CN111572790A - Scalable comprehensive protection control system and method for unmanned aerial vehicle - Google Patents
Scalable comprehensive protection control system and method for unmanned aerial vehicle Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENTS OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D45/00—Aircraft indicators or protectors not otherwise provided for
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention belongs to the technical field of unmanned aerial vehicles, and discloses a telescopic comprehensive protection control system and a telescopic comprehensive protection control method for an unmanned aerial vehicle, wherein an image acquisition module is used for shooting images around the unmanned aerial vehicle according to a certain frequency in the flying process of the unmanned aerial vehicle; the image feature correction module is used for automatically judging whether obstacles exist around the shot image by adopting the SIFT feature point matching technology; the distance measuring module is used for detecting the distance between the unmanned aerial vehicle and the obstacle through the distance sensor; the automatic telescopic module is used for protecting the unmanned aerial vehicle by extending the electric telescopic rod outwards to the connected elastic rubber block when the unmanned aerial vehicle approaches to the obstacle; the telescopic protection device adopted by the invention can comprehensively protect the unmanned aerial vehicle in the flight process, the adopted soft rubber material is light in weight, the power consumption of the unmanned aerial vehicle is saved, the adopted automatic detection structure enables the whole structure to be compact, the air resistance of the unmanned aerial vehicle in the flight process is avoided, and the power consumption of the unmanned aerial vehicle is saved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to a telescopic comprehensive protection control system and method for an unmanned aerial vehicle.
Background
At present, an unmanned aerial vehicle is an unmanned aerial vehicle operated by utilizing a radio remote control device and a self-contained program control device, or is completely or intermittently and autonomously operated by a vehicle-mounted computer, and has the advantages of small volume, low manufacturing cost, convenience in use, low requirement on the operational environment, strong battlefield viability and the like. In the flight process of the unmanned aerial vehicle, if the operation is not good, the obstacle is easily collided, and the damage is caused.
However, most of the existing unmanned aerial vehicles do not have corresponding protective measures, and the occurrence of collision damage is easy to happen; some unmanned aerial vehicles have installed protection device, but protection device wholly covers unmanned aerial vehicle, has improved unmanned aerial vehicle's flight resistance to energy resource consumption has been increased.
Through the above analysis, the problems and defects of the prior art are as follows:
(1) most of the existing unmanned aerial vehicles do not have corresponding protective measures, and the occurrence of collision damage is easy to happen;
(2) some unmanned aerial vehicles have installed protection device, but protection device wholly covers unmanned aerial vehicle, has improved unmanned aerial vehicle's flight resistance to energy resource consumption has been increased.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a telescopic comprehensive protection control system and method for an unmanned aerial vehicle.
The invention is realized in such a way that an unmanned aerial vehicle telescopic comprehensive protection control method comprises the following steps:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
the denoising comprises the following steps:
(1) denoising the unmanned aerial vehicle collected image f (x, y) containing noise by carrying out stationary wavelet transform and neighborhood coefficient shrinkage to obtain sub-band coefficients respectively: low frequency coefficients, horizontal detail coefficients, vertical detail coefficients, and diagonal detail coefficients;
(2) carrying out region segmentation on the low-frequency coefficient of the first layer by using PCNN;
(3) keeping the low-frequency coefficient unchanged, and respectively performing neighborhood threshold processing on the horizontal detail coefficient, the vertical detail coefficient and the diagonal detail coefficient of each layer;
(4) processing the noise unmanned aerial vehicle collected image by adopting a pulse coupling neural network to obtain an entropy sequence En, and taking En as an edge detection operator;
(5) optimizing a threshold to obtain an optimal denoising threshold k;
(6) denoising the unmanned aerial vehicle acquired image by adopting an improved anisotropic diffusion model according to the obtained edge detection operator En and the optimal denoising threshold value k;
the feature extraction of the unmanned aerial vehicle collected image comprises the steps of respectively expanding L unmanned aerial vehicle collected images after noise processing according to rows and then combining the L unmanned aerial vehicle collected images to form a matrix X (X) with L rows and M × N columns1，x2，x3，Λ，xL)T；
Averaging matrix X such that e (X) is 0;
whitening the matrix X such that E (X)TX)＝I；
Initializing the number of independent components to enable n to be 1, wherein n is the number of the independent components;
initialization wnRandomly select wn＝wn/||wn||；
According to the formula wn＝E{Xg(wn TX)}-E{g′(wn TX)}wnCalculating wn；
According to the formula
Iterate out wn+1；
According to the formula
And wn+1＝wn+1/||wn+1| |, iterating to obtain wn+1；
W obtained by judgmentn+1Whether or not to converge, if wn+1Not converging, returning to find wn；
Taking n as n +1, and extracting all independent components one by one under the condition that n is less than M;
forming each independent component into a matrix S ═ S (S)1，s2，Λ，sP)TAccording to the formula P ═ FS-1Calculating characteristics P of L unmanned aerial vehicle collected images;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
Further, the calculating the texture image by using the 3 × 3 second-order gradient gaussian template comprises:
in the horizontal direction:
where I (x, y) represents the pixel intensity of image NL;
in the vertical direction:
calculating gradient values:
further, the improved hierarchical topic model based clustering comprises:
introducing the distance information into a topic model for joint modeling to obtain clustering results, wherein each cluster corresponds to a topic;
selecting a function at a certain distance or a certain random process to depict the intensity change of the theme when modeling the distance information; the Beta distribution has asymmetry relative to other distributions and is used for modeling distance information;
probability density function of Beta distribution:
modeling the special subjects of different levels by using a level model nCRP;
the sampling level is set according to the following conditional probability:
p(zd,n|z-(d,n),c,w,m,π,η)∝p(zd,n|zd,-n,m,π)p(wd,n|z,c,w-(d,n),η)；
zd,nrepresenting the topic assignment of the current word, zd,-nRepresenting the current topic assignment for the rest of the observed data after excluding the topic to be sampled, k is the topic index, and c, w, m, pi, η are the hyperparameters.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
Another object of the present invention is to provide a scalable overall protection control system for an unmanned aerial vehicle, which operates the scalable overall protection control method for an unmanned aerial vehicle, the scalable overall protection control system for an unmanned aerial vehicle comprising:
the image acquisition module is connected with the central control module, comprises a camera and is used for shooting images around the unmanned aerial vehicle according to a certain frequency in the flying process of the unmanned aerial vehicle;
the image feature correction module is connected with the central control module and is used for automatically judging whether obstacles exist around the shot image by adopting the SIFT feature point matching technology;
after the image information is acquired by the unmanned aerial vehicle, the feature points extracted by the improved SIFT can be constructed into a vector P ═ { P ═ by1，p2，…，pnN is the number of the feature points, then the vector P is used for proofreading with the feature total vector of the memory, and when the matching degree reaches 60%, the obstacle is judged to exist;
the distance measuring module is connected with the central control module, comprises two paths of distance sensors and is used for detecting the distance between the unmanned aerial vehicle and the barrier through the distance sensors;
the two distance sensors can measure the linear distance between the unmanned aerial vehicle and the obstacle, the distance from the unmanned aerial vehicle to the obstacle is returned to a flight control terminal of the unmanned aerial vehicle at the frequency of 1s, the high-speed digital signal processor operates a Kalman filter, and data fusion is carried out on the information returned by the two sensors to obtain the optimal estimation of the distance between the unmanned aerial vehicle and the obstacle;
the automatic telescopic module is connected with the central control module, comprises an electric telescopic rod, an elastic rubber block and a pulley, and is used for protecting the unmanned aerial vehicle by the electric telescopic rod extending outwards from the connected elastic rubber block when the unmanned aerial vehicle approaches an obstacle;
the central control module is connected with the image acquisition module, the image characteristic correction module, the distance measurement module and the automatic telescopic module, and comprises a microprocessor, wherein the microprocessor is used for controlling the image acquisition module to acquire images of the periphery of the unmanned aerial vehicle according to a certain set frequency, extracting the characteristics of the acquired peripheral images through the image characteristic correction module and checking the characteristic total set of the stored obstacle images, when the standard matching degree is reached, the existence of an obstacle is automatically judged, the distance measurement module is further controlled to measure and transmit the distance between the unmanned aerial vehicle and the obstacle, and when the measured distance is close to the distance between the unmanned aerial vehicle, the electric telescopic rod is controlled to extend outwards to connect the elastic rubber block to protect the unmanned aerial vehicle;
and the storage module is connected with the central control module, comprises a memory and is used for storing the set of the barrier characteristic points and is controlled by the central control module to be checked with the acquired image characteristic points.
Further, the structure of automatic flexible module is provided with electric telescopic handle, electric telescopic handle's end connection unmanned aerial vehicle, and the elastic rubber piece is connected at the top.
Furthermore, a connecting shaft is arranged inside the elastic rubber block, and a pulley is connected above the connecting shaft.
Furthermore, the pulleys are controlled without power, and are used for changing the running track of the unmanned aerial vehicle after the unmanned aerial vehicle touches the obstacle.
Scalable comprehensive protection control system of unmanned aerial vehicle still further includes:
the wireless signal transmission module is connected with the central control module, comprises a wireless signal transceiver and is used for transmitting the distance information between the unmanned aerial vehicle and the barrier to the control terminal;
the display module is connected with the central control module, comprises a display screen and is used for displaying the state of the unmanned aerial vehicle and the distance information between the unmanned aerial vehicle and the barrier.
By combining all the technical schemes, the invention has the advantages and positive effects that:
(1) the telescopic protection device adopted by the invention can comprehensively protect the unmanned aerial vehicle in the flight process, the adopted soft rubber material is light in weight, the power consumption of the unmanned aerial vehicle is saved, the automatic telescopic structure is compact in overall structure due to the adoption of automatic detection, the increase of the air resistance of the unmanned aerial vehicle in the flight process is avoided, the power consumption of the unmanned aerial vehicle is saved, and the intelligent degree is high.
(2) The electric telescopic rod adopted by the invention can realize automatic extension and retraction of the protective rubber block, and reduce air resistance in the flight process.
(3) The pulley structure adopted by the invention can reduce the friction force between the unmanned aerial vehicle and the obstacle when the unmanned aerial vehicle touches the obstacle, so that the unmanned aerial vehicle can change the flight direction and get rid of the contact with the obstacle in time.
(4) The pulleys of the unmanned aerial vehicle are in unpowered control, and the running direction of the unmanned aerial vehicle can be changed according to the power direction of the unmanned aerial vehicle.
(5) The use method of the unmanned aerial vehicle protection device provided by the invention has high intelligent degree, and can perform human-computer interaction, so that monitoring personnel can know the operation condition of the unmanned aerial vehicle in real time.
(6) The wireless signal transmission module and the display module provided by the invention can realize human-computer interaction, and are convenient for personnel to monitor the unmanned aerial vehicle.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
Fig. 1 is a block diagram of a scalable overall protection control system of an unmanned aerial vehicle according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an automatic expansion module according to an embodiment of the present invention;
fig. 3 is a flowchart of a scalable overall protection control method for an unmanned aerial vehicle according to an embodiment of the present invention;
in the figure: 1. an image acquisition module; 2. an image characteristic proofreading module; 3. a distance measuring module; 4. an automatic telescoping module; 5. a central control module; 7. an electric telescopic rod; 8. an elastic rubber block; 9. a pulley; 10. a wireless signal transmission module; 11. and a display module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a telescopic comprehensive protection control system for an unmanned aerial vehicle, and the invention is described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the scalable comprehensive protection control system for an unmanned aerial vehicle provided by the invention specifically comprises:
the image acquisition module 1 is connected with the central control module 5, comprises a camera and is used for shooting images around the unmanned aerial vehicle according to a certain frequency in the flying process of the unmanned aerial vehicle;
the image feature correction module 2 is connected with the central control module 5 and automatically judges whether obstacles exist around the shot image by adopting the SIFT feature point matching technology;
after the image information is acquired by the unmanned aerial vehicle, the feature points extracted by the improved SIFT can be constructed into a vector P ═ { P ═ by1，p2，…，pnN is the number of the feature points, then the vector P is used for proofreading with the feature total vector of the memory, and when the matching degree reaches 60%, the obstacle is judged to exist;
the distance measuring module 3 is connected with the central control module 5, comprises two paths of distance sensors and is used for detecting the distance between the unmanned aerial vehicle and the barrier through the distance sensors;
the two distance sensors can measure the linear distance between the unmanned aerial vehicle and the obstacle, the distance from the unmanned aerial vehicle to the obstacle is returned to a flight control terminal of the unmanned aerial vehicle at the frequency of 1s, the high-speed digital signal processor operates a Kalman filter, and data fusion is carried out on the information returned by the two sensors to obtain the optimal estimation of the distance between the unmanned aerial vehicle and the obstacle;
the automatic telescopic module 4 is connected with the central control module 5, comprises an electric telescopic rod, an elastic rubber block and a pulley, and is used for protecting the unmanned aerial vehicle by the electric telescopic rod extending outwards from the connected elastic rubber block when the unmanned aerial vehicle approaches an obstacle;
the central control module 5 is connected with the image acquisition module 1, the image characteristic correction module 2, the distance measurement module 3 and the automatic telescopic module 4, and comprises a microprocessor, wherein the microprocessor is used for controlling the image acquisition module 1 to acquire images of the periphery of the unmanned aerial vehicle according to a certain set frequency, extracting the characteristics of the acquired peripheral images through the image characteristic correction module 2 and checking the characteristic total set of the stored obstacle images, when the standard matching degree is reached, the existence of obstacles is automatically judged, the distance measurement module 3 is further controlled to measure and transmit the distance between the unmanned aerial vehicle and the obstacles, and when the measured distance is close to the distance between the unmanned aerial vehicle, the electric telescopic rod is controlled to extend outwards to connect the elastic rubber blocks to protect the unmanned aerial vehicle;
and the storage module 6 is connected with the central control module 5, comprises a memory and is used for storing the set of the barrier characteristic points and is controlled by the central control module 5 to be checked with the acquired image characteristic points.
As shown in fig. 2, the automatic telescopic module 4 provided by the invention is structurally provided with an electric telescopic rod 7, the end part of the electric telescopic rod 7 is connected with an unmanned aerial vehicle, and the top part of the electric telescopic rod is connected with an elastic rubber block 8.
As shown in fig. 2, a connecting shaft is arranged in the elastic rubber block 8, and a pulley 9 is connected above the connecting shaft.
As shown in fig. 3, the scalable comprehensive protection control method for the unmanned aerial vehicle provided by the invention specifically comprises the following steps:
s101: in the flight process of the unmanned aerial vehicle, acquiring images collected by the unmanned aerial vehicle for 5 s/time by using a camera around the unmanned aerial vehicle, and denoising and feature extraction;
s102: calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting characteristic points of the image acquired by the camera after denoising and characteristic extraction through improved SIFT, and checking the characteristic points with the obstacle characteristic points in a memory;
s103: when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the barrier, and the high-speed digital signal processor performs optimal estimation on the collected distance information based on improved hierarchical topic model clustering;
s104: when unmanned aerial vehicle was close to the barrier, microprocessor control electric telescopic handle outwards stretched out the elasticity rubber piece of connection and realizes the protection to unmanned aerial vehicle.
The denoising comprises the following steps:
(1) denoising the unmanned aerial vehicle collected image f (x, y) containing noise by carrying out stationary wavelet transform and neighborhood coefficient shrinkage to obtain sub-band coefficients respectively: low frequency coefficients, horizontal detail coefficients, vertical detail coefficients, and diagonal detail coefficients;
(2) carrying out region segmentation on the low-frequency coefficient of the first layer by using PCNN;
(3) keeping the low-frequency coefficient unchanged, and respectively performing neighborhood threshold processing on the horizontal detail coefficient, the vertical detail coefficient and the diagonal detail coefficient of each layer;
(4) processing the noise unmanned aerial vehicle collected image by adopting a pulse coupling neural network to obtain an entropy sequence En, and taking En as an edge detection operator;
(5) optimizing a threshold to obtain an optimal denoising threshold k;
(6) denoising the unmanned aerial vehicle acquired image by adopting an improved anisotropic diffusion model according to the obtained edge detection operator En and the optimal denoising threshold value k;
the feature extraction of the unmanned aerial vehicle collected image comprises the steps of respectively expanding L unmanned aerial vehicle collected images after noise processing according to rows and then combining the L unmanned aerial vehicle collected images to form a matrix X (X) with L rows and M × N columns1，x2，x3，Λ，xL)T；
Averaging matrix X such that e (X) is 0;
whitening the matrix X such that E (X)TX)＝I；
Initializing the number of independent components to enable n to be 1, wherein n is the number of the independent components;
initialization wnRandomly select wn＝wn/||wn||；
According to the formula wn＝E{Xg(wn TX)}-E{g′(wn TX)}wnCalculating wn；
According to the formula
Iterate out wn+1；
According to the formula
And wn+1＝wn+1/||wn+1| |, iterating to obtain wn+1；
W obtained by judgmentn+1Whether or not to converge, if wn+1Not converging, returning to find wn；
Taking n as n +1, and extracting all independent components one by one under the condition that n is less than M;
forming each independent component into a matrix S ═ S (S)1，s2，Λ，sP)TAccording to the formula P ═ FS-1And calculating the characteristics P of the collected images of the L unmanned planes.
Further, the calculating the texture image by using the 3 × 3 second-order gradient gaussian template comprises:
in the horizontal direction:
where I (x, y) represents the pixel intensity of image NL;
in the vertical direction:
calculating gradient values:
further, the improved hierarchical topic model based clustering comprises:
introducing the distance information into a topic model for joint modeling to obtain clustering results, wherein each cluster corresponds to a topic;
selecting a function at a certain distance or a certain random process to depict the intensity change of the theme when modeling the distance information; the Beta distribution has asymmetry relative to other distributions and is used for modeling distance information;
probability density function of Beta distribution:
modeling the special subjects of different levels by using a level model nCRP;
the sampling level is set according to the following conditional probability:
p(zd,n|z-(d,n),c,w,m,π,η)∝p(zd,n|zd,-n,m,π)p(wd,n|z,c,w-(d,n),η)；
zd,nrepresenting the topic assignment of the current word, zd,-nRepresenting the current topic assignment for the rest of the observed data after excluding the topic to be sampled, k is the topic index, and c, w, m, pi, η are the hyperparameters.
As shown in fig. 1, the scalable comprehensive protection control system for an unmanned aerial vehicle provided by the present invention further includes:
the wireless signal transmission module 10 is connected with the central control module 5, comprises a wireless signal transceiver and is used for transmitting the distance information between the unmanned aerial vehicle and the barrier to the control terminal;
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. The scalable comprehensive protection control method of the unmanned aerial vehicle is characterized by comprising the following steps:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
the denoising comprises the following steps:
(1) denoising the unmanned aerial vehicle collected image f (x, y) containing noise by carrying out stationary wavelet transform and neighborhood coefficient shrinkage to obtain sub-band coefficients respectively: low frequency coefficients, horizontal detail coefficients, vertical detail coefficients, and diagonal detail coefficients;
(2) carrying out region segmentation on the low-frequency coefficient of the first layer by using PCNN;
(3) keeping the low-frequency coefficient unchanged, and respectively performing neighborhood threshold processing on the horizontal detail coefficient, the vertical detail coefficient and the diagonal detail coefficient of each layer;
(4) processing the noise unmanned aerial vehicle collected image by adopting a pulse coupling neural network to obtain an entropy sequence En, and taking En as an edge detection operator;
(5) optimizing a threshold to obtain an optimal denoising threshold k;
(6) denoising the unmanned aerial vehicle acquired image by adopting an improved anisotropic diffusion model according to the obtained edge detection operator En and the optimal denoising threshold value k;
the feature extraction of the unmanned aerial vehicle collected image comprises the steps of respectively expanding L unmanned aerial vehicle collected images after noise processing according to rows and then combining the L unmanned aerial vehicle collected images to form a matrix X (X) with L rows and M × N columns1，x2，x3，Λ，xL)T；
Averaging matrix X such that e (X) is 0;
whitening the matrix X such that E (X)TX)＝I；
Initializing the number of independent components to enable n to be 1, wherein n is the number of the independent components;
initialization wnRandomly select wn＝wn/||wn||；
According to the formula wn＝E{Xg(wn TX)}-E{g′(wn TX)}wnCalculating wn；
According to the formula
Iterate out wn+1；
According to the formula
And wn+1＝wn+1/||wn+1| |, iterating to obtain wn+1；
W obtained by judgmentn+1Whether or not to converge, if wn+1Not converging, returning to find wn；
Taking n as n +1, and extracting all independent components one by one under the condition that n is less than M;
forming each independent component into a matrix S ═ S (S)1，s2，Λ，sP)TAccording to the formula P ═ FS-1Calculating characteristics P of L unmanned aerial vehicle collected images;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
2. The unmanned aerial vehicle scalable full protection control method of claim 1, wherein the computing the texture image using a 3 x 3 second order gradient gaussian template comprises:
in the horizontal direction:
where I (x, y) represents the pixel intensity of image NL;
in the vertical direction:
calculating gradient values:
3. the unmanned aerial vehicle scalable full protection control method of claim 1, wherein the improved hierarchical topic model based clustering comprises:
introducing the distance information into a topic model for joint modeling to obtain clustering results, wherein each cluster corresponds to a topic;
selecting a function at a certain distance or a certain random process to depict the intensity change of the theme when modeling the distance information; the Beta distribution has asymmetry relative to other distributions and is used for modeling distance information;
probability density function of Beta distribution:
modeling the special subjects of different levels by using a level model nCRP;
the sampling level is set according to the following conditional probability:
p(zd,n|z-(d,n),c,w,m,π,η)∝p(zd,n|zd,-n,m,π)p(wd,n|z,c,w-(d,n),η)；
zd,nrepresenting the topic assignment of the current word, zd,-nRepresenting the current topic assignment for the rest of the observed data after excluding the topic to be sampled, k is the topic index, and c, w, m, pi, η are the hyperparameters.
4. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
5. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of:
the method comprises the following steps that firstly, during the flying process of the unmanned aerial vehicle, the camera collects images collected by the unmanned aerial vehicle for 5 s/time around the unmanned aerial vehicle, and denoising and feature extraction are carried out;
calculating a texture image by using a 3 multiplied by 3 second-order gradient Gaussian template, extracting feature points of the image acquired by the camera after denoising and feature extraction through improved SIFT, and checking the feature points with the feature points of the obstacle in the memory;
when the verification feature points are consistent, the microprocessor controls the two paths of distance sensors to collect distance information between the unmanned aerial vehicle and the obstacle, and the collected distance information is optimally estimated based on the improved hierarchical topic model clustering through the high-speed digital signal processor;
step four, when the unmanned aerial vehicle is close to the barrier, the microprocessor controls the electric telescopic rod to stretch out the elastic rubber block for connection so as to protect the unmanned aerial vehicle.
6. An unmanned aerial vehicle scalable comprehensive protection control system operating the unmanned aerial vehicle scalable comprehensive protection control method according to any one of claims 1 to 4, the unmanned aerial vehicle scalable comprehensive protection control system comprising:
the image acquisition module is connected with the central control module, comprises a camera and is used for shooting images around the unmanned aerial vehicle according to a certain frequency in the flying process of the unmanned aerial vehicle;
the image feature correction module is connected with the central control module and is used for automatically judging whether obstacles exist around the shot image by adopting the SIFT feature point matching technology;
after the image information is acquired by the unmanned aerial vehicle, the feature points extracted by the improved SIFT can be constructed into a vector P ═ { P ═ by1，p2，…，pnWhere n is the number of feature points, then by vectorP and the characteristic total vector of the memory are corrected, and when the matching degree reaches 60%, the existence of the obstacle is judged;
the distance measuring module is connected with the central control module, comprises two paths of distance sensors and is used for detecting the distance between the unmanned aerial vehicle and the barrier through the distance sensors;
the two distance sensors can measure the linear distance between the unmanned aerial vehicle and the obstacle, the distance from the unmanned aerial vehicle to the obstacle is returned to a flight control terminal of the unmanned aerial vehicle at the frequency of 1s, the high-speed digital signal processor operates a Kalman filter, and data fusion is carried out on the information returned by the two sensors to obtain the optimal estimation of the distance between the unmanned aerial vehicle and the obstacle;
the automatic telescopic module is connected with the central control module, comprises an electric telescopic rod, an elastic rubber block and a pulley, and is used for protecting the unmanned aerial vehicle by the electric telescopic rod extending outwards from the connected elastic rubber block when the unmanned aerial vehicle approaches an obstacle;
the central control module is connected with the image acquisition module, the image characteristic correction module, the distance measurement module and the automatic telescopic module, and comprises a microprocessor, wherein the microprocessor is used for controlling the image acquisition module to acquire images of the periphery of the unmanned aerial vehicle according to a certain set frequency, extracting the characteristics of the acquired peripheral images through the image characteristic correction module and checking the characteristic total set of the stored obstacle images, when the standard matching degree is reached, the existence of an obstacle is automatically judged, the distance measurement module is further controlled to measure and transmit the distance between the unmanned aerial vehicle and the obstacle, and when the measured distance is close to the distance between the unmanned aerial vehicle, the electric telescopic rod is controlled to extend outwards to connect the elastic rubber block to protect the unmanned aerial vehicle;
and the storage module is connected with the central control module, comprises a memory and is used for storing the set of the barrier characteristic points and is controlled by the central control module to be checked with the acquired image characteristic points.
7. The retractable comprehensive protection control system for the unmanned aerial vehicle as claimed in claim 6, wherein the structure of the automatic retractable module is provided with an electric retractable rod, the end of the electric retractable rod is connected with the unmanned aerial vehicle, and the top of the electric retractable rod is connected with an elastic rubber block.
8. The retractable comprehensive protection control system for the unmanned aerial vehicle as claimed in claim 7, wherein a connecting shaft is arranged inside the elastic rubber block, and a pulley is connected above the connecting shaft.
9. The retractable full protection control system for unmanned aerial vehicles of claim 7, wherein the pulleys are controlled without power for changing the trajectory of the unmanned aerial vehicle after the unmanned aerial vehicle touches an obstacle.
10. The scalable full protection control system of unmanned aerial vehicle of claim 6, further comprising:
the wireless signal transmission module is connected with the central control module, comprises a wireless signal transceiver and is used for transmitting the distance information between the unmanned aerial vehicle and the barrier to the control terminal;
the display module is connected with the central control module, comprises a display screen and is used for displaying the state of the unmanned aerial vehicle and the distance information between the unmanned aerial vehicle and the barrier.
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