CN113220001A - Underwater vehicle and real-time obstacle avoidance method thereof - Google Patents
Underwater vehicle and real-time obstacle avoidance method thereof Download PDFInfo
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Abstract
The invention belongs to the technical field of robot motion control, and provides a submarine vehicle and a real-time obstacle avoidance method of the submarine vehicle, wherein the real-time obstacle avoidance method comprises the following steps: the system comprises a remote shore-based upper computer, a controller, an altimeter, a ranging sonar, a depth finder and a forward-looking sonar, wherein the controller, the altimeter, the ranging sonar, the depth finder and the forward-looking sonar are all arranged inside a submarine vehicle; the controller receives an initial navigation path set by the remote shore-based upper computer and drives the motor to control the propeller to work; the method comprises the steps that data and state data of an altimeter, a ranging sonar, a depth finder and a forward-looking sonar which are measured in real time are sent into a controller computer, and when the forward-looking sonar detects that an obstacle exists on a traveling path of the underwater vehicle, a motor is controlled to rotate a propeller to change a navigation posture according to a set distance threshold, so that autonomous obstacle avoidance is completed.
Description
Technical Field
The invention belongs to the technical field of robot motion control, and particularly relates to an underwater vehicle and a real-time obstacle avoidance method of the underwater vehicle.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The method has abundant resources and development significance in the ocean, and especially has important research values in the aspects of ocean archaeology, scientific investigation, submarine topography mapping, fishery culture, ocean pasture, marine rescue, sunken ship salvage and the like. The current research and development aiming at underwater robot can replace human to work in dangerous water area. However, due to the unknown and complex submarine topography, the navigation path of the underwater vehicle cannot be planned in advance, and therefore a control system of the underwater vehicle is required to have a stable and reliable real-time collision prevention function. In order to realize high-precision environment perception and self-adaptive air route control effects, scientists of various countries gradually develop related research works according to an ocean motion mechanism, and various control strategies and means are adopted, such as intelligent control algorithms of sliding film control, fuzzy control, neural network and the like, so that various underwater robots with different modes appear successively, such as OUTLAND1000 developed by OUTLAND company in the United states, and a sonar system carried by the equipment can be used for surveying and mapping underwater features and has a good obstacle avoidance function; an ABE (extreme ultraviolet absorption) underwater vehicle developed by the Wutz Hall ocean institute, wherein the underwater vehicle designs a set of collision avoidance system by utilizing altimeter information; the university team of Irander Heilong explores the submarine topography tracking problem, and designs a controller by combining a nonlinear theory and a pseudo-spectrum method, wherein the controller is closer to a submersible vehicle controller under real navigation; the Italian national research Committee also carries out a series of researches on the aspect of submarine topography tracking, and processes measurement data by using Tritech ST-1000 sonar and extended Kalman filtering to obtain good tracking effect. Due to the randomness of ocean motion, when the gradient of the submarine topography exceeds the maximum gradient of climbing of the underwater vehicle, the real-time collision avoidance mode needs to be started, and the simple and practical near-domain map navigation method can meet the real-time collision avoidance function. Because of the uncertainty characteristic of the external environment of the ocean, an accurate mathematical model is difficult to establish, the current mainstream control technology adopts nonlinear robust control, but the underwater vehicle is influenced by the seawater pressure during deep-sea navigation and ocean current disturbance during working, and the adoption of a conventional PID control method is a simple and feasible scheme. Accurate hydrodynamic modeling, reasonable sensing equipment configuration and advanced control strategies are the precondition for ensuring that the underwater vehicle can smoothly and efficiently complete the operation task. The extraction of the hydrodynamic coefficient is the basis of the maneuverability research and the control system design of the underwater vehicle, and the conventional method for acquiring the hydrodynamic coefficient mainly comprises a model test method, a computational fluid mechanics method, a semi-theoretical semi-empirical estimation method and a model identification method.
At present, a main stream underwater vehicle has use conditions on land, but when the main stream underwater vehicle works underwater, due to the complexity of an underwater environment and the diversity and incompleteness of submarine landforms, the stable and reliable autonomous working of the underwater vehicle is still an important research subject, a conventional PID control algorithm is simple, the running time is short, the system response time is short, but the autonomous working can not cope with the instantaneous change of the external marine environment, so a fuzzy + PID control strategy is adopted by people, the problem of partial interference is solved by the control strategy, but the control process is mechanically discontinuous, the whole system shakes seriously, people adopt a robust control strategy, however, the robust control strategy is effective on a rigid system of the underwater vehicle, but the underwater vehicle has a complex relation with hydrodynamic action when working underwater, and the underwater vehicle has random interference along with ocean current disturbance and dark current, so people try to adopt an intelligent control technology, such as artificial intelligent algorithms like a neural network and the like, however, certain problems still exist, mainly including that underwater dynamics of the underwater vehicle and ocean background noise are not carefully considered, and the adopted intelligent control algorithms such as the fuzzy neural network and the like increase the operation amount of the controller, so that the problems of poor obstacle avoidance real-time performance, unstable system convergence and the like are brought.
In summary, the conventional underwater vehicle has the following problems:
1. the underwater environment is different from the air, the marine environment is complex and changeable, the submarine landform is unknown, and the underwater vehicle controller has a flexible real-time obstacle avoidance function, so that the reasonable design of the whole structure of the underwater vehicle is of great importance. In addition, error measurement of sensing equipment such as obstacle avoidance sonar carried by the underwater vehicle needs to be statistically estimated so as to carry out compensation design in a control algorithm.
2. Because the operation of the fuzzy neural network algorithm needs a long time, the established mathematical model cannot be too complex, the control system can be complicated and even cannot be realized due to the too complex mathematical model, the actual motion condition of the system cannot be reflected due to the too simple mathematical model, the control performance of the control system is weakened, and the operation amount of a controller needs to be reasonably arranged to ensure the stable convergence of the algorithm.
3. Ocean background noise classification, modeling problems of boat noise and wind noise.
Disclosure of Invention
In order to solve the technical problems in the research background, the invention provides an underwater vehicle and a real-time obstacle avoidance method of the underwater vehicle, which provide the structural composition of the whole system of the underwater vehicle, analyze and research ocean background noise, distinguish the composition of interference and establish a corresponding mathematical model; the measurement error of the sensing equipment carried by the underwater vehicle is counted, and the controller design is completed by adopting a fuzzy neural network control algorithm, so that the requirements for smooth and effective control are met, the calculation amount of the controller is reduced, and the purposes of good real-time performance, excellent control performance and the like are achieved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an intelligent underwater vehicle overall design scheme.
A submersible vehicle comprising: the system comprises a remote shore-based upper computer, a controller, an altimeter, a ranging sonar, a depth finder and a forward-looking sonar, wherein the controller, the altimeter, the ranging sonar, the depth finder and the forward-looking sonar are all arranged inside a submarine vehicle;
the controller receives an initial navigation path set by the remote shore-based upper computer and drives the motor to control the propeller to work; the method comprises the steps that data and state data of an altimeter, a ranging sonar, a depth finder and a forward-looking sonar which are measured in real time are sent into a controller computer, and when the forward-looking sonar detects that an obstacle exists on a traveling path of the underwater vehicle, a motor is controlled to rotate a propeller to change a navigation posture according to a set distance threshold, so that autonomous obstacle avoidance is completed.
The invention provides a real-time obstacle avoidance method of a submarine vehicle.
A real-time obstacle avoidance method for an underwater vehicle, which employs the underwater vehicle of the first aspect, includes:
collecting the ocean background environment noise in real time, and inputting the ocean background environment noise into a ship ocean background environment model;
acquiring the underwater position and pose of the underwater vehicle in real time, and inputting the underwater position and pose into an underwater dynamic model of the underwater vehicle;
setting an initial value of an airway, calculating an expected advancing path of the underwater vehicle by the controller according to the noise quantity of the sensor in the cabin and the ocean background environment after the underwater vehicle works, and simultaneously taking a calculation result as a main reference value of the underwater vehicle to reduce the calculation quantity of the controller, and feeding back the error between the navigating path of the underwater vehicle and the expected path to the controller of the underwater vehicle; when random interference such as crustal motion occurs, the neural network algorithm can perform fast operation so as to complete closed-loop adjustment.
When the forward-looking sonar detects that the advancing path of the underwater vehicle is provided with an obstacle, the controller controls the motor to rotate the propeller according to the predicted navigation path information, and the underwater vehicle changes the navigation attitude to finish autonomous obstacle avoidance.
Compared with the prior art, the invention has the beneficial effects that:
the invention can detect the information around the ocean by sensors such as an altimeter, a depth finder, a ranging sonar and a forward-looking sonar and send the information to the underwater vehicle controller, and when the underwater vehicle detects that an obstacle exists on a traveling road, the forward-looking sonar and the ranging sonar select to climb through the obstacle or change the course to avoid the obstacle according to the distance between the underwater vehicle and the obstacle and the height gradient of the obstacle. Meanwhile, due to the problems of the non-linearity of the dynamic height of the underwater vehicle and the uncertainty of underwater dynamic parameters, and the interference of unknown environmental factors, the controller designed by the fuzzy neural network control algorithm has good robustness and a stable self-adaptive track airway function, thereby meeting the application requirements.
The invention firstly provides the structural composition of the whole system of the underwater vehicle, and the navigation path controller of the underwater vehicle is designed by applying BP fuzzy neural network algorithm based on the hydrodynamic action relation of the underwater vehicle, thereby improving the control performance and the operation efficiency of the underwater vehicle and meeting the self-adaptive tracking function of the navigation path.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a block diagram of the overall structural design of a submersible vehicle in an embodiment of the invention;
FIG. 2 is a schematic block diagram of the path tracking of the underwater vehicle in the embodiment of the invention;
FIG. 3 is a diagram of a control process of the controller according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a ground coordinate system and a motion coordinate system in an embodiment of the invention.
FIG. 5 is a chart of a hypothetical local noise source profile at the sea surface in an embodiment of the present invention;
FIG. 6 is a geometric representation of a ray model in an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
The present embodiment provides a submersible vehicle.
A submersible vehicle comprising: the system comprises a remote shore-based upper computer, a controller, an altimeter, a ranging sonar, a depth finder and a forward-looking sonar, wherein the controller, the altimeter, the ranging sonar, the depth finder and the forward-looking sonar are all arranged inside a submarine vehicle;
the remote shore-based upper computer is provided with an initial navigation path and is sent to a Beidou combined navigation end or a shore-based/water surface base station in a wireless communication mode, and the altimeter, the ranging sonar, the depth finder and the forward-looking sonar send real-time measured data to the controller;
the controller receives an initial navigation path set by the remote shore-based upper computer and drives the motor to control the propeller to work; sending data and state data measured by an altimeter, a ranging sonar, a depth finder and a forward looking sonar in real time into a controller computer; when the forward-looking sonar detects that an obstacle exists on the advancing path of the underwater vehicle, the motor is controlled to rotate the propeller according to the set distance threshold, the underwater vehicle changes the navigation attitude, and the autonomous obstacle avoidance is completed.
As one or more embodiments, a submersible vehicle comprises: the underwater image acquisition module acquires a short-distance underwater image in real time.
As one or more embodiments, the underwater image acquisition module comprises a holder searchlight and a high-definition camera.
As one or more implementation modes, the controller transmits data and state data measured by the altimeter, the ranging sonar, the depth finder and the forward-looking sonar in real time to the shore-based/water-surface base station through infrasonic wave communication, and the shore-based/water-surface base station returns the data to the remote shore-based upper computer through the Beidou satellite.
As one or more embodiments, a submersible vehicle comprises: and the cabin sensor displays the state data of the underwater vehicle.
Specifically, as shown in fig. 1, the overall structure of the underwater vehicle is generally divided into four parts: the specific overall structure block diagram is shown in figure 1. The underwater vehicle is firstly put into water through a crane, the system is powered on, an initial navigation path is set through a remote shore-based upper computer, the underwater vehicle is connected to a Beidou combined navigation system or a shore-based/water surface base station through a wireless communication mode, an altimeter, a depth finder, a ranging sonar and a forward-looking sonar work, data of the altimeter, the depth finder, the ranging sonar and the forward-looking sonar work are sent to a controller of the underwater vehicle, then the controller drives a motor propeller to work, and the underwater vehicle advances according to the initial navigation path set by the remote shore-based upper computer. And (3) starting a tripod head searchlight, acquiring a short-distance underwater image by a high-definition camera in real time, and displaying state data of the underwater vehicle when the sensor in the cabin normally works. According to the command of the remote shore-based upper computer, the controller transmits the state data and the measurement data to the shore-based/water surface base station through infrasonic wave communication, and the shore-based/water surface base station returns the data to the remote shore-based upper computer through the Beidou satellite.
Example two
The embodiment provides a real-time obstacle avoidance method of a submersible vehicle.
A real-time obstacle avoidance method of a submarine vehicle, which adopts the submarine vehicle described in the first embodiment, includes:
collecting the ocean background environment noise in real time, and inputting the ocean background environment noise into a ship ocean background environment model;
acquiring the underwater position and pose of the underwater vehicle in real time, and inputting the underwater position and pose into an underwater dynamic model of the underwater vehicle;
setting an initial value of a navigation path, calculating an expected advancing path of the underwater vehicle by the controller according to the noise quantity of the sensor in the cabin and the ocean background environment after the underwater vehicle works, and simultaneously taking a calculation result as a main reference value of the underwater vehicle to reduce the calculation quantity of the controller;
when the forward-looking sonar detects that the advancing path of the underwater vehicle is provided with an obstacle, the controller calculates a new navigating path in real time, and then controls the motor to rotate the propeller to enable the underwater vehicle to change a track, so that autonomous obstacle avoidance is completed.
Specifically, as shown in fig. 2: firstly, an initial navigation path is set for the underwater vehicle, navigation path information is given through a fuzzy algorithm and is sent to a controller, and the underwater vehicle is started to enter an autonomous working state through an automatic rudder. And the altimeter, the depth finder, the ranging sonar and the forward-looking sonar measure data in real time and send the data to the central processor for operation. Due to the complex and diverse marine environments and submarine landforms, the underwater vehicle is likely to be subjected to course deviation and even crash due to various external interferences. The interference statistics is sent to the fuzzy neural network controller, and the operation of the fuzzy neural network algorithm is time-consuming, so that the established mathematical model cannot be too complex, the control system cannot be complicated due to the too complex operation, the realization cannot be realized, the simplicity cannot be realized, the practical motion condition of the system cannot be reflected due to the too simple operation, and the control performance of the control system is weakened. According to the invention, a two-dimensional fractal sea surface model is added into the BP fuzzy neural network controller, and the interference from the marine environment is cancelled by calculation, so that the operation pressure of the controller is reduced, the control performance of the control system is improved, and the stable tracking effect is achieved. When the forward-looking sonar detects that the traveling path of the underwater vehicle is provided with an obstacle, the motor is controlled to rotate the propeller according to the preset distance threshold, the underwater vehicle changes the navigation attitude, and the autonomous obstacle avoidance is completed.
The preset distance threshold is a fixed value, such as 10 meters of obstacle avoidance distance and 20 meters of obstacle avoidance distance, but is not limited to these two values.
Firstly, establishing a ship ocean background environment model, then starting a fuzzy algorithm, and outputting the fuzzy algorithm as an expected route of the underwater vehicle; random interferences such as thermohaline ocean current, crustal motion, ocean bottom earthquake and the like caused by the temperature and salinity of the seawater are used as the input of the neural network, then the real route of the underwater vehicle is adjusted in real time to tend to the expected route, and the specific control process is shown in figure 3.
According to the control process of the controller, besides the physical cause explanation of random interference, an underwater dynamic model of the underwater vehicle and a ship ocean background environment model need to be intensively explained, and the difference value of the two paths is the basis for self-adaptive adjustment of a fuzzy neural network control algorithm. In the following we will briefly describe the model of the dynamics of the underwater vehicle and the model of the marine background environment of the boat.
(1) Dynamic model of underwater vehicle
The dynamic model of the underwater vehicle is established on the basis of a kinematic model, the component velocities of the O-shaped origin of a motion coordinate system on the X, Y and Z axes are u, V and w respectively, and the angular velocity omega on the X, Y and Z axes is p, q and r respectively. The underwater position and the attitude of the underwater vehicle can be measured by three absolute value components of the O-O in a fixed coordinate system of a moving coordinate system
And attitude angle of the moving coordinate system relative to the fixed coordinate system
To determine, i.e. the fixed coordinate system origin E needs to coincide with the moving coordinate system origin o, thus ξ0＝0，η0＝0，
Attitude angle of underwater vehicle with fixed coordinate system
The instantaneous attitude of the vehicle, expressed as a moving coordinate system, with rotation about the E xi axis by heading angle psi, about the E xi axis
The shaft is rotated to obtain a longitudinal rocking angle theta, and the shaft is rotated around an E eta axis to obtain a transverse rocking angle
As shown in fig. 4.
According to the Euler rotation theorem, the fixed coordinate system can obtain a motion coordinate system through three rotations, namely according to the rotation heading angle psi, the rotation pitch angle theta and the rotation roll angle phi in sequence
We can then obtain a transformation matrix from the moving coordinate system to the fixed coordinate system as:
attitude angle
The value range is as follows:
the angular velocity relationship in the two coordinate systems is as follows:
the gravity center of the underwater vehicle is expressed by a fixed coordinate system (x, y, z), and the kinematic model of the underwater vehicle is as follows:
the dynamic model of the underwater vehicle is then given as:
wherein M is an inertia matrix generated by rigid mass and rotational inertia of the underwater vehicle, C (v) is a Coriolis force and moment matrix caused by earth rotation, D (v) is a damping matrix in the aspect of hydrodynamic force, and g (eta)2) Forces and moments, tau, generated for gravity and buoyancyeηυFor external environmental forces and moments (disturbance of the marine environment by wind, waves, currents), τproFor thrust and torque output, tau, produced by the propellerhydroHydrodynamic force for the underwater vehicle to interact with water, MAAs an additional mass matrix for the diver, CA(v) Is the additional mass part of the Coriolis force and moment, D (| v |) is a water damping force matrix, g (eta) is the restoring force of the underwater vehicle, and delta f is the seawaterTemperature and salinity induced warm salt ocean Currents (Thermohaline Currents).
(2) Ship ocean background environment model
The marine background environment of the boat is mainly divided into three main categories, namely underwater noise of the boat, wind noise and random interference caused by warm salt ocean current or earth crust movement. The ship underwater noise mainly comprises rigid mechanical noise, propeller noise and hydrodynamic noise, and in the patent, a ship underwater noise model formula can be obtained according to the submersible vehicle dynamic model formula given above. The wind noise is that the submarine vehicle works under water, especially in deep sea, and the interference of factors such as wind, wave, stream, acid-base viscosity and the like in the marine environment on the stress and moment of the submarine vehicle is large. The two-dimensional fractal sea surface model is used for simulating an actual sea surface and is specifically represented as follows:
wherein: sigma is the standard deviation of the sea surface height,
to normalize the factor, dSIs fractal dimension, 2 < dSLess than 3; a. b is a scale factor with a space wave number smaller than or larger than the fundamental wave, and is generally larger than 1, and a is 1/b. Vx，VyIs the relative speed of motion, β, between the vehicle and the sea surface in the x and y directions1mAnd beta2nIs the direction angle of motion of the waves, omegam，ΩnIs the m, n spectral component angular frequency, Λm、ΛnIs the wave length, Λm＝Λ0/am，Λn＝Λ0/bnWave number Km＝2π/Λm＝K0am，Kn＝2π/Λn＝K0bn，Λ0Is the wave fundamental wavelength, K0Is the wave number of the fundamental wave of the sea wave; n is a radical of1Is the harmonic order, alpha1m,α2nIs [ - π, π]And the internal random phase satisfies the uniform distribution characteristic. γ is positive power factor, and Γ is correction factor.
U19.5Is the offshore wind speed at a distance of 19.5m from the sea surface height, and g is the gravity constant.
We derive the formula of the cross-spectral density of the wind-induced noise, which represents the spatial characteristics of the noise field, and assume that the local noise source distribution on the sea surface is shown in FIG. 5, and the noise source distribution has a radius R0The depth of the round surface is z'. Here we only consider the case of incoherent noise sources, then the cross-spectral density of two receiving points located within the area source is:
in the formula G (r)1,r2；z1,z2) Is a function of the green's function,
is the sea surface noise source intensity, z1，z2To receive the depth of two points, it reduces to local acoustic intensity when the two field points tend to be at the same location.
We define the noise source spectral intensity as SωThe acoustic field is xiωWe ignore the frequency dependence of the two by way of the xiωFourier synthesis is carried out to obtain a total noise sound field, and the equation is as follows:
Ξω(r,z)＝∫Sω(r')G(r1,r'；z,z')d2r'
the Harrison is used for carrying out ray processing on sound propagation, the geometric representation of a ray model is shown in figure 6, and the sound pressure of a unit point source at a distance r on the sea surface received by a receiver is as follows:
where p represents the path number, θrIs the angle of arrival. Therefore, point source sound pressure and total noise sound fields of different water depths can be obtained simply by sampling the two-dimensional fractal sea surface model value, and the cross spectral density of two receiving points in an area source is obtained. The two-dimensional fractal sea surface model is the basis of calculating the ship ocean background environment model and is the main part of the operand.
(3) Primary sonar measurement error calculation
The measurement error of the environmental perception equipment of the underwater vehicle has certain influence on the control performance of the underwater vehicle, and the measurement error of the environmental perception equipment needs to be counted to design a stable controller, so that reference is provided for compensation of a control algorithm. Typical perception equipment of a submarine vehicle comprises a depth finder and sonar equipment, and the measurement error of a main sonar is mainly discussed here because the submarine vehicle is limited by the navigation depth. At present, the performance indexes of a main sonar system are as follows: the azimuth angle is +/-20 degrees, the elevation angle is +/-10 degrees, and the action distance is 100 m. The maximum absolute measurement error can be obtained from relevant documents and by consulting the relevant technical data of the underwater vehicle: the azimuth angle is less than or equal to 1 percent, the altitude angle is less than or equal to 0.5 percent, and the working distance is +/-1 percent.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A submersible vehicle, comprising: the system comprises a remote shore-based upper computer, a controller, an altimeter, a ranging sonar, a depth finder and a forward-looking sonar, wherein the controller, the altimeter, the ranging sonar, the depth finder and the forward-looking sonar are all arranged inside a submarine vehicle;
the controller receives an initial navigation path set by the remote shore-based upper computer and drives the motor to control the propeller to work; the method comprises the steps that data and state data of an altimeter, a ranging sonar, a depth finder and a forward-looking sonar which are measured in real time are sent into a controller computer, and when the forward-looking sonar detects that an obstacle exists on a traveling path of the underwater vehicle, a motor is controlled to rotate a propeller to change a navigation posture according to a set distance threshold, so that autonomous obstacle avoidance is completed.
2. The underwater vehicle of claim 1, wherein the remote shore-based upper computer is provided with an initial navigation path, and sends the initial navigation path to a Beidou combined navigation end or a shore-based/water surface base station in a wireless communication mode, and the altimeter, the ranging sonar, the depth finder and the forward looking sonar send real-time measured data to the controller.
3. The underwater vehicle of claim 1, characterized in that it comprises: the underwater image acquisition module acquires a short-distance underwater image in real time.
4. The undersea vehicle of claim 3, wherein the underwater image acquisition module comprises a pan-tilt searchlight and a high-definition camera.
5. The underwater vehicle of claim 1, wherein the controller transmits data and status data measured by the altimeter, the ranging sonar, the depth finder and the forward looking sonar in real time to the shore-based/water-surface base station through infrasonic communication, and the shore-based/water-surface base station returns the data to the remote shore-based upper computer through the Beidou satellite.
6. The underwater vehicle of claim 1, characterized in that it comprises: and the cabin sensor displays the state data of the underwater vehicle.
7. A real-time obstacle avoidance method of an underwater vehicle, which adopts the underwater vehicle as claimed in any one of claims 1 to 6, and is characterized by comprising the following steps:
collecting the ocean background environment noise in real time, and inputting the ocean background environment noise into a ship ocean background environment model;
acquiring the underwater position and pose of the underwater vehicle in real time, and inputting the underwater position and pose into an underwater dynamic model of the underwater vehicle;
setting an initial value of a navigation path, calculating an expected advancing path of the underwater vehicle by the controller according to an in-cabin sensor and the noise quantity of the ocean background environment by adopting a fuzzy control algorithm after the underwater vehicle works, simultaneously taking a calculation result as a main reference value of the underwater vehicle to reduce the calculation quantity of the controller, feeding the error between the navigating path of the underwater vehicle and the expected path back to the controller of the underwater vehicle, and when random interference occurs, quickly calculating by using a neural network algorithm so as to complete closed-loop adjustment; when the forward-looking sonar detects that the advancing path of the underwater vehicle is provided with an obstacle, the controller controls the motor to rotate the propeller according to the predicted navigation path information, and the underwater vehicle changes the navigation attitude to finish autonomous obstacle avoidance.
8. The real-time obstacle avoidance method of the underwater vehicle of claim 7, wherein the random disturbance variable comprises: boat underwater noise, wind borne noise, and random disturbances caused by warm salt ocean currents or earth crust motion.
9. The real-time obstacle avoidance method of the underwater vehicle as claimed in claim 7, wherein the underwater vehicle environment sensing device comprises: altimeter, range finding sonar, depth finder and forward looking sonar.
10. The real-time obstacle avoidance method of the underwater vehicle of claim 7, wherein the difference between the output result of the ship marine background environment model and the output result of the underwater vehicle dynamics model is input to the BP neural network model.
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