CN112783185A - Predicted path obtaining method and device based on unmanned aerial vehicle - Google Patents
Predicted path obtaining method and device based on unmanned aerial vehicle Download PDFInfo
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Abstract
The embodiment of the application provides a predicted path obtaining method and device based on an unmanned aerial vehicle, and the predicted path obtaining method based on the unmanned aerial vehicle comprises the following steps: acquiring a historical motion path and a motion attitude of the unmanned aerial vehicle and predicting flight time; judging whether a stored motion path matched with the historical motion path exists in a preset database or not; when a stored motion path matched with the historical motion path does not exist in the preset database, determining a fitting function according to the motion attitude; performing path fitting according to the historical motion path and the fitting function to obtain a fitting path; and intercepting the fitted path according to the predicted flight time to obtain a predicted path. Therefore, by the implementation of the implementation mode, the flight path of the unmanned aerial vehicle can be efficiently predicted in real time, and the flight universality of the unmanned aerial vehicle is improved.
Description
Technical Field
The application relates to the technical field of unmanned aerial vehicles, in particular to a predicted path obtaining method and device based on an unmanned aerial vehicle.
Background
At present, with the rapid development of unmanned aerial vehicle technology, more and more unmanned aerial vehicles are put into various working environments. As is known, during the flight, the unmanned aerial vehicle usually flies according to a preset flight path. In practice, the current flight path is mostly manually preset, so that the unmanned aerial vehicle lacks certain autonomy in flight, and the flying universality of the unmanned aerial vehicle is reduced.
Disclosure of Invention
An object of the embodiments of the present application is to provide a predicted path obtaining method and apparatus based on an unmanned aerial vehicle, which can efficiently predict a flight path of the unmanned aerial vehicle in real time, thereby improving the flight universality of the unmanned aerial vehicle.
The first aspect of the embodiments of the present application provides a predicted path obtaining method based on an unmanned aerial vehicle, including:
acquiring a historical motion path and a motion attitude of the unmanned aerial vehicle and predicting flight time;
judging whether a stored motion path matched with the historical motion path exists in a preset database or not;
when a stored motion path matched with the historical motion path does not exist in the preset database, determining a fitting function according to the motion attitude;
performing path fitting according to the historical motion path and the fitting function to obtain a fitting path;
and intercepting the fitted path according to the predicted flight time to obtain a predicted path.
In the implementation process, the method can preferentially acquire the historical motion path and the motion attitude of the unmanned aerial vehicle and predict the flight time; then, judging whether a storage motion path matched with the historical motion path exists in a preset database or not; when a stored motion path similar to a historical motion path generated by the current unmanned aerial vehicle during flying does not exist in the preset database, determining a fitting function according to the motion attitude, so that the path obtained by fitting later is ensured to be corresponding to the current motion attitude, and a path which turns during the straight flying process or a path which flies during the circling process is avoided; when the fitting function is obtained, the method can further perform path fitting according to the historical motion path and the fitting function to obtain a fitting path; and then intercepting the fitted path according to the predicted flight time to obtain a predicted path. Therefore, by implementing the implementation mode, the flight path of the unmanned aerial vehicle can be automatically predicted in real time according to the composite information and the fitting function, so that the prediction precision and the prediction efficiency of the flight path of the unmanned aerial vehicle can be improved, the unmanned aerial vehicle flight path prediction method can be suitable for more unmanned aerial vehicle flight conditions, and the flight universality of the unmanned aerial vehicle is improved.
Further, the step of determining whether a stored motion path matching the historical motion path exists in a preset database includes:
acquiring the instantaneous flying speed of the unmanned aerial vehicle;
judging whether a storage motion path matched with the instantaneous flying speed and the historical motion path exists in a preset database or not;
and triggering and executing the step of determining the fitting function according to the motion posture when the stored motion path matched with the historical motion path does not exist in the preset database.
In the implementation process, the method can assist in judging whether the corresponding stored motion path exists in the preset database according to the current instantaneous flight speed of the unmanned aerial vehicle, so that the fitting function is determined according to the motion attitude when the corresponding stored motion path does not exist in the preset database, and the method can conveniently perform path fitting according to the fitting function. Therefore, whether corresponding content exists in the preset database can be accurately judged by implementing the implementation mode, so that the condition discovery such as misjudgment is avoided, and the acquisition precision of the predicted path of the whole unmanned aerial vehicle is improved.
Further, the motion attitude comprises a hovering flight attitude, a straight flight attitude and a general flight attitude, and the fitting function comprises a circular fitting function corresponding to the hovering flight attitude, a straight fitting function corresponding to the straight flight attitude and a fitting quadratic function corresponding to the general flight attitude.
In the implementation process, the method can determine the flight condition of the current unmanned aerial vehicle according to the motion attitude, so that a fitting function corresponding to the flight condition of the current unmanned aerial vehicle can be determined, the determination accuracy of the fitting function is improved, and the acquisition accuracy of the prediction path of the whole unmanned aerial vehicle is improved.
Further, when the motion attitude is the general flight attitude, the step of performing path fitting according to the historical motion path and the fitting function to obtain a fitting path includes:
extracting three position coordinates in the historical motion path;
calculating according to the three position coordinates and the fitting quadratic function to obtain a fitting curve;
calculating according to other position coordinates in the historical motion path and the fitted curve to obtain the variance of the other position coordinates relative to the fitted curve;
and when the variance is not greater than a preset threshold value, generating a fitting path according to the fitting curve.
In the implementation process, the method can perform route fitting in any flight process of the unmanned aerial vehicle, and can judge according to the route fitting result in real time, so that the route fitting precision is improved, and a more accurate fitting path can be obtained.
Further, the method further comprises:
when the variance is larger than the preset threshold, correcting the fitting curve according to a preset correction formula to obtain a correction curve;
and generating a fitting path according to the correction curve.
In the implementation process, the method can automatically adjust the fitting curve when the precision of the fitting curve is low, so that the fitting curve is adjusted to be a correction curve which can be used for generating the fitting path with enough precision, the internal adjustment of the fitting path is realized, and the acquisition precision of the fitting path is improved in the process of realizing automatic path prediction.
A second aspect of the embodiments of the present application provides an unmanned aerial vehicle-based predicted route acquiring apparatus, where the unmanned aerial vehicle-based predicted route acquiring apparatus includes:
the acquisition unit is used for acquiring the historical motion path, the motion attitude and the predicted flight time of the unmanned aerial vehicle;
the judging unit is used for judging whether a stored motion path matched with the historical motion path exists in a preset database or not;
the determining unit is used for determining a fitting function according to the motion posture when a stored motion path matched with the historical motion path does not exist in the preset database;
the fitting unit is used for performing path fitting according to the historical motion path and the fitting function to obtain a fitting path;
and the intercepting unit is used for intercepting the fitted path according to the predicted flight time to obtain a predicted path.
In the implementation process, the device can automatically predict the flight path of the unmanned aerial vehicle in real time according to the composite information and the fitting function, so that the prediction precision and the prediction efficiency of the flight path of the unmanned aerial vehicle can be improved, the device can be suitable for more flight conditions of the unmanned aerial vehicle, and the flight universality of the unmanned aerial vehicle is improved.
Further, the judging unit includes:
the acquiring subunit is used for acquiring the instantaneous flying speed of the unmanned aerial vehicle;
the judging subunit is used for judging whether a stored motion path matched with the instantaneous flying speed and the historical motion path exists in a preset database or not;
the judging subunit is further configured to trigger the determining unit to execute the operation of determining the fitting function according to the motion posture when the stored motion path matching the historical motion path does not exist in the preset database.
In the implementation process, the device can more accurately judge whether corresponding content exists in the preset database, so that the condition discovery such as misjudgment is avoided, and the acquisition precision of the prediction path of the whole unmanned aerial vehicle is improved.
Further, the motion attitude comprises a hovering flight attitude, a straight flight attitude and a general flight attitude, and the fitting function comprises a circular fitting function corresponding to the hovering flight attitude, a straight fitting function corresponding to the straight flight attitude and a fitting quadratic function corresponding to the general flight attitude.
In the implementation process, the device can determine the flight condition of the current unmanned aerial vehicle according to the motion attitude, so that a fitting function corresponding to the flight condition of the current unmanned aerial vehicle can be determined, the determination precision of the fitting function is improved, and the acquisition precision of the prediction path of the whole unmanned aerial vehicle is improved.
A third aspect of the embodiments of the present application provides an electronic device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the unmanned aerial vehicle-based predicted path obtaining method according to any one of the first aspect of the embodiments of the present application.
A fourth aspect of the present embodiment provides a computer-readable storage medium, which stores computer program instructions, where the computer program instructions, when read and executed by a processor, perform the method for obtaining a predicted path based on a drone according to any one of the first aspect of the present embodiment.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a predicted path obtaining method based on an unmanned aerial vehicle according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another predicted path obtaining method based on an unmanned aerial vehicle according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a predicted path obtaining apparatus based on an unmanned aerial vehicle according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of another predicted path obtaining apparatus based on an unmanned aerial vehicle according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1
Referring to fig. 1, fig. 1 is a schematic flowchart of a predicted path obtaining method based on an unmanned aerial vehicle according to an embodiment of the present application. The predicted path obtaining method based on the unmanned aerial vehicle comprises the following steps:
s101, obtaining a historical motion path and a motion attitude of the unmanned aerial vehicle and predicting flight time.
In this embodiment, the motion attitude includes a hovering flight attitude, a straight flight attitude, and a general flight attitude, and the fitting function includes a circular fitting function corresponding to the hovering flight attitude, a straight fitting function corresponding to the straight flight attitude, and a fitting quadratic function corresponding to the general flight attitude.
In this embodiment, the method may obtain a past path (i.e., a historical movement path) of the drone, where the historical movement path is composed of a plurality of coordinate points, where each coordinate point includes a longitude x and a latitude y, an instantaneous speed at each time, an engine speed, and a predicted time period (i.e., a predicted flight time period).
In this embodiment, the predicted flight duration is the time length that the unmanned aerial vehicle still needs to fly. For example, the drone still needs to fly for 30min during the flight, and the 30min is the predicted flight duration.
S102, judging whether a stored motion path matched with the historical motion path exists in a preset database, and if so, ending the process; if not, steps S103 to S105 are executed.
In this embodiment, the method may acquire the motion attitude and the flight record (i.e., the stored motion path) of the unmanned aerial vehicle in a preset database, and then determine whether the preset database has a stored motion path matching the motion attitude and the historical motion path.
In this embodiment, there may be some errors in the determination process.
In the present embodiment, the above-described error is generated because of the degree of coincidence between the history movement path and the stored movement path.
In the present embodiment, the error may be represented by an error rate, i.e., a coincidence rate between the history movement path and the stored movement path.
For example, the step is used for indicating whether the method searches a preset database for a path record which is coincident with a past path; specifically, this step may find the closest path within the allowable error range based on the motion attitude and the instantaneous flying speed.
In this embodiment, when the preset database has a stored motion path matching the historical motion path, the method determines the stored motion path as the current flight path, and intercepts the current flight path according to the predicted flight duration to obtain the predicted path.
And S103, determining a fitting function according to the motion attitude.
And S104, performing path fitting according to the historical motion path and the fitting function to obtain a fitting path.
And S105, intercepting the fitted path according to the predicted flight time to obtain a predicted path.
In this embodiment, the method may process a planar display of the predicted path according to the pitch condition of the drone and the engine speed.
In this embodiment, the method may intercept the corresponding portion according to the predicted duration in the flat display result, and then output the predicted path.
In the present embodiment, the predicted time period is a predicted flight time period.
In the embodiment, when the predicted flight time is 30min, the method intercepts a predicted path corresponding to 30min from the fitted path.
Implement this kind of embodiment, can carry out the path prediction to the aerofoil type unmanned aerial vehicle, its prediction becomes to adopt the algorithm based on curve fitting technique to combine aerofoil type unmanned aerial vehicle's structural feature well, can also utilize database storage technique simultaneously, reduce the prediction error to within 10%.
By implementing the embodiment, the unmanned aerial vehicle can have better expansibility and improvement, so that the method can be used in multiple fields of unmanned aerial vehicle path prediction, flight biological research and the like.
In the embodiment of the present application, the execution subject of the method may be a computing device such as a computer and a server, and is not limited in this embodiment.
In this embodiment, an execution subject of the method may also be an intelligent device such as a smart phone and a tablet computer, which is not limited in this embodiment.
As can be seen, by implementing the predicted path obtaining method based on the unmanned aerial vehicle described in this embodiment, the historical motion path, the motion attitude, and the predicted flight duration of the unmanned aerial vehicle can be preferentially obtained; then, judging whether a storage motion path matched with the historical motion path exists in a preset database or not; when a stored motion path similar to a historical motion path generated by the current unmanned aerial vehicle during flying does not exist in the preset database, determining a fitting function according to the motion attitude, so that the path obtained by fitting later is ensured to be corresponding to the current motion attitude, and a path which turns during the straight flying process or a path which flies during the circling process is avoided; when the fitting function is obtained, the method can further perform path fitting according to the historical motion path and the fitting function to obtain a fitting path; and then intercepting the fitted path according to the predicted flight time to obtain a predicted path. Therefore, by implementing the implementation mode, the flight path of the unmanned aerial vehicle can be automatically predicted in real time according to the composite information and the fitting function, so that the prediction precision and the prediction efficiency of the flight path of the unmanned aerial vehicle can be improved, the unmanned aerial vehicle flight path prediction method can be suitable for more unmanned aerial vehicle flight conditions, and the flight universality of the unmanned aerial vehicle is improved.
Example 2
Please refer to fig. 2, fig. 2 is a schematic flowchart of a predicted path obtaining method based on an unmanned aerial vehicle according to an embodiment of the present application. As shown in fig. 2, the predicted path obtaining method based on the drone includes:
s201, obtaining a historical motion path and a motion attitude of the unmanned aerial vehicle and predicting flight time.
S202, acquiring the instantaneous flying speed of the unmanned aerial vehicle.
S203, judging whether a stored motion path matched with the instantaneous flight speed and the historical motion path exists in the preset database, and if not, executing the steps S204-S210; if yes, the flow is ended.
And S204, determining a fitting function according to the motion attitude.
In this embodiment, the motion attitude includes a hovering flight attitude, a straight flight attitude, and a general flight attitude, and the fitting function includes a circular fitting function corresponding to the hovering flight attitude, a straight fitting function corresponding to the straight flight attitude, and a fitting quadratic function corresponding to the general flight attitude.
In this embodiment, when the motion attitude is the hovering flight attitude, the corresponding circle fitting function is (x-x)0)2+(y-y0)2＝R2. Under the motion posture, the method can perform path fitting and path interception according to the formula so as to obtain an arc-shaped predicted path.
In this embodiment, if the spiral path exists in the preset database, the method carries over the plan formed by the existing spiral path, and the predicted path and the spiral path are overlapped for common use.
In this embodiment, when there is no corresponding determination path in the preset database, the method selects the latest three coordinate points, connects the circumscribed circle of the triangle surrounded by the lines, determines the fitting path according to the circumscribed circle, and then executes step S210.
In this embodiment, when the motion attitude is a straight flight attitude, the corresponding straight line fitting function is Ax + By + C is 0. Under the motion attitude, the method can select two nearest coordinates, which are marked as M(xm,ym) And N (x)n,yn). Wherein if xm＝xnIf B is 0; if xm≠xnThen, the column equation system calculates the truncated equation, and then arranges the truncated equation to obtain a general equation, and then performs step S210.
And S205, when the motion attitude is a general flight attitude, extracting three position coordinates from the historical motion path.
In this embodiment, the fitting quadratic function is (y ═ ax)2+bx+c)。
And S206, calculating according to the three position coordinates and the fitting quadratic function to obtain a fitting curve.
In this embodiment, the method selects the latest three coordinates, which are denoted as P1(x1, y1), P2(x2, y2), and P3(x3, y3), and performs the following calculation:
1) by
Solving a quadratic term coefficient a;
2) by
Substituting a into b;
3) by
Substituting a and b to obtain c.
And S207, calculating according to other position coordinates in the historical motion path and the fitting curve to obtain the variance of the other position coordinates relative to the fitting curve.
In this embodiment, the method is used to calculate the variance between the corresponding point on the curve and other historical data, and if the variance is greater than the set value, step S208 is executed.
As an optional implementation manner, after the step of calculating according to the other position coordinates in the historical movement path and the fitted curve to obtain the variance of the other position coordinates relative to the fitted curve, the method further includes:
and when the variance is not greater than the preset threshold, generating a fitting path according to the fitting curve, and performing path fitting according to the historical motion path and the fitting function to obtain the fitting path.
And S208, when the variance is larger than a preset threshold value, correcting the fitted curve according to a preset correction formula to obtain a corrected curve.
In this embodiment, the correction formula is Y + t1ex+t2lnx, wherein t1,t2∈R。
In this embodiment, the method may use the above formula to correct the fitted curve to minimize the variance.
And S209, generating a fitting path according to the correction curve.
And S210, intercepting the fitted path according to the predicted flight time to obtain a predicted path.
It can be seen that, by implementing the predicted path obtaining method based on the unmanned aerial vehicle described in this embodiment, the flight path of the unmanned aerial vehicle can be automatically predicted in real time according to the composite information and the fitting function, so that the prediction accuracy and the prediction efficiency of the flight path of the unmanned aerial vehicle can be improved, and the method can be adapted to more flight conditions of the unmanned aerial vehicle, and can improve the flight universality of the unmanned aerial vehicle.
Example 3
Please refer to fig. 3, fig. 3 is a schematic structural diagram of a predicted path obtaining apparatus based on an unmanned aerial vehicle according to an embodiment of the present application. As shown in fig. 3, the predicted path acquiring apparatus based on the drone includes:
an obtaining unit 310, configured to obtain a historical motion path, a motion attitude, and a predicted flight duration of the unmanned aerial vehicle;
a judging unit 320, configured to judge whether a stored motion path matching the historical motion path exists in the preset database;
a determining unit 330, configured to determine a fitting function according to the motion posture when a stored motion path matching the historical motion path does not exist in the preset database;
the fitting unit 340 is configured to perform path fitting according to the historical motion path and the fitting function to obtain a fitting path;
and the intercepting unit 350 is configured to intercept the fitted path according to the predicted flight duration to obtain a predicted path.
In this embodiment of the application, for explanation of the predicted path obtaining apparatus based on the unmanned aerial vehicle, reference may be made to the description in embodiment 1 or embodiment 2, and details are not repeated in this embodiment.
It can be seen that, implement the prediction route acquisition device based on unmanned aerial vehicle that this embodiment described, can come real-time automatic prediction unmanned aerial vehicle flight path according to combined information and fitting function to can improve unmanned aerial vehicle flight path's prediction precision and with prediction efficiency, and then can be adapted to more unmanned aerial vehicle flight condition, realize the improvement to unmanned aerial vehicle flight universality.
Example 4
Please refer to fig. 4, fig. 4 is a schematic structural diagram of a predicted path acquiring apparatus based on an unmanned aerial vehicle according to an embodiment of the present application. The predicted path acquiring device based on the unmanned aerial vehicle shown in fig. 4 is obtained by optimizing the predicted path acquiring device based on the unmanned aerial vehicle shown in fig. 3. As shown in fig. 4, the judging unit 320 includes:
an obtaining subunit 321, configured to obtain an instantaneous flying speed of the unmanned aerial vehicle;
a judging subunit 322, configured to judge whether a stored motion path matching the instantaneous flight speed and the historical motion path exists in the preset database;
the judging subunit 322 is further configured to trigger the determining unit to perform an operation of determining the fitting function according to the motion posture when the stored motion path matching the historical motion path does not exist in the preset database.
As an alternative embodiment, the motion attitude includes a hover flight attitude, a straight flight attitude, and a general flight attitude, and the fitting function includes a circular fitting function corresponding to the hover flight attitude, a straight fitting function corresponding to the straight flight attitude, and a fitted quadratic function corresponding to the general flight attitude.
As an alternative embodiment, when the motion attitude is a general flight attitude, the fitting unit 340 includes:
an extraction subunit 341, configured to extract three position coordinates in the historical motion path;
the calculating subunit 342 is configured to perform calculation according to the three position coordinates and the fitting quadratic function to obtain a fitting curve;
the calculating subunit 342 is further configured to calculate according to the coordinates of the other positions in the historical movement path and the fitted curve, so as to obtain a variance between the coordinates of the other positions and the fitted curve;
and a fitting subunit 343, configured to generate a fitting path according to the fitting curve when the variance is not greater than the preset threshold.
As an optional implementation, the fitting unit further includes:
a correcting subunit 344, configured to correct the fitted curve according to a preset correction formula when the variance is greater than a preset threshold, to obtain a corrected curve;
and a fitting subunit 343, configured to generate a fitting path according to the correction curve.
In this embodiment of the application, for explanation of the predicted path obtaining apparatus based on the unmanned aerial vehicle, reference may be made to the description in embodiment 1 or embodiment 2, and details are not repeated in this embodiment.
It can be seen that, implement the prediction route acquisition device based on unmanned aerial vehicle that this embodiment described, can come real-time automatic prediction unmanned aerial vehicle flight path according to combined information and fitting function to can improve unmanned aerial vehicle flight path's prediction precision and with prediction efficiency, and then can be adapted to more unmanned aerial vehicle flight condition, realize the improvement to unmanned aerial vehicle flight universality.
The embodiment of the application provides an electronic device, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic device to execute the predicted path acquisition method based on the unmanned aerial vehicle in any one of embodiment 1 and embodiment 2 of the application.
The embodiment of the present application provides a computer-readable storage medium, which stores computer program instructions, and when the computer program instructions are read and executed by a processor, the method for obtaining a predicted path based on an unmanned aerial vehicle according to any one of embodiment 1 or embodiment 2 of the present application is executed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A predicted path obtaining method based on an unmanned aerial vehicle is characterized by comprising the following steps:
acquiring a historical motion path and a motion attitude of the unmanned aerial vehicle and predicting flight time;
judging whether a stored motion path matched with the historical motion path exists in a preset database or not;
when a stored motion path matched with the historical motion path does not exist in the preset database, determining a fitting function according to the motion attitude;
performing path fitting according to the historical motion path and the fitting function to obtain a fitting path;
and intercepting the fitted path according to the predicted flight time to obtain a predicted path.
2. The predicted path acquiring method based on unmanned aerial vehicle according to claim 1, wherein the step of determining whether a stored motion path matching the historical motion path exists in a preset database comprises:
acquiring the instantaneous flying speed of the unmanned aerial vehicle;
judging whether a storage motion path matched with the instantaneous flying speed and the historical motion path exists in a preset database or not;
and triggering and executing the step of determining the fitting function according to the motion posture when the stored motion path matched with the historical motion path does not exist in the preset database.
3. The unmanned aerial vehicle-based predicted path acquisition method of claim 1, wherein the motion attitude comprises a hover flight attitude, a straight flight attitude, and a general flight attitude, and the fitting function comprises a circular fitting function corresponding to the hover flight attitude, a straight fitting function corresponding to the straight flight attitude, and a fitted quadratic function corresponding to the general flight attitude.
4. The predicted path acquisition method based on unmanned aerial vehicle of claim 3, wherein when the motion attitude is the general flight attitude, the step of performing path fitting according to the historical motion path and the fitting function to obtain a fitted path comprises:
extracting three position coordinates in the historical motion path;
calculating according to the three position coordinates and the fitting quadratic function to obtain a fitting curve;
calculating according to other position coordinates in the historical motion path and the fitted curve to obtain the variance of the other position coordinates relative to the fitted curve;
and when the variance is not greater than a preset threshold value, generating a fitting path according to the fitting curve.
5. The drone-based predicted path acquisition method of claim 4, further comprising:
when the variance is larger than the preset threshold, correcting the fitting curve according to a preset correction formula to obtain a correction curve;
and generating a fitting path according to the correction curve.
6. An unmanned aerial vehicle-based predicted path acquisition device, comprising:
the acquisition unit is used for acquiring the historical motion path, the motion attitude and the predicted flight time of the unmanned aerial vehicle;
the judging unit is used for judging whether a stored motion path matched with the historical motion path exists in a preset database or not;
the determining unit is used for determining a fitting function according to the motion posture when a stored motion path matched with the historical motion path does not exist in the preset database;
the fitting unit is used for performing path fitting according to the historical motion path and the fitting function to obtain a fitting path;
and the intercepting unit is used for intercepting the fitted path according to the predicted flight time to obtain a predicted path.
7. The predicted path acquisition apparatus based on unmanned aerial vehicle according to claim 6, wherein the determination unit includes:
the acquiring subunit is used for acquiring the instantaneous flying speed of the unmanned aerial vehicle;
the judging subunit is used for judging whether a stored motion path matched with the instantaneous flying speed and the historical motion path exists in a preset database or not;
the judging subunit is further configured to trigger the determining unit to execute the operation of determining the fitting function according to the motion posture when the stored motion path matching the historical motion path does not exist in the preset database.
8. The drone-based predicted path acquisition device of claim 6, wherein the motion attitude comprises a hover flight attitude, a straight-ahead flight attitude, and a general flight attitude, and the fitting function comprises a circular fitting function corresponding to the hover flight attitude, a straight-line fitting function corresponding to the straight-ahead flight attitude, and a fitted quadratic function corresponding to the general flight attitude.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the drone-based predicted path acquisition method of any one of claims 1 to 5.
10. A readable storage medium having stored therein computer program instructions, which when read and executed by a processor, perform the drone-based predicted path acquisition method of any one of claims 1 to 5.
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