CN117606489A - Unmanned aerial vehicle flight survey method, unmanned aerial vehicle flight survey equipment and unmanned aerial vehicle flight survey medium - Google Patents
Unmanned aerial vehicle flight survey method, unmanned aerial vehicle flight survey equipment and unmanned aerial vehicle flight survey medium Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle flight survey method, unmanned aerial vehicle flight survey equipment and a medium, wherein the unmanned aerial vehicle flight survey method comprises the following steps: obtaining basic environment data and parameter data of the unmanned aerial vehicle; establishing communication between the unmanned aerial vehicle and an edge computing platform, and carrying out global initial path planning; analyzing and setting local environment factors, and carrying out local division on global path planning; carrying out local path planning to obtain a local survey area flight scheme; carrying out multistage fuzzy comprehensive evaluation on the flight scheme of the local survey area: selecting a satisfactory flight plan as an optimal flight plan for the region; after the optimal flight scheme is obtained, the unmanned aerial vehicle starts to execute local survey work, when the unmanned aerial vehicle reaches the next survey point, if the next survey point is a global survey end point, the unmanned aerial vehicle drops, and the survey work is finished; if the next survey point is not the global survey end point, entering the path planning of the next region, and flying along the planned flying scheme until the global survey end point is reached.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle survey path planning, in particular to an unmanned aerial vehicle flight survey method, corresponding electronic equipment and a computer readable storage medium.
Background
Part of the agricultural production in China is in mountain areas, but in the agricultural production process, the agricultural production is often required to be surveyed. However, the traditional surveying method is generally manual monitoring or uses some simple measuring tools, has a certain limitation on areas with complex terrains, requires a great deal of labor cost, and has the problems of slow surveying, inaccurate surveying, dangerous surveying and the like. Compared with the traditional surveying method, the unmanned aerial vehicle is used for surveying, so that the surveying efficiency can be greatly improved, the surveying range is enlarged, and the surveying risk is reduced. With the increasing demand for high frequency surveys for large-scale agriculture, the advantages of unmanned aerial vehicle surveys are becoming more and more apparent.
The existing unmanned aerial vehicle investigation path planning mode has the following two problems:
1. the influence of the communication intensity on unmanned plane survey is not considered and analyzed, for example, the 5G communication intensity can influence the transmission of real-time data and survey images. No consideration or analysis of the effect of static obstructions is made, such as the height of the static obstructions and the minimum radius of the circumcircle determining the path smoothness. The unmanned aerial vehicle emergency is not considered and analyzed, for example, the distance between the unmanned aerial vehicle and the emergency landing point can influence whether the unmanned aerial vehicle can better go to the emergency landing point for avoiding danger in case of emergency.
2. The flight scheme of the unmanned aerial vehicle survey path is not subjected to multistage evaluation and analysis, namely the safety degree and the track complexity in the aspect of influencing the flight scheme are not subjected to the first-stage evaluation and analysis. The factors affecting the safety degree and the track complexity are not subjected to secondary evaluation and analysis, and whether the flight scheme meets the actual requirements cannot be evaluated more objectively and comprehensively.
Disclosure of Invention
An object of the present application is to solve the above-mentioned problems and provide a unmanned aerial vehicle flight survey method, corresponding electronic equipment and computer-readable storage medium.
The technical scheme for solving the technical problems is as follows:
a method of unmanned aerial vehicle flight survey comprising the steps of:
s1, acquiring basic environment data and parameter data of an unmanned aerial vehicle;
s2, establishing communication between the unmanned aerial vehicle and an edge computing platform, and performing global initial path planning;
s3, analyzing and setting local environment factors, and locally dividing the global path planning;
s4, planning a local path to obtain a flight scheme of the local survey area;
s5, carrying out multi-level fuzzy comprehensive evaluation on the flight scheme of the local survey area, and selecting the flight scheme meeting the requirements as the optimal flight scheme of the local survey area;
S6, after the optimal flight scheme is obtained, the unmanned aerial vehicle starts to execute local survey work, and in the flight process, the unmanned aerial vehicle dynamically keeps away the obstacle until the unmanned aerial vehicle reaches the next survey point; if the next survey point is the global survey end point, the unmanned aerial vehicle drops to finish the survey work; if the next survey point is not the global survey end point, the process jumps back to step S4, and the path planning for the next area is entered, and steps S5 and S6 are repeated.
Preferably, in step S1, map data of a survey area and airspace data in a specific height above the map are obtained in advance, and actual operation parameters of the unmanned aerial vehicle are set according to basic environment data; wherein,
the basic environment data acquisition method comprises the following steps:
measuring the intensity of 5G signals and GPS signals in the space above the survey area, and dividing the intensity level and level attenuation range of the 5G signals and the GPS signals in the map; meanwhile, if a static obstacle exists in the map, the height and the minimum circumcircle radius of the static obstacle are required to be obtained;
the method for setting the actual operation parameters of the unmanned aerial vehicle comprises the following steps:
setting the highest safe flight altitude H of the unmanned aerial vehicle according to the basic environment data fmax And minimum safe flying height H fmin The method comprises the steps of carrying out a first treatment on the surface of the Setting the minimum inertial movement distance L according to the actual performance parameter condition of the unmanned aerial vehicle min The method comprises the steps of carrying out a first treatment on the surface of the Setting minimum turning radius r according to actual parameters of unmanned aerial vehicle min The steering radius r is required to be larger than r during the turning process min The method comprises the steps of carrying out a first treatment on the surface of the Setting a global maximum range S according to unmanned aerial vehicle parameters and battery endurance parameters gmax 。
Preferably, in step S2, coordinates of each point to be surveyed in the surveyed area are taken, and under the constraint of the basic environment data obtained in step S1 and the parameter data of the unmanned aerial vehicle, global optimal path planning is performed on coordinates of a central point of the surveyed area, so as to determine the surveying sequence of each surveying point; transmitting real-time data to an edge computing platform in the unmanned aerial vehicle surveying process; meanwhile, the unmanned aerial vehicle needs to conduct real-time image transmission between 5G communication and a survey center in the survey process.
Preferably, in step S3, the step of locally dividing the global path plan is:
the current survey point P i As a starting point, the next survey point P i+1 As the end point, the area between the start point and the end point is set as a local survey area B i Where i denotes the number of the first survey area, i=1, 2, … …, n.
Preferably, in step S3, the step of setting the local environmental factor is:
A1, setting the flight distance of each section of unmanned plane in the local survey area before changing the flight attitude as S ip Each flight distance S ip Requiring greater than minimum inertial movement of the unmanned aerial vehicleDistance of movement L min ；
A2, setting the 5G signal intensity of the p-th flight path in the ith exploration area as G ip Wherein 0 < G ip ＜1，G ip The larger the value, the stronger the 5G signal strength is represented;
a3, setting the GPS signal strength as F ip Wherein 0 < F ip ＜1，F ip The larger the value, the stronger the representative GPS signal strength;
a4, setting the height of the static obstacle as H ij The minimum circumcircle required by bypassing the static obstacle has a flying arc length A ij Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; j denotes the number of the j-th static obstacle in the i-th area to be surveyed, j=1, 2, … …, m;
a5, setting each flight distance S before the unmanned aerial vehicle changes the flight attitude in the local survey area ip The distance from the midpoint of (2) to the nearest emergency drop point is d ip 。
Preferably, in step S4, an objective function is established, and path planning is performed through the objective function, so as to obtain a flight plan of the local survey area.
Preferably, in step S5, the flight scheme of the local survey area is subjected to scheme evaluation by using multi-level fuzzy comprehensive evaluation, so as to obtain a safety degree fuzzy comprehensive evaluation vector, a track complexity fuzzy comprehensive evaluation vector, a flight scheme quality fuzzy comprehensive evaluation vector and a score, respectively; comparing the flight scheme quality evaluation score with the expected score, and if the flight scheme quality evaluation score is greater than the expected score, indicating that the flight scheme meets the requirements of a decision maker; if the flight scheme merit evaluation score is smaller than the expected score, the flight scheme is not satisfied with the requirements of the decision maker, the method returns to the step S4, and the directional factor is introduced and fed back to the local survey area path planning of the step S4, so as to re-plan the flight scheme of the local survey area.
Preferably, in step S6, after the unmanned aerial vehicle receives the flight plan of the local survey area calculated by the edge calculation platform through 5G communication, the standby state is ended, and the unmanned aerial vehicle starts to perform the local survey work; if the unmanned aerial vehicle finds a dynamic obstacle in the survey process, jumping to the step S6 (a); if the unmanned aerial vehicle does not find a dynamic obstacle in the surveying process, the unmanned aerial vehicle works normally until entering the next surveying point, and the step S6 (b) is entered; wherein,
s6 (a), after the unmanned aerial vehicle finds out a dynamic obstacle, the coordinates before the dynamic obstacle avoidance are stored; judging the threat degree of the dynamic obstacle according to the length of the reaction time, selecting different dynamic obstacle avoidance schemes to avoid the obstacle, returning the coordinates stored before the dynamic obstacle avoidance to the normal working state by the unmanned aerial vehicle after the dynamic obstacle avoidance is finished, and continuously executing the flight scheme of the original local survey area;
s6 (b), the unmanned aerial vehicle reaches the next survey point, if the next survey point is a global survey end point, the unmanned aerial vehicle lands, and the survey work is finished: the unmanned aerial vehicle reaches a survey end point, descends to a falling point and finishes the survey work; if the next survey point is not the global survey end point, the step is skipped to step S4, and the path planning of the next area is entered.
An electronic device comprising a central processor and a memory, said central processor being adapted to invoke the steps of running a computer program stored in said memory to perform said unmanned aerial vehicle flight survey method.
A computer-readable storage medium, characterized in that it stores in the form of computer-readable instructions a computer program implemented in accordance with the unmanned aerial vehicle flight survey method according to any one of claims 1 to 8, which, when invoked by a computer to run, performs the steps comprised by the respective method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
(1) According to the unmanned aerial vehicle flight survey method, the operation sequence of the survey points is determined through the global optimal path, and then path planning is carried out on the local survey areas between the survey points; the planned flight scheme can comprehensively consider the safety degree and the flight path complexity factors, so that the flight scheme meets the actual requirements and the wishes of a decision maker, reduces the danger in the unmanned aerial vehicle surveying process, improves the surveying efficiency, and provides help and direction for the surveying work in agricultural production.
(2) According to the unmanned aerial vehicle flight survey method, the flight scheme of the local survey area is subjected to multi-stage fuzzy comprehensive evaluation, so that the advantages and disadvantages of the flight scheme can be comprehensively evaluated from subjective and objective layers, the unmanned aerial vehicle flight survey method is beneficial to making a more comprehensive and scientific flight scheme, and the safety and efficiency of flight are improved.
Drawings
Fig. 1 is an overall flow chart of the unmanned aerial vehicle flight survey method of the present invention.
Fig. 2 is a two-level fuzzy comprehensive evaluation flow chart of the unmanned aerial vehicle flight survey method of the invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
As shown in fig. 1, the unmanned aerial vehicle flight survey method of the present invention includes:
s1, basic environment data and unmanned aerial vehicle parameter data are obtained:
map data of the survey area is obtained in advance, and airspace data in a specific height above the map is obtained. Measuring the intensity of the 5G signal and the GPS signal in the space, and dividing the intensity level and the level attenuation range of the 5G signal and the GPS signal in a map; if a static obstacle exists in the map, acquiring the height and the minimum circumcircle radius of the static obstacle; and the highest safe flying height H of the unmanned aerial vehicle is set according to the basic environment data fmax And minimum safe flying height H fmin The method comprises the steps of carrying out a first treatment on the surface of the Setting the minimum inertial movement distance L according to the actual performance parameter condition of the unmanned aerial vehicle min Wherein the minimum inertial movement distance L min The minimum moving distance required by the unmanned aerial vehicle to keep stable after changing the flying gesture is referred to, the unmanned aerial vehicle needs to frequently change the flying gesture in real-time flying, if the moving distance of the unmanned aerial vehicle is smaller than the minimum inertial moving distance L min The drone may be prevented from stabilizing the survey; setting minimum turning radius r according to actual parameters of unmanned aerial vehicle min The unmanned plane needs to change steering during flight, but the steering radius r is larger than r during turning due to self inertia min Otherwise, the unmanned aerial vehicle may not fly stably, so the global maximum range S is set according to the unmanned aerial vehicle parameters and the battery endurance parameters gmax 。
S2, establishing 5G communication between the unmanned aerial vehicle and the edge computing platform, and carrying out global initial path planning:
acquiring coordinates of each point to be surveyed in a surveying area, carrying out global optimal path planning on the coordinates of a central point of the surveying area under the constraint of basic environment data and unmanned aerial vehicle parameter data in the step S1, determining the surveying sequence of each surveying point, establishing 5G communication between the unmanned aerial vehicle and an edge computing platform, transmitting real-time data to the edge computing platform in the unmanned aerial vehicle surveying process, and carrying out data throughput in real time and dynamic obstacle avoidance on the unmanned aerial vehicle by the edge computing platform according to the data; the process of data transmission requires low transmission delay, so 5G communication is required. Meanwhile, the unmanned aerial vehicle needs to establish real-time image transmission with a survey center in the process of surveying, so that the survey center can conveniently analyze survey results in real time; in addition, the 5G communication can be used for transmitting a large number of clear and coherent pictures at a long distance and high speed, so that the requirement of real-time image transmission is met; at this time, the unmanned aerial vehicle is still in a standby state.
S3, analyzing and setting local environment factors, and carrying out local division on the global path planning;
the current survey point P i As a starting point, the next survey point P i+1 As the end point, the area between the start point and the end point is set as a local survey area B i The method comprises the steps of carrying out a first treatment on the surface of the Where i denotes the number of the first survey area, i=1, 2, … …, n.
The steps for setting the local environmental factors are as follows:
a1: setting the flight distance of each section of unmanned plane before changing the flight attitude in the local survey area as S ip The method comprises the steps of carrying out a first treatment on the surface of the The method aims at facilitating the acquisition of an objective function in the subsequent steps, so that the local range is optimal and shortest as much as possible; setting the flight distance of each section of unmanned plane before changing the flight attitude in the local survey area as S ip . Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; and each flight distance needs to be larger than the minimum inertial movement distance of the unmanned aerial vehicle, namely S ip >L min ；
A2: the 5G signal intensity level and the level attenuation range of the local survey area are required to be obtained, so that the unmanned aerial vehicle and the edge computing platform can normally communicate and keep low-delay transmission, and the unmanned aerial vehicle and the survey center realize low-delay real-time image transmission; setting the 5G signal intensity of the p-th flight path in the ith survey area as G ip Wherein 0 < G ip ＜1，G ip The larger the value, the stronger the 5G signal strength; where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight path in the i-th zone to be surveyed, p=1, 2, … …, r;
a3: the method comprises the steps that the GPS signal intensity level and the level attenuation range of a local survey area are required to be obtained, and real-time GPS coordinates of an unmanned aerial vehicle are obtained in actual flight; the purpose of acquiring the GPS signal intensity level and the level attenuation range of the local survey area is to facilitate the edge computing platform to carry out local path planning; the purpose of acquiring the real-time GPS coordinates of the unmanned aerial vehicle is to ensure that the unmanned aerial vehicle can dynamically avoid the obstacle and feed back the local survey progress and the global survey progress according to real-time positioning; setting the GPS signal strength as F ip Wherein 0 < F ip ＜1，F ip The larger the value, the stronger the GPS signal strength; where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight path in the i-th zone to be surveyed, p=1, 2, … …, r.
A4: the method comprises the steps of obtaining the radius of a minimum circumcircle of a static obstacle in a local exploration area and the height of the static obstacle, and facilitating an edge computing platform to plan a route of leaping or bypassing the static obstacle in the local exploration area; setting the height of the static obstacle as H ij The required flying arc length A of the minimum circumcircle bypassing the static obstacle ij Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; j denotes the i-th area to be surveyedThe number of the j-th static obstacle in the list, j=1, 2, … …, m.
A5: the method comprises the steps of obtaining the distance from the middle point of each flight distance before the unmanned aerial vehicle changes the flight attitude to the nearest emergency drop point in a local survey area, and aiming at enabling the unmanned aerial vehicle to go to the nearest emergency drop point as soon as possible when the unmanned aerial vehicle encounters an emergency, so that the loss is reduced to the greatest extent; setting the distance from the midpoint of each flight distance to the nearest emergency drop point before the unmanned aerial vehicle changes the flight attitude in the local survey area as d ip Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight path in the i-th zone to be surveyed, p=1, 2, … …, r.
S4, carrying out local path planning to obtain a local flight scheme:
in local path planning, a proper objective function needs to be established, and path planning is carried out through the objective function to obtain a flight scheme of a local survey area; the construction steps of the objective function are as follows:
b1: by comprehensively considering that each flight distance before the unmanned aerial vehicle changes the flight attitude in the local exploration area is S ip 5G Signal Strength G ip GPS signal strength F ip Static obstacle height H ij Minimum circumcircle required flying arc length A bypassing static obstacle ij Distance d from midpoint of each flight distance before changing flight attitude of unmanned aerial vehicle to nearest emergency drop point ip These five factors constitute the objective function, as shown in the following equation
In the method, in the process of the invention,for the local track objective function, the path planning algorithm performs initial operation on the objective function, and weights of all partsIs a random value and is carried out by iterationCalculating to change the weight of each part continuouslyUntil the algorithm converges or solves for extremum, each partial weight in the objective functionIs dynamically adjustable based on feedback from subsequent directional factors. When a certain part is weightedThe larger the factor representing the more factors that take this part into account when planning the path; wherein,weights representing the path distance arguments,representing track distance arguments;the weights representing the 5G communication strength argument,representing 5G communication strength argument;weights representing the GPS signal strength argument,representing GPS signal strength argument;the weights representing the static obstacle avoidance arguments,representing static obstacle avoidance independent variables;weights representing emergency safe drop distance arguments, Representing an emergency safe drop distance argument.
B2: in order to represent the path distance independent variable, the invention sums each section of flight distance before the unmanned plane changes the flight attitude in the local survey area to be used as the path distance independent variable, and the following formula is shown:
wherein: i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; and each flight distance needs to be larger than the minimum inertial movement distance of the unmanned aerial vehicle, namely S ip >L min The method comprises the steps of carrying out a first treatment on the surface of the When x is 1 Weight value w of (2) 1 The larger the flight plan, the more the track distance is considered in the path planning, and the smaller the planned flight plan track distance is.
B3: to represent the 5G communication intensity argument, the present invention sums the 5G signal intensity levels for each flight leg within the local survey area as shown in the following equation:
wherein: i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; when x is 2 Weight value w of (2) 2 The larger the 5G communication intensity is considered in the path planning, the better the 5G communication quality of the planned flight scheme is.
B4: to represent the GPS signal strength argument, the present invention sums the GPS signal strength levels for each flight leg within the local survey area as shown in the following equation:
wherein: i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; when x is 3 Weight value w of (2) 3 The larger the GPS signal strength is considered in the path planning, the better the GPS positioning quality of the planned flight scheme is.
B5: to represent the static obstacle avoidance independent variable, the invention changes the minimum circumcircle required by the minimum circumcircle bypassing the static obstacle in the local survey area to the flight arc length A ij Height H of static obstacle ij And respectively summing and linearly combining the two to obtain a static obstacle avoidance independent variable, wherein the static obstacle avoidance independent variable is shown in the following formula:
wherein: i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; j denotes the number of the j-th static obstacle in the i-th area to be surveyed, j=1, 2, … …, m; a, a 1 、a 2 Is a static obstacle avoidance independent variable combination coefficient; when x is 4 Weight value w of (2) 4 The larger the flight plan, the more the avoidance of the static obstacle is considered in the path planning, and the planned flight plan can better fly or bypass the static obstacle.
B6: in order to represent the safe landing distance independent variable, the invention takes the negative of the distance between the current unmanned aerial vehicle and the nearest emergency landing point as the independent variable, and the independent variable is represented by the following formula:
wherein: i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; the purpose of taking the negative value is to ensure that the polarity of the safe landing distance independent variable is the same as the polarity of the 5G communication intensity independent variable, the GPS signal intensity independent variable and the static obstacle avoidance independent variable, and is opposite to the polarity of the track distance independent variable,further, the track distance independent variable and other independent variables in the objective function are guaranteed to be in a contradictory relation; when x is 5 Weight value w of (2) 5 The larger the distance between the unmanned plane and the nearest emergency drop point is considered in the path planning, the closer the planned flight path is to the emergency drop point.
Therefore, after the objective function F (x) is obtained, a path planning is performed to obtain a flight scheme of a local survey area, and the flight scheme of the local survey area considers the factors of track distance, 5G communication safety, GPS signal positioning, static obstacle avoidance and emergency safety landing distance.
S5, carrying out multistage fuzzy comprehensive evaluation on the flight scheme of the local survey area to obtain scheme scores:
step S4, planning a flight scheme of a local survey area, and then carrying out scheme evaluation by utilizing multi-stage fuzzy comprehensive evaluation, wherein the weight coefficient of a first-stage evaluation factor is determined by adopting a hesitation fuzzy method, and the determined weight coefficient can well reflect the hesitation psychology of a decision maker and reduce error influence caused by subjective factors to a certain extent; the method is characterized in that the weight coefficient of the secondary evaluation factor is determined by adopting an entropy weight method, the basic thought of the method is to determine objective weight by utilizing the variability of the factor, and the information entropy of the factor is fully utilized; and (3) comprehensively grading the flight scheme provided by the S4 through multistage fuzzy comprehensive evaluation, comparing the flight scheme with the expected score, and if the score is greater than the expected score, indicating that the flight scheme basically meets the requirements of a decision maker. If the score is smaller than the expected score, the flight scheme is not satisfied with the basic requirement of the decision maker, and the decision maker is presented with a higher tendency to the safety degree or the track complexity according to the factor weight tendency in the first-level fuzzy comprehensive evaluation, and then a directional factor is introduced to be fed back to the local survey area path planning in the step S4, so that the path planning algorithm carries out iterative computation towards the direction of the directional factor, and the flight scheme is re-planned, so that the tendency and the requirement of the decision maker are more satisfied. Thereby forming a closed loop flight plan generation and evaluation.
FIG. 2 is a level fuzzy comprehensive evaluation flowchart, which is more specifically implemented as follows, wherein the step C is aimed at obtaining a fuzzy comprehensive evaluation vector about the safety degree, the step D is aimed at obtaining a fuzzy comprehensive evaluation vector about the track complexity, and the step E is aimed at obtaining a fuzzy comprehensive evaluation vector and a score about the flight scheme goodness:
c1: determining a set of factors that evaluate the degree of security: the factors affecting the safety level are described by an evaluation factor set, which is shown in the following formula:
c2: determining a comment set: the evaluation of the safety degree was classified into 4 grades of excellent, good, medium and bad, and the use of the comment set was expressed as follows:
wherein m is 1 The expression level is "excellent"; m is m 2 The expression level is "good"; m is m 3 The expression level is "medium"; m is m 4 The expression level is "bad".
And C3, determining a fuzzy evaluation matrix: determining a fuzzy evaluation matrix by adopting an expert evaluation method, and taking an average value or a mode value of expert opinions as a membership degree of each grade of a factor set belonging to the comment set; setting a fuzzy evaluation matrix as P 1 The fuzzy evaluation matrix is shown as follows:
in which the first column vector represents a factor set U 1 T Membership to the security level "excellent"; the second column of vectors represents factor set U 1 T Membership to the security level "good"; the third column of vectors represents factor set U 1 T Membership in the security level "middle"; the fourth column of vectors represents factor set U 1 T The degree of membership to the security level "bad" is as followsThe table shows:
in the table, p 11 Membership degree indicating that 5G communication safety degree belongs to level 'excellent', p 22 Membership grade indicating that GPS positioning effective degree belongs to grade "good", p 33 Indicating the membership of the emergency safe drop distance to the class "medium".
And C4: the set of weights is determined using an entropy weight method: carrying out standardization processing on the evaluation factors to obtain information entropy, and further obtaining weight coefficients of the evaluation factors, wherein the information entropy is as follows: the smaller the event occurrence probability is, the larger the information quantity is, and the larger the information entropy is; the larger the event occurrence probability is, the smaller the information quantity is, and the smaller the information entropy is; the method comprises the following specific steps:
c41: intensity of 5G signal G p And (3) carrying out standardization processing, wherein the standardization processing is shown as the following formula:
wherein G is p The signal intensity level of the p-th flight distance 5G in a certain local investigation region; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; t is t 1p Is G p And (5) standardized index data.
C42: the information entropy of the 5G communication security factor is shown as follows:
wherein E is 1 Information entropy, t, of 5G communication safety degree factor 1p Is G p And (5) standardized index data.
C43: and calculating the objective weight of the index by using the information entropy of the 5G communication safety degree factor, wherein the objective weight of the index is shown in the following formula:
wherein w is 1 Weight of 5G communication safety degree factor accounting for safety degree, E 1 And the information entropy is the factor of the 5G communication safety degree.
C44: GPS signal strength F p And (3) carrying out standardization processing, wherein the standardization processing is shown as the following formula:
wherein F is p The p-th flight distance GPS signal intensity level in a certain local survey area; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; t is t 2p Is F p And (5) standardized index data.
C45: the information entropy of the GPS positioning validity factor is shown in the following formula:
wherein E is 2 Information entropy for GPS positioning validity factor, t 2p Is F p And (5) standardized index data.
C46: calculating the objective weight of the index by using the information entropy of the GPS positioning effective degree factor, wherein the objective weight is shown in the following formula:
wherein w is 2 Weighting the GPS positioning effectiveness factor to the safety degree, E 2 And (5) information entropy of a GPS positioning validity factor.
C47: the distance from the midpoint of each flight distance before the unmanned plane changes the flight attitude to the nearest emergency drop point is d p And (3) carrying out standardization processing, wherein the standardization processing is shown as the following formula:
wherein d p The distance from the midpoint of each flight distance to the nearest emergency drop point before the flight attitude of the unmanned aerial vehicle is changed. The method comprises the steps of carrying out a first treatment on the surface of the p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; t is t 3p Is F p And (5) standardized index data.
C48: the information entropy of the emergency safety landing distance factor is shown in the following formula:
wherein E is 3 Information entropy for emergency safety landing distance factor, t 3p Is d p And (5) standardized index data.
The information entropy of the emergency safety landing distance factor calculates the objective weight of the index, and the objective weight is shown as the following formula:
wherein w is 3 Weight of safety factor for emergency safety landing distance factor, E 3 Entropy of information of the emergency safety landing distance factor.
C49: weight set A 1 The following formula is shown:
c5: obtaining a fuzzy comprehensive evaluation vector about the safety degree: setting the comprehensive evaluation vector of the safety degree as R 1 The safety degree comprehensive evaluation vector is shown as follows:
wherein R is 1 Representing securityDegree comprehensive evaluation vector, A 1 Representing a set of security degree weights, P 1 A represents a fuzzy evaluation matrix of safety degree, a 1 A represents the membership of the security degree to the class "excellent", a 2 A represents the membership of the degree of security to the class "good", a 3 Representing the degree of membership of the degree of security to the class "in", a 4 Indicating the degree of membership of the security level to the level "bad".
Specifically, the fuzzy comprehensive evaluation vector of the track complexity is obtained by the following specific steps:
d1: determining a set of factors that evaluate track complexity: describing factors influencing the track complexity by using an evaluation factor set, wherein the track complexity evaluation factor set has the following formula:
d2: determining a comment set: the evaluation of the track complexity is classified into 4 grades of excellent, good, medium and bad, and the evaluation is expressed as the following formula by using a comment set.
Wherein m is 1 The expression level is "excellent"; m is m 2 The expression level is "good"; m is m 3 The expression level is "medium"; m is m 4 The expression level is "bad".
D3: determining a fuzzy evaluation matrix: determining a fuzzy evaluation matrix by adopting an expert evaluation method; taking the average value or the mode value of the expert opinion as the membership degree of each grade of the factor set belonging to the comment set; setting a fuzzy evaluation matrix as P 2 The fuzzy evaluation matrix is shown as follows:
in which the first column vector represents a factor set U 2 T Membership to the track complexity level "excellent"; second column vector substitutionWatch factor set U 2 T Membership to the track complexity level "good"; the third column of vectors represents factor set U 2 T Membership to the "middle" track complexity level; the fourth column of vectors represents factor set U 2 T Membership to track complexity level "bad"; the following table shows:
in the table: p is p ` 11 Membership grade indicating path length and quality degree belonging to grade "excellent", p ` 22 Indicating the degree of membership of the path smoothness to the class "good".
D4: the set of weights is determined using an entropy weight method: respectively carrying out standardization processing on the evaluation factors to obtain information entropy, and further obtaining weight coefficients of the evaluation factors; the information entropy means that: the smaller the event occurrence probability is, the larger the information quantity is, and the larger the information entropy is; the larger the event occurrence probability is, the smaller the information amount is, and the smaller the information entropy is. The method comprises the following specific steps:
d41: each flight distance S before changing the flight attitude of the unmanned aerial vehicle in the local survey area p And (3) carrying out standardization processing, wherein the standardization processing is shown as the following formula:
Wherein S is p Each flight distance before the flight attitude of the unmanned aerial vehicle is changed for the unmanned aerial vehicle in the local survey area; p denotes the number of the p-th flight distance in the i-th zone to be surveyed, p=1, 2, … …, r; t is t 4p Is S p And (5) standardized index data.
The information entropy of the path length quality is shown as follows:
in the method, in the process of the invention,E 4 entropy of information of path length and quality, t 4p Is S p And (5) standardized index data.
D42: and calculating the objective weight of the index by using the information entropy of the path length quality, wherein the objective weight is shown in the following formula:
wherein w is 4 E is the weight of path length and quality factors to track complexity 4 And the information entropy is the path length quality factor.
D43: minimum circumcircle required flight arc length A for bypassing static obstacle ij And height H of static obstacle ij The normalization process is performed as shown in the following formula:
wherein (a) 1 A j +a 2 H j ) Path smoothness for a certain local survey area; j denotes the number of the j-th static obstacle in the i-th area to be surveyed, j=1, 2, … …, m; a, a 1 、a 2 Linear combination coefficients for path smoothness; t is t 5j Is (a) 1 A j +a 2 H j ) And (5) standardized index data.
The information entropy of the path smoothness factor is shown as follows:
wherein E is 5 Information entropy as path smoothness factor, t 5j Is (a) 1 A j +a 2 H j ) And (5) standardized index data.
And calculating the objective weight of the index by using the information entropy of the path smoothness factor, wherein the objective weight is shown in the following formula:
wherein w is 5 Weighting the path smoothness factor by the track complexity, E 5 Entropy of information, which is a path smoothness factor.
D44: obtaining a weight set A 2 The following formula is shown:
d5: obtaining a fuzzy comprehensive evaluation vector about track complexity: setting the comprehensive evaluation vector of track complexity as R 2 The track complexity comprehensive evaluation vector is shown as follows:
wherein: r is R 2 Represents a comprehensive evaluation vector of the safety degree, A 2 Representing a track complexity weight set, P 2 B, representing a track complexity fuzzy evaluation matrix 1 Representing membership of track complexity to level "excellent", b 2 Representing membership of track complexity to class "good", b 3 Representing membership of track complexity to class "middle", b 4 Representing the membership of track complexity to the class "bad".
Specifically, the fuzzy comprehensive evaluation vector and score of the quality degree of the obtained flight scheme comprise the following specific steps:
e1: determining a flight scheme goodness factor set: describing factors influencing the quality of the flight scheme by using an evaluation factor set, wherein the evaluation factor set of the quality of the flight scheme is shown as the following formula:
Wherein, the safety factor is obtained in the step C, and the track complexity is obtained in the step D.
E2: determining a comment set: the evaluation of the safety degree was classified into 4 grades of excellent, good, medium and bad, and the use of the comment set was expressed as follows:
wherein m is 1 Indicating a "good" rating in the range of scores [90,100 ]]；m 2 A score of "good" with a score range of [80, 90); m is m 3 A score of "medium" in the range of 60, 80; m is m 4 The scale is denoted as "bad", and the score range is [0,60 ].
E3: determining a fuzzy evaluation matrix: c, comprehensively evaluating the safety degree obtained in the step R 1 And D, comprehensively evaluating the vector R with the track complexity obtained in the step D 2 And combining to obtain a fuzzy comprehensive evaluation matrix P, wherein the fuzzy comprehensive evaluation matrix P is shown as the following formula:
wherein R is 1 The safety degree comprehensive evaluation vector represents the membership degree of the safety degree factor to the level of 'excellent, good, medium and bad'. R is R 2 The comprehensive evaluation vector is a track complexity comprehensive evaluation vector which represents the membership degree of the track complexity factor to the level of 'excellent, good, medium and bad'; the following table shows:
in the table: a, a 1 A membership degree indicating that the security degree is subordinate to the level "excellent", b 2 Indicating the membership of the track complexity to the class "good".
E4: the weight set is determined using a hesitant blur method: two groups of hesitation blur numbers are defined, namely the safety degree hesitation blur number and the track complexity hesitation blur number. After the hesitation fuzzy number is determined, fuzzy number index entropy is calculated respectively, and finally the weight is calculated respectively by using an information entropy minimization principle.
E41: a set of security hesitation blur numbers is defined. The degree of safety hesitation blur number is shown as follows:
in the formula, h M1 (x i ) Hesitation of fuzzy numbers for a group of security degrees, h M1 σ(1) (x 1 ) Is a as 1 ，h M1 σ(2) (x 2 ) Is a as 2 ，h M1 σ(3) (x 3 ) Is that 3 , h M1 σ(4) (x 4 ) Is a as 4 . Wherein a is 1 、a 2 、a 3 、a 4 Respectively represent the membership degree of the security degree to the level of excellent, good, medium and bad.
E42: a set of track complexity hesitation blur numbers is defined. The track complexity hesitation blur number is shown as follows:
in the formula, h M2 (x i ) Hesitation blur number for a group of track complexity, h M2 σ(1) (x 1 ) B is 1 ，h M2 σ(2) (x 2 ) B is 2 ，h M2 σ(3) (x 3 ) B is 3 ,h M2 σ(4) (x 4 ) B is 4 . Wherein b 1 、b 2 、b 3 、b 4 And respectively representing the membership degree of the track complexity to the grades of excellent, good, medium and poor.
Degree of safety hesitation fuzzy number h M1 (x i ) The exponential entropy of (a) is shown in the following formula:
wherein E (h) M1 (x i ) I=1, 2,3, 4) is the exponential entropy of the hesitant blur number of the degree of safety.
Track complexity hesitation blur number h M2 (x i ) The exponential entropy of (a) is shown in the following formula:
wherein E (h) M2 (x i ) I=1, 2,3, 4) is the exponential entropy of the track complexity hesitation blur number.
The information entropy of the security factor is shown as follows:
wherein E is 1 I=1, 2,3,4, which is the information entropy of the security factor.
The information entropy of the track complexity factor is shown as follows:
wherein E is 2 Information entropy, i=1, 2,3,4, is a track complexity factor.
E43: the safety factor weight is calculated by using the information entropy minimization principle and is shown as the following formula:
in the formula, v 1 The safety degree factor is the weight of the quality of the flight scheme.
E44: and calculating track complexity factor weights by using an information entropy minimization principle, wherein the weights are shown in the following formula:
in the formula, v 2 The flight path complexity factor is the weight of the flight scheme quality.
E45: the resulting weight set A is shown in the following formula:
e5: obtaining a flight scheme evaluation score: setting the comprehensive evaluation vector of the flight scheme quality as R, wherein the evaluation vector of the flight scheme quality is shown as follows:
specifically, the comprehensive evaluation vector of the quality of the flight scheme is weighted average for R, and finally the score of the flight scheme is obtained, wherein R is a row vector of one row and four columns. And comparing the obtained flight scheme score with the expected score, if the flight scheme score is smaller than the expected score, jumping back to the step S4 to carry out path planning again, and introducing a directional factor into the path planning according to the weight trend of the decision maker so as to enable the path planning algorithm to carry out iterative calculation towards the direction expected by the decision maker. The directional factors may guide the path planning algorithm to be more prone to the safety degree or the track complexity, and at this time, the weights of the portions of the objective function are dynamically adjusted according to the evolution direction of the directional factors, for example, when the decision maker is more prone to the safety degree, the directional factors are introduced to enable the path planning algorithm to increase the weights of the objective function about the safety degree factors. If the flight plan score is greater than the desired score, step S6 is entered. The desired score may be freely defined by the decision maker.
S6, carrying out local survey work by the unmanned aerial vehicle: after the appropriate flight plan is obtained in step S5, the edge computing platform transmits the calculated flight plan for the local survey area path plan to the drone via 5G communication. After the unmanned aerial vehicle confirms the receiving scheme and generates the flight instruction, the standby state is ended, and the unmanned aerial vehicle starts to execute local survey work. If a dynamic obstacle is found during the survey, the process jumps to step S6 (a). During the survey, if no dynamic obstacle is found, the operation is continued normally until the next survey point is entered, step S6 (b) is entered, wherein,
s6 (a), carrying out interrupt response, and carrying out dynamic obstacle avoidance by the unmanned aerial vehicle: after the unmanned aerial vehicle finds the dynamic obstacle, the coordinates before the dynamic obstacle avoidance are stored. Setting a relative distance d between a dynamic obstacle and the unmanned aerial vehicle ik Relative velocity v ik Acquiring relative velocity v at a certain moment ik After a short time the relative velocity v ik Remain unchanged. When the relative distance between the dynamic obstacle and the unmanned aerial vehicle is smaller, the reaction time of the unmanned aerial vehicle is smaller, the threat degree is larger, and the relative distance d is larger ik Is inversely related to the threat level. When the relative speed between the dynamic obstacle and the unmanned aerial vehicle is larger, the reaction time of the unmanned aerial vehicle is smaller, the threat degree is larger, and the relative speed v is higher ik Positively correlated with the threat level. Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n. k denotes the number of the kth dynamic obstacle in the ith area to be surveyed, k=1, 2, … …, q. The invention relates to the relative distance d between a dynamic obstacle and an unmanned plane in a local survey area ik Relative velocity v ik And performing division operation to obtain the reaction time, wherein the greater the reaction time is, the smaller the threat degree is. The threat degree of the dynamic obstacle can be judged according to the length of the reaction time, and different dynamic obstacle avoidance schemes are selected. After the dynamic obstacle avoidance is finished, the unmanned aerial vehicle returns to the coordinates stored before the dynamic obstacle avoidance, returns to a normal working state, and continues to execute the original flight scheme.
S6 (b), reaching the next survey point: in the step S6, the unmanned aerial vehicle reaches the next survey point, and if the next survey point is a global survey end point, the step S7 is entered; if the next survey point is not the global survey end point, the step is skipped to step S4, and the path planning of the next area is entered.
S7, unmanned aerial vehicle landing, and ending survey work: the drone reaches the survey end point, descends to the drop point and ends the survey.
Finally, compared with the prior art, the unmanned aerial vehicle flight survey method has the following effects:
(1) The invention determines the operation sequence of the survey points through the global optimal path, and then performs path planning on the local survey area between the survey points. The local survey area comprehensively considers the 5G communication safety degree, GPS positioning effectiveness degree, emergency safety landing distance, path length goodness and path smoothness to carry out path planning, and the planned flight scheme can more comprehensively consider the safety degree and track complexity factors, so that the flight scheme meets the actual requirements and willingness of decision makers, reduces the danger in the unmanned aerial vehicle survey process, improves the survey efficiency, and provides help and direction for the survey work in agricultural production.
(2) According to the invention, through the secondary fuzzy comprehensive evaluation, the safety degree and the track complexity factor affecting the flight scheme are comprehensively evaluated. In terms of the safety level, a plurality of objective factors are considered, the comprehensiveness of the evaluation is ensured through objective evaluation, and the objective factors include the safety level of 5G communication, the effectiveness level of GPS positioning and the distance factor of emergency safety landing. In terms of the track complexity, the length and the quality of the path and the smoothness of the path are considered. Regarding the degree of security, the effect of communication and positioning is of great concern, as well as the provision of countering emergency situations. In terms of track complexity, the length of the path and smoothness are of concern. Through the two-level fuzzy comprehensive evaluation, the method can comprehensively evaluate the advantages and disadvantages of the flight scheme from subjective and objective layers. This helps to formulate a more comprehensive and scientific flight scheme, and improves the safety and efficiency of flight.
(3) The invention establishes a closed loop feedback system between fuzzy comprehensive evaluation and path planning by introducing the orientation factor. The targeting factor is introduced as an important factor affecting the path planning, with the aim of adjusting the path planning according to the decision maker's weight trends. The weight tendency of the decision maker influences the path planning through the directional factors, and the result of the path planning influences the fuzzy comprehensive evaluation in turn, so that the closed loop feedback is realized. The path planning algorithm performs iterative computations under this framework, enabling it to be optimized continuously towards the direction desired by the decision maker. The evolution direction of the orientation factor dynamically adjusts the weight of each part of the objective function, so that the system is more intelligent.
(4) The invention protects the site and realizes site restoration through an interrupt response mechanism. When the unmanned aerial vehicle detects a dynamic obstacle, the system stores the current coordinate as the position before the dynamic obstacle avoidance. By analyzing the length of the unmanned aerial vehicle reaction time, the system can evaluate the threat degree of the dynamic obstacle and correspondingly select different dynamic obstacle avoidance schemes. And after the dynamic obstacle avoidance is finished, the unmanned aerial vehicle returns the coordinates stored before, so that the on-site restoration is realized. The restoration mechanism ensures that the unmanned aerial vehicle can return to the original working state after obstacle avoidance, and continues to execute a preset flight scheme. The mechanism enables the unmanned aerial vehicle to flexibly adjust the obstacle avoidance strategy under the condition of facing different threat degrees, thereby improving the adaptability to complex environments.
The foregoing is illustrative of the present invention and is not to be construed as limiting thereof, but rather as various changes, modifications, substitutions, combinations, and simplifications which may be made therein without departing from the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method of unmanned aerial vehicle flight survey comprising the steps of:
s1, acquiring basic environment data and parameter data of an unmanned aerial vehicle;
s2, establishing communication between the unmanned aerial vehicle and an edge computing platform, and performing global initial path planning;
s3, analyzing and setting local environment factors, and locally dividing the global path planning;
s4, planning a local path to obtain a flight scheme of the local survey area;
s5, carrying out multi-level fuzzy comprehensive evaluation on the flight scheme of the local survey area, and selecting the flight scheme meeting the requirements as the optimal flight scheme of the local survey area;
s6, after the optimal flight scheme is obtained, the unmanned aerial vehicle starts to execute local survey work, and in the flight process, the unmanned aerial vehicle dynamically keeps away the obstacle until the unmanned aerial vehicle reaches the next survey point; if the next survey point is the global survey end point, the unmanned aerial vehicle drops to finish the survey work; if the next survey point is not the global survey end point, the process jumps back to step S4, and the path planning for the next area is entered, and steps S5 and S6 are repeated.
2. The unmanned aerial vehicle flight survey method of claim 1, wherein in step S1, map data of the survey area and airspace data within a specific altitude above the map are obtained in advance, and actual operation parameters of the unmanned aerial vehicle are set according to the basic environment data; wherein,
the basic environment data acquisition method comprises the following steps:
measuring the intensity of 5G signals and GPS signals in the space above the survey area, and dividing the intensity level and level attenuation range of the 5G signals and the GPS signals in the map; meanwhile, if a static obstacle exists in the map, the height and the minimum circumcircle radius of the static obstacle are required to be obtained;
the method for setting the actual operation parameters of the unmanned aerial vehicle comprises the following steps:
setting the highest safe flight altitude H of the unmanned aerial vehicle according to the basic environment data fmax And minimum safe flying height H fmin The method comprises the steps of carrying out a first treatment on the surface of the Setting the minimum inertial movement distance L according to the actual performance parameter condition of the unmanned aerial vehicle min The method comprises the steps of carrying out a first treatment on the surface of the Setting minimum turning radius r according to actual parameters of unmanned aerial vehicle min The steering radius r is required to be larger than r during the turning process min The method comprises the steps of carrying out a first treatment on the surface of the Setting a global maximum range S according to unmanned aerial vehicle parameters and battery endurance parameters gmax 。
3. The unmanned aerial vehicle flight survey method of claim 1, wherein in step S2, coordinates of each point to be surveyed in the survey area are taken, global optimum path planning is performed on coordinates of a central point of the survey area under the constraint of the basic environment data obtained in step S1 and the parameter data of the unmanned aerial vehicle, and the survey sequence of each survey point is determined; transmitting real-time data to an edge computing platform in the unmanned aerial vehicle surveying process; meanwhile, the unmanned aerial vehicle needs to conduct real-time image transmission between 5G communication and a survey center in the survey process.
4. The unmanned aerial vehicle flight survey method of claim 1, wherein in step S3, the step of locally dividing the global path plan is:
the current survey point P i As a starting point, the next survey point P i+1 As the end point, the area between the start point and the end point is set as a local survey area B i Where i denotes the number of the first survey area, i=1, 2, … …, n.
5. The unmanned aerial vehicle flight survey method of claim 4, wherein in step S3, the step of setting the local environmental factor is:
a1, setting the flight distance of each section of unmanned plane in the local survey area before changing the flight attitude as S ip Each flight distance S ip Is required to be greater than the minimum inertial movement distance L of the unmanned aerial vehicle min ；
A2, setting the 5G signal intensity of the p-th flight path in the ith exploration area as G ip Wherein 0 < G ip ＜1，G ip The larger the value, the stronger the 5G signal strength is represented;
a3, setting the GPS signal strength as F ip Wherein 0 < F ip ＜1，F ip The larger the value, the stronger the representative GPS signal strength;
a4, setting the height of the static obstacle as H ij The minimum circumcircle required by bypassing the static obstacle has a flying arc length A ij Where i denotes the number of the i-th area to be surveyed, i=1, 2, … …, n; j denotes the number of the j-th static obstacle in the i-th area to be surveyed, j=1, 2, … …, m;
A5, setting each flight distance S before the unmanned aerial vehicle changes the flight attitude in the local survey area ip The distance from the midpoint of (2) to the nearest emergency drop point is d ip 。
6. The unmanned aerial vehicle flight survey method of claim 1, wherein in step S4, an objective function is established, and path planning is performed by the objective function to obtain a flight plan for the local survey area.
7. The unmanned aerial vehicle flight survey method according to claim 1, wherein in step S5, the flight plan of the local survey area is subjected to plan evaluation by using multi-level fuzzy comprehensive evaluation, and a safety degree fuzzy comprehensive evaluation vector, a track complexity fuzzy comprehensive evaluation vector, a flight plan goodness fuzzy comprehensive evaluation vector and a score are respectively obtained; comparing the flight scheme quality evaluation score with the expected score, and if the flight scheme quality evaluation score is greater than the expected score, indicating that the flight scheme meets the requirements of a decision maker; if the flight scheme merit evaluation score is smaller than the expected score, the flight scheme is not satisfied with the requirements of the decision maker, the method returns to the step S4, and the directional factor is introduced and fed back to the local survey area path planning of the step S4, so as to re-plan the flight scheme of the local survey area.
8. The unmanned aerial vehicle flight survey method of claim 1, wherein in step S6, after the unmanned aerial vehicle receives the flight plan of the local survey area calculated by the edge calculation platform through 5G communication, the standby state is ended and the unmanned aerial vehicle starts to perform the local survey work; if the unmanned aerial vehicle finds a dynamic obstacle in the survey process, jumping to the step S6 (a); if the unmanned aerial vehicle does not find a dynamic obstacle in the surveying process, the unmanned aerial vehicle works normally until entering the next surveying point, and the step S6 (b) is entered; wherein,
s6 (a), after the unmanned aerial vehicle finds out a dynamic obstacle, the coordinates before the dynamic obstacle avoidance are stored; judging the threat degree of the dynamic obstacle according to the length of the reaction time, selecting different dynamic obstacle avoidance schemes to avoid the obstacle, returning the coordinates stored before the dynamic obstacle avoidance to the normal working state by the unmanned aerial vehicle after the dynamic obstacle avoidance is finished, and continuously executing the flight scheme of the original local survey area;
s6 (b), the unmanned aerial vehicle reaches the next survey point, if the next survey point is a global survey end point, the unmanned aerial vehicle lands, and the survey work is finished: the unmanned aerial vehicle reaches a survey end point, descends to a falling point and finishes the survey work; if the next survey point is not the global survey end point, the step is skipped to step S4, and the path planning of the next area is entered.
9. An electronic device comprising a central processor and a memory, characterized in that the central processor is adapted to invoke a computer program stored in the memory for performing the steps of the unmanned aerial vehicle flight survey method of any of claims 1 to 8.
10. A computer-readable storage medium, characterized in that it stores in the form of computer-readable instructions a computer program implemented in accordance with the unmanned aerial vehicle flight survey method according to any one of claims 1 to 8, which, when invoked by a computer to run, performs the steps comprised by the respective method.
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