CN108766035B - Unmanned aerial vehicle terrain matching flight control system under guidance of point density - Google Patents
Unmanned aerial vehicle terrain matching flight control system under guidance of point density Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle terrain matching flight control system under point density guidance, which belongs to the field of unmanned aerial vehicles, and comprises the specific steps of establishing a DEM model, planning a course according to the DEM model, equally dividing the course into a plurality of sections, making an approximate fitting curve of the course, and writing the fitting curve into an unmanned aerial vehicle flight control function.
Description
Technical Field
The invention belongs to the field of unmanned aerial vehicles, and particularly relates to a terrain matching flight control system of an unmanned aerial vehicle under the guidance of point density.
Background
Compared with a manned aircraft, the unmanned aerial vehicle has the advantages of low cost, small size, convenience in use, low production and maintenance cost, strong maneuverability, strong viability and the like. Because no personnel drive, the unmanned aerial vehicle is not limited by the physiology and life risks of personnel, and is suitable for executing tasks of 'boring and dangerous' such as information collection, geological survey, low-altitude investigation and anti-terrorist striking. With the development of science and technology, the production cost of the unmanned aerial vehicle is further reduced, and the unmanned aerial vehicle starts to be developed in the fields of civilian use, scientific research and the like, such as gas pipeline monitoring, area coverage monitoring, disaster emergency search and rescue, agricultural plant protection, public security fire control, remote sensing mapping and the like.
Along with the development of modern agricultural technologies, the application of agricultural unmanned aerial vehicles is undoubtedly an important sign for the development of high-tech agricultural era, and now agricultural unmanned aerial vehicles are increasingly applied to the aspects of pesticide spraying, fertilizer application and the like, so that a large amount of manpower is saved due to the appearance of the agricultural unmanned aerial vehicles, and the working efficiency of the agricultural unmanned aerial vehicles is directly influenced by the accuracy of a navigation method.
During aerial operation of the unmanned aerial vehicle, the unmanned aerial vehicle faces safety threats of tangible obstacles such as mountains, buildings, trees, power transmission lines and the like, and constraints of intangible obstacles such as no-fly areas, dangerous areas and the like. If the person can not avoid the accident, the crash accident can happen, the potential safety hazard can be generated, and even the injury can be caused to the operator or other people; and certain economic loss is caused.
At present, a method for solving the problem of unmanned aerial vehicle navigation comprises GPS navigation, and methods for realizing autonomous navigation comprise an infrared scanning map drawing method, an indoor visual system navigation method, inertial navigation, radio navigation, satellite navigation and the like.
Therefore, it is urgently needed to develop an unmanned aerial vehicle autonomous navigation system which can be used for realizing autonomous navigation flight in mountainous areas, hills and the like and has lower cost.
Disclosure of Invention
According to the unmanned aerial vehicle control center, the DEM geographic model is adopted, the flight route of the unmanned aerial vehicle is planned in advance, the flight route is input into the unmanned aerial vehicle in advance, the unmanned aerial vehicle does not need to solve the flight route in real time in the flight process, only the preset route is required to be installed for autonomous flight, and the unmanned aerial vehicle control center can be used for an unmanned aerial vehicle control center with low computing capability.
In order to realize the purpose in real time, the invention is realized by the following technical scheme: the unmanned aerial vehicle terrain matching flight control system comprises the following steps:
step1, establishing a DEM model of a flight area;
step2, planning a flight route according to the DEM model;
step3, dividing the flight route into a plurality of sections of short-distance flight routes;
step 4, making an approximate fitting route for the flight route;
and 5, determining a flight control function of the unmanned aerial vehicle through the fitted route.
Further, the DEM model of the flight area is established in the step1, and the specific model is the DEM model based on TIN.
Further, step3 divides the flight path into multiple sections of short-distance flight paths, and the specific steps are as follows:
step1, dividing the whole flight route into a plurality of sections of short-distance flight routes, wherein dividing boundary points between each section of short-distance flight route are track points;
step2, planning the flight height of each section of short flight route;
step3, calculating a track point space coordinate value of each section of flight route;
and further, the step 4 of approximately fitting the flight route, wherein the specific method is to adopt the spatial coordinate value of the track point of each section of the flight route as a fitting point and adopt a B spline curve fitting algorithm to fit an approximate track curve.
Further, the step 5 determines the flight control function of the unmanned aerial vehicle through the fitted flight path, and the specific method is that the fitted curve of the flight path is used as the input quantity of the PID flight control algorithm, and the automatic flight control is carried out on the unmanned aerial vehicle through the PID flight control algorithm.
Further, the TIN-based DEM model specifically adopts a grid-based triangulation algorithm in the TIN algorithm.
The invention has the beneficial effects that:
1. according to the unmanned aerial vehicle flight route calculation method, the DEM geographic model is adopted, the flight route of the unmanned aerial vehicle is planned in advance, the flight route is input into the unmanned aerial vehicle in advance, the unmanned aerial vehicle does not need to calculate the flight route in real time in the flight process, and only the preset route is required to be installed for autonomous flight.
2. The unmanned aerial vehicle can realize autonomous navigation flight of terrain matching under the complex geographic environment, and can be used for a low-cost unmanned aerial vehicle control center with lower computing capability.
3. The unmanned aerial vehicle can realize the terrain matching autonomous navigation function in a complex environment without too many hardware devices.
Drawings
FIG. 1 is a block diagram of a PID flight control algorithm;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention and the accompanying drawings, 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.
The unmanned aerial vehicle flight control system comprises the following steps: the flight route is planned in advance, the whole flight route is decomposed into a section of short-distance flight route, then curve fitting is carried out to obtain a fitting curve of the flight route, and the unmanned aerial vehicle can avoid collision only by installing control parameters of the fitting curve of the flight route to fly.
Step1, establishing a DEM model of a flight area; the method comprises the steps of establishing a DEM model of a flight area based on TIN, wherein the TIN is an abbreviation of an irregular triangular net, the TIN is widely applied to a geographic information system, the area is divided into an equal triangular surface network according to a limited point set of the area, digital elevations consist of continuous triangular surfaces, the shapes and the sizes of the triangular surfaces depend on the positions and the densities of measuring points which are irregularly distributed, data redundancy when the terrain is flat can be avoided, and digital elevation features can be expressed according to terrain feature points.
The low-precision DEM geographic model can be downloaded freely on a DEM website, but the high-precision DEM model needs to be manufactured according to the requirement. In the flight process of the unmanned aerial vehicle, because the control precision of the unmanned aerial vehicle is very high, a high-precision DEM model is needed, generally a TIN-based algorithm is adopted to generate the high-precision DEM model based on TIN, a grid-based triangulation network generation algorithm in the TIN algorithm is adopted in the invention, and the specific method of the grid-based triangulation network generation algorithm is as follows:
a. forming a Thiessen polygon;
the structural analysis and the feature extraction of the data are realized by transforming the morphological structure of the raster data by a mathematical morphology method and adopting a morphological transformation principle. The binary morphology (function value field is defined at 0 or 1) is that the graphics are regarded as a set, and are converted into a new morphological structure under the action of structural elements through set logic operation and set morphological transformation. And if the gray value of the pixel corresponding to the data point is 1, the gray values of the other pixels are 0, the gray value of the pixel where the reference point is located is 1, and the gray values of the other pixels are 0, performing morphological transformation on the binary image to establish TIN.
Let X be the reference point pixel set, then the rest part after removing these reference points is the rest set X of XcI.e. a Thiessen polygon of TIN.
The skeleton SK (A) of the continuous image A is the set of the centers of the largest inscribed circles of A. The skeleton can be obtained by utilizing the conditional order inertia refining morphological transformation, and the topological adjacency relation of each component in the A can be ensured. The result is a set of pixels of continuous single pixel width and isotropy. The specific algorithm is as follows:
is provided with CkIs a grid circle of radius K, A is a subset of the image, and
A0＝A
selecting a structural element Li(i ═ 1, 2,. 8), then
SK(A)＝AO{Lk}；(Ak) (2)
I.e. the skeleton of a is generated by a conditional sequential refinement transformation of a. The iteration termination condition is
The above skeleton algorithm yields SK (X)c) I.e. the desired thiessen polygon.
b. Forming a triangular net;
if X is the set of reference points, PiE any reference point where X is X will be related to PiReference point and P in Thiessen polygon adjacent to Thiessen polygoniAre connected to form PiAll triangle sides that are vertices. The method comprises the following steps:
(i) will PiThe polygon expands to the boundary (i.e., skeleton of y)
Then P will beiConditional sequential dilation is performed until the Thiessen polygon is filled without crossing the polygon boundaries.
(ii) Extraction of and PiThe Thiessen polygon DiA set of adjacent polygons. First, make H pair DiCross the boundary, and then expand DiRemoving elements of (D) to leave DiIs conditionally expanded sequentially with respect to the boundary of the adjacent polygon, if the boundary is not exceeded (i.e., X)cThe skeleton of (a). DiSet of adjacent polygons D'i：
(iii) Extracting D'iOf points belonging to X, i.e. the extraction is located at PiReference point sets in the Thiessen polygons adjacent to the Thiessen polygon:
Qi＝D′i∩X (6)
sequentially connect PiAnd QiAnd generating an edge corresponding to the TIN.
The same treatment is carried out on each point in X, and the adjacent dots and related information are recorded and stored to construct
A TIN triangulation based DEM ground model is presented.
Step2, planning a flight route according to the DEM model; and selecting a flight path suitable for flying by using the DEM model, and calculating the total route and the flying height of the flight path.
Step3, dividing the flight route into a plurality of sections of short-distance flight routes; the method comprises the following specific steps:
and step1, dividing the whole flight route into a plurality of sections of short-distance flight routes, wherein dividing boundary points between each section of short-distance flight route are track points. The flight path point is divided into inflection points of the flight path, and is also called a control point of the flight path. All track points are combined to form the point density described in the title.
Step2, planning the flight height of each section of short flight route; confirm unmanned aerial vehicle's flying height in each section short distance course, unmanned aerial vehicle's each section flying height can be different, also can be the same, will change unmanned aerial vehicle's flying height, only need to change the height of two track points and can accomplish.
Step3, calculating a track point space coordinate value of each section of flight route; and (3) determining the space coordinate value of the track point of the unmanned aerial vehicle in the flight path through the DEM model calculated in the steps 1 and 2 and the determined flight path.
Step 4, making an approximate fitting route for the flight route; and 4, performing approximate fitting on the flight route, wherein the specific method is to adopt the space coordinate value of the route point of each section of the flight route as a fitting point and adopt a B-spline curve fitting algorithm to fit an approximate route curve. The general equation for B-spline curve fitting is as follows:
wherein P isi(i ═ 0,1.. n) is the course point of the flight path, Fi,kAnd (t) is a B-spline basis function of the K order.
In the three-dimensional space, if the coordinates of n +1 track points are respectively (x)0,y0,z0)...(xn,yn,zn) The coordinates of the three-dimensional B-spline curve can then be determined by the following formula:
the maximum and minimum values of t depend on the number of waypoints selected.
B-spline basis function Fi,k(t) is obtained by the recursive formula of equations (9) and (10) given below:
where Knot is called a node function and is determined by:
when the flight route is determined, the designed B-spline curve is expected to start at the starting point and end at the target point, and a determined tangent vector is provided, namely the initial velocity vector of the unmanned aerial vehicle. So, in the form of open B-spline curves, open uniform shaping node functions are generated by the company (11). The polynomial curve of the open B spline passes through the first track point and the last track point, wherein the K value determines the smoothness degree of the curve, the larger the K value is, the smoother the curve is, in the course calculation, if the K value is too large, the flight course can not be matched with an actually planned course, so that the K is selected to be 3, and the fitted course is most matched with the planned course.
And finally, generating a flight path by using a B spline fitting curve, wherein the flight path comprises (1) path point data (P) divided according to a path planned by the DEM model0,P1,P2,...Pn) Setting the takeoff position of the unmanned aerial vehicle as P0Landing position is Pn，P0,P1,P2,…PnAnd (3) obtaining a flight route fitting curve for all the flight path control points through a formula (7).
And 5, determining a flight control function of the unmanned aerial vehicle through the fitted route. The method specifically comprises the steps of adopting a fitting curve of a flight route as an input quantity of a PID flight control algorithm, and automatically controlling the flight of the unmanned aerial vehicle through the PID flight control algorithm. The PID control is a control quantity formed by linearly combining the error signal P, the integral I, and the differential D, and is called PID control. Because the control circuit has contained three control quantity x, y, z, consequently design 3 independent controllers and control the unmanned aerial vehicle flight. Input signals of a GPS position sensor and an attitude sensor of the unmanned aerial vehicle are input as feedback signals of the current position of the unmanned aerial vehicle, and a flight route curve fitted by a B sample of a planning route is input as a control signal for the flight of the unmanned aerial vehicle. Firstly, a four-rotor unmanned plane kinetic equation is made as follows:
wherein the content of the first and second substances,
is the yaw angle, theta is the pitch angle,
for roll angle, the Sin function and the Cos function are respectively expressed by S and C, m is the mass of the unmanned aerial vehicle, g is the gravitational constant, and IX,IY,IZIs the rotation inertia of the machine body around three axes.
Then determining PID control rate, set kp,ki,kdProportional, integral and differential coefficients, respectively, from equation (12) the position control loop equation can be derived as follows:
wherein x isd,yd,zdThe displacement amount is obtained by integrating the acceleration measured by the GPS position sensor.
The attitude control loop also has psi as yaw angle, theta as pitch angle,
setting k as three control values of the roll anglep,ki,kdProportional, integral and differential coefficients, respectively, from equation (12) the attitude control loop equation can be derived as follows:
wherein the content of the first and second substances,
θd,φdthe displacement amount is integrated with the acceleration measured by the attitude sensor.
Through the steps, a grid-based triangulation network generation algorithm is used for generating a TIN high-precision DEM model of the target flight area. Planning a flight route on the generated high DEM model, dividing the flight route into countless sections of flight routes at the inflection point of the flight route, wherein the divided inflection point is a track point; the unmanned aerial vehicle has the advantages that an unmanned aerial vehicle is loaded through the B-spline curve fitting algorithm, the flight route is combined with an attitude sensor and a GPS (global position system) position sensor of the unmanned aerial vehicle, the flight route serves as a given control quantity input signal controlled by a PID (proportion integration differentiation), the attitude sensor and the GPS position sensor serve as feedback quantities controlled by the PID, and the unmanned aerial vehicle can have an autonomous flight function matched with terrain by using the PID control algorithm.
Finally, it is noted that the above preferred embodiments are merely illustrative of the technical solutions of the present invention and not restrictive, and although the present invention has been described in detail with reference to the above preferred embodiments, it should be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the present invention.
Claims (1)
1. The utility model provides an unmanned aerial vehicle topography matching flight control system under point density guide which characterized in that: the unmanned aerial vehicle terrain matching flight control system comprises the following steps:
step1, establishing a DEM model of a flight area, wherein the DEM model of the flight area is established in the step1, and the specific model is a DEM model based on TIN;
step2, planning a flight route according to the DEM model;
step3, dividing the flight route into a plurality of sections of short-distance flight routes, and specifically comprising the following steps:
step1, dividing the whole flight route into a plurality of sections of short-distance flight routes, wherein dividing boundary points between each section of short-distance flight route are track points;
step2, planning the flight height of each section of short flight route;
step3, calculating a track point space coordinate value of each section of flight route;
step 4, performing approximate fitting route on the flight route, adopting the track point space coordinate value of each flight route as a fitting point, and adopting a B spline curve fitting algorithm to fit an approximate track curve;
step 5, determining a flight control function of the unmanned aerial vehicle through a fitted flight path, wherein the specific method is that a fitted curve of the flight path is used as an input quantity of a PID flight control algorithm, and the unmanned aerial vehicle is automatically subjected to flight control through the PID flight control algorithm;
the DEM model based on the TIN is established, and a grid-based triangular network generation algorithm in the TIN algorithm is specifically adopted.
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