CN109856638B - Method for automatically searching and positioning specific underwater target - Google Patents
Method for automatically searching and positioning specific underwater target Download PDFInfo
- Publication number
- CN109856638B CN109856638B CN201910148370.2A CN201910148370A CN109856638B CN 109856638 B CN109856638 B CN 109856638B CN 201910148370 A CN201910148370 A CN 201910148370A CN 109856638 B CN109856638 B CN 109856638B
- Authority
- CN
- China
- Prior art keywords
- auv
- underwater
- underwater vehicle
- autonomous
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Abstract
The invention discloses a method for automatically searching and positioning a specific underwater target, which is characterized in that an Autonomous Underwater Vehicle (AUV) is used as a movable sonar signal receiving transducer array, so that underwater sonar images are collected more conveniently, efficiently and comprehensively, the positioning precision of the autonomous underwater vehicle is improved by adopting an EKF-SLAM algorithm and a chip-level atomic clock (CSAC), an optical and acoustic image data characteristic set of the specific underwater target is formed by machine learning, possible target data are returned to a mother ship for artificial secondary judgment after the Autonomous Underwater Vehicle (AUV) performs characteristic identification, and the target searching efficiency is greatly improved.
Description
Technical Field
The invention provides a method for automatically searching and positioning a specific underwater target, belonging to the field of underwater detection.
Background
The earth is a large water ball. The total ocean area is about 3.6 hundred million square kilometers, accounting for about 71% of the earth's surface area. Ocean shipping is one of the most important transportation modes in international commodity exchange, and the proportion of the freight transportation amount to the whole international freight transportation amount is about more than 80%. The ocean is also a source of abundant biological and mineral resources. The submarine mineral products are rich, and contain rich energy mineral products, coal, petroleum, natural gas, combustible ice and the like, 880 million tons of rare earth minerals can be used for extracting minerals such as manganese, iron, nickel and the like, and future energy deuterium and tritium can be extracted from seawater.
A search positioning method for a specific underwater target is needed in marine resource investigation, evaluation, exploration, seabed sampling, hydrological investigation, seabed landform photography and drawing, search and rescue of a disabled ship, special water area warning and the like.
Meanwhile, positioning and map building (SLAM) is a target positioning and unknown environment detection algorithm which is widely applied, and allows the robot to sense the environment by using an external sensor carried by the robot in a completely unknown environment, extract useful information in the environment, perform self positioning by using the information, and build an environment map in an incremental manner.
The Gaussian noise model is a noise model commonly used in an underwater environment, and the EKF-SLAM based on the extended Kalman filter is the most widely used SLAM algorithm, is suitable for solving various nonlinear problems, and is very suitable for solving the positioning problem of an underwater target.
The EKF-SLAM algorithm is mainly divided into three parts of prediction, updating and expansion. The method comprises the steps of storing the state of the AUV and map features in an independent state vector, estimating the state of a system by a recursion process of predicting and observing updating, and adding new features into the state vector if the new features exist so as to realize the estimation of the position of the AUV and the creation of a map.
The prediction means that the AUV performs positioning on the attitude at the next moment according to the attitude at the current moment by using data such as acceleration, angular velocity and the like provided by a sensor carried by the AUV, for example, a vertical gyroscope and the like, but the positioning algorithm is generally low in precision and needs to be updated according to observation data of the sensor.
The first part is the robot attitude after updating and predicting by utilizing data provided by sensors such as a Doppler velocity meter DVL, a digital compass and the like. And the second part is that forward looking sonar carried by the AUV is used for scanning the environmental characteristics of the seabed to obtain sonar measurement data, processing the sonar measurement data, extracting point characteristics, converting the measurement of the point characteristics into a global coordinate system, and performing data association with the characteristics in a map. If the feature is observed for the first time, entering an expansion stage, and adding the feature into a map; otherwise, entering an updating stage. And obtaining the optimal estimation of the AUV state and the map characteristics by using an extended Kalman filtering algorithm.
With the discovery of coherent layout trapping (CPT) phenomenon and the development of MEMS technology, atomic clocks, which are the most accurate timing devices, have entered the miniaturized era. There are already low cost, low power consumption, high performance commercial chip atomic clocks (CSACs) on the market that can be integrated into handheld devices. The timing precision can be greatly improved by adopting CSAC equipment, and the positioning precision can be greatly improved when the CSAC equipment is applied to navigation positioning.
Currently, for underwater target positioning, the following problems exist:
1) the underwater wireless communication bandwidth is limited, and real-time image data return cannot be met.
2) The method is limited by the fixed shape and distribution of the sonar arrays, and can acquire more accurate underwater sonar images by continuously adjusting the positions of the receiving transducers by using a plurality of sonar arrays.
3) Because the electromagnetic wave is attenuated in water at a very high speed, the underwater unmanned underwater vehicle cannot accurately position the position of the underwater unmanned underwater vehicle through a GPS signal, and the positioning error is larger and exceeds an acceptable range only by the inertial navigation of the unmanned underwater vehicle.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a method for automatically searching and positioning a specific underwater target, which is characterized in that an Autonomous Underwater Vehicle (AUV) is used as a movable sonar signal receiving transducer array, so that underwater sonar images are more conveniently, efficiently and comprehensively acquired, the positioning precision of the autonomous underwater vehicle is improved by adopting an EKF-SLAM algorithm and a chip-level atomic clock (CSAC), an optical and acoustic image data characteristic set of the specific underwater target is formed by machine learning, possible target data are returned to a mother ship for artificial secondary judgment after the Autonomous Underwater Vehicle (AUV) performs characteristic identification, and the target searching efficiency is greatly improved.
The invention is realized by the following technical scheme:
a method for automatic search and location of a specific underwater target, the method comprising:
s0, the automatic searching and positioning system for the specific underwater target comprises a mother ship, a buoy and an autonomous underwater vehicle AUV;
s1, acquiring optical and acoustic image characteristic data sets of a specific underwater target and storing the data sets in an Autonomous Underwater Vehicle (AUV);
s2, acquiring a submarine sonar image of the target underwater area;
s3, analyzing the submarine sonar image, and selecting a specific underwater target potential area;
s4, carrying out clock synchronization on the mother ship, the buoy and the autonomous underwater vehicle AUV, and launching the autonomous underwater vehicle AUV to carry out autonomous search on the specific underwater target potential area according to a programmed route and judge targets which possibly accord with the characteristics;
and S5, analyzing the data information returned by the AUV, and determining the position of the specific underwater target after discrimination.
Preferably, the functional module of the buoy comprises:
the underwater communication module comprises a transmitting transducer, a receiving transducer and an acoustic wave modem;
the overwater communication module is used for signal transfer between buoys or communication between the buoys and the mother ship;
the GPS module is used for accurately positioning the buoy;
the CSAC clock is integrated by the chip atomic clock and is used for clock synchronization and improving the positioning precision of the autonomous underwater vehicle AUV;
an energy module: the solar energy power supply system comprises a solar cell panel, a storage battery and a detachable power supply cable connected with a mother ship.
Preferably, the autonomous underwater vehicle AUV function module comprises
The underwater communication module comprises a transmitting transducer, a receiving transducer and an acoustic wave modem;
the overwater communication module is used for signal transfer between the Autonomous Underwater Vehicles (AUV) or communication between the Autonomous Underwater Vehicles (AUV) and the mother ship or the buoy;
the GPS module is used for accurately positioning the autonomous underwater vehicle AUV;
the CSAC clock is integrated by the chip atomic clock and is used for clock synchronization and improving the positioning precision of the autonomous underwater vehicle AUV;
the inertial imaging module comprises a Doppler velocity meter DVL, a fiber optic gyroscope FOG and a depth meter, and is used for measuring various motion parameters of the autonomous underwater vehicle AUV and improving the positioning precision of the autonomous underwater vehicle AUV;
the optical imaging module comprises a high-definition underwater camera and is used for collecting images for target feature judgment and returning artificial secondary judgment of a mother ship;
and the acoustic imaging module comprises an active forward-looking imaging sonar which is used for forming a sonar image of an underwater target object to judge the target characteristics and position the Autonomous Underwater Vehicle (AUV).
Preferably, the optical and acoustic image feature data set of the specific underwater target is obtained in S1, specifically, the method includes collecting optical and acoustic data of the specific underwater target in a simulation environment, forming a feature data set through training of a machine learning neural network, or using an existing similar feature data set.
Preferably, the acquiring of the underwater sonar image of the underwater region of the target in S2 specifically includes: the mother ship provided with the high-power sonar equipment transmits sonar signals underwater, the Autonomous Underwater Vehicle (AUV) serves as a movable transducer array, the reflected sound wave signals are acquired in a distributed mode, and sonar imaging is formed after data processing.
Preferably, when the specific underwater target potential area is autonomously searched according to the programmed route in S4, the positioning accuracy of the autonomous underwater vehicle AUV is improved by using communication between the autonomous underwater vehicle AUV and the beacon and data fusion of the autonomous underwater vehicle AUV sensor, and using the EKF-SLAM algorithm.
Preferably, the autonomous underwater vehicle AUV at the surface can act as a mobile buoy providing positioning information.
Preferably, the underwater autonomous underwater vehicle AUV can be used as a data transfer station to provide data information transfer for the underwater autonomous underwater vehicle AUV in a deeper water area or a water area where signal propagation is difficult.
Drawings
Fig. 1 is a schematic block diagram of a method for automatically searching and positioning a specific underwater target in the invention.
Fig. 2 is a block diagram of main functional modules of the buoy system of the invention.
Fig. 3 is a block diagram of main functional modules of the autonomous underwater vehicle AUV system of the present invention.
Fig. 4 is a block diagram of a process of acquiring a set of optical and acoustic data features of the present invention.
Fig. 5 is a coordinate system configuration of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
S0, the automatic searching and positioning system for the specific underwater target comprises a mother ship, a buoy and an autonomous underwater vehicle AUV;
fig. 2 shows a block diagram of main functional modules of the buoy system of the present invention. The functional module of the buoy comprises:
the underwater communication module comprises a transmitting transducer, a receiving transducer and an acoustic wave modem;
the overwater communication module is used for signal transfer between buoys or communication between the buoys and the mother ship;
the GPS module is used for accurately positioning the buoy;
the CSAC clock is integrated by the chip atomic clock and is used for clock synchronization and improving the positioning precision of the autonomous underwater vehicle AUV;
an energy module: the solar energy power supply system comprises a solar cell panel, a storage battery and a detachable power supply cable connected with a mother ship.
Fig. 3 is a block diagram showing main functional modules of the autonomous underwater vehicle AUV system of the present invention. The autonomous underwater vehicle AUV function module comprises:
the underwater communication module comprises a transmitting transducer, a receiving transducer and an acoustic wave modem;
the overwater communication module is used for signal transfer between the Autonomous Underwater Vehicles (AUV) or communication between the Autonomous Underwater Vehicles (AUV) and the mother ship or the buoy;
the GPS module is used for accurately positioning the autonomous underwater vehicle AUV;
the CSAC clock is integrated by the chip atomic clock and is used for clock synchronization and improving the positioning precision of the autonomous underwater vehicle AUV;
the inertial imaging module comprises a Doppler velocity meter DVL, a fiber optic gyroscope FOG and a depth meter, and is used for measuring various motion parameters of the autonomous underwater vehicle AUV and improving the positioning precision of the autonomous underwater vehicle AUV;
the optical imaging module comprises a high-definition underwater camera and is used for collecting images for target feature judgment and returning artificial secondary judgment of a mother ship;
and the acoustic imaging module comprises an active forward-looking imaging sonar which is used for forming a sonar image of an underwater target object to judge the target characteristics and position the Autonomous Underwater Vehicle (AUV).
Fig. 1 is a schematic block diagram illustrating a method for automatically searching and locating a specific underwater target according to the present invention. S1, acquiring optical and acoustic image feature data sets of a specific underwater target and storing the optical and acoustic image feature data sets in an Autonomous Underwater Vehicle (AUV), as shown in FIG. 4, the method is a process block diagram for acquiring the optical and acoustic data feature sets, and the specific method is that optical and acoustic original image data of the specific underwater target are acquired under a simulation environment, firstly, the data are cleaned, sampled and structured, and a proper model and a model evaluation method are selected. And then, feature extraction is carried out, the dimension of the obtained original data is very large, the dimension reduction is carried out on the data by adopting methods such as wavelet analysis, Fourier analysis and the like, the modeling is facilitated, and new features are automatically constructed from the original data. And then, selecting features, wherein the feature quantity obtained by the feature extraction step is still large, and 1, the correlation between the features and the problem to be solved, 2, the influence of the features on the model precision and 3, the redundancy existing among the features are considered. Removing some unnecessary features from the feature set will result in a better feature set, and selecting useful features from a large number of features. And then, feature construction is carried out, wherein partial modes and structural information in the original data set need to be summarized and extracted by people, features with high value are artificially selected according to a specific underwater environment, and new features are artificially constructed from the original data set. And then, feature learning is carried out, features are automatically recognized and used from the original data set, and a part of new related image data is adopted for discriminant training to form a feature data set. Or may directly employ existing similar feature data sets.
And S2, acquiring the submarine sonar image of the target underwater area, driving the mother ship carrying the buoy and the autonomous underwater vehicle AUV to the target underwater area, and putting 4-5 autonomous underwater vehicles AUV. The autonomous underwater vehicle AUV is located above the water surface, and the accurate position of the autonomous underwater vehicle AUV is obtained through the GPS module. The sound wave modem on the mother ship transmits a modulated sound wave signal to the underwater through the transmitting transducer. And the sound wave signals are reflected by the underwater target and then received by receiving transducers on the mother ship and the Autonomous Underwater Vehicle (AUV). And the Autonomous Underwater Vehicle (AUV) modulates the received reflected sound wave signal and the position information and then sends the modulated reflected sound wave signal to the mother ship. The mother ship can control the autonomous underwater vehicle AUV to a specific position through the overwater communication module, and continuously receives the sound wave signals sent by the mother ship to perform distributed sonar imaging. And finally, forming an underwater sonar image by the sonar signals of the autonomous underwater vehicle AUV and the mother ship through a fusion algorithm.
And S3, analyzing the submarine sonar image, selecting a specific underwater target potential area, specifically, determining a possible underwater target existence area through difference by comparing and analyzing the submarine sonar image data with the existing submarine sonar image data, or judging the possible underwater target existence area on a sonar map by collecting the hydrological data of the current water area and combining the underwater physical characteristics of a specific target object, such as shape, sound wave reflection characteristics, stress deformation and the like.
And S4, carrying out clock synchronization on the mother ship, the buoy and the autonomous underwater vehicle AUV, and carrying out autonomous search on the specific underwater target potential area by putting the buoy and the autonomous underwater vehicle AUV according to a programmed route, wherein the autonomous underwater vehicle AUV is provided with an active forward-looking sonar and has an automatic obstacle avoidance function. The buoy is placed above the water surface of the possible underwater target existence area, and the autonomous underwater vehicle AUV submerges into the water bottom to further search the possible underwater target existence area.
Because the electromagnetic wave is greatly attenuated underwater, the modulated sound wave signal is transmitted underwater through the buoy at regular time, the sound wave signal carries the accurate position information of the buoy and the time node t1 for transmitting the sound wave, and if the sound wave transducer of the autonomous underwater vehicle AUV receives the modulated sound wave signal of the buoy at the time t2, the buoy and the autonomous underwater vehicle AUV both have accurate CSAC clocks and are synchronized through the clocks.
The distance 1 between the buoy and the autonomous underwater vehicle AUV can be obtained by the following formula
l＝(t2-t1)*vw (1)
Wherein Vw is the propagation speed of the sound wave under water at that time, and can be measured by a sensor on the mother ship.
Due to the fact that the CSAC clock has high accuracy and time stability, time errors of distance 1 measurement of the buoy and the autonomous underwater vehicle AUV are reduced.
And when the Autonomous Underwater Vehicle (AUV) autonomously searches the specific underwater target potential area according to a programmed route, further reducing the positioning error in the GPS-free environment by adopting an EKF-SLAM algorithm.
The autonomous underwater vehicle AUV has the motion model of
XV(k+1)＝f(XV(k+1)，u(k))+ω(k) (2)
In the formula, the state vector X of AUVV(k) Including its own parameters such as position, velocity, acceleration, attitude angle, etc., which uniquely determine the state of the AUV at the time of day, u (k) is the control input, f is the state transfer function, and ω (k) is used to represent the dynamic noise of the system and the uncertainty of the system modeling itself.
The observation model of the sensor describes the interrelationship (mainly distance and direction) between the sensor observation data and the robot position, and the equation is
z(k)＝h(X(k)，θi)+vk) (3)
Where z (k) is the observed quantity at time k, h (.) is a function of the observation with respect to the system state, a function of the robot state x (k) and the observation target θ i at time k of the robot, and v (k) is the observation noise.
The distance and the direction of the environmental characteristics are taken as observed quantities, and the pose of the robot can be expressed as
Xv＝[xv yv θv] (4)
The position of the target object detected by the autonomous underwater vehicle AUV can be expressed as
θi＝[xi yi] (5)
The observation function z can be specifically expressed as
vrIs the observation of noise, vθThe noise is observed in the direction.
Scanning the submarine environment with imaging sonar, processing the sonar data into point features, and locating in a global coordinate system using thetai＝[xi yi]Where i 1, 2, n is the number of environmental features, since the features are stationary, a model of the environmental features may be expressed as
In the EKF-SLAM algorithm, two coordinate systems need to be considered, and FIG. 5 is a coordinate system configuration of the present invention. Namely a global coordinate system and a robot coordinate system. In fig. 5, the large coordinate system is a global coordinate system, and the small coordinate system is a robot coordinate system. The global coordinate system takes the position of the robot at step 0 as a base point, the y-axis points to north, and the x-axis points to east. The robot coordinate system is that the current AUV position is taken as the origin, the orientation of the robot head is taken as the y-axis, and the clockwise rotation is taken as the x-axis by 90 degrees when viewed from the top
The system state vector includes two parts: state vector of AUV and vector composed of all features in map
X＝[XV Xm]T (8)
Wherein
XV＝[x y θ vx vy]T (9)
State vector representing AUV
Xm＝[x1 y1 …… xn yn]T (10)
A vector representing the composition of all features in the map is referred to herein as a map feature vector. XVWhere x, y, theta denote the position and orientation information of the robot in the global coordinate system, vx、vyWhich represents the linear velocity in both directions in the robot coordinate system. In Xm (x)i，yi) And indicating the position information of the ith map feature in the global coordinate system.
The state estimation and covariance estimation of the system are respectively composed of state vectors and covariance estimation of AUV and map features, and the specific form is as follows:
wherein
And
are each XvAnd XmState estimation of
Wherein P isVIs a covariance matrix, P, of the AUV state vector, XVmFor map feature state vector XmCovariance matrix of (P)vmIs a cross covariance matrix of Xv and Xm.
Along with the increase of the environmental characteristics, the dimension of the system state vector is also increased partially, the SLAM algorithm needs to continuously predict and update the system state vector, initial values of the system state vector and the covariance matrix need to be set before prediction updating,
X＝XV＝[x y θ vx vy]T (13)
wherein X (0) ═ XV(0)＝[05*1]，P(0)＝Pv(0)＝[05*5]
Since the map is not yet constructed at the initial time, the system state vector contains only the state of the AUV and does not contain any map features.
The EKF-SLAM prediction process means that the AUV estimates the state of the AUV at the next moment on the basis of the current moment measured value, and in the process, because the environmental features described by the map are always kept static, the state vector of the AUV is only influenced at the stage, and the state vector used for describing the map is kept unchanged.
Thus, it is X in the system state vector that the prediction phase changesvPart, and P in the system correlation matrixvAnd PvmAnd X ismAnd PmThe item remains unchanged.
The established AUV motion model is
Xv(k+1)＝f(X(k)，U(k)) (14)
Wherein x, y, theta, vx，vyX and y coordinates representing a robot state vector, heading, and speed in x and y directions, respectively
u(k)＝[ωn ax ay]To control the vector, ωn、ax、ayThe angular velocity and the acceleration of the robot in the x and y directions.
Since the prediction phase only changes the state information of the AUV, but not the map information, assuming that the studied environmental features are stationary, the system state at time k +1 is
The covariance matrix is:
P-(k+1)＝GP(k)GT+HQHT (17)
q is an additive white noise, wherein
Wherein G isvAnd HvThe Jacobian matrix of the nonlinear model f function with respect to the state XV and the controlled variable u. The speed measured value provided by the DVL and the angle value of the optical fiber gyroscope are used as direct observed values of the speed and the course angle of the robot to update the speed and the angle value in the system state vector, the speed and the course angle are put together, and the predicted value is updated at the frequency of 1Hz
Observations regarding speed and angle are:
Zd＝[θ Vx Vy] (20)
where the subscript θ represents the heading angle and Vx and Vy represent the linear velocities of the AUV in the x and y directions in the robot coordinate system.
Because all measured values are directly observed on the AUV state, a linear observation model is adopted
Z＝HdX+sd (21)
Wherein the observation matrix
Hd＝[03*2 I3*3 03*1 03*2n] (22)
The noise Sd of the observation model is white gaussian noise with a mean value of 0 and a variance of R.
Directly adopting an EKF algorithm to update the state vector of the system, wherein an innovation Vd and an innovation covariance matrix Sd are represented as follows:
Vd＝Zd-HdX (23)
and finally updating the state estimation through an EKF updating equation, and simultaneously calculating the posterior estimation Kalman gain of the state vector covariance matrix:
updated system state vector:
P+＝P--WdSdWT (27)
the algorithm uses the measured values of speed, angle and the like to update the state vector of the robot and simultaneously update the environmental map features. In practice, the characteristics of the environment map are basically independent from the measured values, and for the convenience of calculation, when the sensor data is used for updating, only the attitude vector and the covariance matrix of the robot can be updated, and the part related to the map in the system state vector and the covariance matrix does not need to be updated
And carrying out data fusion on the obtained positioning data of the AUV with a certain weight by combining the distance 1 and the depth of the buoy and the autonomous underwater vehicle AUV, thereby further improving the positioning accuracy.
The mother ship can be used as an emission source of the water sound wave signal, and putting a plurality of buoys is beneficial to searching several possible target areas simultaneously, so that the efficiency of searching and positioning is improved.
When the Autonomous Underwater Vehicle (AUV) searches possible underwater areas, the sonar can be started preferentially to sample and compare underwater targets when the visualization condition is not met, and the high-definition underwater camera can be started to perform feature matching on optical images of the underwater targets when the visualization condition is met. And when the object meeting the characteristic matching exists, sending image information to the buoy or the mother ship through the underwater communication system, and sending information to the mother ship through the overwater communication module by the buoy. When the underwater sound wave signal transmission is needed, 1-2 Autonomous Underwater Vehicles (AUV) can be instructed to serve as AUV data transfer stations of autonomous underwater vehicles in deeper water areas when the water areas are deeper or the underwater environment is not favorable for long-distance transmission of sound wave signals.
It will be apparent to those skilled in the art that various changes and modifications may be made in the invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (7)
1. A method for automatic search and location of a specific underwater target, the method comprising:
s0: the automatic searching and positioning system for the specific underwater target comprises a mother ship, a buoy and an Autonomous Underwater Vehicle (AUV);
s1: acquiring optical and acoustic original image data of different specific underwater targets in a simulated environment, making a characteristic data set through machine learning, and storing the characteristic data set in the autonomous underwater vehicle AUV;
s2: acquiring a submarine sonar image of an underwater area;
s3: analyzing the seabed sonar images, comparing the images by using the characteristic data set, and selecting a potential area of the specific underwater target;
s4, carrying out clock synchronization on the mother ship, the buoy and the autonomous underwater vehicle AUV, and throwing the autonomous underwater vehicle AUV to carry out autonomous search on the specific underwater target potential area according to a programmed route;
s5, analyzing the data information returned by the AUV, determining the position of the specific underwater target after discrimination,
and S4, establishing searched map information when performing autonomous search according to the autonomous underwater vehicle AUV, wherein the map information comprises a motion model, an environment characteristic model and the position of a specific underwater target of the autonomous underwater vehicle AUV.
2. The method for automatically searching and positioning the specific underwater target according to claim 1, wherein: the functional module of the buoy comprises:
the underwater communication module comprises a transmitting transducer, a receiving transducer and an acoustic wave modem;
the overwater communication module is used for signal transfer between buoys or communication between the buoys and the mother ship;
the GPS module is used for accurately positioning the buoy;
the CSAC clock is integrated by the chip atomic clock and is used for clock synchronization and improving the positioning precision of the autonomous underwater vehicle AUV;
an energy module: the solar energy power supply system comprises a solar cell panel, a storage battery and a detachable power supply cable connected with a mother ship.
3. The method for automatically searching and positioning the specific underwater target according to claim 1, wherein: the autonomous underwater vehicle AUV functional module comprises
The overwater communication module is used for signal transfer between the Autonomous Underwater Vehicles (AUV) or communication between the Autonomous Underwater Vehicles (AUV) and the mother ship or the buoy;
the GPS module is used for accurately positioning the autonomous underwater vehicle AUV;
the inertial imaging module comprises a Doppler velocity meter DVL, a fiber optic gyroscope FOG and a depth meter, and is used for measuring various motion parameters of the autonomous underwater vehicle AUV and improving the positioning precision of the autonomous underwater vehicle AUV;
the optical imaging module comprises a high-definition underwater camera and is used for judging target characteristics and returning artificial secondary judgment of a mother ship after images are collected;
and the acoustic imaging module comprises an active forward-looking imaging sonar which is used for forming a sonar image of an underwater target object to judge the target characteristics and position the Autonomous Underwater Vehicle (AUV).
4. The method for automatically searching and positioning the specific underwater target according to claim 1, wherein: s2, acquiring the underwater sonar image of the target underwater area, specifically: the mother ship provided with the high-power sonar equipment transmits modulated sonar signals underwater, the autonomous underwater vehicle AUV serves as a movable transducer array, reflected sound wave signals are acquired in a distributed mode, and sonar images are formed after data processing.
5. The method of claim 1, wherein when performing autonomous search on the potential area of the specific underwater target according to a programmed route in S4, the positioning accuracy of the autonomous underwater vehicle AUV is improved by using communication between the autonomous underwater vehicle AUV and the buoy and data fusion of the autonomous underwater vehicle AUV sensor and using the EKF-SLAM algorithm.
6. The method of claim 1, wherein the autonomous underwater vehicle AUV on the water surface can be used as a mobile buoy to provide positioning information.
7. The method as claimed in claim 1, wherein the autonomous underwater vehicle AUV under water can be used as a data relay station to provide data information relay for the autonomous underwater vehicle AUV in a deeper water area or a water area where signal propagation is difficult.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910148370.2A CN109856638B (en) | 2019-02-28 | 2019-02-28 | Method for automatically searching and positioning specific underwater target |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910148370.2A CN109856638B (en) | 2019-02-28 | 2019-02-28 | Method for automatically searching and positioning specific underwater target |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109856638A CN109856638A (en) | 2019-06-07 |
| CN109856638B true CN109856638B (en) | 2021-09-17 |
Family
ID=66899305
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910148370.2A Active CN109856638B (en) | 2019-02-28 | 2019-02-28 | Method for automatically searching and positioning specific underwater target |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109856638B (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110320520B (en) * | 2019-06-26 | 2022-08-02 | 哈尔滨工程大学 | Robust back-end graph optimization method for depth measurement information synchronous positioning and graph building |
| CN110456357B (en) * | 2019-08-27 | 2023-04-07 | 吉林大学 | Navigation positioning method, device, equipment and medium |
| CN110646857B (en) * | 2019-09-12 | 2021-06-08 | 北京自动化控制设备研究所 | Underwater magnetic target distributed searching method |
| CN110703206B (en) * | 2019-09-19 | 2023-07-25 | 上海船舶电子设备研究所(中国船舶重工集团公司第七二六研究所) | Underwater UUV communication positioning integrated system |
| CN110824485A (en) * | 2019-09-25 | 2020-02-21 | 中国人民解放军军事科学院国防科技创新研究院 | Bistatic sonar underwater target feature extraction and classification method based on AUV |
| CN110764094A (en) * | 2019-10-25 | 2020-02-07 | 南京奥达升智能科技有限公司 | Underwater three-dimensional visual detection system and detection method thereof |
| CN111045453A (en) * | 2019-12-25 | 2020-04-21 | 南京工程学院 | Cooperative control system and method based on unmanned ship and multi-underwater robot |
| CN112445244B (en) * | 2020-11-09 | 2022-03-04 | 中国科学院沈阳自动化研究所 | Target searching method for multiple autonomous underwater robots |
| CN113654553B (en) * | 2021-08-12 | 2024-01-16 | 浙江大学 | Cylinder array positioning system and positioning method based on inverse ultrashort baseline |
| CN115201799A (en) * | 2022-09-09 | 2022-10-18 | 昆明理工大学 | Time-varying Kalman filtering tracking method for sonar |
| CN116659510A (en) * | 2023-06-02 | 2023-08-29 | 海南大学 | Underwater robot positioning and obstacle avoidance method, device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012101423A2 (en) * | 2011-01-25 | 2012-08-02 | Subsea Asset Location Technologies Limited | Identification, detection and positioning of underwater acoustic reflectors |
| CN103852764A (en) * | 2012-12-07 | 2014-06-11 | 新昌县冠阳技术开发有限公司 | Submarine navigation monitoring system based on laser polarization detection |
| GB201617203D0 (en) * | 2016-10-10 | 2016-11-23 | Gardiner Simon | Autonomous unmanned subersible vehicle docking |
| CN106506361A (en) * | 2016-11-17 | 2017-03-15 | 河海大学常州校区 | A kind of underwater sound sensing network routing algorithm and system using mediation potential field |
| CN106772324A (en) * | 2016-11-18 | 2017-05-31 | 中国电子科技集团公司第三研究所 | A kind of method of underwater sound signal simulation, underwater sound signal simulator and Imaging sonar |
| JP2018017502A (en) * | 2016-07-25 | 2018-02-01 | 株式会社エス・イー・エイ | Underwater sound positioning system |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8534959B2 (en) * | 2005-01-17 | 2013-09-17 | Fairfield Industries Incorporated | Method and apparatus for deployment of ocean bottom seismometers |
| US9255803B2 (en) * | 2009-08-14 | 2016-02-09 | IPOZ Systems, LLC | Devices, program products and computer implemented methods for touchless metrology having virtual zero-velocity and position update |
| CN104443325A (en) * | 2014-08-25 | 2015-03-25 | 鲁正祥 | Large aircraft carrier composed of small submarines |
| CN105387842A (en) * | 2015-11-17 | 2016-03-09 | 中国海洋大学 | Self-propulsion type submarine topography and landform mapping system and method based on perception driving |
| CN108681338A (en) * | 2018-05-29 | 2018-10-19 | 中国科学院声学研究所 | A kind of telemetering and remote control system of submarine navigation device |
| CN109270541A (en) * | 2018-08-21 | 2019-01-25 | 中国科学院声学研究所 | A kind of wireless sonar system and method based on underwater sound communication |
-
2019
- 2019-02-28 CN CN201910148370.2A patent/CN109856638B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2012101423A2 (en) * | 2011-01-25 | 2012-08-02 | Subsea Asset Location Technologies Limited | Identification, detection and positioning of underwater acoustic reflectors |
| CN103852764A (en) * | 2012-12-07 | 2014-06-11 | 新昌县冠阳技术开发有限公司 | Submarine navigation monitoring system based on laser polarization detection |
| JP2018017502A (en) * | 2016-07-25 | 2018-02-01 | 株式会社エス・イー・エイ | Underwater sound positioning system |
| GB201617203D0 (en) * | 2016-10-10 | 2016-11-23 | Gardiner Simon | Autonomous unmanned subersible vehicle docking |
| CN106506361A (en) * | 2016-11-17 | 2017-03-15 | 河海大学常州校区 | A kind of underwater sound sensing network routing algorithm and system using mediation potential field |
| CN106772324A (en) * | 2016-11-18 | 2017-05-31 | 中国电子科技集团公司第三研究所 | A kind of method of underwater sound signal simulation, underwater sound signal simulator and Imaging sonar |
Non-Patent Citations (2)
| Title |
|---|
| 《AUV回收控制的关键技术研究》;姜言清;《中国优秀博硕士学位论文全文数据库（博士） 工程科技II辑》;20171215(第12期);正文第55-95页 * |
| 《Inverse speed analysis and low speed control of underwater vehicle》;YE L et.al;《Journal of Central South University》;20141231;第21卷(第7期);第2652-2659页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109856638A (en) | 2019-06-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109856638B (en) | Method for automatically searching and positioning specific underwater target | |
| Wu et al. | Survey of underwater robot positioning navigation | |
| Salavasidis et al. | Terrain‐aided navigation for long‐endurance and deep‐rated autonomous underwater vehicles | |
| Bao et al. | Integrated navigation for autonomous underwater vehicles in aquaculture: A review | |
| Zhang et al. | Autonomous underwater vehicle navigation: a review | |
| Eustice et al. | Experimental results in synchronous-clock one-way-travel-time acoustic navigation for autonomous underwater vehicles | |
| Maki et al. | Navigation method for underwater vehicles based on mutual acoustical positioning with a single seafloor station | |
| Eustice et al. | Recent advances in synchronous-clock one-way-travel-time acoustic navigation | |
| Bovio et al. | Autonomous underwater vehicles for scientific and naval operations | |
| CN101900558B (en) | Combined navigation method of integrated sonar micro navigation autonomous underwater robot | |
| Meduna et al. | Low-cost terrain relative navigation for long-range AUVs | |
| Zhang et al. | NavNet: AUV navigation through deep sequential learning | |
| Ridao et al. | USBL/DVL navigation through delayed position fixes | |
| Stanway | Dead reckoning through the water column with an acoustic Doppler current profiler: Field experiences | |
| Medagoda et al. | Water column current profile aided localisation for autonomous underwater vehicles | |
| JP2017534526A (en) | A method for calculating the surface (water surface) speed of at least one ship, and a method for estimating each drift (floating) vector at all points in the route of the ship. | |
| Sirikonda et al. | Integration of low-cost IMU with MEMS and NAVIC/IRNSS receiver for land vehicle navigation | |
| Medagoda et al. | Water column current profile aided localisation combined with view-based SLAM for autonomous underwater vehicle navigation | |
| Wang et al. | Wireless sensor networks for underwater localization: A survey | |
| Quraishi et al. | Easily deployable underwater acoustic navigation system for multi-vehicle environmental sampling applications | |
| RU198953U1 (en) | DEVICE FOR DETERMINING VESSEL MOVEMENT PARAMETERS | |
| JP3234889B2 (en) | Bathymetry system using GPS kinematic positioning method | |
| CN112379395A (en) | Positioning navigation time service system | |
| Wang et al. | Study on the observability degree of integrated inertial navigation system of autonomous underwater vehicle | |
| Inzartsev et al. | Integrated positioning system of autonomous underwater robot and its application in high latitudes of arctic zone |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |