Is a position of (c). The method is as follows
Wherein->
The fitness function value corresponding to the particle at the first iteration in the past is represented.
The calculation process of the global optimum position may be that, in all particles at the nth iteration, a global optimum solution g of the particle swarm g is obtained b-g-n I.e. the position of the particle corresponding to the minimum of the fitness function of all particles in the whole particle swarm g is iterated at the present time. The method is as follows
Wherein->
And represents the fitness function value corresponding to the particle at the current nth iteration.
As an optional embodiment of the present invention, the calculating the fitness function in step 103 includes: obtaining a first relative measurement distance based on the long machine position information and the particle position of each subgroup; obtaining a second relative measurement distance based on the anchor point position information and the particle position of each sub-group; and obtaining the fitness function based on the first relative measurement distance and the second relative measurement distance.
Illustratively, the first relative measured distance is a relative measured distance of the particles of each of the subgroups from the elongate machine; the second relative measurement distance is the relative measurement distance between the particles of each subgroup and the anchor point.
The embodiment of the invention also discloses an unmanned aerial vehicle cluster positioning device, as shown in fig. 2, which comprises:
an acquisition module 201, configured to acquire number information and location information of an unmanned aerial vehicle cluster; the location information includes long-range location information and anchor location information in the unmanned aerial vehicle cluster. The details of step 101 of any of the above method embodiments are described in the exemplary embodiments, and are not repeated here.
The dividing module 202 is configured to divide the unmanned aerial vehicle cluster into different subgroups based on the number information, and obtain parameter information of each subgroup. Illustratively, the details of step 102 of any of the above method embodiments are described in detail, and are not repeated herein.
The first optimizing module 203 is configured to obtain an individual optimal position and a global optimal position of each of the subgroups based on the parameter information, the long-range position information, and the anchor point position information of each of the subgroups. For example, the details of step 103 of any of the above method embodiments are described in detail, and are not repeated here.
A second optimizing module 204, configured to obtain a reference optimal position of each of the subgroups based on the individual optimal position and the global optimal position of each of the subgroups. Illustratively, the details of step 104 of any of the above method embodiments are described in detail, and are not repeated herein.
A first updating module 205, configured to update parameter information of each of the subgroups based on a reference optimal position of each of the subgroups. Illustratively, the details of step 105 of any of the above method embodiments are described in detail, and are not repeated herein.
The iteration module 206 is configured to return, based on the updated parameter information, to perform the steps from obtaining the individual optimal position and the global optimal position of each of the subgroups based on the parameter information, the long-machine position information, and the anchor point position information of each of the subgroups to updating the parameter information of each of the subgroups based on the reference optimal position of each of the subgroups until the maximum number of iterations is reached, and obtain the final reference optimal position. Illustratively, the details of step 106 of any of the above method embodiments are described in detail, and are not repeated herein.
The best position determining module 207 is configured to obtain the best position of each of the subgroups based on the last reference best position. Illustratively, the details of step 107 of any of the above method embodiments are described in detail, and are not repeated herein.
The invention provides an unmanned aerial vehicle cluster positioning device, which comprises: the acquisition module is used for acquiring the quantity information and the position information of the unmanned aerial vehicle clusters; the position information comprises long-distance information and anchor point position information in the unmanned aerial vehicle cluster; the dividing module is used for dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information and acquiring parameter information of each subgroup; the first optimizing module is used for obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup; the second optimizing module is used for obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group; a first updating module, configured to update parameter information of each sub-group based on a reference optimal position of each sub-group; the iteration module is used for returning and executing the steps of obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-machine position information and the anchor point position information of each subgroup based on the updated parameter information to the step of updating the parameter information of each subgroup based on the reference optimal position of each subgroup until the final reference optimal position is obtained after the maximum iteration times are reached; and the optimal position determining module is used for obtaining the optimal position of each subgroup based on the last reference optimal position. The unmanned aerial vehicle to be detected is divided into different subgroups according to the dividing module, the unmanned aerial vehicle is positioned by using a particle swarm algorithm, wherein the throwing of the example in the particle subgroup can reduce the number of particles thrown in the boundary according to the coverage range of the unmanned aerial vehicle to be detected, so that the unmanned aerial vehicle has lower implementation complexity, a particle enhancement scheme with optimal reference is introduced, and the positioning precision of the unmanned aerial vehicle cluster can be improved by combining the global optimal with the individual optimal linear combination.
As an optional embodiment of the present invention, the parameter information includes location information, speed information of the subgroup, and coverage of the unmanned aerial vehicle in the subgroup, and the first optimizing module 203 includes: the particle delivery module is used for obtaining the particle number and the particle position of each subgroup based on the position information of the subgroup and the coverage range of unmanned aerial vehicles in the subgroup; the first optimizing sub-module is used for obtaining the individual optimal position and the global optimal position of each sub-group based on the particle number and the particle position of each sub-group, the long-machine position information and the anchor point position information. For example, the details of step 103 are described above, and will not be repeated here.
As an optional embodiment of the present invention, the fitness function in the first optimizing module 203 includes: the first relative measuring and calculating distance module is used for obtaining a first relative measuring and calculating distance based on the long machine position information and the particle position of each subgroup; a second relative measuring distance module, configured to obtain a second relative measuring distance based on the anchor point position information and the particle position of each of the subgroups; and the fitness function module is used for obtaining the fitness function based on the first relative measuring distance and the second relative measuring distance. For example, the details of the calculation process of the fitness function in step 103 are described above, and will not be described herein.
Embodiments of the present invention also provide a computer device, as shown in fig. 3, which may include a processor 301 and a memory 302, where the processor 301 and the memory 302 may be connected by a bus or otherwise, and in fig. 3, the connection is exemplified by a bus.
The processor 301 may be a central processing unit (Central Processing Unit, CPU). Processor 301 may also be a chip such as another general purpose processor, a digital information processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, or a combination thereof.
The memory 302, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the unmanned aerial vehicle cluster positioning method in the embodiment of the present invention. The processor 301 executes various functional applications of the processor and data processing, i.e. implements the unmanned aerial vehicle cluster localization method in the above-described method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 302.
The one or more modules are stored in the memory 302, which when executed by the processor 301, performs the drone cluster localization method in the embodiment shown in fig. 1.
The details of the above computer device may be understood correspondingly with respect to the corresponding relevant descriptions and effects in the embodiment shown in fig. 1, which are not repeated here.
It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations are within the scope of the invention as defined by the appended claims.
Claims (10)
1. The unmanned aerial vehicle cluster positioning method is characterized by comprising the following steps of:
acquiring the quantity information and the position information of unmanned aerial vehicle clusters; the position information comprises long-distance information and anchor point position information in the unmanned aerial vehicle cluster;
dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information, and acquiring parameter information of each subgroup;
obtaining an individual optimal position and a global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup;
obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group;
updating parameter information of each sub-group based on the reference optimal position of each sub-group;
returning to execute the steps of obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-machine position information and the anchor point position information of each subgroup based on the updated parameter information to update the parameter information of each subgroup based on the reference optimal position of each subgroup until the maximum iteration number is reached and obtaining the final reference optimal position;
and obtaining the optimal position of each subgroup based on the final reference optimal position.
2. The method of claim 1, wherein the parameter information comprises position information, speed information, and coverage of the drones in the subgroup,
the obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup comprises the following steps:
obtaining the particle number and the particle position of each subgroup based on the position information of the subgroup and the coverage range of unmanned aerial vehicles in the subgroup;
and obtaining the individual optimal position and the global optimal position of each subgroup based on the particle number and the particle position of each subgroup, the long-distance position information and the anchor point position information.
3. The method of claim 2, wherein said deriving the individual optimal position and the global optimal position for each of said sub-groups based on the number and position of particles, the long-machine position information, and the anchor position information for each of said sub-groups comprises:
obtaining a fitness function of particles in each subgroup based on the particle position, the long-machine position information and the anchor point position information of each subgroup;
obtaining individual optima of particles of each of the subgroups based on the fitness function;
and obtaining the global optimum of the particles of each sub-group based on the particle number of each sub-group and the fitness function of the particles.
4. A method according to claim 3, wherein said deriving an fitness function for the particles in each of said subgroups based on the particle position, the long machine position information, and the anchor point position information for each of said subgroups comprises:
obtaining a first relative measurement distance based on the long machine position information and the particle position of each subgroup;
obtaining a second relative measurement distance based on the anchor point position information and the particle position of each sub-group;
and obtaining the fitness function based on the first relative measurement distance and the second relative measurement distance.
5. The method of claim 1, wherein deriving a reference optimal position for each of the sub-groups based on the individual optimal position and the global optimal position for each of the sub-groups comprises:
and linearly superposing the individual optimum and the global optimum to obtain the optimum position, wherein the weight coefficient of the linear superposition is determined by an exhaustion method.
6. The method of claim 1, wherein said updating parameter information for each of said subgroups based on a reference optimal position for each of said subgroups comprises:
updating the position information, the speed information and the fitness function of the particles of each of the sub-groups based on the optimal position of each of the sub-groups.
7. The method of claim 6, wherein the location information, the velocity information, and the fitness function of the particles of each of the subgroups are updated according to the following formula,
P gi-n up ＝P gi-n +v gi-n up ，
f＝f gi-n (P gi-n up )，
v gi-n up indicating the updated particle velocity, g indicating the subgroup number, gi indicating the ith particle in the subgroup numbered g, gi-n indicating the nth iteration of the ith particle in the subgroup numbered g,
to select inertial weights, c is a constant, δ=rand (0, 1) is a gaussian distributed random number, r b-gi-n Reference optimum after the nth iteration representing the ith particle in subgroup numbered g, P gi-n Represents the first of subgroups numbered gThe position after the nth iteration of the i particles; p (P) gi-n up Representing the position after the nth iteration of the ith particle in the updated subgroup numbered g; f (f) gi-n Representing the fitness function after the nth iteration of the ith particle in the subgroup numbered g.
8. An unmanned aerial vehicle cluster positioning device is characterized by comprising,
the acquisition module is used for acquiring the quantity information and the position information of the unmanned aerial vehicle clusters; the position information comprises long-distance information and anchor point position information in the unmanned aerial vehicle cluster;
the dividing module is used for dividing the unmanned aerial vehicle cluster into different subgroups based on the quantity information and acquiring parameter information of each subgroup;
the first optimizing module is used for obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-distance position information and the anchor point position information of each subgroup;
the second optimizing module is used for obtaining a reference optimal position of each sub-group based on the individual optimal position and the global optimal position of each sub-group;
a first updating module, configured to update parameter information of each sub-group based on a reference optimal position of each sub-group;
the iteration module is used for returning and executing the steps of obtaining the individual optimal position and the global optimal position of each subgroup based on the parameter information, the long-machine position information and the anchor point position information of each subgroup based on the updated parameter information to the step of updating the parameter information of each subgroup based on the reference optimal position of each subgroup until the final reference optimal position is obtained after the maximum iteration times are reached;
and the optimal position determining module is used for obtaining the optimal position of each subgroup based on the last reference optimal position.
9. A computer device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the unmanned aerial vehicle cluster localization method of any of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the unmanned aerial vehicle cluster localization method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111368081.7A CN113867411B (en) | 2021-11-18 | 2021-11-18 | Unmanned aerial vehicle cluster positioning method and device and computer equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111368081.7A CN113867411B (en) | 2021-11-18 | 2021-11-18 | Unmanned aerial vehicle cluster positioning method and device and computer equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113867411A CN113867411A (en) | 2021-12-31 |
| CN113867411B true CN113867411B (en) | 2023-06-16 |
Family
ID=78984949
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111368081.7A Active CN113867411B (en) | 2021-11-18 | 2021-11-18 | Unmanned aerial vehicle cluster positioning method and device and computer equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113867411B (en) |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107346458A (en) * | 2017-05-10 | 2017-11-14 | 西北工业大学 | A kind of unmanned plane paths planning method and its device |
| CN108398958B (en) * | 2018-03-14 | 2021-04-23 | 广州亿航智能技术有限公司 | Unmanned aerial vehicle formation path matching method and device and storage medium |
| CN111830461A (en) * | 2019-04-18 | 2020-10-27 | 中国民航大学 | Airborne UWB positioning method for unmanned aerial vehicle |
| CN110068335B (en) * | 2019-04-23 | 2021-07-30 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster real-time positioning method and system under GPS rejection environment |
| KR102386832B1 (en) * | 2019-07-19 | 2022-04-14 | 배재대학교 산학협력단 | System and method for controlling cluster flight of unmanned aerial vehicle |
| US20200005656A1 (en) * | 2019-09-13 | 2020-01-02 | Intel Corporation | Direction finding in autonomous vehicle systems |
| CN112001309A (en) * | 2020-08-21 | 2020-11-27 | 上海特金无线技术有限公司 | Target searching method, device and equipment based on unmanned aerial vehicle cluster and storage medium |
| CN112836425A (en) * | 2021-01-12 | 2021-05-25 | 中铁第四勘察设计院集团有限公司 | Cylinder fitting method and device based on particle swarm optimization algorithm |
| CN112887994B (en) * | 2021-01-20 | 2022-03-25 | 华南农业大学 | Wireless sensor network optimization method based on improved binary particle swarm and application |
| CN112995898B (en) * | 2021-03-10 | 2022-06-10 | 南京航空航天大学 | Unmanned aerial vehicle cluster belief propagation cooperative positioning method based on CASSA (computer-aided single-station analysis) optimization |
-
2021
- 2021-11-18 CN CN202111368081.7A patent/CN113867411B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113867411A (en) | 2021-12-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110501712B (en) | Method, device and equipment for determining position attitude data in unmanned driving | |
| US9660740B2 (en) | Signal strength distribution establishing method and wireless positioning system | |
| WO2018056391A1 (en) | Method for creating positioning geomagnetism map, position measurement method, noise measurement method, and system for creating positioning geomagnetism map | |
| CN110658490B (en) | RSS (really simple syndication) and AOA (automatic optical inspection) based three-dimensional wireless sensor network non-cooperative positioning method | |
| Fabresse et al. | Undelayed 3D RO-SLAM based on Gaussian-mixture and reduced spherical parametrization | |
| CN112218330A (en) | Positioning method and communication device | |
| CN109981195B (en) | Method and device for processing wireless signal strength | |
| Zhang et al. | Wi-Fi-inertial indoor pose estimation for microaerial vehicles | |
| CN113867411B (en) | Unmanned aerial vehicle cluster positioning method and device and computer equipment | |
| WO2020107434A1 (en) | Coordinate calibration method and device for rtk reference station, and storage medium | |
| Alamleh et al. | A weighting system for building RSS maps by crowdsourcing data from smartphones | |
| Csík et al. | Comparison of different radio communication-based technologies for indoor localization using trilateration | |
| CN109769206B (en) | Indoor positioning fusion method and device, storage medium and terminal equipment | |
| Suseenthiran et al. | Indoor positioning utilizing bluetooth low energy RSSI on LoRa system | |
| RU137394U1 (en) | DEVICE FOR PROCESSING INFORMATION OF NETWORK DISTANCED IN THE SPACE OF PELENGATION POST | |
| Petukhov et al. | Adaptive Kalman filtering for hybrid ToA/AoA navigation system | |
| Hao et al. | A novel three-dimensional coordinate positioning algorithm based on factor graph | |
| CN111314848B (en) | Wireless terminal positioning method and system | |
| CN114543810A (en) | Passive positioning method and device for unmanned aerial vehicle cluster in complex environment | |
| CN107250831B (en) | Providing an indication of the direction of travel of a mobile device | |
| Peng et al. | Maneuvering Target Tracking Using Current Statistical Model Based Adaptive UKF for Wireless Sensor Network. | |
| Stanculeanu et al. | Enhanced RSSI localization system for asset tracking services using Non expensive IMU | |
| CN111562547A (en) | 3D visualization method and system for monitoring element | |
| Kabiri et al. | Pose Graph Optimization for a MAV Indoor Localization Fusing 5GNR TOA with an IMU | |
| Coleman et al. | Incorporating observability via control barrier functions with application to range-based target tracking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |