CN107748499B - Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle - Google Patents

Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle Download PDF

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CN107748499B
CN107748499B CN201711026685.7A CN201711026685A CN107748499B CN 107748499 B CN107748499 B CN 107748499B CN 201711026685 A CN201711026685 A CN 201711026685A CN 107748499 B CN107748499 B CN 107748499B
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unmanned aerial
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point
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CN107748499A (en
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杨善林
朱默宁
罗贺
胡笑旋
马华伟
雷星
马滢滢
夏维
靳鹏
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Hefei University of Technology
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    • 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
    • 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

Abstract

The embodiment of the invention relates to an optimization method and device for planning multi-region detection tasks of a fixed-wing unmanned aerial vehicle. The method provided by the embodiment of the invention can automatically obtain the area access sequence and flight path planning of the unmanned aerial vehicle in the area detection task according to the preset model and algorithm, so that the unmanned aerial vehicle can automatically complete the area detection task in an optimal mode under the condition of no manual operation, the path length for executing the detection task is shortest, and the detection efficiency is effectively improved.

Description

Optimization method and device for multi-zone detection task planning of fixed-wing unmanned aerial vehicle
Technical Field
The embodiment of the invention relates to the technical field of unmanned aerial vehicles, in particular to an optimization method and device for multi-zone detection task planning of a fixed-wing unmanned aerial vehicle.
Background
With the continuous deepening of the agricultural mechanization degree, the unmanned aerial vehicle rapidly becomes an important mode in the agricultural operation process with the advantages of high operation efficiency, low labor intensity, low comprehensive cost and the like, and has wide application in agricultural aviation operations such as precision seeding, vegetation detection, pesticide spraying and the like. For example, the germination condition and the weed degree of herbaceous plants can be detected by an unmanned aerial vehicle, or the planthoppers can be controlled by spraying pesticides on rice fields by using the unmanned aerial vehicle, and the like.
Current drones can be broadly classified into two broad categories, multi-rotor (e.g., quad-rotor, six-rotor, or eight-rotor drones, etc.) and fixed-wing. Wherein fixed wing unmanned aerial vehicle is comparatively wide application in agricultural operation with advantages such as long, the area of cruising is big, flying speed is fast, height.
However, in the process of the invention creation, the inventor finds that, in the prior art, when a fixed-wing drone is used for operation, a manual remote control mode is mainly adopted, and flight path planning is only performed on the operation of a single area, and the access sequence and the task flight path planning among a plurality of areas to be detected are not comprehensively considered, so that the shortest total flight path cannot be ensured.
Disclosure of Invention
An embodiment of the invention provides an optimization method and device for multi-zone detection mission planning of a fixed-wing unmanned aerial vehicle, which are used for overcoming the defect that in the prior art, when the fixed-wing unmanned aerial vehicle is used for operating, a manual remote control mode is mainly adopted, and flight path planning is only carried out on the operation of a single zone, the planning of an access sequence and mission flight paths among a plurality of zones to be detected is not comprehensively considered, and the shortest total flight path cannot be ensured.
In a first aspect, an embodiment of the present invention provides an optimization method for multi-zone probe mission planning of a fixed-wing drone, where when one fixed-wing drone executes multiple probe missions on multiple polygonal regions to be probed, the method includes:
acquiring information of a region to be detected and information of a fixed-wing unmanned aerial vehicle;
performing circumscribed rectangle approximation processing on each polygonal region to be detected by adopting a circumscribed rectangle approximation calculation method to obtain a processed region to be detected;
acquiring a UAV-MROC model, wherein the UAV-MROC model comprises an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function enabling the fixed wing unmanned aerial vehicle to complete the detection task in the shortest path; the constraint condition comprises that each processed region to be detected is only accessed once;
taking the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected as the input of the UAV-MROC model, and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model;
and optimizing a solution set of the flight path output by the UAV-MROC model by adopting a minimum element iterative algorithm to obtain an optimal solution, and taking the optimal solution as a task allocation result of the fixed wing unmanned aerial vehicle to a plurality of detection areas.
In a second aspect, an embodiment of the present invention provides an optimization apparatus for multi-zone mission planning of a fixed-wing drone, where when one fixed-wing drone executes multiple mission tasks on multiple polygonal areas to be surveyed, the apparatus includes:
the information acquisition unit is used for acquiring information of a region to be detected and information of the fixed-wing unmanned aerial vehicle;
the approximation processing unit is used for carrying out circumscribed rectangle approximation processing on each polygonal region to be detected to obtain a processed region to be detected;
the model acquisition unit is used for acquiring a UAV-MROC model, wherein the UAV-MROC model comprises an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function which enables the fixed wing unmanned aerial vehicle to complete the detection task in the shortest path; the constraint condition comprises that each area to be detected is only accessed once;
the model execution unit is used for taking the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected as the input of the UAV-MROC model and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model;
and the model solving unit is used for optimizing the solution set of the flight path output by the UAV-MROC model by adopting a minimum element iterative algorithm to obtain an optimal solution, and taking the optimal solution as a task allocation result of the fixed-wing unmanned aerial vehicle to a plurality of detection areas.
An embodiment of the invention provides a method for optimizing a regional detection task of a fixed-wing unmanned aerial vehicle, which comprises the steps of firstly obtaining information of a region to be detected and fixed-wing unmanned aerial vehicle for executing the task according to the condition that one fixed-wing unmanned aerial vehicle executes detection tasks on a plurality of regions to be detected, obtaining an optimal solution for enabling the unmanned aerial vehicle to execute an operation task in a shortest path according to the information based on a preset UAV-MROC model and a minimum element iteration algorithm, and using the optimal solution as a task allocation and flight path planning result of the operation, thereby effectively improving the operation efficiency. Compared with the existing manual remote control mode, the method provided by the invention can automatically obtain the task and flight path planning of the unmanned aerial vehicle in the operation according to the preset model and algorithm, so that the unmanned aerial vehicle can automatically execute the operation task according to the task and flight path planning, and the influence of manual operation is avoided. In addition, because the method provided by the invention takes the optimal solution of the preset shortest path model as the flight path planning result, the unmanned aerial vehicle executing the operation task based on the result can also efficiently complete the detection task in the shortest time while executing the task, so that the operation form of the unmanned aerial vehicle can be applied to wider detection tasks.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of an embodiment of a method and an apparatus for optimizing multi-zone mission planning of a fixed-wing drone according to the present invention;
fig. 2(a) -2(b) are schematic diagrams illustrating a detection mode of an area to be detected according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a method for approximating a circumscribed rectangle of a polygonal region to be detected according to an embodiment of the present invention;
4(a) -4(c) are schematic diagrams of turn paths for parallel scanning within a region provided by embodiments of the present invention;
FIG. 5 is a schematic diagram of region entry points provided by the present invention;
FIG. 6 is a schematic diagram of a two-dimensional Dubins path according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a path between two connectable points provided by an embodiment of the present invention;
FIG. 8 is a schematic diagram of 4 regions to be detected according to an embodiment of the present invention;
fig. 9 is a schematic diagram of an optimal solution for solving detection tasks performed on 4 regions to be detected by the minimum element row iterative algorithm according to the embodiment of the present invention;
fig. 10 is a schematic structural diagram of an embodiment of a method and an apparatus for optimizing multi-zone probe mission planning of a fixed-wing drone provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In a first aspect, an embodiment of the present invention provides an optimization method and an apparatus for multi-zone probe mission planning of a fixed-wing drone, where when one fixed-wing drone executes probe missions on multiple to-be-probed zones, as shown in fig. 1, the method includes:
s101, acquiring information of a region to be detected and information of a fixed-wing unmanned aerial vehicle;
s102, carrying out circumscribed rectangle approximation processing on each polygonal region to be detected by adopting a circumscribed rectangle approximation calculation method to obtain a processed region to be detected;
s103, acquiring a UAV-MROC model, wherein the UAV-MROC model comprises an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function for enabling the fixed wing unmanned aerial vehicle to complete the detection task at the shortest path; the constraint condition comprises that each processed region to be detected is only accessed once;
s104, taking the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected as the input of the UAV-MROC model, and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model;
and S105, optimizing a solution set of the flight path output by the UAV-MROC model by adopting a minimum element iterative algorithm to obtain an optimal solution, and taking the optimal solution as a task allocation result of the fixed wing unmanned aerial vehicle to a plurality of detection areas.
An embodiment of the invention provides a method for optimizing a regional detection task of a fixed-wing unmanned aerial vehicle, which comprises the steps of firstly obtaining information of a region to be detected and fixed-wing unmanned aerial vehicle for executing the task according to the condition that one fixed-wing unmanned aerial vehicle executes detection tasks on a plurality of regions to be detected, obtaining an optimal solution for enabling the unmanned aerial vehicle to execute an operation task in a shortest path according to the information based on a preset UAV-MROC model and a minimum element iteration algorithm, and using the optimal solution as a task allocation and flight path planning result of the operation, thereby effectively improving the operation efficiency. Compared with the existing manual remote control mode, the method provided by the embodiment of the invention can automatically obtain the task and the flight path planning of the unmanned aerial vehicle in the operation according to the preset model and algorithm, so that the unmanned aerial vehicle can automatically execute the operation task according to the task and the flight path planning, and the influence of manual operation is avoided. In addition, because the method provided by the embodiment of the invention takes the optimal solution of the preset shortest path model as the flight path planning result, the unmanned aerial vehicle executing the operation task based on the result can also efficiently complete the detection task in the shortest time while executing the task, so that the operation form of the unmanned aerial vehicle can be applied to wider detection tasks.
In the implementation, it can be understood that the objective function and the constraint condition included in the UAV-MROC model in the above method are important bases for obtaining the optimal planning result of the present invention, and may be set in various ways, and one of the alternative setting ways is described in detail below.
The UAV-MROC model is a combinatorial optimization problem. The performance of the fixed-wing unmanned aerial vehicle, the size of the area to be detected, the path of the fixed-wing unmanned aerial vehicle during task execution and the like also affect the result of task allocation. The specific parameters and settings in the specific model are as follows:
fixed wing unmanned aerial vehicle
U denotes a fixed-wing drone performing the mission to be detected, with a minimum turning radius R at the optimal cruising speedU(ii) a When the operation task is executed, the unmanned aerial vehicle starts from the starting point, and finally returns to the starting point after the detection tasks of all the areas are completed. In the whole operation process, referring to fig. 2(a) and 2(b), the fixed-wing drone carries a detection radius RdWhen the unmanned aerial vehicle flies at the optimal detection height, the detection area of the sensor is 2Rd×2RdSquare of (2). RUMay be less than or equal to Rd。
By combining the characteristics of the fixed-wing unmanned aerial vehicle for executing the detection task, the embodiment of the invention makes the following assumptions:
(1) the cruising height of the fixed-wing unmanned aerial vehicle is generally more than 1000 meters when the fixed-wing unmanned aerial vehicle executes a detection task, so that the problem of obstacle avoidance is not considered;
(2) the fixed wing unmanned aerial vehicle selects the optimal cruising speed and cruising height to fly according to the performance of the sensor, so that the optimal detection effect is achieved when the influence of other factors is not considered;
(3) the influence of the external environment on the flight track of the fixed-wing unmanned aerial vehicle is not considered in the flight process of the fixed-wing unmanned aerial vehicle;
(4) the single endurance time of the fixed-wing unmanned aerial vehicle generally exceeds 10 hours, which is enough to complete the detection tasks of all areas to be detected;
(II) area to be detected
With A0Representing the starting point and the end point of the fixed-wing unmanned aerial vehicle, the starting point and the end point are the same in the embodiment of the invention, A0Having only one vertex v0；
By using
Figure GDA0002530248460000061
Represents NABlock area to be detected, and area to be detected AkIs a polygon of an arbitrary shape, and each block is approximated by its circumscribed rectangle. Referring to fig. 3, a specific approximation process may include: obtaining a region A to be detectedkSelecting the minimum value x of the x and y coordinates of each vertexminAnd yminMaximum value xmaxAnd ymaxAnd generating coordinates of four vertexes of the circumscribed rectangle by combining two pairs. For example: the coordinate of the lower left vertex of the circumscribed rectangle is (xmin)minThe coordinate of the upper left vertex of the circumscribed rectangle is (xmax)minThe coordinate of the upper right vertex of the circumscribed rectangle is (xmax)maxThe coordinate of the lower right vertex of the circumscribed rectangle is (xmin)max。
The vertex coordinates of the circumscribed rectangle after the approximation processing are (A)i1,Ai2,Ai3,Ai4) (ii) a The set of the starting point, the terminal point and the area to be detected of the fixed-wing unmanned aerial vehicle is
Figure GDA0002530248460000071
When the fixed wing unmanned aerial vehicle treats the detection area Ak(k＝1,…,NA) When coverage type scanning is carried out, the entry point of the fixed-wing unmanned aerial vehicle flying into the area to be detected is vi(i∈[8(k-1)+1,8k]) And the fixed-wing unmanned aerial vehicle can leave after the whole processed region to be detected is completely detected. Meanwhile, each processed region to be detected can be detected only once at most.
(III) flight path
In the process of executing the detection task by the fixed-wing unmanned aerial vehicle, two points vi,vjThe path of flight therebetween can be broken down into two parts, the first part being from viPoint entering area and parallel scanning to cover the area to be detected, and the second part is flying from the leaving point of the area to vjAnd (4) point. The sum of the path lengths of the two parts is viAnd vjThe flight path length therebetween.
The length of the flight path of the detection scanning of the fixed-wing unmanned aerial vehicle depends on the relationship among the side length of the rectangle, the minimum turning radius of the unmanned aerial vehicle and the detection radius of the sensor, and the relationship among the three is reflected in fig. 4. FIG. 4(a) is a view showing a radius R detected by a sensordWith minimum turning radius R of unmanned aerial vehicleuThe relationship between is Rd>RuWhen the scanning is carried out, the turning path schematic diagram of parallel scanning in the region is shown; FIG. 4(b) is Rd＝RuTurning path schematic diagram of parallel scanning in the time region; FIG. 4(c) is Rd<RuThe turning path diagram of the parallel scanning in the time region.
When the fixed-wing unmanned aerial vehicle adopts a parallel scanning strategy to execute a regional detection task, an entry point of an external rectangle needs to be determined first. Fig. 5 shows the positions of 8 entry points of the circumscribed rectangle.
According to the generation principle of the Dubins path, the shortest Dubins path between two points can be generated by combining an arc-segment path and a straight-segment path, and there are six cases where D ═ { LSL, RSR, RSL, LSR, RLR, LRL }. Wherein, L represents a section of arc that unmanned aerial vehicle turned to the left, and R represents a section of arc that unmanned aerial vehicle turned to the right, and S represents that unmanned aerial vehicle flies with the straight line mode. For example, as shown in fig. 6, LSL represents a Dubins path in which the drone first turns left from the starting point and flies along the arc path (L), then flies in the straight line manner (S), and finally flies left again along the arc path (L) to the ending point.
Based on the above assumptions, in the embodiment of the present invention, the information of the area to be detected, the information of the fixed-wing drone and the processed information of the area to be detected are used as inputs of the UAV-MROC model, and the step of obtaining a solution set of flight paths for executing the current detection task based on the UAV-MROC model may include:
and step one, replacing the area to be detected with the circumscribed rectangle subjected to approximate processing to generate 8 entry points of the area, and numbering the entry points in the clockwise direction from the first point on the right side of the lower left corner of the rectangle. For example, referring to FIG. 5, the location of the entry point # 1 is at the right R side of the lower left vertex of the rectangledThe unmanned aerial vehicle is positioned at the position of the (sensor detection radius) and on the bottom edge, and the course angle of the unmanned aerial vehicle at the 1# entry point is perpendicular to the bottom edge and points to the inside of the rectangle; the position of the entry point # 2 is above the lower left vertex of the rectangle Rd(sensor detection radius) and on the left side, the heading angle of the drone at the 2# entry point is perpendicular to the left and points inside the rectangle;
step two, according to the minimum turning radius of the unmanned aerial vehicle, the detection radius of a sensor, the coordinates of 8 entry points of each area and the side length of the edge where the entry points are located, the turning times corresponding to each entry point are calculated, and then the turning radius of the unmanned aerial vehicle during operation in the area is obtained, so that the unmanned aerial vehicle is guaranteed to adopt the shortest path to complete the detection task in the area;
step three, calculating according to the coordinates of each entry point and the corresponding turning times to obtain the corresponding departure point coordinates and the path length of the operation in the area to be detected, wherein 8 entry points correspond to 8 departure points, but the path length is only 2, wherein the internal path lengths of the entry points 1#, 4#, 5#, 8# are the same, and the internal path lengths of the entry points 2#, 3#, 6#, and 7# are the same;
calculating to obtain the flight path length of the unmanned aerial vehicle from each exit point to the entry points of other areas outside the areas according to the coordinates of the exit points corresponding to the entry points;
and step five, taking the sum of the internal path length and the external path length as the distance between two access points, and taking the sum of the lengths as an output result of the UAV-MROC model.
Based on this, the objective function of the UAV-MROC model can be obtained as follows:
Figure GDA0002530248460000081
the constraint conditions of the UAV-MROC model are as follows:
NV＝8×NA+1 (2)
Figure GDA0002530248460000094
Figure GDA0002530248460000091
Figure GDA0002530248460000092
Figure GDA0002530248460000093
wherein N isARepresenting the area A to be detectedkThe number of (2); a. the0Representing a starting point and an end point of the fixed-wing unmanned aerial vehicle, wherein the starting point and the end point are the same point; w is aijIndicating fixed wing drone slave viThe point enters the processed area to be detected and flies to v after completing the detection task according to the parallel scanning strategyjPath length of the point; xijRepresenting fixed-wing drone pair viPoints v and vjAccess situation of point, if Xij1, then fixed wing drone slave viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint, otherwise fixed wing drone is not from viPoint enters the area AkFlying after completing detection task according to parallel scanning strategyTo vjAnd (4) point.
It is understood that, after the flight path solution set is obtained based on the UAV-MROC model, the method provided by the embodiment of the present invention may solve the optimal solution of the UAV-MROC model according to a preset algorithm. The predetermined algorithm for obtaining the optimal solution can be implemented by various methods, and one of the alternative ways is described in detail below.
The invention uses a minimum element iterative algorithm to solve the optimization problem of executing detection tasks on a plurality of areas by one fixed wing unmanned aerial vehicle. The method specifically comprises the following steps of:
step one, establishing all flight path lengths wijTable of values, using (N)V+1)×(NV+1) matrix
Figure GDA0002530248460000095
Storing, wherein the first row of the matrix is starting point A0To all points vj(j＝1,…,NV) Flight path length w of0jThe first column of the matrix being all points vi(i＝1,…,NV) To the end point A0Flight path length w ofi0. Therefore, element D in matrix DmnAnd wijThe correspondence of (a) is as follows: m is i +1, n is j +1, and d is given according to the constraint conditions of the formulas (3) and (6)mmInfinity and
Figure GDA0002530248460000096
Figure GDA0002530248460000097
step two, setting L to be used for storing the path length of the unmanned aerial vehicle, setting the initial value of L to be 0, and simultaneously setting a vector theta to be used for storing the node access sequence of the unmanned aerial vehicle, because the unmanned aerial vehicle starts from a starting point A0Starting, so the first element in θ is 0, i.e., θ ═ 0 };
step three, finding D with the minimum value from the first row of the D matrix1nD is mixing1nThe corresponding node number n-1 is stored in a vector theta, i.e. theta ═{ theta, (n-1) }, mixing d1nStored in D, i.e. L ═ L + D1n；
Step four, finding D with the minimum numerical value from the nth row of the D matrixnx；
Step five, judging dnxWhether the corresponding node number (x-1) is in the vector theta or not, if (x-1) is not in the vector theta, storing (x-1) in the vector theta, namely, theta is equal to { theta, (x-1) }, and simultaneously, d is addednxStored in L, i.e. L ═ L + dnx. If (x-1) is already in θ, d will benxIs modified to infinity, and then the step four is repeated;
and step six, judging whether the ending condition is met, if the warp beam condition is not met, replacing n with x, and repeating the step four. And if the end condition is met, outputting theta and L to obtain an optimal solution, wherein the optimal solution can be used as an approximate optimal scheme for executing detection tasks on a plurality of polygonal areas to be detected by one fixed-wing unmanned aerial vehicle.
It is understood that for every two blocks of the area to be detected, the shortest path (included in A) shown in FIG. 7 can be obtained based on the above methodiInternal flight path of the interior and in AiAnd AjThe outer flight path in between). Further, in the case of multiple areas to be detected as shown in fig. 8, the fixed-wing drone shown in fig. 9 is driven from a based on the above method0The point-departure routes A1, A2, A3 and A4 regions finally return to A0And the shortest path of the point is also the optimal execution scheme of the task.
In a second aspect, an embodiment of the present invention further provides an optimization apparatus for multi-zone probe mission planning of a fixed-wing drone, where when one fixed-wing drone executes multiple probe missions on multiple rectangular areas to be probed, the apparatus includes:
an information obtaining unit 201, configured to obtain information of an area to be detected and information of a fixed-wing drone;
an approximation processing unit 202, configured to perform circumscribed rectangle approximation processing on each polygonal region to be detected to obtain a processed region to be detected;
a model obtaining unit 203, configured to obtain a UAV-MROC model, where the UAV-MROC model includes an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function for enabling the fixed-wing drone to complete the detection task in a shortest path; the constraint condition comprises that each area to be detected is only accessed once;
the model execution unit 204 is configured to take the information of the area to be detected, the information of the fixed-wing drone and the processed information of the area to be detected as input of the UAV-MROC model, and obtain a solution set of a flight path for executing the current detection task based on the UAV-MROC model;
and the model solving unit 205 is configured to optimize a solution set of the flight path output by the UAV-MROC model by using a minimum element iterative algorithm to obtain an optimal solution, and use the optimal solution as a task allocation result of the fixed-wing drone to the plurality of detection regions.
Optionally, the approximation processing unit 202 is further configured to perform the following steps:
acquiring x and y coordinates of each vertex of the area to be detected, and respectively selecting the minimum value x in the x and y coordinates of each vertexminAnd yminMaximum value xmaxAnd ymaxGenerating coordinates of four vertexes of the circumscribed rectangle by combining two by two;
accordingly, the model executing unit 204 is further configured to execute the following steps:
replacing a to-be-detected area with an approximately processed circumscribed rectangle, generating eight entry points of the area, and numbering the eight entry points in a clockwise direction from a first point on the right side of the lower left corner of the rectangle;
step two, calculating to obtain the turning times corresponding to each entry point according to the minimum turning radius of the unmanned aerial vehicle, the detection radius of the sensor, the coordinates of the eight entry points of each area and the side length of the edge where the entry points are located, and further obtaining the turning radius of the unmanned aerial vehicle during operation in the area;
calculating to obtain a corresponding departure point coordinate and an internal path length of the operation in the to-be-detected area according to the coordinate of each entry point and the corresponding turning times;
calculating to obtain the external path length of the unmanned aerial vehicle flying outside the region from each exit point to other entry points of the region to be tested according to the coordinates of the exit points corresponding to each entry point;
and step five, taking the sum of the internal path length and the external path length as the distance between two entry points, and taking the sum of the lengths as an output result of the UAV-MROC model.
Optionally, the objective function of the UAV-MROC model is:
Figure GDA0002530248460000121
the constraint conditions of the UAV-MROC model are as follows:
NV＝8×NA+1 (2)
Figure GDA0002530248460000125
Figure GDA0002530248460000122
Figure GDA0002530248460000123
Figure GDA0002530248460000124
wherein N isARepresenting the area A to be detectedkThe number of (2); a. the0Representing a starting point and an end point of the fixed-wing unmanned aerial vehicle, wherein the starting point and the end point are the same point; w is aijIndicating fixed wing drone slave viThe point enters the processed area to be detected and flies to v after completing the detection task according to the parallel scanning strategyjPath length of the point; xijRepresenting fixed-wing drone pair viPoints v and vjAccess situation of point, if Xij1, then fixed wing drone slave viClick inInto region AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint, otherwise fixed wing drone is not from viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint;
the parallel scanning strategy is that the to-be-detected region external rectangle flies in a mode of being parallel to the long edge or the short edge of the to-be-detected region external rectangle, the to-be-detected region enters the to-be-detected region from a first entry point on the long edge or the short edge in a direction perpendicular to the long edge or the short edge of the to-be-detected region external rectangle, and the distance between the first entry point and the top point of the to-be-detected region is the detection radius of the fixed-wing unmanned aerial vehicle.
Optionally, the model solving unit 205 is further configured to perform the following steps:
step one, establishing all flight path lengths wijTable of values, using (N)V+1)×(NV+1) matrix
Figure GDA0002530248460000126
Storing, wherein the first row of the matrix is starting point A0To all points (j ═ 1, …, NV) Flight path length w of0jThe first column of the matrix being all points vi(i＝1,…,NV) To the end point A0Flight path length w ofi0(ii) a Element D in matrix DmnAnd wijThe correspondence of (a) is as follows: m is i +1, n is j +1, and d is set according to the constraint conditions of the formula (3) and the formula (6)mmInfinity and
Figure GDA0002530248460000131
Figure GDA0002530248460000132
step two, setting L to be used for storing the path length of the unmanned aerial vehicle, setting the initial value of L to be 0, and simultaneously setting a vector theta to be used for storing the node access sequence of the unmanned aerial vehicle, because the unmanned aerial vehicle starts from a starting point A0Starting, so the first element in θ is 0, i.e., θ ═ 0 };
step three, finding D with the minimum value from the first row of the D matrix1nD is mixing1nThe corresponding node number n-1 is stored in a vector theta, i.e. theta ═ theta, (n-1) }, d is stored1nStored in L, i.e. L ═ L + d1n；
Step four, finding D with the minimum numerical value from the nth row of the D matrixnx；
Step five, judging dnxWhether the corresponding node number (x-1) is in the vector theta or not, if (x-1) is not in the vector theta, storing (x-1) in the vector theta, namely, theta is equal to { theta, (x-1) }, and simultaneously, d is addednxStored in L, i.e. L ═ L + dnx(ii) a If (x-1) is already in θ, d will benxIs modified to infinity, and then the step four is repeated;
step six, judging whether an ending condition is met, if the ending condition is not met, replacing n with x, and repeating the step four; and if the end condition is met, outputting theta and L to obtain the optimal solution.
Optionally, the fixed-wing drone executes the detection task in a preset combined optimization flight mode, where the preset combined optimization flight mode includes a parallel scanning flight mode inside the region to be detected and a Dubins path flight mode between the regions to be detected;
the flight mode of the parallel scanning is as follows: entering a region to be detected from a first entry point on a first edge in a direction perpendicular to the first edge of a circumscribed rectangle of the region to be detected, wherein the distance between the first entry point and the vertex of the nearest region to be detected is the scanning radius of the unmanned aerial vehicle, the first edge is any edge of the region to be detected, and the first entry point is any entry point of the region to be detected; when the turning is needed, the turning flight is carried out in a mode of ensuring the shortest path in the area;
the Dubins path flight mode is as follows: the minimum turning radius based constraint of the drone flies in a combination of curved turning and straight travel.
Since the optimization device for multi-zone probe mission planning of a fixed-wing drone described in this embodiment is a device capable of executing the optimization method for multi-zone probe mission planning of a fixed-wing drone in the embodiment of the present invention, based on the optimization method for multi-zone probe mission planning of a fixed-wing drone described in the embodiment of the present invention, a person skilled in the art can understand the specific implementation manner and various variations of the optimization device for multi-zone probe mission planning of a fixed-wing drone in this embodiment, and therefore, how to implement the optimization method for multi-zone probe mission planning of a fixed-wing drone in the embodiment of the present invention by the optimization device for multi-zone probe mission planning of a fixed-wing drone is not described in detail here. As long as those skilled in the art implement the device adopted by the method for optimizing multi-zone probe mission planning of a fixed-wing drone in the embodiment of the present invention, the scope of the present application is intended to be protected.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Some component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components of a gateway, proxy server, system according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.

Claims (6)

1. A method for optimizing multi-zone detection task planning of a fixed-wing unmanned aerial vehicle is characterized in that when one fixed-wing unmanned aerial vehicle executes multiple detection tasks on a plurality of polygonal areas to be detected, the method comprises the following steps:
acquiring information of a region to be detected and information of a fixed-wing unmanned aerial vehicle;
performing circumscribed rectangle approximation processing on each polygonal region to be detected by adopting a circumscribed rectangle approximation calculation method to obtain a processed region to be detected;
acquiring a UAV-MROC model, wherein the UAV-MROC model comprises an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function enabling the fixed wing unmanned aerial vehicle to complete the detection task in the shortest path; the constraint condition comprises that each processed region to be detected is only accessed once;
taking the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected as the input of the UAV-MROC model, and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model;
optimizing a solution set of the flight path output by the UAV-MROC model by adopting a minimum element iterative algorithm to obtain an optimal solution, and taking the optimal solution as a task allocation result of the fixed wing unmanned aerial vehicle to a plurality of detection areas;
the UAV-MROC model objective function is:
Figure FDA0002530248450000011
the constraint conditions of the UAV-MROC model are as follows:
NV＝8×NA+1 (2)
Figure FDA0002530248450000012
Figure FDA0002530248450000013
Figure FDA0002530248450000014
Figure FDA0002530248450000015
wherein D isijIndicating that the fixed-wing drone follows a preset path from viVertex to vjThe flight path length of the vertex, i, j is the number of the vertex; n is a radical ofVRepresenting the number of all vertexes; n is a radical ofARepresenting the area A to be detectedkK is the number of the area to be detected; a. the0Representing a starting point and an end point of the fixed-wing unmanned aerial vehicle, wherein the starting point and the end point are the same point; w is aijIndicating fixed wing drone slave viThe point enters the processed area to be detected and flies to v after completing the detection task according to the parallel scanning strategyjPath length of the point; xijRepresenting that the fixed-wing drone is paired with two connectable points v satisfying the constraint of formula (3)iPoints v and vjAccess situation of point, if Xij1, then fixed wing drone slave viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint, otherwise fixed wing drone is not from viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to viPoint;
the parallel scanning strategy is that the to-be-detected area is flown in a mode of being parallel to the long edge or the short edge of the to-be-detected area external rectangle inside the to-be-detected area external rectangle, and enters the to-be-detected area from a first entry point on the long edge or the short edge in a direction perpendicular to the long edge or the short edge of the to-be-detected area external rectangle, and the distance between the first entry point and the vertex of the to-be-detected area is the detection radius of the fixed-wing unmanned aerial vehicle;
the solving of the UAV-MROC model by adopting the minimum element iterative algorithm to obtain an optimal solution comprises the following steps:
step one, establishing all flight path lengths wijTable of values, using (N)V+1)×(NV+1) matrix
Figure FDA0002530248450000021
Storing, wherein the first row of the matrix is starting point A0To all points vj(j＝1,…,NV) Flight path length w of0jThe first column of the matrix being all points vi(i＝1,…,NV) To the end point A0Flight path length w ofi0(ii) a Element D in matrix DmnAnd wijThe correspondence of (a) is as follows: m is i +1, n is j +1, and d is set according to the constraint conditions of the formula (3) and the formula (6)mmInfinity and
Figure FDA0002530248450000022
Figure FDA0002530248450000023
step two, setting L to be used for storing the path length of the unmanned aerial vehicle, setting the initial value of L to be 0, and simultaneously setting a vector theta to be used for storing the node access sequence of the unmanned aerial vehicle, because the unmanned aerial vehicle starts from a starting point A0Starting, so the first element in θ is 0, i.e., θ ═ 0 };
step three, finding D with the minimum value from the first row of the D matrix1nD is mixing1nThe corresponding node number n-1 is stored in a vector theta, i.e. theta ═ theta, (n-1) }, d is stored1nStored in L, i.e. L ═ L + d1nJudgment of NAWhether the current value is equal to 1 or not, if so, turning to the step six, otherwise, turning to the step four;
step four, finding D with the minimum numerical value from the nth row of the D matrixnx；
Step five, judging dnxWhether the corresponding node number (x-1) is in the vector theta or not, if (x-1) is not in the vector theta, storing (x-1) in the vector theta, namely, theta is equal to { theta, (x-1) }, and simultaneously, d is addednxStored in L, i.e. L ═ L + dnx(ii) a If (x-1) is already in θ, d will benxIs modified to infinity, and then the step four is repeated;
step six, judging whether an ending condition is met, namely the number of elements in theta is equal to NA+1, if the ending condition is not met, replacing n with x, and repeating the step four; and if the end condition is met, outputting theta and L to obtain the optimal solution.
2. The method of claim 1, wherein the circumscribed rectangle approximation calculation method comprises:
acquiring x and y coordinates of each vertex of the area to be detected, and respectively selecting the minimum value x in the x and y coordinates of each vertexminAnd yminMaximum value xmaxAnd ymaxGenerating coordinates of four vertexes of the circumscribed rectangle by combining two by two;
correspondingly, the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected are used as the input of the UAV-MROC model, and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model comprises:
replacing a to-be-detected area with an approximately processed circumscribed rectangle, generating eight entry points of the area, and numbering the eight entry points in a clockwise direction from a first point on the right side of the lower left corner of the rectangle;
step two, calculating to obtain the turning times corresponding to each entry point according to the minimum turning radius of the unmanned aerial vehicle, the detection radius of the sensor, the coordinates of the eight entry points of each area and the side length of the edge where the entry points are located, and further obtaining the turning radius of the unmanned aerial vehicle during operation in the area;
calculating to obtain a corresponding departure point coordinate and an internal path length of the operation in the to-be-detected area according to the coordinate of each entry point and the corresponding turning times;
calculating to obtain the external path length of the unmanned aerial vehicle flying outside the region from each exit point to other entry points of the region to be tested according to the coordinates of the exit points corresponding to each entry point;
and step five, taking the sum of the internal path length and the external path length as the distance between two entry points, and taking the sum of the lengths as an output result of the UAV-MROC model.
3. The method of claim 1, wherein the fixed-wing drone performs the mission in a preset, jointly optimized flight regime comprising a parallel scan flight regime inside the area to be probed and a Dubins path flight regime between the areas to be probed;
the flight mode of the parallel scanning is as follows: entering a region to be detected from a first entry point on a first edge in a direction perpendicular to the first edge of a circumscribed rectangle of the region to be detected, wherein the distance between the first entry point and the vertex of the nearest region to be detected is the scanning radius of the unmanned aerial vehicle, the first edge is any edge of the region to be detected, and the first entry point is any entry point of the region to be detected; when the turning is needed, the turning flight is carried out in a mode of ensuring the shortest path in the area;
the Dubins path flight mode is as follows: the minimum turning radius based constraint of the drone flies in a combination of curved turning and straight travel.
4. The utility model provides an optimizing apparatus of fixed wing unmanned aerial vehicle multizone survey mission planning, its characterized in that treats the survey area and carries out multiple survey task when a fixed wing unmanned aerial vehicle, the device includes:
the information acquisition unit is used for acquiring information of a region to be detected and information of the fixed-wing unmanned aerial vehicle;
the approximation processing unit is used for carrying out circumscribed rectangle approximation processing on each polygonal region to be detected to obtain a processed region to be detected;
the model acquisition unit is used for acquiring a UAV-MROC model, wherein the UAV-MROC model comprises an objective function and a constraint condition, and the objective function of the UAV-MROC model is an objective function which enables the fixed wing unmanned aerial vehicle to complete the detection task in the shortest path; the constraint condition comprises that each area to be detected is only accessed once;
the model execution unit is used for taking the information of the area to be detected, the information of the fixed-wing unmanned aerial vehicle and the processed information of the area to be detected as the input of the UAV-MROC model and obtaining a solution set of a flight path for executing the detection task based on the UAV-MROC model;
the model solving unit is used for optimizing a solution set of the flight path output by the UAV-MROC model by adopting a minimum element iterative algorithm to obtain an optimal solution, and the optimal solution is used as a task allocation result of the fixed wing unmanned aerial vehicle to a plurality of detection areas;
the objective function of the UAV-MROC model is:
Figure FDA0002530248450000051
the constraint conditions of the UAV-MROC model are as follows:
NV＝8×NA+1 (2)
Figure FDA0002530248450000052
Figure FDA0002530248450000053
Figure FDA0002530248450000054
Figure FDA0002530248450000055
wherein N isARepresenting the area A to be detectedkThe number of (2); a. the0Representing a starting point and an end point of the fixed-wing unmanned aerial vehicle, wherein the starting point and the end point are the same point; w is aijIndicating fixed wing drone slave viThe point enters the processed area to be detected and flies to v after completing the detection task according to the parallel scanning strategyjPath length of the point; xijRepresenting fixed-wing drone pair viPoints v and vjAccess situation of point, if Xij1, then fixed wing drone slave viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint, otherwise fixed wing drone is not from viPoint enters the area AkAfter completing the detection task according to the parallel scanning strategy, the flying vehicle flies to vjPoint;
the parallel scanning strategy is that the to-be-detected area is flown in a mode of being parallel to the long edge or the short edge of the to-be-detected area external rectangle inside the to-be-detected area external rectangle, and enters the to-be-detected area from a first entry point on the long edge or the short edge in a direction perpendicular to the long edge or the short edge of the to-be-detected area external rectangle, and the distance between the first entry point and the vertex of the to-be-detected area is the detection radius of the fixed-wing unmanned aerial vehicle;
the model solving unit adopts the line minimum element iterative algorithm to execute the following steps:
step one, establishing all flight path lengths wijTable of values, using (N)V+1)×(NV+1) matrix
Figure FDA0002530248450000056
Storing, wherein the first row of the matrix is starting point A0To all points vj(j＝1,…,NV) Flying roadRadial length w0jThe first column of the matrix being all points vi(i＝1,…,NV) To the end point A0Flight path length w ofi0(ii) a Element D in matrix DmnAnd wijThe correspondence of (a) is as follows: m is i +1, n is j +1, and d is set according to the constraint conditions of the formula (3) and the formula (6)mmInfinity and
Figure FDA0002530248450000061
Figure FDA0002530248450000062
step two, setting L to be used for storing the path length of the unmanned aerial vehicle, setting the initial value of L to be 0, and simultaneously setting a vector theta to be used for storing the node access sequence of the unmanned aerial vehicle, because the unmanned aerial vehicle starts from a starting point A0Starting, so the first element in θ is 0, i.e., θ ═ 0 };
step three, finding D with the minimum value from the first row of the D matrix1nD is mixing1nThe corresponding node number n-1 is stored in a vector theta, i.e. theta ═ theta, (n-1) }, d is stored1nStored in L, i.e. L ═ L + d1n；
Step four, finding D with the minimum numerical value from the nth row of the D matrixnx；
Step five, judging dnxWhether the corresponding node number (x-1) is in the vector theta or not, if (x-1) is not in the vector theta, storing (x-1) in the vector theta, namely, theta is equal to { theta, (x-1) }, and simultaneously, d is addednxStored in L, i.e. L ═ L + dnx(ii) a If (x-1) is already in θ, d will benxIs modified to infinity, and then the step four is repeated;
step six, judging whether an ending condition is met, namely the number of elements in theta is equal to NA+1, if the ending condition is not met, replacing n with x, and repeating the step four; and if the end condition is met, outputting theta and L to obtain the optimal solution.
5. The apparatus of claim 4, wherein the approximation processing unit is further configured to perform the steps of:
acquiring x and y coordinates of each vertex of the area to be detected, and respectively selecting the minimum value x in the x and y coordinates of each vertexminAnd yminMaximum value xmaxAnd ymaxGenerating coordinates of four vertexes of the circumscribed rectangle by combining two by two;
accordingly, the model execution unit is further configured to perform the steps of:
replacing a to-be-detected area with an approximately processed circumscribed rectangle, generating eight entry points of the area, and numbering the eight entry points in a clockwise direction from a first point on the right side of the lower left corner of the rectangle;
step two, calculating to obtain the turning times corresponding to each entry point according to the minimum turning radius of the unmanned aerial vehicle, the detection radius of the sensor, the coordinates of the eight entry points of each area and the side length of the edge where the entry points are located, and further obtaining the turning radius of the unmanned aerial vehicle during operation in the area;
calculating to obtain a corresponding departure point coordinate and an internal path length of the operation in the to-be-detected area according to the coordinate of each entry point and the corresponding turning times;
calculating to obtain the external path length of the unmanned aerial vehicle flying outside the region from each exit point to other entry points of the region to be tested according to the coordinates of the exit points corresponding to each entry point;
and step five, taking the sum of the internal path length and the external path length as the distance between two entry points, and taking the sum of the lengths as an output result of the UAV-MROC model.
6. The apparatus of claim 4, wherein the fixed-wing drone performs the mission in a preset, jointly optimized flight regime comprising a parallel scan flight regime inside the area to be probed and a Dubins path flight regime between the areas to be probed;
the flight mode of the parallel scanning is as follows: entering a region to be detected from a first entry point on a first edge in a direction perpendicular to the first edge of a circumscribed rectangle of the region to be detected, wherein the distance between the first entry point and the vertex of the nearest region to be detected is the scanning radius of the unmanned aerial vehicle, the first edge is any edge of the region to be detected, and the first entry point is any entry point of the region to be detected; when the turning is needed, the turning flight is carried out in a mode of ensuring the shortest path in the area;
the Dubins path flight mode is as follows: the minimum turning radius based constraint of the drone flies in a combination of curved turning and straight travel.
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