CN107748499A - The optimization method and device of fixed-wing unmanned plane multizone detection mission planning - Google Patents

The optimization method and device of fixed-wing unmanned plane multizone detection mission planning Download PDF

Info

Publication number
CN107748499A
CN107748499A CN201711026685.7A CN201711026685A CN107748499A CN 107748499 A CN107748499 A CN 107748499A CN 201711026685 A CN201711026685 A CN 201711026685A CN 107748499 A CN107748499 A CN 107748499A
Authority
CN
China
Prior art keywords
region
detected
unmanned plane
point
fixed
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.)
Granted
Application number
CN201711026685.7A
Other languages
Chinese (zh)
Other versions
CN107748499B (en
Inventor
杨善林
朱默宁
罗贺
胡笑旋
马华伟
雷星
马滢滢
夏维
靳鹏
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hefei University of Technology
Original Assignee
Hefei University of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hefei University of Technology filed Critical Hefei University of Technology
Priority to CN201711026685.7A priority Critical patent/CN107748499B/en
Publication of CN107748499A publication Critical patent/CN107748499A/en
Application granted granted Critical
Publication of CN107748499B publication Critical patent/CN107748499B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The present embodiments relate to a kind of optimization method and device of fixed-wing unmanned plane multizone detection mission planning, the situation that a frame fixed-wing unmanned plane performs job task to polylith polygon region to be detected is directed in this method, the type for performing this subtask, area information to be detected, the unmanned machine information of fixed-wing, the sensor type for performing required by task are obtained first, the shortest path for completing this multizone detection mission, and the optimum results using the shortest path as this detection mission are obtained based on default model and algorithm then according to these information.Method provided in an embodiment of the present invention can automatically obtain the region access order of unmanned plane and flight path planning in this regionally detecting task according to default model and algorithm, unmanned plane is allowd automatically to complete regionally detecting task according to optimum way in the case of no manual operation, so that the path length for performing detection mission is most short, so as to effectively improve detection efficient.

Description

The optimization method and device of fixed-wing unmanned plane multizone detection mission planning
Technical field
The present embodiments relate to unmanned air vehicle technique field, and in particular to a kind of fixed-wing unmanned plane multizone detection mission The optimization method and device of planning.
Background technology
With deepening constantly for Agricultural Mechanization Degree, unmanned plane is so that its operating efficiency is high, labor intensity is small, integrated cost The advantage of low aspect, rapidly become a kind of important mode during agricultural operation, precision drilling, vegetation detection, Had a wide range of applications in the agricultural aviation operation such as pesticide spraying.For example, unmanned plane can be utilized to herbal germination shape Condition and weeds degree are detected, or carry out pesticide spraying to rice field using unmanned plane to control plant hopper etc..
Current unmanned plane can substantially be roughly divided into more rotors (such as four rotors, six rotors or eight rotor wing unmanned aerial vehicles etc.) And the major class of fixed-wing two.Wherein fixed-wing unmanned plane is so that flying distance is long, cruise area is big, flying speed is fast, highly high Advantage is applied in agricultural operation by relatively broad.
However, during innovation and creation, inventor has found, is made in the prior art using fixed-wing unmanned plane During industry, mainly by the way of manual remote control, and trajectory planning is carried out only for the operation in single region, without considering The planning of access order and task flight path between polylith region to be detected, can not ensure that total flight path is most short.
The content of the invention
An embodiment provides a kind of optimization method of fixed-wing unmanned plane multizone detection mission planning And device, for overcoming in the prior art when carrying out operation using fixed-wing unmanned plane, mainly by the way of manual remote control, And carry out trajectory planning only for the operation in single region, without consider between polylith region to be detected access order and The planning of task flight path, the defects of total flight path is most short can not be ensured.
In a first aspect, the embodiments of the invention provide a kind of optimization side of fixed-wing unmanned plane multizone detection mission planning Method, when a frame fixed-wing unmanned plane performs a variety of detection missions to polylith polygon region to be detected, methods described includes：
Obtain the unmanned machine information of area information and fixed-wing to be detected；
The region to be detected of polygon every piece described is carried out at boundary rectangle approximation using boundary rectangle approximate calculation method Reason, the region to be detected after being handled；
UAV-MROC models are obtained, wherein, UAV-MROC models include object function and constraints, the UAV-MROC The object function of model is so that fixed-wing unmanned plane completes the object function of this detection mission with shortest path；The constraint The region to be detected that condition includes after each processing is only accessed once；
Using the information in the region to be detected after the area information to be detected, the unmanned machine information of fixed-wing and processing as The input of the UAV-MROC models, obtain performing the solution of the flight path of this detection mission based on the UAV-MROC models Collection；
The disaggregation of the flight path exported using row least member iterative algorithm to described UAV-MROC models carries out excellent Change obtains optimal solution, and using task allocation result of the optimal solution as fixed-wing unmanned plane to polylith search coverage.
Second aspect, the embodiments of the invention provide a kind of optimization dress of fixed-wing unmanned plane multizone detection mission planning Put, when a frame fixed-wing unmanned plane performs a variety of detection missions to polylith polygon region to be detected, described device includes：
Information acquisition unit, for obtaining the unmanned machine information of area information and fixed-wing to be detected；
Approximate processing unit, for carrying out boundary rectangle approximate processing for the region to be detected of polygon every piece described, obtain Region to be detected after to processing；
Model acquiring unit, for obtaining UAV-MROC models, wherein, UAV-MROC models include object function and constraint Condition, the object function of the UAV-MROC models is so that fixed-wing unmanned plane completes this detection mission with shortest path Object function；The constraints includes each region to be detected and is only accessed once；
Model performing unit, for will be treated after the area information to be detected, the unmanned machine information of fixed-wing and processing Input of the information of search coverage as the UAV-MROC models, obtains performing this detection based on the UAV-MROC models The disaggregation of the flight path of task；
Model solution unit, for the flight exported using row least member iterative algorithm to described UAV-MROC models The disaggregation in path optimizes to obtain optimal solution, and using task of the optimal solution as fixed-wing unmanned plane to polylith search coverage Allocation result.
An embodiment provides a kind of optimization method of fixed-wing unmanned plane regionally detecting task, this method In for a frame fixed-wing unmanned plane to polylith region to be detected perform detection mission situation, first obtain perform this subtask The unmanned machine information of area information and fixed-wing to be detected, default UAV-MROC models and row are based on according to above- mentioned information Least member iterative algorithm, the optimal solution that unmanned plane can be made to perform job task with shortest path is obtained, and by the optimal solution Task distribution and trajectory planning result as this subjob, so as to effectively improve the efficiency of operation.Compared to existing people The mode of work remote control, method provided by the invention can automatically obtain unmanned plane in this subjob according to default model and algorithm Task and trajectory planning so that unmanned plane can perform job task automatically according to the task and trajectory planning, avoid Influenceed by manual operation.Further, since method provided by the invention is by the optimal solution work of default shortest path model For trajectory planning result, therefore the unmanned plane for performing based on the result job task also can be with most short while the task of execution Time efficiently complete detection mission so that unmanned plane operation form can be applied to wider detection mission in.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area Technical staff will be clear understanding.Accompanying drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention Limitation.And in whole accompanying drawing, identical part is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 is a kind of optimization method and device of fixed-wing unmanned plane multizone detection mission planning provided by the invention Embodiment flow chart；
Fig. 2 (a) -2 (b) is regionally detecting schematic diagram to be detected provided in an embodiment of the present invention；
Fig. 3 is that the boundary rectangle approximate method provided in an embodiment of the present invention that carried out to polygon region to be detected is illustrated Figure；
Fig. 4 (a) -4 (c) is the turning path schematic diagram of interior parallel scanning in region provided in an embodiment of the present invention；
Fig. 5 is the region inlet point schematic diagram of the bright offer of this hair embodiment；
Fig. 6 is two-dimentional Dubins path schematic diagrams provided in an embodiment of the present invention；
Fig. 7 is the path schematic diagram between 2 points connected provided in an embodiment of the present invention；
Fig. 8 is 4 area schematics to be detected provided in an embodiment of the present invention；
Fig. 9 is that row least member iterative algorithm solution provided in an embodiment of the present invention performs detection to 4 regions to be detected The schematic diagram of the optimal case of task；
Figure 10 is a kind of optimization method and device of fixed-wing unmanned plane multizone detection mission planning provided by the invention Example structure schematic diagram.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made Embodiment, belong to the scope of protection of the invention.
In a first aspect, the embodiments of the invention provide a kind of optimization side of fixed-wing unmanned plane multizone detection mission planning Method and device, when a frame fixed-wing unmanned plane performs detection mission to polylith region to be detected, as shown in figure 1, methods described Including：
S101, obtain area information and the unmanned machine information of fixed-wing to be detected；
S102, using boundary rectangle approximate calculation method to the region to be detected of polygon every piece described carry out boundary rectangle it is near Like processing, the region to be detected after being handled；
S103, UAV-MROC models are obtained, wherein, UAV-MROC models include object function and constraints, described The object function of UAV-MROC models is so that fixed-wing unmanned plane completes the object function of this detection mission with shortest path； The region to be detected that the constraints includes after each processing is only accessed once；
S104, the letter by the region to be detected after the area information to be detected, the unmanned machine information of fixed-wing and processing The input as the UAV-MROC models is ceased, obtains performing the flight road of this detection mission based on the UAV-MROC models The disaggregation in footpath；
S105, the disaggregation of the flight path exported using row least member iterative algorithm to described UAV-MROC models are entered Row optimization obtains optimal solution, and using task allocation result of the optimal solution as fixed-wing unmanned plane to polylith search coverage.
An embodiment provides a kind of optimization method of fixed-wing unmanned plane regionally detecting task, this method In for a frame fixed-wing unmanned plane to polylith region to be detected perform detection mission situation, first obtain perform this subtask The unmanned machine information of area information and fixed-wing to be detected, default UAV-MROC models and row are based on according to above- mentioned information Least member iterative algorithm, the optimal solution that unmanned plane can be made to perform job task with shortest path is obtained, and by the optimal solution Task distribution and trajectory planning result as this subjob, so as to effectively improve the efficiency of operation.Compared to existing people The mode of work remote control, method provided in an embodiment of the present invention can be automatically obtained in this subjob according to default model and algorithm The task and trajectory planning of unmanned plane so that unmanned plane can perform operation automatically according to the task and trajectory planning and appoint Business, avoids being influenceed by manual operation.Further, since method provided in an embodiment of the present invention is by default shortest path mould The optimal solution of type performs the unmanned plane of job task while the task of execution as trajectory planning result, therefore based on the result Also detection mission can efficiently be completed with the most short time so that unmanned plane operation form can be applied to widely detection In task.
In the specific implementation, it is to be understood that object function that the UAV-MROC models in the above method include and Constraints is the important evidence that the present invention can obtain optimum programming result, and it can be set in several ways, below The optional set-up mode of one of which is described in detail.
Described UAV-MROC models are a combinatorial problems.Fixed-wing unmanned plane self performance, area to be detected Path when size in domain performs task with fixed-wing unmanned plane etc. also has an impact to the result of task distribution.Specific mould Design parameter and setting in type is as follows：
(1) fixed-wing unmanned plane
Represented to perform the fixed-wing unmanned plane of task to be detected with U, minimum of the unmanned plane under optimal cruising speed turns Curved radius is RU；When performing job task, unmanned plane finally returns that from starting point after completing the detection mission to all areas To the starting point.In whole operation process, it is R to carry radius of investigation referring to Fig. 2 (a) and Fig. 2 (b) fixed-wings unmanned planed's Sensor, when unmanned plane is with optimal detection altitude, the search coverage of sensor is a 2Rd×2RdSquare.RU R may also be more than or equal to by being likely less thand。
With reference to fixed-wing unmanned plane perform detection mission the characteristics of, the embodiment of the present invention make it is assumed hereinafter that：
(1) cruising altitude of the fixed-wing unmanned plane when performing detection mission is general all more than 1000 meters, therefore, does not examine The problem of considering avoidance；
(2) fixed-wing unmanned plane selects optimal cruising speed and cruising altitude to fly according to the performance of sensor, so as to Reach optimal Effect on Detecting when not considering that other factors influence；
(3) fixed-wing unmanned plane does not consider shadow of the external environment to fixed-wing unmanned plane during flying track in flight course Ring；
(4) typically above 10 hours in the single cruising time of fixed-wing unmanned plane, it is sufficient to complete to all to be detected The detection mission in region；
(2) region to be detected
Use A0The beginning and end of fixed-wing unmanned plane is represented, beginning and end is identical in the embodiment of the present invention, A0Only one Individual vertex v0；
WithRepresent NABlock region to be detected, and region A to be detectedkIt is the polygon of arbitrary shape Shape, approximate processing is carried out with its boundary rectangle to each piece of region.Referring to Fig. 3, specific approximate processing process can include：Obtain Take region A to be detectedkX, the y-coordinate on each summit, the minimum value x in x, y-coordinate is chosen respectivelyminAnd yminMaximum xmaxWith ymax, pass through the coordinate on four summits of combination of two generation boundary rectangle.Such as：The coordinate of boundary rectangle bottom left vertex is (xmin, ymin), the coordinate of boundary rectangle left upper apex is (xmin,ymax), the coordinate of boundary rectangle right vertices is (xmax,ymax), it is external The coordinate of rectangle bottom right vertex is (xmax,ymin)。
The apex coordinate of boundary rectangle after approximate processing is (Ai1,Ai2,Ai3,Ai4)；The starting point of fixed-wing unmanned plane, end Point and the collection in region to be detected are combined intoWhen fixed-wing unmanned plane treats search coverage Ak(k= 1,…,NA) cover type scanning when, the inlet point that fixed-wing unmanned plane flies into region to be detected is vi(i ∈ [8k+1,8 (k+1)]), Assuming that the fixed-wing unmanned plane can just leave after must detecting the region to be detected after monoblock processing completely.At the same time, it is each Region to be detected after individual processing can only be at most detected once.
(3) flight path
During fixed-wing unmanned plane performs detection mission, in two point vi,vjBetween the path flown can decompose For two parts, Part I is from viPoint treats search coverage progress cover type detection into region and using parallel sweep method, Part II is from region to leave the v that a little flies to after completing detection missionjPoint.Two-part path length sum is viWith vjIt Between flight path length.
Wherein, it is minimum to depend on the length of side of rectangle, unmanned plane for the flight path length of the detection scanning of fixed-wing unmanned plane Relation between radius of turn and sensor radius of investigation three, Fig. 4 have reacted this relation of three.Fig. 4 (a) is sensing Device radius of investigation RdWith unmanned plane min. turning radius RuBetween relation be Rd>RuWhen, the turning road of region interior parallel scanning Footpath schematic diagram；Fig. 4 (b) is Rd=RuThe turning path schematic diagram of time domain interior parallel scanning；Fig. 4 (c) is Rd<RuTime domain The turning path schematic diagram of interior parallel scanning.
Fixed-wing unmanned plane is used during parallel sweep strategy execution regionally detecting task, it is necessary to first determine entering for boundary rectangle Access point.Fig. 5 gives the position of 8 inlet points of boundary rectangle.
According to the generation principle in Dubins paths, the most short Dubins paths of point-to-point transmission can be by segmental arc path and straightway road Footpath combination producing, following six kinds of situations, i.e. D={ LSL, RSR, RSL, LSR, RLR, LRL } be present.Wherein, L represent unmanned plane to The one section of arc to turn left, R represent one section of arc that unmanned plane bends to right, and S represents that unmanned plane flies in a linear fashion.Such as Fig. 6 Shown, LSL represents that unmanned plane is first turned to the left from starting point and flown along segmental arc path (L), but flies again along linear fashion (S) OK, last unmanned plane is turned along one section of Dubins path reaching home of segmental arc path (L) flight to the left again.
Based on it is above-mentioned it is assumed that in the embodiment of the present invention by the area information to be detected, the unmanned machine information of fixed-wing and Input of the information in the region to be detected after processing as the UAV-MROC models, is held based on the UAV-MROC models The step of disaggregation of the flight path of this detection mission of row, can include：
Step 1: replacing region to be detected with the boundary rectangle after approximate processing, 8 inlet points in the region are generated, from First point starts on the right side of the rectangle lower left corner, and inlet point is numbered along clockwise direction.For example, with reference to Fig. 5,1# inlet points Position rectangle bottom left vertex right side Rd(sensor radius of investigation) place and on the base, unmanned plane is in 1# inlet points Course angle is perpendicular to base and points to rectangle inside；The position of 2# inlet points R above rectangle bottom left vertexd(sensor detects Radius) place and on the left side, unmanned plane perpendicular to the left side and points to rectangle inside in the course angle of 2# inlet points；
Step 2: enter point coordinates according to unmanned plane min. turning radius, sensor radius of investigation, 8 of each region And the length of side on side where inlet point, number of turns corresponding to each inlet point is calculated, and then obtain unmanned plane in region Radius of turn during inside processing, to ensure unmanned plane using the detection mission inside most short path completion region；
Step 3: a seat is left according to corresponding to being calculated it in the coordinate of each inlet point and corresponding number of turns It is marked with and the path length in region inside processing to be detected, 8 corresponding 8 of inlet points leave a little, but path length only has 2 Value, the internal path length of wherein 1#, 4#, 5#, 8# inlet point is identical, the internal path length phase of 2#, 3#, 6#, 7# inlet point Together；
Step 4: leaving point coordinates according to corresponding to each inlet point, each leave is calculated and a little enters to other regions Flight path length of the unmanned plane of access point in region exterior；
Step 5: using internal path length and external path length sum as the distance between two inlet points, by length Spend output result of the sum as UAV-MROC models.
Based on this, the object function that can obtain the UAV-MROC models is：
The constraints of the UAV-MROC models is：
NV=8 × NA+1 (2)
Wherein, NARepresent region A to be detectedkNumber；A0The starting point and terminal of fixed-wing unmanned plane are represented, that is, is originated Point and terminal are same point；wijRepresent fixed-wing unmanned plane from viPoint is into the region to be detected after the processing and according to parallel V is arrived in flight after scanning strategy completes detection missionjThe path length of point；XijRepresent fixed-wing unmanned plane to viPoint and vjThe visit of point Situation is asked, if Xij=1, then it represents that fixed-wing unmanned plane is from viPoint enters region AkDetection mission is completed according to parallel sweep strategy V is arrived in flight afterwardsjPoint, otherwise fixed-wing unmanned plane is not from viPoint enters region AkDetection mission is completed according to parallel sweep strategy V is arrived in flight afterwardsjPoint.
Understandable to be, after flight path disaggregation is obtained based on UAV-MROC models, the embodiment of the present invention provides Method can be according to the optimal solution of default Algorithm for Solving UAV-MROC models.Wherein this preset algorithm for asking for optimal solution The optional mode of one of which can be described in detail below by accomplished in many ways.
The present invention uses row least member iterative algorithm and multiple regions is performed to solve a frame fixed-wing unmanned plane The optimization problem of detection mission.The specific implementation step for the row least member iterative algorithm that the present invention uses is as follows：
Step 1: establish all flight path length wijThe table of value, with (NV+1)×(NV+ 1) matrixDeposit Storage, wherein the first behavior starting point A of matrix0To there is point Vj(j=1 ..., NV) flight path length w0j, the first row of matrix To be there is point Vi(i=1 ..., NV) arrive terminal A0Flight path length wi0.So element d in matrix DmnWith wijCorrespondence Relation is as follows：M=i+1, n=j+1, according to formula (3) and the constraints of formula (6), if dmm=+∞ and
Step 2: setting L storing the path length of unmanned plane, the initial value for setting L is 0, at the same set vectorial θ to The node visit order of unmanned plane is stored, because unmanned plane is from starting point A0Set out, so first in θ element is 0, i.e. θ= {0}；
Step 3: the minimum d of numerical value is found from the first row of D matrix1n, by d1nCorresponding node serial number n-1 be stored in Measure in θ, i.e. θ={ θ, (n-1) }, by d1nIt is stored in D, i.e. L=L+d1n；
Step 4: the minimum d of numerical value is found from the line n of D matrixnx；
Step 5: judge dnxCorresponding node serial number (x-1) whether in vectorial θ, if (x-1) not in θ, will (x-1) it is stored in vectorial θ, i.e. θ={ θ, (x-1) }, meanwhile, by dnxIt is stored in L, i.e. L=L+dnx.(if x-1) in θ, Then by dnxValue be revised as infinity, then repeat step four；
Step 6: judging whether to meet termination condition, if discontented foot channel beam condition, n, repeat step four are substituted with x. θ and L are exported if termination condition is met, obtains optimal solution, the optimal solution can be used as a frame fixed-wing unmanned plane to polylith Polygon region to be detected performs the near optimal solution of detection mission.
It is understandable to be, for every two pieces of regions to be detected, can be obtained based on the above method it is as shown in Figure 7 most Short path (is included in AiInternal inside flight path and in AiAnd AjBetween external flight path).And then for Fig. 8 institutes The situation in the multiple regions to be detected shown, based on above method fixed-wing unmanned plane as shown in Figure 9 from A0Put the approach that takes off A1, A2, A3, A4 region are eventually returned to A0The shortest path of point, namely the optimal of this subtask carry into execution a plan.
Second aspect, the embodiment of the present invention additionally provide a kind of optimization of fixed-wing unmanned plane multizone detection mission planning Device, when a frame fixed-wing unmanned plane performs a variety of detection missions to polylith rectangle region to be detected, described device includes：
Information acquisition unit 201, for obtaining the unmanned machine information of area information and fixed-wing to be detected；
Approximate processing unit 202, for carrying out boundary rectangle approximate processing for the region to be detected of polygon every piece described, Region to be detected after being handled；
Model acquiring unit 203, for obtaining UAV-MROC models, wherein, UAV-MROC models include object function and Constraints, the object function of the UAV-MROC models to cause fixed-wing unmanned plane, with shortest path completion, this time appoint by detection The object function of business；The constraints includes each region to be detected and is only accessed once；
Model performing unit 204, for by after the area information to be detected, the unmanned machine information of fixed-wing and processing Input of the information in region to be detected as the UAV-MROC models, obtains performing this spy based on the UAV-MROC models The disaggregation of the flight path of survey task；
Model solution unit 205, for what is exported using row least member iterative algorithm to described UAV-MROC models The disaggregation of flight path optimizes to obtain optimal solution, and using the optimal solution as fixed-wing unmanned plane to polylith search coverage Task allocation result.
Alternatively, the approximate processing unit 202, it is further used for performing following steps：
X, the y-coordinate on the region summit to be detected are obtained, chooses the minimum value x in x, y-coordinate respectivelyminAnd yminIt is maximum Value xmaxAnd ymax, pass through the coordinate on four summits of combination of two generation boundary rectangle；
Correspondingly, the model performing unit 204 is further used for performing following steps：
Step 1: replacing region to be detected with the boundary rectangle after approximate processing, eight inlet points in the region are generated, from First point starts on the right side of the rectangle lower left corner, and eight inlet points are numbered along clockwise direction；
Step 2: enter point coordinates according to unmanned plane min. turning radius, sensor radius of investigation, eight of each region And the length of side on side where inlet point, number of turns corresponding to each inlet point is calculated, and then obtain unmanned plane in region Radius of turn during inside processing；
Step 3: a seat is left according to corresponding to being calculated it in the coordinate of each inlet point and corresponding number of turns It is marked with and the internal path length in region inside processing to be detected；
Step 4: leaving point coordinates according to corresponding to each inlet point, each leave is calculated and a little arrives other areas to be measured The external path length that the unmanned plane of domain inlet point flies in region exterior；
Step 5: using the internal path length and the external path length sum as between two inlet points away from From the output result using the length sum as the UAV-MROC models.
Alternatively, the object function of the UAV-MROC models is：
The constraints of the UAV-MROC models is：
NV=8 × NA+ 1 formula two
Wherein, NARepresent region A to be detectedkNumber；A0The starting point and terminal of fixed-wing unmanned plane are represented, that is, is originated Point and terminal are same point；wijRepresent fixed-wing unmanned plane from viThe region to be detected that point enters after the processing is simultaneously internal and presses V is arrived in flight after completing detection mission according to parallel sweep strategyjThe path length of point；XijRepresent fixed-wing unmanned plane to viPoint and vj The access situation of point, if Xij=1, then it represents that fixed-wing unmanned plane is from viPoint enters region AkComplete to visit according to parallel sweep strategy V is arrived in flight after survey taskjPoint, otherwise fixed-wing unmanned plane is not from viPoint enters region AkComplete to visit according to parallel sweep strategy V is arrived in flight after survey taskjPoint；
Wherein, the parallel sweep strategy is to be used inside the region boundary rectangle to be detected parallel to area to be detected The long side of domain boundary rectangle or the mode of short side are flown, and with perpendicular to the direction of boundary rectangle long side in region to be detected or short side Enter region to be detected, first inlet point and the distance on region summit to be detected from the first inlet point in long side or short side For fixed-wing unmanned plane radius of investigation.
Alternatively, the model solution unit 205, it is further used for performing following steps：
Step 1: establish all flight path length wijThe table of value, with (NV+1)×(NV+ 1) matrixDeposit Storage, wherein the first behavior starting point A of matrix0To there is point Vj(j=1 ..., NV) flight path length w0j, the first row of matrix To be there is point Vi(i=1 ..., NV) arrive terminal A0Flight path length wi0；Element d in matrix DmnWith wijCorresponding relation It is as follows：M=i+1, n=j+1, according to the constraints of formula three and formula six, if dmm=+∞ and
Step 2: setting L storing the path length of unmanned plane, the initial value for setting L is 0, at the same set vectorial θ to The node visit order of unmanned plane is stored, because unmanned plane is from starting point A0Set out, so first in θ element is 0, i.e. θ= {0}；
Step 3: the minimum d of numerical value is found from the first row of D matrix1n, by d1nCorresponding node serial number n-1 be stored in Measure in θ, i.e. θ={ θ, (n-1) }, by d1nIt is stored in L, i.e. L=L+d1n；
Step 4: the minimum d of numerical value is found from the line n of D matrixnx；
Step 5: judge dnxCorresponding node serial number (x-1) whether in vectorial θ, if (x-1) not in θ, will (x-1) it is stored in vectorial θ, i.e. θ={ θ, (x-1) }, meanwhile, by dnxIt is stored in L, i.e. L=L+dnx；(if x-1) in θ, Then by dnxValue be revised as infinity, then repeat step four；
Step 6: judging whether to meet termination condition, if being unsatisfactory for constraints, n, repeat step four are substituted with x； θ and L are exported if termination condition is met, obtains the optimal solution.
Alternatively, the fixed-wing unmanned plane performs detection mission with default combined optimization flying method, described default Combined optimization flying method be included in parallel sweep flying method inside region to be detected and between region to be detected Dubins paths flying method；
The flying method of the parallel sweep is：With perpendicular to the direction on the side of region boundary rectangle first to be detected from first The first inlet point on side enters region to be detected, and first inlet point and the distance on nearest region summit to be detected are nothing Man-machine sweep radius, wherein first side is any one side in region to be detected, first inlet point is region to be detected Any one inlet point；When needing to be turned, turning flight is carried out in a manner of ensureing that region inner track is most short；
Dubins paths flying method is：The constraint of min. turning radius based on unmanned plane is with arc turning and directly The mode that line traveling combines is flown.
By the optimization device that the fixed-wing unmanned plane multizone detection mission that the present embodiment is introduced is planned is to hold The device of the optimization method of fixed-wing unmanned plane multizone detection mission planning in the row embodiment of the present invention, so it is based on this hair The method of the optimization of fixed-wing unmanned plane multizone detection mission planning described in bright embodiment, the affiliated technology people in this area Member can understand the present embodiment fixed-wing unmanned plane multizone detection mission planning optimization device embodiment with And its various change form, so how real for the optimization device of fixed-wing unmanned plane multizone detection mission planning herein The optimization method of fixed-wing unmanned plane multizone detection mission planning in the existing embodiment of the present invention is no longer discussed in detail.If this Those skilled in the art implements the optimization method institute that fixed-wing unmanned plane multizone detection mission is planned in the embodiment of the present invention The device of use, belong to the scope to be protected of the application.
In the specification that this place provides, numerous specific details are set forth.It is to be appreciated, however, that the implementation of the present invention Example can be put into practice in the case of these no details.In some instances, known method, structure is not been shown in detail And technology, so as not to obscure the understanding of this description.
Similarly, it will be appreciated that in order to simplify the disclosure and help to understand one or more of each inventive aspect, Above in the description to the exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes In example, figure or descriptions thereof.However, the method for the disclosure should be construed to reflect following intention：I.e. required guarantor The application claims of shield features more more than the feature being expressly recited in each claim.It is more precisely, such as following Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore, Thus the claims for following embodiment are expressly incorporated in the embodiment, wherein each claim is in itself Separate embodiments all as the present invention.
Those skilled in the art, which are appreciated that, to be carried out adaptively to the module in the equipment in embodiment Change and they are arranged in one or more equipment different from the embodiment.Can be the module or list in embodiment Member or component be combined into a module or unit or component, and can be divided into addition multiple submodule or subelement or Sub-component.In addition at least some in such feature and/or process or unit exclude each other, it can use any Combination is disclosed to all features disclosed in this specification (including adjoint claim, summary and accompanying drawing) and so to appoint Where all processes or unit of method or equipment are combined.Unless expressly stated otherwise, this specification (including adjoint power Profit requires, summary and accompanying drawing) disclosed in each feature can be by providing the alternative features of identical, equivalent or similar purpose come generation Replace.
In addition, it will be appreciated by those of skill in the art that although some embodiments in this include institute in other embodiments Including some features rather than further feature, but the combination of the feature of different embodiments means to be in the scope of the present invention Within and form different embodiments.For example, in the following claims, embodiment claimed it is any it One mode can use in any combination.
Some unit embodiments of the present invention can be realized with hardware, or to be run on one or more processor Software module realize, or realized with combinations thereof.It will be understood by those of skill in the art that it can use in practice Microprocessor or digital signal processor (DSP) are realized in gateway according to embodiments of the present invention, proxy server, system Some or all parts some or all functions.The present invention is also implemented as being used to perform side as described herein The some or all equipment or program of device (for example, computer program and computer program product) of method.It is such Realizing the program of the present invention can store on a computer-readable medium, or can have the shape of one or more signal Formula.Such signal can be downloaded from internet website and obtained, and either be provided or with any other shape on carrier signal Formula provides.
It should be noted that the present invention will be described rather than limits the invention for above-described embodiment, and ability Field technique personnel can design alternative embodiment without departing from the scope of the appended claims.In the claims, Any reference symbol between bracket should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not Element or step listed in the claims.Word "a" or "an" before element does not exclude the presence of multiple such Element.The present invention can be by means of including the hardware of some different elements and being come by means of properly programmed computer real It is existing.In if the unit claim of equipment for drying is listed, several in these devices can be by same hardware branch To embody.The use of word first, second, and third does not indicate that any order.These words can be explained and run after fame Claim.

Claims (10)

  1. A kind of 1. optimization method of fixed-wing unmanned plane multizone detection mission planning, it is characterised in that when a frame fixed-wing without Man-machine to perform a variety of detection missions to polylith polygon region to be detected, methods described includes：
    Obtain the unmanned machine information of area information and fixed-wing to be detected；
    Boundary rectangle approximate processing is carried out to the region to be detected of polygon every piece described using boundary rectangle approximate calculation method, obtained Region to be detected after to processing；
    UAV-MROC models are obtained, wherein, UAV-MROC models include object function and constraints, the UAV-MROC models Object function to cause fixed-wing unmanned plane to complete the object function of this detection mission with shortest path；The constraints Only it is accessed once including the region to be detected after each processing；
    Using the information in the region to be detected after the area information to be detected, the unmanned machine information of fixed-wing and processing as described in The input of UAV-MROC models, obtain performing the disaggregation of the flight path of this detection mission based on the UAV-MROC models；
    The disaggregation of the flight path exported using row least member iterative algorithm to described UAV-MROC models is optimized To optimal solution, and using task allocation result of the optimal solution as fixed-wing unmanned plane to polylith search coverage.
  2. 2. according to the method for claim 1, it is characterised in that the boundary rectangle approximate calculation method, including：
    X, the y-coordinate on all summits in region to be detected are obtained, chooses the minimum value x in all summit x, y-coordinate respectivelymin And yminMaximum xmaxAnd ymax, pass through the coordinate on four summits of combination of two generation boundary rectangle；
    Correspondingly, by the information in the region to be detected after the area information to be detected, the unmanned machine information of fixed-wing and processing As the input of the UAV-MROC models, obtain performing the flight path of this detection mission based on the UAV-MROC models Disaggregation include：
    Step 1: replacing region to be detected with the boundary rectangle after approximate processing, eight inlet points in the region are generated, from rectangle First point starts on the right side of the lower left corner, and eight inlet points are numbered along clockwise direction；
    Step 2: according to unmanned plane min. turning radius, sensor radius of investigation, eight of each region enter point coordinates and The length of side on side, is calculated number of turns corresponding to each inlet point, and then obtain unmanned plane inside region where inlet point Radius of turn during operation；
    Step 3: left according to corresponding to being calculated it in the coordinate of each inlet point and corresponding number of turns point coordinates with And the internal path length in region inside processing to be detected；
    Step 4: leaving point coordinates according to corresponding to each inlet point, each leave is calculated and a little enters to other regions to be measured The external path length that the unmanned plane of access point flies in region exterior；
    Step 5: using the internal path length and the external path length sum as the distance between two inlet points, Output result using the length sum as the UAV-MROC models.
  3. 3. according to the method for claim 1, it is characterised in that：
    The UAV-MROC model objective functions are：
    The UAV-MROC models constraints is：
    NV=8 × NA+ 1 formula two
    Wherein, DijRepresent that fixed-wing unmanned plane presses default path from viSummit is to vjThe flight path length on summit, i, j are top The numbering of point；NVRepresent the number on all summits；NARepresent region A to be detectedkNumber, k be region to be detected numbering；A0 The starting point and terminal of fixed-wing unmanned plane are represented, i.e., starting point and terminal are same point；wijRepresent fixed-wing unmanned plane from vi V is arrived in flight after point completes detection mission into the region to be detected after the processing and according to parallel sweep strategyjThe path of point Length；XijRepresent fixed-wing unmanned plane to meet formula three constrain connect two point viPoint and vjThe access situation of point, if Xij =1, then it represents that fixed-wing unmanned plane is from viPoint enters region AkV is arrived in flight after completing detection mission according to parallel sweep strategyj Point, otherwise fixed-wing unmanned plane is not from viPoint enters region AkV is arrived in flight after completing detection mission according to parallel sweep strategyj Point；
    Wherein, the parallel sweep strategy is to be used inside the region boundary rectangle to be detected outside parallel to region to be detected Connect the long side of rectangle or the mode of short side flown, and with perpendicular to the direction of boundary rectangle long side in region to be detected or short side from length The first inlet point on side or short side enters region to be detected, and first inlet point and the distance on region summit to be detected are solid Determine wing unmanned plane radius of investigation.
  4. 4. according to the method for claim 3, it is characterised in that described to use row least member iterative algorithm algorithm to described UAV-MROC model solutions obtain optimal solution, including：
    Step 1: establish all flight path length wijThe table of value, with (NV+1)×(NV+ 1) matrixStorage, Wherein the first behavior starting point A of matrix0To there is point Vj(j=1 ..., NV) flight path length w0j, the first of matrix is classified as There is point Vi(i=1 ..., NV) arrive terminal A0Flight path length wi0；Element d in matrix DmnWith wijCorresponding relation such as Under：M=i+1, n=j+1, according to the constraints of formula three and formula six, if dmm=+∞ and
    Step 2: setting L to store the path length of unmanned plane, the initial value for setting L is 0, while sets vectorial θ to store The node visit order of unmanned plane, because unmanned plane is from starting point A0Set out, so first in θ element is 0, i.e. θ={ 0 }；
    Step 3: the minimum d of numerical value is found from the first row of D matrix1n, by d1nCorresponding node serial number n-1 is stored in vectorial θ In, i.e. θ={ θ, (n-1) }, by d1nIt is stored in L, i.e. L=L+d1n, judge NAWhether 1 is equal to, if equal to 1 goes to step six, it is no Then go to step four；
    Step 4: the minimum d of numerical value is found from the line n of D matrixnx；
    Step 5: judge dnxCorresponding node serial number (x-1) whether in vectorial θ, if (x-1) not in θ, by (x- 1) it is stored in vectorial θ, i.e. θ={ θ, (x-1) }, meanwhile, by dnxIt is stored in L, i.e. L=L+dnx；(if x-1) in θ, By dnxValue be revised as infinity, then repeat step four；
    Step 6: judging whether to meet termination condition, i.e. element number in θ is equal to NA+ 1, if being unsatisfactory for termination condition, N, repeat step four are substituted with x；θ and L are exported if termination condition is met, obtains the optimal solution.
  5. 5. according to the method for claim 1, it is characterised in that the fixed-wing unmanned plane is flown with default combined optimization Mode performs detection mission, and the default combined optimization flying method is included in the parallel sweep flight inside region to be detected Mode and the Dubins paths flying method between region to be detected；
    The flying method of the parallel sweep is：With perpendicular to region boundary rectangle first to be detected while direction from first while on The first inlet point enter region to be detected, first inlet point and the distance on nearest region summit to be detected are unmanned plane Sweep radius, wherein first side is any one side in region to be detected, first inlet point is appointed for region to be detected One inlet point of meaning；When needing to be turned, turning flight is carried out in a manner of ensureing that region inner track is most short；
    Dubins paths flying method is：The constraint of min. turning radius based on unmanned plane is turned with arc and linear rows The mode for entering combination is flown.
  6. A kind of 6. optimization device of fixed-wing unmanned plane multizone detection mission planning, it is characterised in that when a frame fixed-wing without Man-machine to perform a variety of detection missions to polylith rectangle region to be detected, described device includes：
    Information acquisition unit, for obtaining the unmanned machine information of area information and fixed-wing to be detected；
    Approximate processing unit, for carrying out boundary rectangle approximate processing for the region to be detected of polygon every piece described, obtain everywhere Region to be detected after reason；
    Model acquiring unit, for obtaining UAV-MROC models, wherein, UAV-MROC models include object function and constraint bar Part, the object function of the UAV-MROC models is so that fixed-wing unmanned plane completes the mesh of this detection mission with shortest path Scalar functions；The constraints includes each region to be detected and is only accessed once；
    Model performing unit, for will be to be detected after the area information to be detected, the unmanned machine information of fixed-wing and processing Input of the information in region as the UAV-MROC models, obtains performing this detection mission based on the UAV-MROC models Flight path disaggregation；
    Model solution unit, for the flight path exported using row least member iterative algorithm to described UAV-MROC models Disaggregation optimize to obtain optimal solution, and the task of polylith search coverage is distributed using the optimal solution as fixed-wing unmanned plane As a result.
  7. 7. device according to claim 6, it is characterised in that the approximate processing unit, it is as follows to be further used for execution Step：
    X, the y-coordinate on the region summit to be detected are obtained, chooses the minimum value x in x, y-coordinate respectivelyminAnd yminMaximum xmaxAnd ymax, pass through the coordinate on four summits of combination of two generation boundary rectangle；
    Correspondingly, the model performing unit is further used for performing following steps：
    Step 1: replacing region to be detected with the boundary rectangle after approximate processing, eight inlet points in the region are generated, from rectangle First point starts on the right side of the lower left corner, and eight inlet points are numbered along clockwise direction；
    Step 2: according to unmanned plane min. turning radius, sensor radius of investigation, eight of each region enter point coordinates and The length of side on side, is calculated number of turns corresponding to each inlet point, and then obtain unmanned plane inside region where inlet point Radius of turn during operation；
    Step 3: left according to corresponding to being calculated it in the coordinate of each inlet point and corresponding number of turns point coordinates with And the internal path length in region inside processing to be detected；
    Step 4: leaving point coordinates according to corresponding to each inlet point, each leave is calculated and a little enters to other regions to be measured The external path length that the unmanned plane of access point flies in region exterior；
    Step 5: using the internal path length and the external path length sum as the distance between two inlet points, Output result using the length sum as the UAV-MROC models.
  8. 8. device according to claim 7, it is characterised in that：
    The object function of the UAV-MROC models is：
    The constraints of the UAV-MROC models is：
    NV=8 × NA+ 1 formula two
    Wherein, NARepresent region A to be detectedkNumber；A0Represent fixed-wing unmanned plane starting point and terminal, i.e., starting point with Terminal is same point；wijRepresent fixed-wing unmanned plane from viThe region to be detected that point enters after the processing is simultaneously internal and according to flat V is arrived in flight after row scanning strategy completes detection missionjThe path length of point；XijRepresent fixed-wing unmanned plane to viPoint and vjPoint Access situation, if Xij=1, then it represents that fixed-wing unmanned plane is from viPoint enters region AkDetection is completed according to parallel sweep strategy to appoint V is arrived in flight after businessjPoint, otherwise fixed-wing unmanned plane is not from viPoint enters region AkDetection is completed according to parallel sweep strategy to appoint V is arrived in flight after businessjPoint；
    Wherein, the parallel sweep strategy is to be used inside the region boundary rectangle to be detected outside parallel to region to be detected Connect the long side of rectangle or the mode of short side flown, and with perpendicular to the direction of boundary rectangle long side in region to be detected or short side from length The first inlet point on side or short side enters region to be detected, and first inlet point and the distance on region summit to be detected are solid Determine wing unmanned plane radius of investigation.
  9. 9. device according to claim 8, it is characterised in that the model solution unit, it is as follows to be further used for execution Step：
    Step 1: establish all flight path length wijThe table of value, with (NV+1)×(NV+ 1) matrixStorage, Wherein the first behavior starting point A of matrix0To there is point Vj(j=1 ..., NV) flight path length w0j, the first of matrix is classified as There is point Vi(i=1 ..., NV) arrive terminal A0Flight path length wi0；Element d in matrix DmnWith wijCorresponding relation such as Under：M=i+1, n=j+1, according to the constraints of formula three and formula six, if dmm=+∞ and
    Step 2: setting L to store the path length of unmanned plane, the initial value for setting L is 0, while sets vectorial θ to store The node visit order of unmanned plane, because unmanned plane is from starting point A0Set out, so first in θ element is 0, i.e. θ={ 0 }；
    Step 3: the minimum d of numerical value is found from the first row of D matrix1n, by d1nCorresponding node serial number n-1 is stored in vectorial θ In, i.e. θ={ θ, (n-1) }, by d1nIt is stored in L, i.e. L=L+d1n；
    Step 4: the minimum d of numerical value is found from the line n of D matrixnx；
    Step 5: judge dnxCorresponding node serial number (x-1) whether in vectorial θ, if (x-1) not in θ, by (x- 1) it is stored in vectorial θ, i.e. θ={ θ, (x-1) }, meanwhile, by dnxIt is stored in L, i.e. L=L+dnx；(if x-1) in θ, By dnxValue be revised as infinity, then repeat step four；
    Step 6: judging whether to meet termination condition, i.e. element number in θ is equal to NA+ 1, if being unsatisfactory for termination condition, N, repeat step four are substituted with x；θ and L are exported if termination condition is met, obtains the optimal solution.
  10. 10. device according to claim 6, it is characterised in that the fixed-wing unmanned plane is flown with default combined optimization Line mode performs detection mission, and the parallel sweep that the default combined optimization flying method is included in inside region to be detected flies Line mode and the Dubins paths flying method between region to be detected；
    The flying method of the parallel sweep is：With perpendicular to region boundary rectangle first to be detected while direction from first while on The first inlet point enter region to be detected, first inlet point and the distance on nearest region summit to be detected are unmanned plane Sweep radius, wherein first side is any one side in region to be detected, first inlet point is appointed for region to be detected One inlet point of meaning；When needing to be turned, turning flight is carried out in a manner of ensureing that region inner track is most short；
    Dubins paths flying method is：The constraint of min. turning radius based on unmanned plane is turned with arc and linear rows The mode for entering combination is flown.
CN201711026685.7A 2017-10-27 2017-10-27 Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle Active CN107748499B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711026685.7A CN107748499B (en) 2017-10-27 2017-10-27 Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711026685.7A CN107748499B (en) 2017-10-27 2017-10-27 Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle

Publications (2)

Publication Number Publication Date
CN107748499A true CN107748499A (en) 2018-03-02
CN107748499B CN107748499B (en) 2020-09-01

Family

ID=61254200

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711026685.7A Active CN107748499B (en) 2017-10-27 2017-10-27 Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle

Country Status (1)

Country Link
CN (1) CN107748499B (en)

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108919832A (en) * 2018-07-23 2018-11-30 京东方科技集团股份有限公司 Unmanned machine operation flight course planning method, unmanned plane application method and device
CN109683473A (en) * 2018-10-26 2019-04-26 中国飞行试验研究院 A kind of comprehensive pilot-aircraft closed loop system modeling and verification method
FR3079296A1 (en) * 2018-03-22 2019-09-27 Thales METHOD AND SYSTEM FOR ASSISTING AN OPERATOR TO DEVELOP A FLIGHT PLAN OF AN AIRCRAFT THROUGH A SET OF MISSION ZONES TO BE COVERED
CN116026341A (en) * 2023-03-27 2023-04-28 中国人民解放军国防科技大学 Multi-unmanned aerial vehicle balanced path planning method and device
CN116299727A (en) * 2023-03-03 2023-06-23 中国地质调查局地球物理调查中心 Unmanned aerial vehicle frequency domain multi-frequency electromagnetic detection method and detection system
CN117890999A (en) * 2024-03-15 2024-04-16 中国民用航空飞行学院 Unmanned aerial vehicle lightning emission control method and device, electronic equipment and storage medium

Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09146908A (en) * 1995-11-17 1997-06-06 Atr Ningen Joho Tsushin Kenkyusho:Kk Problem solving method
JPH11212959A (en) * 1998-01-29 1999-08-06 Micro Technology Kk Chaos solution of traveling salesman problem
JP2000048003A (en) * 1998-07-27 2000-02-18 Fujitsu Ltd Hierarchical processing method for traveling salesman problem and its program recording medium
US20010034578A1 (en) * 2000-02-03 2001-10-25 Ryuichi Ugajin Information processing method and information processing apparatus
US20020059025A1 (en) * 2000-11-12 2002-05-16 Hong-Soo Kim Method for finding shortest path to destination in traaffic network using Dijkstra algorithm or Floyd-warshall algorithm
CN101118609A (en) * 2007-09-13 2008-02-06 北京航空航天大学 Cloud model microenvironment self-adapting ant colony optimizing method for resolving large scale TSP
CN101494590A (en) * 2008-01-23 2009-07-29 中兴通讯股份有限公司 Optimum path selection method of communication network based on load balance
CN102136104A (en) * 2011-03-22 2011-07-27 西安电子科技大学 Load balance and Lin-Kernighan (LK) algorithm based vehicle route planning method
CN102420392A (en) * 2011-07-30 2012-04-18 山东鲁能智能技术有限公司 Transformer substation inspection robot global path planning method based on magnetic navigation
CN103337162A (en) * 2013-07-16 2013-10-02 四川大学 Real-time planning and dynamic scheduling system for urban emergency rescue channel
CN104457775A (en) * 2014-12-12 2015-03-25 北京航天宏图信息技术有限责任公司 Path determination method and device, and navigation instrument
CN104850011A (en) * 2015-05-22 2015-08-19 上海电力学院 Optimal path planning method for TSP obstacle avoidance in obstacle environment
CN106600147A (en) * 2016-12-15 2017-04-26 合肥工业大学 Resolvable task oriented task assigning method and apparatus for multiple unmanned aerial vehicles
CN106842963A (en) * 2017-04-14 2017-06-13 合肥工业大学 Multiple no-manned plane detection mission is distributed and trajectory planning combined optimization method and device
CN106873629A (en) * 2017-04-14 2017-06-20 合肥工业大学 Unmanned plane aviation job task distribution method and device
CN106920015A (en) * 2017-04-11 2017-07-04 东南大学 Suitable for the most short loop method for dynamically partitioning of power distribution network reconfiguration representation
CN107037826A (en) * 2017-04-14 2017-08-11 合肥工业大学 Unmanned plane detection mission distribution method and device

Patent Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH09146908A (en) * 1995-11-17 1997-06-06 Atr Ningen Joho Tsushin Kenkyusho:Kk Problem solving method
JPH11212959A (en) * 1998-01-29 1999-08-06 Micro Technology Kk Chaos solution of traveling salesman problem
JP2000048003A (en) * 1998-07-27 2000-02-18 Fujitsu Ltd Hierarchical processing method for traveling salesman problem and its program recording medium
US20010034578A1 (en) * 2000-02-03 2001-10-25 Ryuichi Ugajin Information processing method and information processing apparatus
US20020059025A1 (en) * 2000-11-12 2002-05-16 Hong-Soo Kim Method for finding shortest path to destination in traaffic network using Dijkstra algorithm or Floyd-warshall algorithm
CN101118609A (en) * 2007-09-13 2008-02-06 北京航空航天大学 Cloud model microenvironment self-adapting ant colony optimizing method for resolving large scale TSP
CN101494590A (en) * 2008-01-23 2009-07-29 中兴通讯股份有限公司 Optimum path selection method of communication network based on load balance
CN102136104A (en) * 2011-03-22 2011-07-27 西安电子科技大学 Load balance and Lin-Kernighan (LK) algorithm based vehicle route planning method
CN102420392A (en) * 2011-07-30 2012-04-18 山东鲁能智能技术有限公司 Transformer substation inspection robot global path planning method based on magnetic navigation
CN103337162A (en) * 2013-07-16 2013-10-02 四川大学 Real-time planning and dynamic scheduling system for urban emergency rescue channel
CN104457775A (en) * 2014-12-12 2015-03-25 北京航天宏图信息技术有限责任公司 Path determination method and device, and navigation instrument
CN104850011A (en) * 2015-05-22 2015-08-19 上海电力学院 Optimal path planning method for TSP obstacle avoidance in obstacle environment
CN106600147A (en) * 2016-12-15 2017-04-26 合肥工业大学 Resolvable task oriented task assigning method and apparatus for multiple unmanned aerial vehicles
CN106920015A (en) * 2017-04-11 2017-07-04 东南大学 Suitable for the most short loop method for dynamically partitioning of power distribution network reconfiguration representation
CN106842963A (en) * 2017-04-14 2017-06-13 合肥工业大学 Multiple no-manned plane detection mission is distributed and trajectory planning combined optimization method and device
CN106873629A (en) * 2017-04-14 2017-06-20 合肥工业大学 Unmanned plane aviation job task distribution method and device
CN107037826A (en) * 2017-04-14 2017-08-11 合肥工业大学 Unmanned plane detection mission distribution method and device

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
马华伟等: "可选时间窗VRP的禁忌搜索算法", 《计算机工程与应用》 *
马良: "旅行推销员问题的算法综述", 《数学的实践与认识》 *

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3079296A1 (en) * 2018-03-22 2019-09-27 Thales METHOD AND SYSTEM FOR ASSISTING AN OPERATOR TO DEVELOP A FLIGHT PLAN OF AN AIRCRAFT THROUGH A SET OF MISSION ZONES TO BE COVERED
EP3543654A3 (en) * 2018-03-22 2019-12-11 Thales Method and system for assisting an operator with creating a flight plan of an aircraft passing through a set of mission areas to be covered
CN108919832A (en) * 2018-07-23 2018-11-30 京东方科技集团股份有限公司 Unmanned machine operation flight course planning method, unmanned plane application method and device
US11479359B2 (en) 2018-07-23 2022-10-25 Beijing Boe Technology Development Co., Ltd. UAV operation route planning method, UAV pesticide spreading method and device
CN109683473A (en) * 2018-10-26 2019-04-26 中国飞行试验研究院 A kind of comprehensive pilot-aircraft closed loop system modeling and verification method
CN109683473B (en) * 2018-10-26 2021-12-24 中国飞行试验研究院 Comprehensive man-machine closed-loop system modeling and verifying method
CN116299727A (en) * 2023-03-03 2023-06-23 中国地质调查局地球物理调查中心 Unmanned aerial vehicle frequency domain multi-frequency electromagnetic detection method and detection system
CN116026341A (en) * 2023-03-27 2023-04-28 中国人民解放军国防科技大学 Multi-unmanned aerial vehicle balanced path planning method and device
CN117890999A (en) * 2024-03-15 2024-04-16 中国民用航空飞行学院 Unmanned aerial vehicle lightning emission control method and device, electronic equipment and storage medium
CN117890999B (en) * 2024-03-15 2024-05-31 中国民用航空飞行学院 Unmanned aerial vehicle lightning emission control method and device, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN107748499B (en) 2020-09-01

Similar Documents

Publication Publication Date Title
CN107748499A (en) The optimization method and device of fixed-wing unmanned plane multizone detection mission planning
Khosiawan et al. A system of UAV application in indoor environment
Qiu et al. Field-based high-throughput phenotyping for maize plant using 3D LiDAR point cloud generated with a “Phenomobile”
Kim et al. Real-time path planning with limited information for autonomous unmanned air vehicles
CN107037827A (en) Unmanned plane aviation job task is distributed and trajectory planning combined optimization method and device
Valente et al. Aerial coverage optimization in precision agriculture management: A musical harmony inspired approach
Pandey et al. Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm
Faigl et al. Goal assignment using distance cost in multi-robot exploration
CN109426273A (en) A kind of flying vehicles control method and device
CN106054920A (en) Unmanned aerial vehicle flight path planning method and device
Rahman et al. Optimum harvesting area of convex and concave polygon field for path planning of robot combine harvester
Easton et al. A coverage algorithm for multi-robot boundary inspection
CN106873629B (en) Unmanned plane aviation job task distribution method and device
Majeed et al. A multi-objective coverage path planning algorithm for UAVs to cover spatially distributed regions in urban environments
CN109506654A (en) Low latitude Route planner and device, aircraft
US11662726B2 (en) Controlling movement of a device
CN108036793A (en) Localization method, device and electronic equipment based on a cloud
CN109154503A (en) The planing method and ground end equipment in unmanned machine operation course line
Geng et al. UAV surveillance mission planning with gimbaled sensors
O’Hara et al. Pervasive sensor-less networks for cooperative multi-robot tasks
CN108871330A (en) UAV Formation Flight determining method of path and device
CN107037826B (en) Unmanned plane detection mission distribution method and device
Tevyashov et al. Algorithm for multi-drone path planning and coverage of agricultural fields
Pinkam et al. Rapid coverage of regions of interest for environmental monitoring
CN112699517B (en) Three-dimensional route planning method, system, equipment and medium

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