CN118444697B - Hanging control method, system, terminal and medium for unmanned aerial vehicle tower climbing anti-falling device - Google Patents
Hanging control method, system, terminal and medium for unmanned aerial vehicle tower climbing anti-falling device Download PDFInfo
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Abstract
The invention discloses a hanging control method, a hanging control system, a hanging control terminal and a hanging control medium for a tower climbing anti-falling device of an unmanned aerial vehicle, which relate to the technical field of unmanned aerial vehicles and have the technical scheme that: according to the invention, based on artificial intelligent algorithms such as computer vision and binocular depth stereoscopic vision of deep learning, the most difficult hanging of the anti-falling device operated by a flight hand is replaced, the automatic hanging of the anti-falling device is realized, the unmanned aerial vehicle only needs to vertically take off from the ground near the tower, the unmanned aerial vehicle remains hovering after the flight height of the unmanned aerial vehicle is higher than a certain distance of the tower, the corresponding mounting point is automatically selected and the automatic hanging function is started in the unmanned aerial vehicle, the high-difficulty hanging operation which can only be finished by a high-order flight hand in the past can be automatically finished, and the demands of groups on the high-order flight hand are greatly reduced.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a hanging control method, a hanging control system, a hanging control terminal and a hanging control medium for a tower climbing anti-falling device of an unmanned aerial vehicle.
Background
In recent years, electric power production accidents frequently occur, particularly, high-altitude falling accidents in tower climbing operation become prominent problems, the danger and high occurrence of the accidents are highlighted, and the accidents become important hidden dangers for electric power safety production. Safety of tower climbing operation is critical to the power industry, but reliable tower climbing anti-falling protection tools are lacking in the market, and high-altitude falling accidents are caused.
Currently, three types of falling protection devices for tower climbing exist: 1. anti-falling double-extension ropes, double-hook anti-falling devices and safety rings, however, the devices require operators to change original operation habits, physical energy consumption is increased, but the risk of falling is possibly increased, so that the first-line team is not strong in use intention. 2. Install anti-falling cat ladder, anti-falling guide rail, anti-falling steel strand wires additional, although these devices can prevent effectively that falling, nevertheless need have a power failure to install additional, with high costs, invest in is big, and the later maintenance degree of difficulty is also big, and is only applicable to newly built iron tower, is unfavorable for stock iron tower transformation and popularization on a large scale. 3. The unmanned aerial vehicle carries and steps on tower anti-falling device, carry through unmanned aerial vehicle and step on tower protection fixed bolster and security rope and fly to the iron tower top, under real-time video image transmission assistance, realize the quick installation of fixed bolster at the top of the tower and demolish, although reduced the trade pain point to a certain extent, but whole mounting process needs the manual operation unmanned aerial vehicle of flight hand to accomplish and hang and get the operation, job site environment is complicated, weather is various, in order to prevent unmanned aerial vehicle and fry the machine, unmanned aerial vehicle and carry and step on tower protection fixed bolster all adopt soft link to be connected, the strong wind can cause the support that unmanned aerial vehicle carried to shake violently, lead to support and shaft tower mount point to be difficult to aim at, especially tear down when getting, the support does not have the burden and leads to rocking aggravate, need try repeatedly many times just can accomplish and hang and get.
Therefore, how to study and design a hanging control method, a hanging control system, a hanging control terminal and a hanging control medium for an unmanned aerial vehicle tower climbing anti-falling device, which can overcome the defects, are the problems which are urgently needed to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide a hanging control method, a system, a terminal and a medium for the anti-falling device of the unmanned aerial vehicle, which are based on artificial intelligent algorithms such as computer vision for deep learning, binocular depth stereoscopic vision and the like, to replace the hanging of the anti-falling device which is the most difficult to operate by a flight, so that the automatic hanging of the anti-falling device is realized, the unmanned aerial vehicle only needs to vertically take off from the ground near a pole, the unmanned aerial vehicle is kept hovering after the flight height of the unmanned aerial vehicle is higher than the pole by a certain distance, the corresponding hanging point is automatically selected and the automatic hanging function is started on the unmanned aerial vehicle, the high-difficulty hanging operation which can only be completed by the flight of a high order can be automatically completed, and the demands of groups on the flight of high order flight are greatly reduced.
The technical aim of the invention is realized by the following technical scheme:
in a first aspect, a hanging control method for a tower climbing anti-falling device of an unmanned aerial vehicle is provided, which is characterized by comprising the following steps:
Controlling an unmanned aerial vehicle mounted with the anti-falling device to fly above a mounting area in a transmission tower;
Collecting first image information of a mounting area through a camera configured by the unmanned aerial vehicle, and identifying at least one group of symmetrical mounting points from the first image information;
calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth;
constructing constraint conditions according to the mounting positions of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all mounting points;
Determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle;
controlling the unmanned aerial vehicle to fly horizontally to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle;
And controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point, so that the anti-falling device is mounted at the optimal mounting point.
Further, the process of identifying at least one group of symmetrical mounting points from the first image information specifically includes:
Collecting picture data of the top of the tower, marking mounting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a first identification model, and identifying a plurality of groups of mounting points with confidence coefficient not lower than a preset confidence coefficient from first image information through the first identification model;
Or, collecting the picture data of the top of the tower, marking the mounting points and the supporting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a second identification model, and identifying a plurality of groups of mounting points with the distance between the mounting points and the supporting points not smaller than a preset distance from the first image information through the second identification model.
Further, the process of calculating the mounting distance of each mounting point by adopting the fusion algorithm of monocular nerve reasoning and binocular depth specifically comprises the following steps:
Calibrating a binocular camera system, and determining internal parameters and external parameters of the camera, wherein the internal parameters comprise focal length and optical center coordinates, and the external parameters comprise the relative position relationship between two cameras;
extracting characteristic points from left and right view images acquired by two cameras respectively, and matching the characteristic points in the left and right views by adopting SIFT, SURF and/or ORB algorithm;
Calculating the parallax of each pair of matched characteristic points on the left view image plane and the right view image plane, wherein the parallax is displacement in the horizontal direction;
Calculating the three-dimensional depth of each feature point according to the internal parameters, the base line length and the parallax of the camera;
Combining the three-dimensional depth of the feature points with the plane coordinates of the corresponding feature points in the left view image and the right view image to obtain coordinates of the corresponding feature points in a three-dimensional space;
And taking the unmanned aerial vehicle as an origin in the three-dimensional space, and determining the mounting distance of each mounting point according to the coordinates of the characteristic points in the three-dimensional space.
Further, the process of selecting the optimal mounting point with the smallest mounting distance from all the mounting points according to the constraint condition built according to the mounting direction of the unmanned aerial vehicle specifically comprises the following steps:
in a group of symmetrical mounting points P0 and P1, the vector of the mounting point P0 pointing to the mounting point P1 is the indication azimuth of the mounting point P0, and the vector of the mounting point P1 pointing to the mounting point P0 is the indication azimuth of the mounting point P1;
Taking an azimuth included angle between the mounting azimuth and the indication azimuth of the unmanned aerial vehicle as a constraint condition, and selecting effective mounting points meeting the constraint condition from all mounting points;
and selecting a mounting point with the smallest mounting distance from the effective mounting points as an optimal mounting point, wherein the mounting distance is the linear distance between the mounting point and the unmanned aerial vehicle in the three-dimensional space.
Further, the process of determining the course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle specifically comprises the following steps:
Determining an initial horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the first image information, wherein the distribution direction points to the right side of the first image information;
Controlling the unmanned aerial vehicle to horizontally rotate by a preset angle according to the initial horizontal included angle, wherein the unmanned aerial vehicle rotates anticlockwise when the initial horizontal included angle is an acute angle, and rotates clockwise when the initial horizontal included angle is an obtuse angle;
The unmanned aerial vehicle horizontally rotates by a preset angle, then acquires second image information of the optimal mounting point, and resets after image acquisition is completed;
Determining a reference horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the second image information;
calculating an angle difference value between the initial horizontal included angle and the reference horizontal included angle, and calculating an angle coefficient according to the ratio of a preset angle to the angle difference value;
And calculating a course correction angle of the unmanned aerial vehicle according to the product of the initial horizontal included angle and the angle coefficient, wherein the rotating direction of the course correction angle is the same as the direction of the horizontal rotation of the unmanned aerial vehicle by a preset angle.
Further, the process of controlling the unmanned aerial vehicle to fly horizontally to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle specifically comprises the following steps:
Determining coordinates of characteristic points contained in the optimal mounting points in a three-dimensional space when the mounting distance of the optimal mounting points is calculated, wherein the characteristic points are central points of the optimal mounting points;
Determining a horizontal movement direction and a horizontal movement distance according to an X-axis coordinate and a Y-axis coordinate of the coordinates of the feature points in the three-dimensional space;
after the unmanned aerial vehicle completes horizontal rotation according to the course correction angle, horizontal movement is performed according to the horizontal movement direction and the horizontal movement distance.
Further, the method further comprises:
dividing the full period of the vertical flight of the unmanned aerial vehicle into a first period and a second period;
Collecting vibration information of the unmanned aerial vehicle in a first period and the actual wind speed of the environment in which the unmanned aerial vehicle is located in real time;
Determining a direction deflection angle between the actual wind speed and the distribution direction of the optimal mounting points;
determining the component of the actual wind speed in the indication direction of the optimal mounting point according to the direction deflection angle to obtain a first wind speed;
Determining the maximum polarization time of the anti-falling device between the interval sub-alignment optimal mounting points according to the vibration information of the unmanned aerial vehicle;
Establishing a correlation function between the maximum polarization time and the first wind speed;
Predicting a second wind speed of a second period according to the first wind speed of the first period, and estimating estimated polarization time of the second period by combining the correlation function;
And dynamically determining the downward moving distance of the vertical flight of the unmanned aerial vehicle according to the mounting distance of the optimal mounting point, and dynamically adjusting the downward moving speed of the vertical flight of the unmanned aerial vehicle, so that the anti-falling device mounted on the unmanned aerial vehicle is mounted when aiming at the optimal mounting point and being in the minimum estimated polarization time.
In a second aspect, there is provided a hanging control system of a tower climbing anti-falling device of an unmanned aerial vehicle, which is used for implementing the hanging control method of the tower climbing anti-falling device of the unmanned aerial vehicle according to any one of the first aspects, including:
the initial control module is used for controlling the unmanned aerial vehicle mounted with the anti-falling device to fly above a mounting area in the transmission tower;
The image processing module is used for acquiring first image information of the mounting area through a camera configured by the unmanned aerial vehicle and identifying at least one group of symmetrical mounting points from the first image information;
The distance calculation module is used for calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth;
The mounting identification module is used for constructing constraint conditions according to the mounting direction of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all the mounting points;
The course correction module is used for determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle;
the translation control module is used for controlling the unmanned aerial vehicle to horizontally fly to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the course correction angle of the unmanned aerial vehicle;
And the vertical control module is used for controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point so as to complete the mounting of the anti-falling device at the optimal mounting point.
In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method for controlling hanging of the unmanned aerial vehicle tower landing protection device according to any one of the first aspects when the processor executes the program.
In a fourth aspect, there is provided a computer readable medium having stored thereon a computer program, wherein execution of the computer program by a processor implements the method for controlling hanging of a tower landing gear of an unmanned aerial vehicle according to any one of the first aspects.
Compared with the prior art, the invention has the following beneficial effects:
1. According to the unmanned aerial vehicle tower climbing anti-falling device hanging control method, based on the computer vision of deep learning, binocular depth stereoscopic vision and other artificial intelligent algorithms, the hanging of the anti-falling device which is the most difficult to operate by a flight is replaced, the automatic hanging of the anti-falling device is realized, the unmanned aerial vehicle only needs to vertically take off from the ground near the tower, the unmanned aerial vehicle keeps hovering after the flight height of the unmanned aerial vehicle is higher than the tower by a certain distance, the corresponding hanging point is automatically selected by the unmanned aerial vehicle, the automatic hanging function is started, the high-difficulty hanging operation which can only be completed by a high-order flight in the past can be automatically completed, and the demands of groups on the high-order flight are greatly reduced;
2. According to the invention, the symmetrical mounting points in pairs are identified, and the mounting points with stable reliability are screened out by combining the confidence coefficient or the supporting point, so that the difficulty of visual processing can be effectively reduced, and the mounting direction and the mounting distance of the unmanned aerial vehicle are considered, so that the control workload of the unmanned aerial vehicle can be reduced and the automatic mounting working time of the unmanned aerial vehicle can be reduced when the mounting points which do not interfere the anti-falling rope connected with the anti-falling device are identified;
3. when the course correction angle of the unmanned aerial vehicle is determined, the difference of shooting angles of the cameras in the process of collecting images is considered, and the course correction angle with smaller error can be calculated after angle correction is carried out according to the angle coefficient pair, so that the anti-falling device is aligned with the mounting point;
4. According to the invention, when the unmanned aerial vehicle flies vertically, the downward moving speed of the unmanned aerial vehicle flies vertically is dynamically adjusted in consideration of the influence of vibration caused by the external wind speed and the mechanical transmission process of the unmanned aerial vehicle, so that the anti-falling device mounted on the unmanned aerial vehicle is hung when aiming at the optimal mounting point and being in the minimum estimated polarization time, and the probability of the unmanned aerial vehicle frying is effectively reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart in embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of the recognition result of the mounting point in embodiment 1 of the present invention;
FIG. 3 is a schematic diagram of analysis of mounting distance in embodiment 1 of the present invention;
fig. 4 is a system block diagram in embodiment 2 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1: the hanging control method of the unmanned aerial vehicle tower climbing anti-falling device, as shown in fig. 1, comprises the following steps:
s1: controlling an unmanned aerial vehicle mounted with the anti-falling device to fly above a mounting area in a transmission tower;
S2: collecting first image information of a mounting area through a camera configured by the unmanned aerial vehicle, and identifying at least one group of symmetrical mounting points from the first image information;
s3: calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth;
s4: constructing constraint conditions according to the mounting positions of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all mounting points;
s5: determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle;
S6: controlling the unmanned aerial vehicle to fly horizontally to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle;
s7: and controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point, so that the anti-falling device is mounted at the optimal mounting point.
As a possible implementation manner, the process of identifying at least one group of symmetrical mounting points from the first image information is specifically as follows: and acquiring picture data of the top of the pole tower, marking mounting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a first identification model, and identifying a plurality of groups of mounting points with the confidence coefficient not lower than a preset confidence coefficient from the first image information through the first identification model, wherein the mounting points are shown in fig. 2.
As another possible implementation manner, the process of identifying at least one set of symmetrical mounting points from the first image information is specifically: and acquiring picture data of the top of the tower, marking mounting points and supporting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a second identification model, and identifying a plurality of groups of mounting points with the distance between the mounting points and the supporting points not smaller than a preset distance from the first image information through the second identification model, wherein the supporting points can be areas fixedly mounted through the supporting plates.
In step S3, as shown in fig. 3, in order to achieve horizontal movement of the unmanned aerial vehicle to a position directly above the mounting point, the relative horizontal offset distances x, y and the height z between the mounting point and the unmanned aerial vehicle are calculated. And fusion of monocular nerve reasoning and binocular depth is adopted. And running mounting point identification detection on a camera, fusing an identification result with a binocular stereo parallax depth result, generating the stereo parallax result in real time and in parallel based on semi-global matching (SGBM), and then re-projecting depth data of an identification target at a 3D position (x, y and z coordinates, unit: mm) in a physical space.
(1) Calibrating the binocular camera system, and determining internal parameters and external parameters of the camera, wherein the internal parameters comprise focal length and optical center coordinates, and the external parameters comprise the relative position relationship between the two cameras.
(2) Characteristic points are respectively extracted from left and right view images acquired by the two cameras, and characteristic points in the left and right views are matched by adopting SIFT (Scale-INVARIANT FEATURE TRANSFORM), SURF (Speeded Up Robust Features), ORB (Oriented FAST and Rotated BRIEF) and other algorithms.
(3) And calculating the parallax of each pair of matched characteristic points on the left view image plane and the right view image plane, wherein the parallax is displacement in the horizontal direction.
The parallax d has the following relationship with the three-dimensional depth z of the object: d=f B/z; where f is the focal length of the camera and B is the base length (distance between the optical centers of the two cameras) of the binocular camera.
(4) And calculating the three-dimensional depth of each feature point according to the internal parameters of the camera, the base line length and the parallax.
z = f * B / d。
(5) And combining the three-dimensional depth of the feature point with plane coordinates (u, v) of the corresponding feature point P in the left view image and the right view image to obtain coordinates (x, y and z) of the corresponding feature point in a three-dimensional space.
x = (u - cx) * z / f
y = (v - cy) *z / f；
Wherein, (cx, cy) is the principal point coordinate of the image, and is the intersection point of the optical axis of the camera and the imaging plane. Typically at the approximate geometric center of the image.
(6) And taking the unmanned aerial vehicle as an origin in the three-dimensional space, and determining the mounting distance of each mounting point according to the coordinates of the characteristic points in the three-dimensional space.
In step S4, a constraint condition is constructed according to the mounting direction of the unmanned aerial vehicle, and the process of selecting the optimal mounting point with the smallest mounting distance from all the mounting points is specifically as follows: in a group of symmetrical mounting points P0 and P1, the vector of the mounting point P0 pointing to the mounting point P1 is the indication azimuth of the mounting point P0, and the vector of the mounting point P1 pointing to the mounting point P0 is the indication azimuth of the mounting point P1; taking an azimuth included angle between the mounting azimuth and the indication azimuth of the unmanned aerial vehicle as a constraint condition, and selecting effective mounting points meeting the constraint condition from all mounting points; and selecting a mounting point with the smallest mounting distance from the effective mounting points as an optimal mounting point, wherein the mounting distance is the linear distance between the mounting point and the unmanned aerial vehicle in the three-dimensional space.
According to the invention, the symmetrical mounting points in pairs are identified, and the mounting points with stable reliability are screened out by combining the confidence coefficient or the supporting point, so that the difficulty of visual processing can be effectively reduced, and the mounting direction and the mounting distance of the unmanned aerial vehicle are considered, so that the control workload of the unmanned aerial vehicle can be reduced and the automatic mounting working time of the unmanned aerial vehicle can be reduced when the mounting points which do not interfere the anti-falling rope connected with the anti-falling device are identified.
In step S5, the process of determining the heading correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle specifically includes: determining an initial horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the first image information, wherein the distribution direction points to the right side of the first image information; controlling the unmanned aerial vehicle to horizontally rotate by a preset angle according to the initial horizontal included angle, wherein the unmanned aerial vehicle rotates anticlockwise when the initial horizontal included angle is an acute angle, and rotates clockwise when the initial horizontal included angle is an obtuse angle; the unmanned aerial vehicle horizontally rotates by a preset angle, then acquires second image information of the optimal mounting point, and resets after image acquisition is completed; determining a reference horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the second image information; calculating an angle difference value between the initial horizontal included angle and the reference horizontal included angle, and calculating an angle coefficient according to the ratio of a preset angle to the angle difference value; and calculating a course correction angle of the unmanned aerial vehicle according to the product of the initial horizontal included angle and the angle coefficient, wherein the rotating direction of the course correction angle is the same as the direction of the horizontal rotation of the unmanned aerial vehicle by a preset angle.
According to the invention, when the course correction angle of the unmanned aerial vehicle is determined, the difference of shooting angles of the cameras in the image acquisition process is considered, and the course correction angle with smaller error can be calculated after angle correction is carried out according to the angle coefficient pair, so that the anti-falling device is aligned with the mounting point.
In step S6, the process of controlling the unmanned aerial vehicle to fly horizontally to right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle specifically includes: determining coordinates of characteristic points contained in the optimal mounting points in a three-dimensional space when the mounting distance of the optimal mounting points is calculated, wherein the characteristic points are central points of the optimal mounting points; determining a horizontal movement direction and a horizontal movement distance according to an X-axis coordinate and a Y-axis coordinate of the coordinates of the feature points in the three-dimensional space; after the unmanned aerial vehicle completes horizontal rotation according to the course correction angle, horizontal movement is performed according to the horizontal movement direction and the horizontal movement distance.
In step S7, the vertical flight of the unmanned aerial vehicle may be moved down at a fixed speed. In order to reduce the probability of unmanned aerial vehicle frying, the invention takes the environmental wind speed and the vibration condition of the unmanned aerial vehicle into consideration in the vertical flight of the unmanned aerial vehicle, so as to adaptively adjust the descending speed of the unmanned aerial vehicle, and the specific implementation comprises the following steps: dividing the full period of the vertical flight of the unmanned aerial vehicle into a first period and a second period; collecting vibration information of the unmanned aerial vehicle in a first period and the actual wind speed of the environment in which the unmanned aerial vehicle is located in real time; determining a direction deflection angle between the actual wind speed and the distribution direction of the optimal mounting points; determining the component of the actual wind speed in the indication direction of the optimal mounting point according to the direction deflection angle to obtain a first wind speed; determining the maximum polarization time of the anti-falling device between the interval sub-alignment optimal mounting points according to the vibration information of the unmanned aerial vehicle; establishing a correlation function between the maximum polarization time and the first wind speed; predicting a second wind speed of a second period according to the first wind speed of the first period, and estimating estimated polarization time of the second period by combining the correlation function; and dynamically determining the downward moving distance of the vertical flight of the unmanned aerial vehicle according to the mounting distance of the optimal mounting point, and dynamically adjusting the downward moving speed of the vertical flight of the unmanned aerial vehicle, so that the anti-falling device mounted on the unmanned aerial vehicle is mounted when aiming at the optimal mounting point and being in the minimum estimated polarization time.
According to the invention, when the unmanned aerial vehicle flies vertically, the downward moving speed of the unmanned aerial vehicle flies vertically is dynamically adjusted in consideration of the influence of vibration caused by the external wind speed and the mechanical transmission process of the unmanned aerial vehicle, so that the anti-falling device mounted on the unmanned aerial vehicle is hung when aiming at the optimal mounting point and being in the minimum estimated polarization time, and the probability of the unmanned aerial vehicle frying is effectively reduced.
The method for controlling the hanging of the unmanned aerial vehicle tower climbing anti-falling device can be applied to the dismantling control of the unmanned aerial vehicle tower climbing anti-falling device, and particularly can be used for controlling the automatic dismantling of the unmanned aerial vehicle tower climbing anti-falling device when a hanging rod or a hanging hook configured by the unmanned aerial vehicle is aligned with the anti-falling device in the connecting process of the anti-falling device, namely, the identification mounting point is replaced by the identification positioning structure.
Example 2: the system is used for realizing the unmanned aerial vehicle tower climbing anti-falling device hanging control method as described in the embodiment 1, and comprises an initial control module, an image processing module, a distance calculation module, a hanging identification module, a course correction module, a translation control module and a vertical control module as shown in fig. 4.
The initial control module is used for controlling the unmanned aerial vehicle on which the anti-falling device is mounted to fly above a mounting area in the transmission tower; the image processing module is used for acquiring first image information of the mounting area through a camera configured by the unmanned aerial vehicle and identifying at least one group of symmetrical mounting points from the first image information; the distance calculation module is used for calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth; the mounting identification module is used for constructing constraint conditions according to the mounting direction of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all the mounting points; the course correction module is used for determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle; the translation control module is used for controlling the unmanned aerial vehicle to horizontally fly to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the course correction angle of the unmanned aerial vehicle; and the vertical control module is used for controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point so as to complete the mounting of the anti-falling device at the optimal mounting point.
The invention also discloses a computer terminal which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the hanging control method of the unmanned aerial vehicle tower climbing anti-falling device according to any one of the first aspect when executing the program.
The invention also describes a computer-readable medium on which a computer program is stored, characterized in that the computer program is executed by a processor to implement the unmanned aerial vehicle tower landing protection device hanging control method as in any one of the first aspects.
Working principle: according to the invention, based on artificial intelligent algorithms such as computer vision and binocular depth stereoscopic vision of deep learning, the most difficult hanging of the anti-falling device operated by a flight hand is replaced, the automatic hanging of the anti-falling device is realized, the unmanned aerial vehicle only needs to vertically take off from the ground near the tower, the unmanned aerial vehicle remains hovering after the flight height of the unmanned aerial vehicle is higher than a certain distance of the tower, the corresponding mounting point is automatically selected and the automatic hanging function is started in the unmanned aerial vehicle, the high-difficulty hanging operation which can only be finished by a high-order flight hand in the past can be automatically finished, and the demands of groups on the high-order flight hand are greatly reduced.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. The hanging control method of the unmanned aerial vehicle tower climbing anti-falling device is characterized by comprising the following steps of:
Controlling an unmanned aerial vehicle mounted with the anti-falling device to fly above a mounting area in a transmission tower;
Collecting first image information of a mounting area through a camera configured by the unmanned aerial vehicle, and identifying at least one group of symmetrical mounting points from the first image information;
calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth;
constructing constraint conditions according to the mounting positions of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all mounting points;
Determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle;
controlling the unmanned aerial vehicle to fly horizontally to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle;
controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point so as to finish the mounting of the anti-falling device at the optimal mounting point;
The process for calculating the mounting distance of each mounting point by adopting the fusion algorithm of monocular nerve reasoning and binocular depth comprises the following steps:
Calibrating a binocular camera system, and determining internal parameters and external parameters of the camera, wherein the internal parameters comprise focal length and optical center coordinates, and the external parameters comprise the relative position relationship between two cameras;
extracting characteristic points from left and right view images acquired by two cameras respectively, and matching the characteristic points in the left and right views by adopting SIFT, SURF and/or ORB algorithm;
Calculating the parallax of each pair of matched characteristic points on the left view image plane and the right view image plane, wherein the parallax is displacement in the horizontal direction;
Calculating the three-dimensional depth of each feature point according to the internal parameters, the base line length and the parallax of the camera;
Combining the three-dimensional depth of the feature points with the plane coordinates of the corresponding feature points in the left view image and the right view image to obtain coordinates of the corresponding feature points in a three-dimensional space;
Using the unmanned aerial vehicle as an origin in a three-dimensional space, and determining the mounting distance of each mounting point according to the coordinates of the characteristic points in the three-dimensional space;
And running mounting point identification detection on a camera, fusing an identification result with a binocular stereo parallax depth result, generating the stereo parallax result in real time and in parallel based on semi-global matching, and then re-projecting depth data of an identification target at a 3D position in a physical space.
2. The method for controlling the hanging of the anti-falling device for the unmanned aerial vehicle to climb a tower according to claim 1, wherein the process of identifying at least one group of symmetrical hanging points from the first image information is specifically as follows:
Collecting picture data of the top of the tower, marking mounting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a first identification model, and identifying a plurality of groups of mounting points with confidence coefficient not lower than a preset confidence coefficient from first image information through the first identification model;
Or, collecting the picture data of the top of the tower, marking the mounting points and the supporting points in the picture data, training by adopting a semantic segmentation algorithm to obtain a second identification model, and identifying a plurality of groups of mounting points with the distance between the mounting points and the supporting points not smaller than a preset distance from the first image information through the second identification model.
3. The method for controlling the hanging of the unmanned aerial vehicle tower climbing anti-falling device according to claim 1, wherein the process of constructing constraint conditions according to the hanging direction of the unmanned aerial vehicle and selecting the optimal hanging point with the smallest hanging distance from all the hanging points is specifically as follows:
in a group of symmetrical mounting points P0 and P1, the vector of the mounting point P0 pointing to the mounting point P1 is the indication azimuth of the mounting point P0, and the vector of the mounting point P1 pointing to the mounting point P0 is the indication azimuth of the mounting point P1;
Taking an azimuth included angle between the mounting azimuth and the indication azimuth of the unmanned aerial vehicle as a constraint condition, and selecting effective mounting points meeting the constraint condition from all mounting points;
and selecting a mounting point with the smallest mounting distance from the effective mounting points as an optimal mounting point, wherein the mounting distance is the linear distance between the mounting point and the unmanned aerial vehicle in the three-dimensional space.
4. The method for controlling the hanging of the unmanned aerial vehicle tower climbing anti-falling device according to claim 1, wherein the process of determining the heading correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle is specifically as follows:
Determining an initial horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the first image information, wherein the distribution direction points to the right side of the first image information;
Controlling the unmanned aerial vehicle to horizontally rotate by a preset angle according to the initial horizontal included angle, wherein the unmanned aerial vehicle rotates anticlockwise when the initial horizontal included angle is an acute angle, and rotates clockwise when the initial horizontal included angle is an obtuse angle;
The unmanned aerial vehicle horizontally rotates by a preset angle, then acquires second image information of the optimal mounting point, and resets after image acquisition is completed;
Determining a reference horizontal included angle between the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle in the second image information;
calculating an angle difference value between the initial horizontal included angle and the reference horizontal included angle, and calculating an angle coefficient according to the ratio of a preset angle to the angle difference value;
And calculating a course correction angle of the unmanned aerial vehicle according to the product of the initial horizontal included angle and the angle coefficient, wherein the rotating direction of the course correction angle is the same as the direction of the horizontal rotation of the unmanned aerial vehicle by a preset angle.
5. The method for controlling the hanging of the anti-falling device of the unmanned aerial vehicle on the tower according to claim 1, wherein the process of controlling the unmanned aerial vehicle to fly horizontally to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the heading correction angle of the unmanned aerial vehicle is specifically as follows:
Determining coordinates of characteristic points contained in the optimal mounting points in a three-dimensional space when the mounting distance of the optimal mounting points is calculated, wherein the characteristic points are central points of the optimal mounting points;
Determining a horizontal movement direction and a horizontal movement distance according to an X-axis coordinate and a Y-axis coordinate of the coordinates of the feature points in the three-dimensional space;
after the unmanned aerial vehicle completes horizontal rotation according to the course correction angle, horizontal movement is performed according to the horizontal movement direction and the horizontal movement distance.
6. The unmanned aerial vehicle tower climbing anti-falling device hanging control method according to claim 1, wherein the method further comprises:
dividing the full period of the vertical flight of the unmanned aerial vehicle into a first period and a second period;
Collecting vibration information of the unmanned aerial vehicle in a first period and the actual wind speed of the environment in which the unmanned aerial vehicle is located in real time;
Determining a direction deflection angle between the actual wind speed and the distribution direction of the optimal mounting points;
determining the component of the actual wind speed in the indication direction of the optimal mounting point according to the direction deflection angle to obtain a first wind speed;
Determining the maximum polarization time of the anti-falling device between the interval sub-alignment optimal mounting points according to the vibration information of the unmanned aerial vehicle;
Establishing a correlation function between the maximum polarization time and the first wind speed;
Predicting a second wind speed of a second period according to the first wind speed of the first period, and estimating estimated polarization time of the second period by combining the correlation function;
And dynamically determining the downward moving distance of the vertical flight of the unmanned aerial vehicle according to the mounting distance of the optimal mounting point, and dynamically adjusting the downward moving speed of the vertical flight of the unmanned aerial vehicle, so that the anti-falling device mounted on the unmanned aerial vehicle is mounted when aiming at the optimal mounting point and being in the minimum estimated polarization time.
7. The unmanned aerial vehicle tower climbing anti-falling device hanging control system is characterized in that the system is used for realizing the unmanned aerial vehicle tower climbing anti-falling device hanging control method according to any one of claims 1-6, and comprises the following steps:
the initial control module is used for controlling the unmanned aerial vehicle mounted with the anti-falling device to fly above a mounting area in the transmission tower;
The image processing module is used for acquiring first image information of the mounting area through a camera configured by the unmanned aerial vehicle and identifying at least one group of symmetrical mounting points from the first image information;
The distance calculation module is used for calculating the mounting distance of each mounting point by adopting a fusion algorithm of monocular nerve reasoning and binocular depth;
The mounting identification module is used for constructing constraint conditions according to the mounting direction of the unmanned aerial vehicle, and selecting the optimal mounting point with the smallest mounting distance from all the mounting points;
The course correction module is used for determining a course correction angle of the unmanned aerial vehicle by combining the distribution direction of the optimal mounting points and the mounting direction of the unmanned aerial vehicle;
the translation control module is used for controlling the unmanned aerial vehicle to horizontally fly to the position right above the optimal mounting point according to the mounting distance of the optimal mounting point and the course correction angle of the unmanned aerial vehicle;
And the vertical control module is used for controlling the unmanned aerial vehicle to fly vertically according to the mounting distance of the optimal mounting point so as to complete the mounting of the anti-falling device at the optimal mounting point.
8. A computer terminal comprising a memory, a processor and a computer program stored in the memory and operable on the processor, wherein the processor implements the unmanned aerial vehicle tower landing protection device hanging control method according to any one of claims 1 to 6 when executing the program.
9. A computer-readable medium having stored thereon a computer program, wherein execution of the computer program by a processor realizes the unmanned aerial vehicle tower entry fall arrest device hanging control method according to any one of claims 1 to 6.
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| CN114010973A (en) * | 2021-11-12 | 2022-02-08 | 广东电网有限责任公司 | Artificial tower climbing protection device based on unmanned aerial vehicle technology and use method thereof |
| CN115554629A (en) * | 2022-09-30 | 2023-01-03 | 广东冠能电力科技发展有限公司 | Tower climbing protection device and control method thereof |
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| CN118004418A (en) * | 2024-02-04 | 2024-05-10 | 国网山东省电力公司东营供电公司 | A fall protection connector for drone suspension |
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| CN114010973A (en) * | 2021-11-12 | 2022-02-08 | 广东电网有限责任公司 | Artificial tower climbing protection device based on unmanned aerial vehicle technology and use method thereof |
| CN115554629A (en) * | 2022-09-30 | 2023-01-03 | 广东冠能电力科技发展有限公司 | Tower climbing protection device and control method thereof |
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