CN106773709A - A kind of immersion unmanned plane drives flight system - Google Patents
A kind of immersion unmanned plane drives flight system Download PDFInfo
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Abstract
Flight system, including airborne device, terrestrial information processing unit, unmanned plane failure detector are driven the invention provides a kind of immersion unmanned plane, airborne device, unmanned plane failure detector all pass through wireless connection with terrestrial information processing unit.The present invention realizes the virtual transplanting that unmanned plane drives visual angle, us are enable more intuitively to observe the flight visual angle of unmanned plane, the flight experience of immersion is obtained, and fault detect can be carried out to unmanned plane, so as to be taken measures in time when unmanned plane breaks down.
Description
Technical Field
The invention relates to the field of unmanned aerial vehicles, in particular to an immersive unmanned aerial vehicle driving flight system.
Background
In the related technology, the unmanned aerial vehicle still mainly depends on the eyes to observe the flying height and attitude of the unmanned aerial vehicle in situ and remotely and controls the flying by a remote controller, and due to the influence of the distance and the speed of the unmanned aerial vehicle, an operator needs to control the flying state of the unmanned aerial vehicle in real time, so that the pictures shot by a tripod head of the unmanned aerial vehicle cannot be well controlled; moreover, the existing plane display screen can not achieve the real flying feeling during flying driving, and the accidents of air crash, air explosion or injury of people are easy to happen due to personal operation errors.
Disclosure of Invention
Aiming at the problems, the invention provides an immersive unmanned aerial vehicle driving flight system.
The purpose of the invention is realized by adopting the following technical scheme:
an immersive unmanned aerial vehicle driving flight system comprises an airborne device, a ground information processing device and an unmanned aerial vehicle fault detection device, wherein the airborne device and the unmanned aerial vehicle fault detection device are wirelessly connected with the ground information processing device; the airborne device is positioned on the unmanned aerial vehicle, controls the flight state of the unmanned aerial vehicle, collects the position information of the unmanned aerial vehicle and the image of the surrounding environment, sends the image and the position information to the ground information processing device, and receives the head angle information and the control instruction of the ground information processing device; the unmanned aerial vehicle fault detection device is used for carrying out fault detection on an unmanned aerial vehicle and sending a fault detection result to the ground information processing device; the ground information processing device receives the position information and the image sent by the airborne device for analysis, carries out computer augmented reality technical processing through the ground information processing device, carries out analog simulation on the surrounding environment information, the road information, the traffic information, the marking information and the landmark information of the position of the unmanned aerial vehicle, then superimposes the information on the picture space of the real world, presents the information to an operator through virtual reality glasses, and carries out corresponding alarm according to a fault detection result.
The invention has the beneficial effects that: the virtual transplantation of unmanned aerial vehicle driving visual angle is realized, so that the flying visual angle of the unmanned aerial vehicle can be observed more intuitively, the immersive flying experience is obtained, and the fault detection can be carried out on the unmanned aerial vehicle, so that measures can be taken in time when the unmanned aerial vehicle breaks down.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a schematic view of the structural connection of the present invention;
fig. 2 is a block diagram of the structure of the unmanned aerial vehicle fault detection device.
Reference numerals:
the system comprises an airborne device 1, a ground information processing device 2, an unmanned aerial vehicle fault detection device 3, a historical data acquisition module 11, a data preprocessing module 12, a feature extraction module 13, a real-time fault diagnosis feature vector acquisition module 14, a fault diagnosis model establishing module 15 and a fault diagnosis identification module 16.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1, the embodiment provides an immersive unmanned aerial vehicle driving flight system, which includes an airborne device 1, a ground information processing device 2, and an unmanned aerial vehicle fault detection device 3, where the airborne device 1 and the unmanned aerial vehicle fault detection device 3 are both wirelessly connected with the ground information processing device 2; the airborne device 1 is positioned on the unmanned aerial vehicle, controls the flight state of the unmanned aerial vehicle, collects the position information of the unmanned aerial vehicle and the image of the surrounding environment, sends the image and the position information to the ground information processing device 2, and receives the head angle information and the control instruction of the ground information processing device 2; the unmanned aerial vehicle fault detection device 3 is used for carrying out fault detection on the unmanned aerial vehicle and sending a fault detection result to the ground information processing device 2; the ground information processing device 2 receives the position information and the image sent by the airborne device 1 for analysis, carries out computer augmented reality technical processing through the ground information processing device 2, carries out analog simulation on the surrounding environment information, the road information, the traffic information, the marking information and the landmark information of the position of the unmanned aerial vehicle, then superimposes the information on the picture space of the real world, presents the information to an operator through virtual reality glasses, and carries out corresponding alarm according to a fault detection result.
Preferably, the airborne device 1 comprises a three-axis pan-tilt head, a binocular camera and a GPS.
Preferably, the ground information processing device 2 comprises virtual reality glasses, a computer and an alarm, wherein the computer is respectively connected with the virtual reality glasses and the alarm; the computer is used for analyzing the acquired flying height, attitude and GPS positioning information of the unmanned aerial vehicle, simulating and simulating the surrounding environment information of the position of the unmanned aerial vehicle through the existing augmented reality technology, superposing the simulated and simulated surrounding environment information to a real world picture space, and transmitting the information to the virtual reality glasses; the virtual reality glasses are used for displaying image information after the augmented reality effect is superposed by the computer; the alarm is used for alarming when the fault detection result shows that the unmanned aerial vehicle sends a fault.
The embodiment of the invention realizes the virtual transplantation of the driving visual angle of the unmanned aerial vehicle, so that the flying visual angle of the unmanned aerial vehicle can be observed more visually, immersive flying experience is obtained, and the fault detection can be carried out on the unmanned aerial vehicle, so that measures can be taken in time when the unmanned aerial vehicle breaks down.
Preferably, the unmanned aerial vehicle fault detection apparatus 3 includes:
(1) the historical data acquisition module 11 is used for acquiring historical vibration signal data of a plurality of measuring points when the unmanned aerial vehicle runs in a normal state and various fault states through a sensor;
(2) the data preprocessing module 12 is used for preprocessing the acquired original historical vibration signal data;
(3) the feature extraction module 13 is configured to extract wavelet packet singular value features from the filtered historical vibration signal data, and use the extracted wavelet packet singular value features as a fault diagnosis feature vector sample;
(4) the real-time fault diagnosis feature vector acquisition module 14 is used for acquiring real-time fault diagnosis feature vectors of the unmanned aerial vehicle;
(5) the fault diagnosis model establishing module 15 is used for establishing a fault diagnosis model based on the improved support vector machine, training the fault diagnosis model by using the fault diagnosis characteristic vector sample, and calculating the optimal solution of the parameters of the fault diagnosis model to obtain a trained fault diagnosis model;
(6) and the fault diagnosis and identification module 16 is used for inputting the real-time fault diagnosis feature vector of the unmanned aerial vehicle into the trained fault diagnosis model to complete the diagnosis and identification of the fault of the unmanned aerial vehicle.
Preferably, the data preprocessing module 12 preprocesses the acquired original historical vibration signal data by using a digital filter according to a filtering formula, where the filtering formula is:
wherein Ω is historical vibration signal data obtained after filtering, Ω' is collected original historical vibration signal data, L is the number of measuring points, and Ψ is 1, 2, 3 … L-1; τ is a constant determined by the characteristics of the digital filter itself and θ is the natural acquisition frequency of the sensor used.
According to the data preprocessing method and device, the data are preprocessed through the digital filter according to the filtering formula, different vibration signals can be self-adapted, time domain waveform distortion in original historical vibration signal data can be eliminated, accordingly, the data preprocessing precision is improved, and the accuracy of fault recognition of the unmanned aerial vehicle is guaranteed.
Preferably, when extracting the wavelet packet singular value feature, the feature extraction module 13 specifically executes:
(1) setting the historical vibration signal at a moment measured from a measuring point M when the unmanned aerial vehicle is in the state H as HM(Ω), M1, …, L being the number of stations, and HM(omega) toLayer discrete wavelet packet decomposition, extractingIn a layerA decomposition coefficient, reconstructing all the decomposition coefficients toIs shown asReconstructing signals of each node of the layer and constructing a characteristic matrixWhereinThe value of (A) is determined by combining historical experience and actual conditions;
(2) for feature matrix T [ H ]M(Ω)]Singular value decomposition is carried out to obtain the characteristic matrix T [ H ]M(Ω)]A feature vector of (1), wherein W1,W2,…,WvIs composed of a feature matrix T [ H ]M(Ω)]Singular values of decomposition, v being defined by the feature matrix T [ H ]M(Ω)]Number of singular values decomposed:
(3) definition HM(omega) corresponding fault diagnosis feature vectorComprises the following steps:
wherein,representing feature vectorsThe maximum singular value of (a) is,representing feature vectorsThe smallest singular value of;
(4) screening the calculated fault diagnosis feature vectors, eliminating unqualified fault diagnosis feature vectors, and setting the quantity of the eliminated unqualified fault diagnosis feature vectors as L', so that the fault diagnosis feature vector sample at the fixed moment when the unmanned aerial vehicle is in the state H is as follows:
the wavelet packet singular value feature is extracted to serve as the fault diagnosis feature vector in the preferred embodiment, compared with the fact that other features are extracted to serve as the fault diagnosis feature vector, the method and the device have high accuracy and short calculation time, fault tolerance of unmanned aerial vehicles can be improved, and therefore accurate diagnosis of faults of the unmanned aerial vehicles is facilitated.
Preferably, when the feature extraction module 13 filters the calculated fault diagnosis feature vector, the following steps are specifically performed:
(1) taking all the fault diagnosis characteristic vectors obtained by calculation at the moment when the unmanned aerial vehicle is in the state H as a characteristic vector screening sample set at the moment, and calculating the standard deviation sigma of the characteristic vector screening sample setHAnd expected value muH；
(2) If the calculated fault diagnosis feature vector is obtainedNot meet the requirements ofThe fault diagnosis feature vector is rejected, wherein,is a desired value muHThe maximum likelihood estimate of (a) is,is the standard deviation sigmaHMaximum likelihood estimation of (1).
This preferred embodiment adopts above-mentioned mode to get rid of unqualified failure diagnosis eigenvector, and objective science has improved the accuracy of carrying out the fault diagnosis to unmanned aerial vehicle.
Preferably, the feature extraction module 13 further stores the rejected faulty diagnosis feature vectors in a temporary data storage, and extracts the faulty diagnosis feature vectors in the feature extraction module 13Further correcting the values, specifically: if it is ThenThe value of (A) is modified on the basis of the combination of the original historical experience and the actual situationIf it isThenThe value of (A) is modified on the basis of the combination of the original historical experience and the actual situationWherein L is the number of the measuring points, L' is the number of unqualified fault diagnosis feature vectors, and D is an integer threshold value set manually.
This preferred embodiment has further reduced the influence that unqualified fault diagnosis eigenvector carries out fault diagnosis to unmanned aerial vehicle.
Preferably, the fault diagnosis model based on the improved support vector machine is established as follows:
(1) adopting a radial basis function as a kernel function, and mapping the fault diagnosis characteristic vector sample from an original space to a high-dimensional space by utilizing the kernel function;
(2) constructing an optimal decision function in a high-dimensional space to realize fault diagnosis feature vector sample classification, wherein the construction of the optimal decision function is as follows:
wherein x is an input fault diagnosis feature vector sample, ξ (x) is an output corresponding to the input fault diagnosis feature vector sample, J (x) represents a radial basis function, c is a weight vector, e is a bias, and further,the method comprises the steps of introducing optimization factors, wherein L is the number of measured points, and L' is the number of unqualified fault diagnosis feature vectors;
(3) defining an objective function of a support vector machine and a constraint condition of the support vector machine, solving the objective function of the support vector machine, calculating a weight vector and a deviation, and substituting the calculated weight vector and the calculated deviation into an optimal decision function to obtain an established fault diagnosis model; wherein the objective function of the support vector machine is defined as:
the constraints of the support vector machine are defined as:
yα≥1-α，α≥0,α＝1,…,M
in the formula,for the objective function of the support vector machine,. psi*In order to optimize the penalty factor after the optimization,αis an introduced error variable; m is the number of the fault diagnosis feature vector samples;
in addition, xαFor the α th fault diagnosis feature vector sample of input, yαThe output corresponding to the α th fault diagnosis feature vector sample of the input, c is the weight vector, and e is the deviation.
By introducing the optimization factors, the optimal embodiment reduces the influence of unqualified fault diagnosis feature vectors on fault diagnosis of the unmanned aerial vehicle, further improves the actual accuracy of the optimal decision function, provides a good function basis for establishing a fault diagnosis model, and accordingly establishes a more accurate fault diagnosis model, and can ensure the accuracy of fault diagnosis of the unmanned aerial vehicle.
The penalty factor and the value of the radius parameter of the kernel function are optimized according to the following modes:
averagely dividing all fault diagnosis feature vector samples into subsets which are not included, setting a penalty factor and a value range of a radius parameter value of the kernel function, and carrying out two-dimensional coding on a position vector of each particle to generate an initial particle swarm; selecting a training set for cross validation of parameters corresponding to each particle, taking the obtained classification accuracy of the prediction model as a target function value corresponding to the particle, and iterating the particles in the particle swarm; evaluating all particles by using the objective function value, taking the current evaluation value of a certain particle as the optimal historical evaluation of the particle when the current evaluation value of the particle is superior to the historical evaluation value of the particle, and recording the optimal position vector of the current particle; and searching a global optimal solution, if the value of the global optimal solution is superior to the current historical optimal solution, updating, stopping searching when a set termination criterion is reached, outputting an optimal penalty factor and the value of the radius parameter of the kernel function, and returning to search again if the value of the global optimal solution is not superior to the current historical optimal solution.
This preferred embodiment adopts above-mentioned mode to optimize punishment factor and the value of the radius parameter of this kernel function, and optimization time is shorter relatively, and optimization effect is good, can obtain the better support vector machine of performance, further improves the precision of carrying out fault diagnosis to unmanned aerial vehicle.
In accordance with the above examples, the inventors performed a series of tests, and the following were experimental data obtained by performing the tests:
the experimental data show that the unmanned aerial vehicle fault detection system can perform good flight visual angle observation, obtain immersive flight experience, and accurately perform fault detection on the unmanned aerial vehicle, so that accidents are prevented, and timely and safe maintenance can be ensured when the unmanned aerial vehicle breaks down.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (5)
1. An immersive unmanned aerial vehicle driving flight system is characterized by comprising an airborne device, a ground information processing device and an unmanned aerial vehicle fault detection device, wherein the airborne device and the unmanned aerial vehicle fault detection device are wirelessly connected with the ground information processing device; the airborne device is positioned on the unmanned aerial vehicle, controls the flight state of the unmanned aerial vehicle, collects the position information of the unmanned aerial vehicle and the image of the surrounding environment, sends the image and the position information to the ground information processing device, and receives the head angle information and the control instruction of the ground information processing device; the unmanned aerial vehicle fault detection device is used for carrying out fault detection on an unmanned aerial vehicle and sending a fault detection result to the ground information processing device; the ground information processing device receives the position information and the image sent by the airborne device for analysis, carries out computer augmented reality technical processing through the ground information processing device, carries out analog simulation on the surrounding environment information, the road information, the traffic information, the marking information and the landmark information of the position of the unmanned aerial vehicle, then superimposes the information on the picture space of the real world, presents the information to an operator through virtual reality glasses, and carries out corresponding alarm according to a fault detection result.
2. The immersive unmanned aerial vehicle flying system of claim 1, wherein the airborne device comprises a three-axis pan-tilt head, a binocular camera, and a GPS.
3. The immersive unmanned aerial vehicle driving flight system of claim 2, wherein the ground information processing device comprises virtual reality glasses, a computer and an alarm, and the computer is respectively connected with the virtual reality glasses and the alarm; the computer is used for analyzing the acquired flying height, attitude and GPS positioning information of the unmanned aerial vehicle, simulating and simulating the surrounding environment information of the position of the unmanned aerial vehicle through the existing augmented reality technology, superposing the simulated and simulated surrounding environment information to a real world picture space, and transmitting the information to the virtual reality glasses; the virtual reality glasses are used for displaying image information after the augmented reality effect is superposed by the computer; the alarm is used for alarming when the fault detection result shows that the unmanned aerial vehicle sends a fault.
4. The immersive unmanned aerial vehicle piloting flight system of claim 3, wherein the unmanned aerial vehicle fault detection device comprises:
(1) the historical data acquisition module 11 is used for acquiring historical vibration signal data of a plurality of measuring points when the unmanned aerial vehicle runs in a normal state and various fault states through a sensor; ,
(2) the data preprocessing module 12 is used for preprocessing the acquired original historical vibration signal data;
(3) the feature extraction module 13 is configured to extract wavelet packet singular value features from the filtered historical vibration signal data, and use the extracted wavelet packet singular value features as a fault diagnosis feature vector sample;
(4) the real-time fault diagnosis feature vector acquisition module 14 is used for acquiring real-time fault diagnosis feature vectors of the unmanned aerial vehicle;
(5) the fault diagnosis model establishing module 15 is used for establishing a fault diagnosis model based on the improved support vector machine, training the fault diagnosis model by using the fault diagnosis characteristic vector sample, and calculating the optimal solution of the parameters of the fault diagnosis model to obtain a trained fault diagnosis model;
(6) and the fault diagnosis and identification module 16 is used for inputting the real-time fault diagnosis feature vector of the unmanned aerial vehicle into the trained fault diagnosis model to complete the diagnosis and identification of the fault of the unmanned aerial vehicle.
5. The immersive piloted flight system of unmanned aerial vehicle as claimed in claim 4, wherein the data preprocessing module 12 preprocesses the collected original historical vibration signal data using a digital filter according to a filtering formula, wherein the filtering formula is:
wherein Ω is historical vibration signal data obtained after filtering, Ω' is collected original historical vibration signal data, L is the number of measuring points, and Ψ is 1, 2, 3 … L-1; τ is a constant determined by the characteristics of the digital filter itself and θ is the natural acquisition frequency of the sensor used.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019062127A1 (en) * | 2017-09-28 | 2019-04-04 | 深圳市道通智能航空技术有限公司 | Control method and apparatus for unmanned aircraft |
| CN110401479A (en) * | 2019-07-24 | 2019-11-01 | 中国电子科技集团公司第三十六研究所 | The echo signal recognition methods of equipment and ground installation is carried based on rotor wing unmanned aerial vehicle |
| US11074730B1 (en) | 2020-01-23 | 2021-07-27 | Netapp, Inc. | Augmented reality diagnostic tool for data center nodes |
| CN113625595A (en) * | 2021-08-02 | 2021-11-09 | 中国人民解放军军事科学院国防科技创新研究院 | Unmanned aerial vehicle deduction and fault diagnosis method and system |
| CN114627569A (en) * | 2022-03-10 | 2022-06-14 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Abnormity processing method and device for power line inspection unmanned aerial vehicle and computer equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1999005580A2 (en) * | 1997-07-23 | 1999-02-04 | Duschek Horst Juergen | Method for controlling an unmanned transport vehicle and unmanned transport vehicle system therefor |
| CN103984357A (en) * | 2014-05-30 | 2014-08-13 | 中国人民解放军理工大学 | Unmanned aerial vehicle automatic obstacle avoidance flight system based on panoramic stereo imaging device |
| CN105222761A (en) * | 2015-10-29 | 2016-01-06 | 哈尔滨工业大学 | The first person immersion unmanned plane control loop realized by virtual reality and binocular vision technology and drive manner |
| CN105759833A (en) * | 2016-02-23 | 2016-07-13 | 普宙飞行器科技（深圳）有限公司 | Immersive unmanned aerial vehicle driving flight system |
| CN105928695A (en) * | 2016-05-11 | 2016-09-07 | 中国矿业大学 | Fault diagnosis system and method for mechanical parts of small-size unmanned helicopter |
| CN106094863A (en) * | 2015-04-23 | 2016-11-09 | 鹦鹉无人机股份有限公司 | The system of unmanned plane is driven for immersion |
-
2017
- 2017-01-12 CN CN201710022039.7A patent/CN106773709A/en not_active Withdrawn
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1999005580A2 (en) * | 1997-07-23 | 1999-02-04 | Duschek Horst Juergen | Method for controlling an unmanned transport vehicle and unmanned transport vehicle system therefor |
| CN103984357A (en) * | 2014-05-30 | 2014-08-13 | 中国人民解放军理工大学 | Unmanned aerial vehicle automatic obstacle avoidance flight system based on panoramic stereo imaging device |
| CN106094863A (en) * | 2015-04-23 | 2016-11-09 | 鹦鹉无人机股份有限公司 | The system of unmanned plane is driven for immersion |
| CN105222761A (en) * | 2015-10-29 | 2016-01-06 | 哈尔滨工业大学 | The first person immersion unmanned plane control loop realized by virtual reality and binocular vision technology and drive manner |
| CN105759833A (en) * | 2016-02-23 | 2016-07-13 | 普宙飞行器科技（深圳）有限公司 | Immersive unmanned aerial vehicle driving flight system |
| CN105928695A (en) * | 2016-05-11 | 2016-09-07 | 中国矿业大学 | Fault diagnosis system and method for mechanical parts of small-size unmanned helicopter |
Non-Patent Citations (1)
| Title |
|---|
| 吴祖迥等: "小波包与SVM用于压缩机在线故障检测的研究", 《五邑大学学报（自然科学版）》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2019062127A1 (en) * | 2017-09-28 | 2019-04-04 | 深圳市道通智能航空技术有限公司 | Control method and apparatus for unmanned aircraft |
| CN110401479A (en) * | 2019-07-24 | 2019-11-01 | 中国电子科技集团公司第三十六研究所 | The echo signal recognition methods of equipment and ground installation is carried based on rotor wing unmanned aerial vehicle |
| US11074730B1 (en) | 2020-01-23 | 2021-07-27 | Netapp, Inc. | Augmented reality diagnostic tool for data center nodes |
| US11610348B2 (en) | 2020-01-23 | 2023-03-21 | Netapp, Inc. | Augmented reality diagnostic tool for data center nodes |
| CN113625595A (en) * | 2021-08-02 | 2021-11-09 | 中国人民解放军军事科学院国防科技创新研究院 | Unmanned aerial vehicle deduction and fault diagnosis method and system |
| CN114627569A (en) * | 2022-03-10 | 2022-06-14 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Abnormity processing method and device for power line inspection unmanned aerial vehicle and computer equipment |
| CN114627569B (en) * | 2022-03-10 | 2023-09-12 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Abnormality processing method and device for power line inspection unmanned aerial vehicle and computer equipment |
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Application publication date: 20170531 |