CN112214037B - Unmanned aerial vehicle remote sensing networking flight path planning method based on field station - Google Patents

Unmanned aerial vehicle remote sensing networking flight path planning method based on field station Download PDF

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CN112214037B
CN112214037B CN202011047738.5A CN202011047738A CN112214037B CN 112214037 B CN112214037 B CN 112214037B CN 202011047738 A CN202011047738 A CN 202011047738A CN 112214037 B CN112214037 B CN 112214037B
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赵红颖
李芹
程印乾
刘旭林
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Peking University
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Abstract

The invention discloses an unmanned aerial vehicle remote sensing networking flight path planning method based on a field station, which comprises the steps of firstly optimizing a single-machine flight path, searching an optimal flight direction through a minimum span algorithm, and determining an optimal task starting point according to a task starting point selection criterion based on the station; then on the basis of the optimized single-machine flight path, considering the flight path of each unmanned aerial vehicle to and from the station, and using an unmanned aerial vehicle networking task allocation algorithm based on the station, under the condition of ensuring the highest single-machine operation efficiency, fully utilizing the cruising ability of the unmanned aerial vehicle, and performing task allocation by taking the minimum number of operation unmanned aerial vehicles as targets; and finally, obtaining a station-based unmanned aerial vehicle networking track planning result. The invention solves the problem of unmanned aerial vehicle networking remote sensing operation track planning based on the field station during large-area remote sensing operation by two parts of single machine track optimization based on the field station and multi-machine networking task allocation based on the field station.

Description

Unmanned aerial vehicle remote sensing networking flight path planning method based on field station
Technical Field
The invention relates to the technical field of unmanned aerial vehicle track planning, in particular to an unmanned aerial vehicle remote sensing networking track planning method based on a field station.
Background
The unmanned aerial vehicle has the characteristics of quickness, maneuverability, flexibility and the like, is widely applied to remote sensing observation, is mainly applied to the aspects of ecological environment monitoring, natural disaster situation assessment, emergency rescue and the like, and provides technical support for the development of remote sensing major.
At present, the single-machine operation has the problems of low efficiency and low response speed, along with the development of the remote sensing observation technology, high-frequency observation becomes a new direction for ground observation, and the single-machine operation is difficult to meet the high-frequency requirement, so that multi-machine networking observation is needed, and along with the gradual improvement of field station resources of unmanned aerial vehicles, the demand of the method for distributing safe and efficient multi-machine observation tasks is more and more urgent. The existing unmanned aerial vehicle observation method of the field station is mainly applied to military search and is not suitable for remote sensing observation, and in remote sensing operation, no matter single-machine observation or multi-machine networking observation, the existing flight path planning method does not consider the flight path of the unmanned aerial vehicle to and from a take-off and landing field and the influence of the flight path planning method on task distribution.
Aiming at the problems, the method for solving the problems that the single machine operation in the prior art is low in efficiency and slow in response speed, and the influence of the voyage to and from the take-off and landing field on task distribution is neglected in single machine operation and multi-machine networking observation is provided so as to realize the more efficient and convenient application of the unmanned aerial vehicle.
Disclosure of Invention
Aiming at the defects, the technical problem to be solved by the invention is to provide an unmanned aerial vehicle remote sensing networking flight path planning method based on a field station, so as to solve the problems that the single machine operation in the prior art has low efficiency and slow response speed, and the influence of the flight distance from a take-off and landing place on task distribution is ignored during single machine operation and multi-machine networking observation.
The invention provides an unmanned aerial vehicle remote sensing networking flight path planning method based on a field station, which comprises the following specific steps:
step 1, obtaining an optimized operation track of a single unmanned aerial vehicle in an area to be observed according to relevant data of a field station, the unmanned aerial vehicle, a load and the area to be observed;
step 2, inputting relevant information of a single-machine track planning result, wherein the input relevant information comprises shooting point coordinates, the length of each route and the total number of routes;
step 3, planning the networking flight path of the unmanned aerial vehicle according to the single flight path planning result;
and 4, outputting the task information of each airplane, wherein the output task information comprises a task starting point, shooting point coordinates, the number of air routes and a total air route.
Preferably, the step 1 specifically comprises the following steps:
step 1.1, determining the flight direction of the unmanned aerial vehicle according to relevant data of an area to be observed;
step 1.2, planning an operation track of the unmanned aerial vehicle according to the flight direction of the unmanned aerial vehicle, the area to be observed, the surveying and mapping task requirement and relevant parameters of the unmanned aerial vehicle and a load;
and step 1.3, determining a starting point and an end point of a task according to the planned operation track and the position of the field station.
Preferably, the step 3 comprises
Step 3.1, inputting an unmanned aerial vehicle number m, wherein the initial unmanned aerial vehicle number m is 1;
step 3.2, determining a task starting point of the 1 st unmanned aerial vehicle according to a single-machine track planning result;
step 3.3, calculating the total range of the mth unmanned aerial vehicle after finishing the 1 st route by taking the number n of the initial routes as 1
Figure BDA0002708523640000021
If it is
Figure BDA0002708523640000022
The task allocation of the mth aircraft is finished, and the task starting point, the number n of air routes and the total range of the mth aircraft are output
Figure BDA0002708523640000023
Step 3.4, updating the number N of the remaining routes to be N-N, wherein N is the total number of routes, if N is greater than 0, executing step 3.5, otherwise, ending the distribution task;
step 3.5, updating the information of the shooting points of the rest areas to be observed and the length L of each routeiAnd the number m of the unmanned aerial vehicle is m +1, the updated first shooting point of the first route is used as the task starting point of the m +1 th airplane, and then the step 3.3 is executed.
Preferably, in said step 3.3, if
Figure BDA0002708523640000024
And if the number n of the routes is n +1, calculating the total range of the unmanned aerial vehicle after the unmanned aerial vehicle finishes flying the route
Figure BDA0002708523640000025
If it is
Figure BDA0002708523640000026
Then
Figure BDA0002708523640000027
Completing the task distribution of the mth aircraft, and outputting the task starting point, the number n of air routes and the total range of the mth aircraft
Figure BDA0002708523640000028
Otherwise, the above process is repeated until the condition is satisfied
Figure BDA0002708523640000029
Preferably, the step 1.1 comprises the following specific steps:
step 1.1.1, marking each vertex of the region to be observed as follows according to the clockwise direction: upsilon is1、υ2…υa+1Wherein a is the number of vertexes;
step 1.1.2, marking the coordinate of each vertex, namely the vertex upsiloni(i∈[1，a+1]) Has the coordinates of (x)i,yi)；
Step 1.1.3, respectively calculating the upsilon of the divided vertex on the region to be observed according to the sequencei、υi+1The remaining a-2 vertices to the side upsiloniυi+1The distance d of (d) is:
Figure BDA0002708523640000031
step 1.1.4, comparing calculated values of d obtained by each side on the region to be observed, and selecting vertex upsilon corresponding to the required d value and corresponding side upsilonlυl+1V is on the sidelυl+1The direction of the straight line is the flight direction of the unmanned aerial vehicle remote sensing operation.
Preferably, the step 1.2 comprises the following specific steps:
step 1.2.1, calculating the effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the unmanned aerial vehicle and the relevant parameters of the loadinIs composed of
Figure BDA0002708523640000032
Wherein S is the area of the region to be observed, H is the flying height of the unmanned aerial vehicle, mu is the size of the sensor pixel, H is the number of pixels vertical to the flying direction, f is the focal length, P ishIs the lateral overlap ratio;
step 1.2.2, calculating the non-effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the field station and the relevant parameters of the unmanned aerial vehicleoutIs composed of
Figure BDA0002708523640000033
Wherein L ispFor the range of the unmanned aerial vehicle to and from the field station, LcFor averaging each turn, D is the width of the area to be observed, WhIn order to take a picture in a side direction at an interval,
Figure BDA0002708523640000034
the number of turns is;
step 1.2.3, according to the effective operation range LinAnd a non-productive work leg LoutObtaining the operation efficiency of the unmanned aerial vehicle
Figure BDA0002708523640000035
Is composed of
Figure BDA0002708523640000036
Wherein C is the operation range of the area to be observed, and C is a constant;
and step 1.2.4, determining the operation track of the unmanned aerial vehicle according to the operation efficiency of the unmanned aerial vehicle.
Preferably, the step 1.3 includes the following specific steps:
step 1.3.1, marking the head and tail shooting points of the head and tail air routes as P11, P12, P21 and P22 respectively, and recording the distances between the four shooting points and the station as L respectively11、L12、L21And L22；
Step 1.3.2, calculating the sum of the distance between the station and the task starting point and the distance between the station and the task ending point according to the parity of the number of air routes in the planned flight path, and determining the air route going to and from the field station according to the sum of the distances;
and step 1.3.3, determining a task starting point according to the selected route.
Preferably, the step 1.3.2 comprises the following specific steps:
step 1.3.2.1, executing step 1.3.2.2 when judging that the number of the routes is even, and executing step 1.3.2.3 when judging that the number of the routes is odd;
step 1.3.2.2, calculate and compare distance and L, respectively11+L21、L12+L22The magnitude of the value, the two required shot points being determined andthe distances between the corresponding two shooting points and the stations outside the field are respectively recorded as upsilon1、υ2And L1m、L2mStep 1.3.3 is executed;
step 1.3.2.3, calculate and compare distance and L, respectively11+L22、L12+L21Determining the distance between two required shooting points and the distance between the two corresponding shooting points and the off-site station according to the magnitude of the numerical value, and respectively recording the distances as upsilon1、υ2And L1m、L2mStep 1.3.3 is performed.
According to the scheme, the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station provided by the invention comprises the steps of optimizing a single-machine flight path, searching for an optimal flight direction through a minimum span algorithm, and determining an optimal task starting point according to a task starting point selection criterion based on the field station; secondly, on the basis of the optimized single-machine flight path, considering the range of each unmanned aerial vehicle from the station to the station, and using an unmanned aerial vehicle networking task allocation algorithm based on the station, under the condition of ensuring the highest single-machine operation efficiency, fully utilizing the cruising ability of the unmanned aerial vehicle, and performing task allocation by taking the operation unmanned aerial vehicle with the least quantity as a target; and finally, obtaining an unmanned aerial vehicle networking flight path planning result based on the field station. The invention solves the problem of unmanned aerial vehicle networking remote sensing operation track planning based on a field station during large-area remote sensing operation, has obvious effect and is suitable for wide popularization.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a first process block diagram of a remote sensing networking flight path planning method for an unmanned aerial vehicle based on a field station according to an embodiment of the present invention;
fig. 2 is a process block diagram of a unmanned aerial vehicle remote sensing networking track planning method based on a field station according to an embodiment of the present invention;
fig. 3 is a process block diagram of a third method for planning a flight path of an unmanned aerial vehicle remote sensing networking based on a field station according to an embodiment of the present invention;
fig. 4 is a process block diagram of a fourth method for planning a flight path of an unmanned aerial vehicle remote sensing networking based on a field station according to an embodiment of the present invention;
fig. 5 is a process block diagram of a method for planning a flight path of an unmanned aerial vehicle remote sensing networking based on a field station according to an embodiment of the present invention;
fig. 6 is a process block diagram six of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station provided by the embodiment of the invention;
fig. 7 is a first model schematic diagram of step 1.3 of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station shown in fig. 3;
fig. 8 is a model schematic diagram two of step 1.3 of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station shown in fig. 3;
fig. 9 is a third model schematic diagram of step 1.3 of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station shown in fig. 3;
fig. 10 is a fourth model schematic diagram of step 1.3 of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station shown in fig. 3;
fig. 11 is a first simulation result model diagram of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station according to the embodiment of the invention;
fig. 12 is a simulation result model schematic diagram of a remote sensing networking flight path planning method for an unmanned aerial vehicle based on a field station according to an embodiment of the present invention;
fig. 13 is a third simulation result model schematic diagram of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station according to the embodiment of the invention;
fig. 14 is a fourth simulation result model schematic diagram of the unmanned aerial vehicle remote sensing networking flight path planning method based on the field station provided by the embodiment of the 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.
Referring to fig. 1 to fig. 10, a description will now be given of an embodiment of a method for planning a flight path of an unmanned aerial vehicle remote sensing networking based on a field station according to the present invention.
The remote sensing flight path planning of the unmanned aerial vehicle mainly aims at single machine operation in a small area, and the single machine flight path planning method mainly comprises two methods: the sequential flight band method and the interval flight band method are used for planning the single-machine flight path of which the observation area is a complex polygon, so that the applicability of the remote sensing observation of the unmanned aerial vehicle is improved. One of the core problems of the route planning algorithm for multi-machine cooperative networking operation is task allocation, for example, the task allocation considering the performance index of the unmanned aerial vehicle is used for allocating task areas according to the probability that a moving target appears in a certain area, and the allocation of tasks is performed after the areas are decomposed by using a decomposition algorithm.
The unmanned aerial vehicle remote sensing networking flight path planning method based on the field station comprises the following specific steps:
s1, obtaining an optimized operation track of the unmanned aerial vehicle single machine in the area to be observed according to the relevant data of the field station, the unmanned aerial vehicle, the load and the area to be observed;
the specific implementation steps of the step can be as follows:
s1.1, determining the flight direction of the unmanned aerial vehicle according to relevant data of an area to be observed;
the number of turns has been decided to unmanned aerial vehicle's direction of flight, and when the direction of flight was perpendicular with minimum span direction, the number of times that can guarantee to turn was minimum. The determination of the optimal flight direction is the determination of the minimum span of the observation region. Illustratively, the calculation is carried out on a convex polygon area, the width of the convex polygon only exists in the span of the point-edge type, and the flight direction of the unmanned aerial vehicle is determined by adopting the minimum span algorithm of the point-edge type.
The specific implementation steps of the step can be as follows:
s1.1.1, marking each vertex of an area to be observed in a clockwise direction as follows: upsilon is1、υ2…υa+1Wherein a is the number of vertexes;
s1.1.2, labeling the coordinates of each vertex, vi(i∈[1，a+1]) Has the coordinates of (x)i,yi)；
S1.1.3, calculating the division vertex upsilon on the region to be observed respectively according to the sequencei、υi+1The remaining a-2 vertices to the side upsiloniυi+1The distance d of (d) is:
Figure BDA0002708523640000061
s1.1.4, comparing the calculated value of d obtained by each side on the region to be observed, and obtaining the minimum span, namely the vertex upsilon corresponding to the minimum value of d and the corresponding side upsilonlυl+1(l∈[1，a+1]) V is on the sidelυl+1The direction of the straight line is the flight direction of the unmanned aerial vehicle remote sensing operation.
S1.2, planning an operation track with highest operation efficiency of the unmanned aerial vehicle according to the flight direction of the unmanned aerial vehicle, the area to be observed, the surveying and mapping task requirement and relevant parameters of the unmanned aerial vehicle and a load;
divide into effective operation range and non-effective operation range with unmanned aerial vehicle's total range, unmanned aerial vehicle's total range L is: l ═ Lin+LoutWherein L isinFor effective working voyage, LoutIs an ineffective operation voyage.
The effective operation course refers to a straight course when the unmanned aerial vehicle executes a remote sensing task and is constrained by the overlapping rate and the ground resolution (flight height).
The non-effective operation range refers to the total turning range of the unmanned aerial vehicle during operation, and the sum of the ranges from the field station to the task starting point and from the task end point to the field station, is also called the range of the unmanned aerial vehicle from and to the station, and is constrained by the turning range and the range of the unmanned aerial vehicle from and to the field station.
The specific implementation steps of the step can be as follows:
s1.2.1, calculating the effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the unmanned aerial vehicle and the relevant parameters of the loadinIs composed of
Figure BDA0002708523640000071
Wherein S is the area of the region to be observed, H is the flying height of the unmanned aerial vehicle, mu is the size of the sensor pixel, H is the number of pixels vertical to the flying direction, f is the focal length, P ishIs the lateral overlap ratio;
s1.2.2, calculating the non-effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the field station and the relevant parameters of the unmanned aerial vehicleoutIs composed of
Figure BDA0002708523640000072
Wherein L ispFor the range of the unmanned aerial vehicle to and from the field station, LcFor averaging each turn, D is the width of the area to be observed, WhIn order to take a picture in a side direction at an interval,
Figure BDA0002708523640000073
the number of turns is;
two parallel lines far enough apart are made on the plane, the parallel lines stop immediately when gradually approaching to the center of the plane and intersecting with the convex polygon, the distance between the two parallel lines is the span of the polygon, and the minimum value in all the spans is the width of the convex polygon.
S1.2.3 according to effective working range LinAnd a non-productive work leg LoutObtaining the operation efficiency of the unmanned aerial vehicle
Figure BDA0002708523640000074
Is composed of
Figure BDA0002708523640000075
Wherein C is the operation range of the area to be observed, and C is a constant;
s1.2.4 selecting the operating efficiency of the unmanned aerial vehicle according to the operating efficiency of the unmanned aerial vehicle
Figure BDA0002708523640000076
The highest flight path.
According to
Figure BDA0002708523640000077
Can know when
Figure BDA0002708523640000078
When the minimum value is taken, the minimum value is obtained,
Figure BDA0002708523640000079
maximum, i.e. when the non-effective operation range is shortest, the operation efficiency of the unmanned aerial vehicle is highest, wherein LcCan be regarded as constant, i.e. by finding the minimum number of turns and LpTo judge the working efficiency of the unmanned aerial vehicle
Figure BDA00027085236400000710
And S1.3, determining a starting point and an end point of a task according to the planned operation track and the position of the field station.
The position relationship between the field station and the observation area mainly comprises the following two types: the unmanned aerial vehicle station is located inside the observation region and is located outside the observation region. Voyage L of unmanned aerial vehicle to and from field stationpIs composed of
Figure BDA0002708523640000081
Wherein
Figure BDA0002708523640000082
For the voyage of the field station to the starting point of the task,
Figure BDA0002708523640000083
and returning the distance of the field station for the task terminal. According to the change of the performance of the unmanned aerial vehicle along with the operation time, performing the taskWhen the service starting point is selected, a point far away from the station is selected as the task starting point, and when the energy of the unmanned aerial vehicle is less, the unmanned aerial vehicle is in a range near to the station, so that the occurrence of accidents is reduced. And analyzing according to the position relation between the station and the area to be observed to determine the starting point and the end point of the task, wherein the distance is calculated according to the Euclidean distance. The specific position relationship can be divided into that the field station is positioned outside the area to be observed and the field station is positioned inside the area to be observed.
When the station is located outside the observation area, the relationship between the two can be divided into two types: the stations are located in an orientation parallel to the flight direction and the stations are located in an orientation perpendicular to the flight direction. It is analytically known that the determination of the task starting point and ending point is related to the parity of the number of routes, and the following two principles need to be followed: 1) the distance sum of the station and the task starting point and the task ending point is minimum; 2) and on the premise of meeting 1), a point far away from the station is a task starting point.
The specific implementation steps of the step can be as follows:
s1.3.1, marking head and tail shooting points of the head and tail routes as P11, P12, P21 and P22 respectively, and marking the distances between the four shooting points and the station as L respectively11、L12、L21And L22；
S1.3.2, calculating the distance sum of the station and the task starting point and the task ending point according to the parity of the number of routes in the planned flight path and the Euclidean distance calculation method, and selecting the route with the minimum distance sum;
the specific implementation steps of the step can be as follows:
s1.3.2.1, executing S1.3.2.2 when the number of the routes is judged to be even, and executing S1.3.2.3 when the number of the routes is judged to be odd;
s1.3.2.2, calculating and comparing distance and L respectively11+L21、L12+L22The values of the values, the distances of the values, the two smaller shooting points and the distances between the two corresponding shooting points and the off-site station are respectively recorded as upsilon1、υ2And L1m、L2mExecution S1.3.3;
s1.3.2.3, calculating and comparing distance and L respectively11+L22、L12+L21The values of the values, the distances of the values, the two smaller shooting points and the distances between the two corresponding shooting points and the off-site station are respectively recorded as upsilon1、υ2And L1m、L2mAnd S1.3.3 is executed.
S1.3.3, according to the selected route, selecting a point far away from the station as a task starting point.
The method comprises comparing the distance L between two shooting points and the outdoor station1mAnd L2mThe shooting point with long distance to the outdoor station is the starting point of the task, and the other shooting point is the end point of the task. If is L1m<L2mThen v is2Is the starting point of the task; if L is1m>L2mThen v is1Is the starting point of the task.
When the station is located inside an observation area, the relationship between the two is a simple containment relationship. The determination of the task starting point and the task ending point is related to the parity of the number of the flight paths, and the principle and the specific operation which need to be met are the same as those of the station which is positioned outside the observation area, so that the detailed description is omitted.
S2, inputting relevant information of a single-machine track planning result, wherein the input relevant information comprises shooting point coordinates coordinate (the coordinate refers to an array name for storing all shooting point coordinates), the length Li of each route and the total number N of routes;
the networking observation mode researched by the invention is single-point multitask, namely the same performance platform and load execute the same task, so that all platforms and load parameters and task information are considered to be the same. Recording the maximum range of the unmanned aerial vehicle at the optimal operation speed as L, and recording the length of the ith route as LiAnd average turn course per turn is recorded as LcThe stability of the unmanned aerial vehicle can be worsened when fuel and battery power are insufficient, the safety of the aircraft is affected, and therefore a safe range L is set for each aircraftsFrom this, it can be seen that:
Figure BDA0002708523640000091
wherein L isINFor unmanned aerial vehicle to operate in observation areaVoyage of, LcFor the course of the turn, n is the number of routes, LmFor the total voyage of the mth unmanned aerial vehicle, in order to ensure that the unmanned aerial vehicle can safely return, the remaining voyage must be ensured not to be less than the safe voyage Ls。
S3, calculating the unmanned aerial vehicle networking track by applying a multi-machine networking task allocation algorithm according to the single-machine track planning result;
the specific implementation steps of the step can be as follows:
s3.1, inputting an unmanned aerial vehicle number m, wherein the initial unmanned aerial vehicle number m is 1;
s3.2, determining a task starting point of the 1 st unmanned aerial vehicle according to a single-machine track planning result;
specifically, according to the single-machine track planning result, head and tail shooting points of the head and tail air routes are respectively marked as P11, P12, P21 and P22, and the distances between the four shooting points and the station are respectively marked as L11、L12、L21And L22The distance is calculated by Euclidean distance, and L is compared11、L12、L21And L22And selecting the shooting point corresponding to the shortest distance as the task starting point spt of the 1 st unmanned aerial vehicle.
S3.3, calculating the total range of the mth unmanned aerial vehicle after finishing the 1 st route when the number n of the initial routes is 1
Figure BDA0002708523640000092
If it is
Figure BDA0002708523640000093
The task allocation of the mth aircraft is finished, and the task starting point, the number n of air routes and the total range of the mth aircraft are output
Figure BDA0002708523640000094
In S3.3, if
Figure BDA0002708523640000101
And if the number n of the routes is n +1, calculating the total range of the unmanned aerial vehicle after the unmanned aerial vehicle finishes flying the route
Figure BDA0002708523640000102
If it is
Figure BDA0002708523640000103
Then
Figure BDA0002708523640000104
Completing the task distribution of the mth aircraft, and outputting the task starting point, the number n of air routes and the total range of the mth aircraft
Figure BDA0002708523640000105
Otherwise, the above process is repeated until the condition is satisfied
Figure BDA0002708523640000106
S3.4, updating the number N of the remaining routes to be N-N, wherein N is the total number of the routes, if N is greater than 0, executing the step 3.5, otherwise, ending the distribution task;
s3.5, updating information coordinate of the shooting points of the rest area to be observed and the length L of each routeiAnd taking the updated first shooting point of the first route in the remaining region to be observed as a task starting point of the m +1 th airplane, and then executing S3.3.
And S4, outputting task information of each airplane, wherein the output task information comprises shooting point coordinates, the number n of routes, a task starting point and a total voyage L.
The multi-machine networking task allocation algorithm considers the voyage of each unmanned aerial vehicle going to and from the station, makes full use of the cruising ability of the unmanned aerial vehicles, is different from the voyage average allocation strategy, makes full use of the cruising ability and the operation ability of each unmanned aerial vehicle, reduces the number of the unmanned aerial vehicles for networking operation, and improves the overall working benefit of the networking operation of multiple unmanned aerial vehicles. In addition, in order to avoid the danger of collision of unmanned aerial vehicles, the tasks are sequentially distributed according to the sequence of air routes when the tasks are distributed, and the obstacle avoidance problem of the unmanned aerial vehicles is effectively solved under the condition that the air routes among different unmanned aerial vehicles are not in cross distribution. And simultaneously, selecting a task starting point spt in the same direction during task allocation, and finally obtaining an optimized unmanned aerial vehicle networking track planning result based on the station.
The unmanned aerial vehicle remote sensing networking flight path planning method based on the field station is based on the optimized single flight path, according to an unmanned aerial vehicle networking task allocation algorithm based on the field station, the flight distance of each unmanned aerial vehicle from and to the station is considered, task allocation is carried out by taking the minimum number of unmanned aerial vehicles as a target, when large-area remote sensing operation is carried out, the problem of unmanned aerial vehicle networking remote sensing operation flight path planning based on the field station is solved, networking operation of each unmanned aerial vehicle in the optimal flight direction and the optimal task starting point is guaranteed, the cruising ability of the unmanned aerial vehicle is fully utilized, and therefore the efficiency of networking operation is improved. The method comprises the steps of single machine track optimization based on a field station and multi-machine networking task allocation based on the field station, and under the condition that the single machine operation efficiency is highest, the task allocation of multi-machine networking observation is carried out according to the position of the field station and the cruising ability of the unmanned aerial vehicle by taking the minimum number of the unmanned aerial vehicles as a target.
For example, referring to fig. 11 to 14, the simulation test is performed by using the method, specifically, the steps are recorded clockwise for each vertex of the region to be observed: { upsilon1，υ2，υ3，υ4，υ5And the corresponding coordinates of each vertex are respectively: s { (14.5,2), (5.6,17), (21,29), (30,20), (21,5.3) } × 102According to the minimum width algorithm, obtaining the vertex and the side which correspond to the minimum span of the observation region S and are respectively upsilon1And upsilon4υ5And determining the flight direction as a side upsilon4υ5In the direction of the beam.
And in the simulation process, the flight path planning is carried out in a scanning line mode. Firstly, coordinate system conversion is carried out to obtain a side upsilon4υ5The direction of the X-axis is converted; under the coordinate system, determining the positions of all routes, namely the y coordinates of shooting points according to the sideward shooting intervals; then, according to the intersection point of the upper and lower boundaries of each navigation band and the polygon and the vertex of the polygon, the minimum external connection of each navigation band is determinedThe minimum external rectangle can completely cover the area of each flight band; determining the x coordinate of the first shooting point of each route according to the minimum circumscribed rectangle, namely determining the position of the first shooting point of each route; then sequentially determining the x coordinates of the other shooting points of each route except the head and the tail of the two shooting points according to the course shooting interval; determining the x coordinate of the shooting point at the tail end of each route according to the minimum external rectangle of each route, namely determining the coordinates of all the shooting points in the observation area, wherein the shooting points completely cover the observation area and have no redundant shooting points; and finally, converting the coordinate system.
In the simulation example, the sidewise photographing interval Wh300m, heading shot interval WlSetting the safe range of the unmanned aerial vehicle as 10% of the total range as 200m, changing the position of the field station and the maximum endurance range of the unmanned aerial vehicle to carry out unmanned aerial vehicle networking task allocation, and obtaining a networking track planning result.
According to the simulation example, the method is applied to planning the flight path, and the shortest operation flight path is obtained by selecting the optimal flight direction and the optimal task starting point to optimize the single-machine flight path, so that the operation efficiency is improved; on the basis of the optimized single-machine track, the task allocation of multi-machine networking remote sensing observation is carried out by taking the minimum number of unmanned aerial vehicles as a target according to the position of a station and the cruising ability of the unmanned aerial vehicles, and the unmanned aerial vehicle networking remote sensing observation track planning result based on the field station is obtained.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. Details which are not described in detail in the embodiments of the invention belong to the prior art which is known to the person skilled in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (6)

1. An unmanned aerial vehicle remote sensing networking flight path planning method based on a field station is characterized by comprising the following specific steps:
step 1, obtaining an optimized operation track of a single unmanned aerial vehicle in an area to be observed according to relevant data of a field station, the unmanned aerial vehicle, a load and the area to be observed;
step 2, inputting relevant information of a single-machine track planning result, wherein the input relevant information comprises shooting point coordinates, the length of each route and the total number of routes;
step 3, planning the networking flight path of the unmanned aerial vehicle according to the single flight path planning result;
step 4, outputting task information of each airplane, wherein the output task information comprises a task starting point, shooting point coordinates, the number of air routes and a total air route;
the step 1 specifically comprises the following steps:
step 1.1, determining the flight direction of the unmanned aerial vehicle according to relevant data of an area to be observed;
step 1.2, planning an operation track of the unmanned aerial vehicle according to the flight direction of the unmanned aerial vehicle, the area to be observed, the surveying and mapping task requirement and relevant parameters of the unmanned aerial vehicle and a load;
step 1.3, determining a starting point and an end point of a task according to the planned operation track and the position of a field station;
the step 3 specifically comprises the following steps:
step 3.1, inputting an unmanned aerial vehicle number m, wherein the initial unmanned aerial vehicle number m is 1;
step 3.2, determining a task starting point of the 1 st unmanned aerial vehicle according to a single-machine track planning result;
step 3.3, calculating the total range of the mth unmanned aerial vehicle after finishing the 1 st route by taking the number n of the initial routes as 1
Figure FDA0003118968060000011
If it is
Figure FDA0003118968060000012
The task allocation of the mth aircraft is finished, and the task starting point, the number n of air routes and the total range of the mth aircraft are output
Figure FDA0003118968060000013
Wherein L iscTo average the course of each turn,
Figure FDA0003118968060000014
for the voyage of the field station to the starting point of the task,
Figure FDA0003118968060000015
returning the distance of the field station for the task end point;
step 3.4, updating the number N of the remaining routes to be N-N, wherein N is the total number of routes, if N is greater than 0, executing step 3.5, otherwise, ending the distribution task;
step 3.5, updating the information of the shooting points of the rest areas to be observed and the length L of each routeiAnd the number m of the unmanned aerial vehicle is m +1, the updated first shooting point of the first route is used as the task starting point of the m +1 th airplane, and then the step 3.3 is executed.
2. The method for planning unmanned aerial vehicle remote sensing networking track based on field station as claimed in claim 1, wherein in step 3.3, if yes, the method comprises
Figure FDA0003118968060000021
And if the number n of the routes is n +1, calculating the total range of the unmanned aerial vehicle after the unmanned aerial vehicle finishes flying the route
Figure FDA0003118968060000022
If it is
Figure FDA0003118968060000023
Then
Figure FDA0003118968060000024
n is n-1, completing the task distribution of the mth aircraft, and outputting the task starting point, the number n of routes and the total range of the mth aircraft
Figure FDA0003118968060000025
Otherwise, the above process is repeated until the condition is satisfied
Figure FDA0003118968060000026
3. The unmanned aerial vehicle remote sensing networking track planning method based on the field station as claimed in claim 2, wherein the step 1.1 comprises the following steps:
step 1.1.1, marking each vertex of the region to be observed as follows according to the clockwise direction: upsilon is1、υ2…υa+1Wherein a is the number of vertexes;
step 1.1.2, marking the coordinate of each vertex, namely the vertex upsiloni(i∈[1，a+1]) Has the coordinates of (x)i,yi)；
Step 1.1.3, respectively calculating the upsilon of the divided vertex on the region to be observed according to the sequencei、υi+1The remaining a-2 vertices to the side upsiloniυi+1The distance d of (d) is:
Figure FDA0003118968060000027
step 1.1.4, comparing calculated values of d obtained by each side on the region to be observed, and selecting vertex upsilon corresponding to the required d value and corresponding side upsilonlυl+1V is on the sidelυl+1The direction of the straight line is the flight direction of the unmanned aerial vehicle remote sensing operation.
4. The unmanned aerial vehicle remote sensing networking track planning method based on the field station as claimed in claim 2, wherein the step 1.2 comprises the following steps:
step 1.2.1, calculating the effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the unmanned aerial vehicle and the relevant parameters of the loadinIs composed of
Figure FDA0003118968060000028
Wherein S is the area of the region to be observed, H is the flying height of the unmanned aerial vehicle, mu is the size of the sensor pixel, H is the number of pixels vertical to the flying direction, f is the focal length, P ishIs the lateral overlap ratio;
step 1.2.2, calculating the non-effective operation range L of the unmanned aerial vehicle in the area to be observed according to the area to be observed, the field station and the relevant parameters of the unmanned aerial vehicleoutIs composed of
Figure FDA0003118968060000029
Wherein L ispFor the range of the unmanned aerial vehicle to and from the field station, LcFor averaging each turn, D is the width of the area to be observed, WhIn order to take a picture in a side direction at an interval,
Figure FDA00031189680600000210
the number of turns is;
step 1.2.3, according to the effective operation range LinAnd a non-productive work leg LoutObtaining the operation efficiency of the unmanned aerial vehicle
Figure FDA0003118968060000031
Is composed of
Figure FDA0003118968060000032
Wherein C is the operation range of the area to be observed, and C is a constant;
and step 1.2.4, determining the operation track of the unmanned aerial vehicle according to the operation efficiency of the unmanned aerial vehicle.
5. The unmanned aerial vehicle remote sensing networking track planning method based on the field station as claimed in claim 2, wherein the step 1.3 comprises the following steps:
step 1.3.1, marking the head and tail shooting points of the head and tail air routes as P11, P12, P21 and P22 respectively, and recording the distances between the four shooting points and the station as L respectively11、L12、L21And L22；
Step 1.3.2, calculating the sum of the distance between the station and the task starting point and the distance between the station and the task ending point according to the parity of the number of air routes in the planned flight path, and determining the air route going to and from the field station according to the sum of the distances;
and step 1.3.3, determining a task starting point according to the selected route.
6. The unmanned aerial vehicle remote sensing networking track planning method based on the field station as claimed in claim 5, wherein the step 1.3.2 comprises the following steps:
step 1.3.2.1, executing step 1.3.2.2 when judging that the number of the routes is even, and executing step 1.3.2.3 when judging that the number of the routes is odd;
step 1.3.2.2, calculate and compare distance and L, respectively11+L21、L12+L22Determining the distance between two required shooting points and the distance between the two corresponding shooting points and the off-site station according to the magnitude of the numerical value, and respectively recording the distances as upsilon1、υ2And L1m、L2mStep 1.3.3 is executed;
step 1.3.2.3, calculate and compare distance and L, respectively11+L22、L12+L21Determining the distance between two required shooting points and the distance between the two corresponding shooting points and the off-site station according to the magnitude of the numerical value, and respectively recording the distances as upsilon1、υ2And L1m、L2mStep 1.3.3 is performed.
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