CN115793716B - Automatic optimization method and system for unmanned aerial vehicle route - Google Patents
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Abstract
The invention provides an automatic optimization method and system for an unmanned aerial vehicle route, and relates to the technical field of route optimization. Firstly, acquiring a flight route and related information, converting uninteresting photographing points in the route into transition points according to preset rules, and updating the flight route. And then, respectively calculating the distance between each transition point and the surrounding obstacles to obtain a safety threshold corresponding to the transition point. And finally, calculating an optimized distance corresponding to the transition point by combining the information of the adjacent two waypoints before and after the transition point, comparing the optimized distance with a safety threshold value to obtain and delete part of the transition points according to an optimized result, thereby optimizing the flight route. On one hand, unnecessary photographing points in the original route are converted into transition points, so that the operation time of photographing and the like is shortened; on the other hand, through a threshold optimization algorithm, the waypoints deviating from the straight line in the space are intelligently and safely deleted, so that the flight route is automatically optimized, and the automatic flight efficiency of the unmanned aerial vehicle is improved.
Description
Technical Field
The invention relates to the technical field of route optimization, in particular to an automatic route optimization method and system for an unmanned aerial vehicle.
Background
Along with the automatic flight of large-scale unmanned aerial vehicle, unmanned aerial vehicle operation mode has been by traditional manual teaching manual operation unmanned aerial vehicle flight, and after the transition was planned the route in advance, put unmanned aerial vehicle into flight, let its automatic execution route. In the process of automatically executing the route, no one can stay on a preset waypoint and take a picture. Through the series of actions, the aim of automatically flying and acquiring data of the unmanned aerial vehicle is finally achieved. The unmanned aerial vehicle automatically flies, so that the problems of insufficient experience of operators, accurate track flying, accurate point position photographing and the like can be solved, and the unmanned aerial vehicle is a key process for realizing robot operation in the future.
Although unmanned aerial vehicle automated flight can solve many problems, in the course of executing automated course fly-away, if course design is unreasonable, unmanned aerial vehicle automated flight's efficiency can greatly reduced. The main problems are that: the unmanned aerial vehicle can mechanically stay at each waypoint in the process of an automatic flying route so as to execute corresponding tasks. If the current waypoints are too many, the unmanned aerial vehicle may not fly the complete route before the power is exhausted. In addition, during the flight, a lot of photographing data of invalid waypoints are recorded, and the operator may only be interested in part of the key waypoints of the current route, and then obtain the photographing data from the interested waypoints. Therefore, how to automatically optimize the flight route of the unmanned aerial vehicle, so that the unmanned aerial vehicle can quickly execute the interest point photographing task is a problem to be solved urgently.
Disclosure of Invention
The invention aims to provide an automatic optimization method and system for an unmanned aerial vehicle route, which can intelligently delete the waypoints deviating from the straight line in the space according to an actual threshold value, automatically optimize the flight route and improve the automatic flight efficiency of the unmanned aerial vehicle.
Embodiments of the present invention are implemented as follows:
in a first aspect, embodiments of the present application provide a method for automatic optimization of an unmanned aerial vehicle route, including:
acquiring a loaded flight route, and extracting waypoint information and corresponding barrier information; the navigation points comprise transition points and photographing points;
converting uninteresting photographing points into transition points according to preset rules, and updating a flight route;
based on the updated flight route, respectively calculating the distance between each transition point and surrounding obstacles according to the obstacle information to obtain a safety threshold corresponding to the transition point;
calculating an optimized distance corresponding to the transition point by combining information of two adjacent waypoints before and after the transition point, comparing the optimized distance with the safety threshold value to obtain and optimize a flight route according to an optimized result; according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to a straight line formed by the two adjacent navigation points before and after to obtain an optimized distance corresponding to the transition point; if the optimized distance corresponding to the transition point is smaller than the safety threshold, marking the transition point; if the optimized distance corresponding to the transition point is not smaller than the safety threshold, marking is not carried out; and deleting the marked transition points to obtain the optimized airplane route.
Further, the step of converting the uninteresting photographing points into transition points according to the preset rule and updating the flight route includes:
acquiring and marking interested photographing points in the waypoint set according to preset photographing point information of interest of operators;
and converting the remaining unlabeled photographing points in the navigation point set into transition points, and updating the flight route.
In a second aspect, embodiments of the present application provide an automated optimization system for an unmanned aerial vehicle, comprising:
the information extraction module is used for acquiring the loaded flight route and extracting the waypoint information and the corresponding barrier information; the navigation points comprise transition points and photographing points;
the first round of optimization module is used for converting uninteresting photographing points into transition points according to preset rules and updating a flight route;
the distance calculation module is used for calculating the distance between each transition point and surrounding obstacles according to the updated flight route and the obstacle information respectively to obtain a safety threshold corresponding to the transition point;
the second round of optimization module is used for combining the information of the adjacent two navigation points before and after the transition point, calculating the optimization distance corresponding to the transition point, comparing the optimization distance with the safety threshold value, and obtaining and optimizing the flight route according to the optimization result; according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to a straight line formed by the two adjacent navigation points before and after to obtain an optimized distance corresponding to the transition point; if the optimized distance corresponding to the transition point is smaller than the safety threshold, marking the transition point; if the optimized distance corresponding to the transition point is not smaller than the safety threshold, marking is not carried out; and deleting the marked transition points to obtain the optimized airplane route.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory for storing one or more programs; a processor. The method as described in any one of the first aspects is implemented when the one or more programs are executed by the processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described in any of the first aspects above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the embodiment of the application provides an automatic optimization method and system for an unmanned aerial vehicle route, wherein firstly, a loaded flight route is obtained, and navigation point information and corresponding obstacle information are extracted. The navigation points comprise transition points and photographing points. And then, converting uninteresting photographing points into transition points according to preset rules, and updating the flight route. Therefore, unnecessary photographing tasks are reduced, and the time for the unmanned aerial vehicle to execute the tasks is shortened. And then, based on the updated flight route, respectively calculating the distance between each transition point and surrounding obstacles according to the obstacle information to obtain a safety threshold corresponding to the transition point. And finally, calculating the optimized distance corresponding to the transition point by combining the information of the adjacent two waypoints before and after the transition point, comparing the optimized distance with the safety threshold value to obtain and delete part of the transition points according to the optimized result, thereby optimizing the flight route and improving the operation efficiency. Overall, the present application optimizes the flight path in two main aspects. On one hand, unnecessary photographing tasks are reduced and the time for the unmanned aerial vehicle to execute the tasks is shortened by converting photographing points which are not interested by operators in the original flight route into transition points; on the other hand, through a threshold optimization algorithm, the waypoints deviating from the straight line in the space are intelligently and safely deleted, so that the flight route is automatically optimized, and the automatic flight efficiency of the unmanned aerial vehicle is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram illustrating steps of an embodiment of a method for automatic optimization of unmanned aerial vehicle routes according to the present invention;
FIG. 2 is a schematic diagram of a flight route in an embodiment of a method for automatic optimization of unmanned aerial vehicle route according to the present invention;
FIG. 3 is a schematic diagram of route optimization in an embodiment of the present invention for an automatic unmanned aerial vehicle route optimization method;
FIG. 4 is a block diagram of a configuration for an automatic unmanned aerial vehicle route optimization system provided by the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 1. a memory; 2. a processor; 3. a communication interface; 11. an information extraction module; 12. a first round optimization module; 13. a distance calculation module; 14. and a second round of optimization module.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
In general, in the process of executing an automatic course flight, the unmanned aerial vehicle mainly comprises the following steps:
1. the drone is turned on and automatically loads the route to be executed. The airlines are planned in advance by operators, and each section of airlines has a certain distance from the obstacle so as to ensure the smooth execution of the flight tasks;
2. and after the unmanned aerial vehicle is successfully loaded into the route, executing the action of the blade and flying to the first flying spot. After the first flying spot is executed, flying to the next flying spot;
3. fly to the next waypoint and perform the relevant actions. Wherein, each navigation point needing to execute corresponding action is divided into a transition point and a photographing point. At the transition point, no one has the opportunity to perform a pause and a fuselage rotation action; at the photographing point, the unmanned aerial vehicle performs a plurality of actions such as pause, body rotation, cradle head rotation, photographing and the like, and stores the obtained related pictures and data. Repeating the steps 2 and 3 after the unmanned plane flies to the current waypoint and executes related actions;
4. and the unmanned aerial vehicle flies to each waypoint, and after the corresponding action task is executed, the current route is considered to be executed. And then, the unmanned aerial vehicle automatically executes the return command. After the return voyage is completed, the unmanned aerial vehicle stops the blade, so that the whole automatic route flying and operation are completed.
And then, the operator can read the relevant pictures and data from the memory card of the unmanned aerial vehicle.
However, sometimes the operator is only interested in a part of the key waypoints of the current route, and only needs the photographing data obtained on a part of the key waypoints. Therefore, how to automatically optimize the flight route of the unmanned aerial vehicle, so that the unmanned aerial vehicle can quickly execute the interest point photographing task is a problem to be solved urgently.
In view of the above problems, an embodiment of the present application provides an automatic optimization method for an unmanned aerial vehicle route, specifically referring to fig. 1, the method includes the following steps:
step S1: acquiring a loaded flight route, and extracting waypoint information and corresponding barrier information; the waypoints comprise transition points and photographing points.
In the above steps, a schematic diagram of the flight path of the unmanned aerial vehicle is shown in fig. 2. Because the route track of the unmanned aerial vehicle is formed on the three-dimensional space, the route operation efficiency of the unmanned aerial vehicle is improved, and the route points cannot be deleted at will, so that the unmanned aerial vehicle is prevented from being hit by obstacles in the air flight process to cause the frying machine.
Step S2: and converting uninteresting photographing points into transition points according to preset rules, and updating the flight route.
In the above steps, firstly, the interested photographing points in the waypoint set are marked according to the preset photographing point information of the interested photographing points of the operators. And then, converting the remaining unlabeled photographing points in the waypoint set into transition points, so that the flight route is updated, unnecessary photographing tasks are reduced, and the time for the unmanned aerial vehicle to execute the tasks is shortened.
Specifically, assume that the set of waypoints of the original flight route is: [ A1, A2, A3, A4, A5, A6, A7, A8 ]. Wherein A2, A3, A4, A6, A8 with "] symbols are the original shooting points, and the other points are transition points. According to the preset information, the photographing points of interest of the operator are only A3#, A6#, and A8#, so that the three points are marked, and the unmarked points A2#, A4# areconverted into transition points, so that the flying route is updated, and a new waypoint set is obtained: [ A1, A2, A3, A4, A5, A6, A7, A8, ], an ]. Thus, the first round of optimization of the flight route is completed.
Step S3: and respectively calculating the distance between each transition point and surrounding obstacles according to the obstacle information based on the updated flight route, and obtaining a safety threshold corresponding to the transition point.
For example, as shown in fig. 3, assume that the current transition point is A2, around which there are three obstacles, obstacle 1, obstacle 2, and obstacle 3. According to the space coordinates of each point, the distances from the transition point A2 to three barriers are calculated to be D1, D2 and D3 respectively. In order to ensure safe execution of the navigation task, the shortest distance among three obstacle distances is selected as a safety threshold corresponding to the transition point. Therefore, the unmanned aerial vehicle can not touch the obstacle when executing the section of the route within the safety threshold range. The method is equivalent to obtaining a spherical safety area taking the transition point as the center of a circle and taking the safety threshold value as the radius, and the unmanned aerial vehicle can fly within the spherical range without touching the obstacle, so that the flight safety is ensured. Therefore, if the transition point is not on the straight line formed by the two adjacent navigation points, namely, deviates from the straight line, the transition point can be optimally judged according to the safety threshold value corresponding to the transition point so as to judge whether the transition point can be deleted or not. If the unmanned aerial vehicle can be deleted, the unmanned aerial vehicle can directly fly in a straight line, so that the flight path is shortened.
Step S4: and calculating the optimization distance corresponding to the transition point by combining the information of the two adjacent waypoints before and after the transition point, comparing the optimization distance with the safety threshold value to obtain and optimize the flight route according to the optimization result.
In the above step, firstly, according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to the straight line formed by the two adjacent navigation points before and after to obtain the optimized distance corresponding to the transition point. If the optimized distance corresponding to the transition point is smaller than the safety threshold, marking the transition point; and if the optimized distance corresponding to the transition point is not smaller than the safety threshold, marking is not carried out. And then deleting the marked transition points to complete the second round of optimization, and obtaining the optimized airplane route.
For example, referring to fig. 3, assume that the current transition point A2 has spatial coordinates of (x 2, y2, z 2), the previous waypoint A1 has spatial coordinates of (x 1, y1, z 1), and the next waypoint A3 has spatial coordinates of (x 3, y3, z 3). First, the spatial distances between the three points are calculated, respectively. The spatial distance s1 from the current transition point A2 to the waypoint A1 is:
the spatial distance s2 from the current transition point A2 to the waypoint A3 is:
the spatial distance s3 from waypoint A1 to waypoint A3 is:
then, according to the obtained distances s1, s2 and s3, the included angle alpha between the straight line A1A2 and the straight line A1A3 can be obtained by the cosine law:
then, according to the included angle α, the distance D from the current transition point A2 to the straight line A1A3 can be defined by the sine theorem, that is, the optimized distance corresponding to the current transition point A2:
and finally, judging whether the transition point can be deleted or not according to the optimized distance and the safety threshold value corresponding to the transition point. If the optimized distance D is less than the safety threshold, indicating that the point can be deleted, and marking the point as B1; if the optimized distance D is not less than the safety threshold, the point is not deleted. And then sequentially carrying out the calculation and judgment on each transition point to obtain points B2, B3.
Based on the same inventive concept, the invention also provides an automatic optimization system for the unmanned aerial vehicle route, please refer to fig. 4, and fig. 4 is a structural block diagram of the automatic optimization system for the unmanned aerial vehicle route provided by the embodiment of the application. The system comprises:
the information extraction module 11 is used for acquiring the loaded flight route and extracting the waypoint information and the corresponding obstacle information; the navigation points comprise transition points and photographing points;
the first round of optimization module 12 is configured to convert the uninteresting photographing points into transition points according to a preset rule, and update the flight route;
the distance calculation module 13 is configured to calculate, based on the updated flight route, a distance between each transition point and surrounding obstacles according to the obstacle information, so as to obtain a safety threshold corresponding to the transition point;
the second round of optimization module is used for combining the information of the adjacent two navigation points before and after the transition point, calculating the optimization distance corresponding to the transition point, comparing the optimization distance with the safety threshold value, and obtaining and optimizing the flight route according to the optimization result; according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to a straight line formed by the two adjacent navigation points before and after to obtain an optimized distance corresponding to the transition point; if the optimized distance corresponding to the transition point is smaller than the safety threshold, marking the transition point; if the optimized distance corresponding to the transition point is not smaller than the safety threshold, marking is not carried out; and deleting the marked transition points to obtain the optimized airplane route.
Referring to fig. 5, fig. 5 is a block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 1, a processor 2 and a communication interface 3, wherein the memory 1, the processor 2 and the communication interface 3 are electrically connected with each other directly or indirectly so as to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 1 may be used to store software programs and modules, such as program instructions/modules provided in the embodiments of the present application for use in an unmanned aerial vehicle route automatic optimization system, and the processor 2 executes the software programs and modules stored in the memory 1 to perform various functional applications and data processing. The communication interface 3 may be used for communication of signaling or data with other node devices.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (5)
1. The automatic optimization method for the unmanned aerial vehicle route is characterized by comprising the following steps of:
acquiring a loaded flight route, and extracting waypoint information and corresponding barrier information; the navigation points comprise transition points and photographing points, the transition points are used for the unmanned aerial vehicle to execute pause and body rotation actions, and the photographing points are used for the unmanned aerial vehicle to execute pause, body rotation, cradle head rotation and photographing actions;
converting uninteresting photographing points into transition points according to preset rules, and updating a flight route;
based on the updated flight route, respectively calculating the distance between each transition point and surrounding obstacles according to the obstacle information to obtain a safety threshold corresponding to the transition point;
calculating an optimized distance corresponding to the transition point by combining information of two adjacent waypoints before and after the transition point, comparing the optimized distance with the safety threshold value to obtain and optimize a flight route according to an optimized result; according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to a straight line formed by the two adjacent navigation points before and after to obtain an optimized distance corresponding to the transition point; if the optimized distance corresponding to the transition point is smaller than the safety threshold value, marking the transition point; if the optimized distance corresponding to the transition point is not smaller than the safety threshold value, marking is not carried out; and deleting the marked transition points to obtain the optimized airplane route.
2. The method for automatic optimization of unmanned aerial vehicle course according to claim 1, wherein the step of converting the uninteresting photo-taking points into transition points according to the preset rule and updating the flight course comprises:
acquiring and marking interested photographing points in the waypoint set according to preset photographing point information of interest of operators;
and converting the remaining unlabeled photographing points in the navigation point set into transition points, and updating the flight route.
3. Be used for unmanned aerial vehicle route automatic optimization system, its characterized in that includes:
the information extraction module is used for acquiring the loaded flight route and extracting the waypoint information and the corresponding barrier information; the navigation points comprise transition points and photographing points, the transition points are used for the unmanned aerial vehicle to execute pause and body rotation actions, and the photographing points are used for the unmanned aerial vehicle to execute pause, body rotation, cradle head rotation and photographing actions;
the first round of optimization module is used for converting uninteresting photographing points into transition points according to preset rules and updating a flight route;
the distance calculation module is used for calculating the distance between each transition point and surrounding obstacles according to the updated flight route and the obstacle information respectively to obtain a safety threshold corresponding to the transition point;
the second round of optimization module is used for combining the information of the adjacent two navigation points before and after the transition point, calculating the optimization distance corresponding to the transition point, comparing the optimization distance with the safety threshold value, and obtaining and optimizing the flight route according to the optimization result; according to the space coordinates of the transition point and the two adjacent navigation points before and after the transition point, calculating the distance from the transition point to a straight line formed by the two adjacent navigation points before and after to obtain an optimized distance corresponding to the transition point; if the optimized distance corresponding to the transition point is smaller than the safety threshold value, marking the transition point; if the optimized distance corresponding to the transition point is not smaller than the safety threshold value, marking is not carried out; and deleting the marked transition points to obtain the optimized airplane route.
4. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the method for unmanned aerial vehicle route auto-optimization of any of claims 1-2 is implemented when the one or more programs are executed by the processor.
5. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements a method for automatic optimization of a drone route according to any one of claims 1-2.
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| 考虑无人机杆塔巡视避障的两阶段路径优化;李朝钦;彭晓涛;;计算机测量与控制(第04期);全文 * |
| 适用于城市区域航拍的无人机航线规划研究;张磊;朱励轩;张滕远;徐为驰;李标;;公路交通科技(应用技术版)(第02期);全文 * |
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