1286 results about "Underwater robot" patented technology

The invention relates to an underwater robot navigation positioning method and system. The method comprises the following steps: S1, acquiring angular velocity and acceleration signals through an inertial measurement unit; s2, resolving a three-dimensional velocity observation value according to the beam radial velocity vector signal in combination with the angular velocity signal, and extracting environment feature point cloud data according to the acoustic image signal; s3, multi-source data time synchronization is carried out, and a fusion input signal with time-space alignment is generated; s4, constructing an adaptive factor graph optimization model, and dynamically adjusting an inertial navigation solution node based on a real-time weight coefficient; inputting the environment feature point cloud data into a closed-loop detection module to generate a loopback factor node, and adaptively correcting the weight of the node according to the feature matching degree; and S5, solving the adaptive factor graph optimization model through a nonlinear optimization algorithm. According to the underwater robot navigation positioning method and system, the problem that the fusion positioning precision of a multi-source heterogeneous sensor is insufficient in an underwater GPS-free environment can be solved.

The invention discloses an underwater robot multi-target dynamic resource scheduling method, and relates to the technical field of robot resource scheduling. The method comprises the following steps: constructing an underwater robot resource scheduling problem model, and establishing a target optimization model which comprises the steps of minimizing task maximum execution time, minimizing total power consumption and balancing load; generating reference points and distributing the reference points in a target space; a population is coded and initialized, a scheduling scheme is represented by adopting multi-segment chromosome coding, the scheduling scheme comprises a task allocation sequence and tasks allocated by a robot, and an initial parent population is generated in a random mode; a non-dominated solution set is obtained through evolutionary iteration, and the non-dominated solution set is obtained through reference point niche selection and combined evolutionary iteration of crossover and mutation operation based on an improved NSGA-III algorithm; and obtaining an optimal scheduling scheme, and screening an optimal compromise solution from the non-dominated solution set as the optimal scheduling scheme. According to the invention, a data-driven intelligent scheduling scheme is provided for collaborative operation of underwater robots in a complex marine environment.

The invention relates to the technical field of underwater robots and high-precision positioning, and particularly discloses a high-precision positioning system for collaborative operation of an underwater robot cluster. The system comprises a multi-source fusion positioning base station network module, a cross-medium cooperative positioning engine module, a cluster relative positioning subsystem module, a dynamic environment perception compensation module, an elastic positioning fault-tolerant system module and a cooperative positioning decision center module. An absolute positioning reference is constructed through a multi-source fusion positioning reference station network, a cross-medium cooperative positioning engine is combined to realize accurate pose calculation, a cluster relative positioning subsystem is utilized to establish a dynamic topological relation between robots, an environment perception compensation and elastic fault-tolerant mechanism is introduced, and finally resource configuration is optimized through a cooperative decision center. The method solves the problem of high-precision positioning of robot cluster collaborative operation in a complex underwater environment, and can be widely applied to the fields of ocean exploration, underwater engineering and the like.

The invention provides a multi-mode underwater detection robot. The robot comprises a body section assembly formed by connecting a plurality of cabin pipe assemblies and power cabin assemblies in series. Joints between the cabin pipe assemblies are connected through joint connecting pieces (7); a steering engine joint piece (3) is arranged at the joint connecting piece, a propeller propelling device (4) is arranged at the power cabin assembly, and when the underwater posture and the propelling working condition of the robot are adjusted, a steering engine of the steering engine joint piece applies force to the joint connecting piece to change the included angle of the joint connecting piece, so that the body section assembly is twisted, and the posture of the robot is converted into different modes; the propelling direction of the propeller propelling device is changed; the problems that an existing underwater robot is single in mode, incapable of adapting to complex environments, low in energy efficiency, difficult in structure maintenance and poor in function expansion capacity can be solved.

The invention discloses an underwater robot real-time three-dimensional map reconstruction system and method fused with an SLAM algorithm, and belongs to the technical field of three-dimensional map reconstruction. By constructing a confidence coefficient system of three-dimensional points, a spatial trajectory fluctuation index and an inter-frame texture change rate are introduced as dynamic feature evaluation indexes; and a regression model is adopted to score and grade the severity of the false points, and differential processing of different grades of point clouds is realized in combination with a dynamic suppression mechanism, so that an unstable structure is effectively eliminated, and the stability and reliability of the sparse three-dimensional map are remarkably improved. Therefore, the positioning precision, the path planning capability and the task execution safety of the underwater robot in a complex environment are enhanced.

The invention discloses a multi-mode water-air-land cross-medium cooperative sensing system and method based on an MEMS (Micro Electro Mechanical System). The system comprises an underwater robot, a buoy station, an unmanned aerial vehicle and a ground control station, each node is provided with an MEMS sensor integrating acoustics, optics, magnetism, inertia and other modes, and data transmission and relay are achieved through acoustics, optical communication and radio links. And the ground control station carries out fusion processing, target identification and decision control on multi-source data through time synchronization and space registration, and issues a cooperative instruction to each node to realize a perception-decision-re-perception closed loop. The method comprises five steps of node deployment and calibration, multi-modal data acquisition, hierarchical transmission and sharing, adaptive weighted fusion and closed-loop cooperative control. According to the invention, global coverage, high-precision and multi-mode environment and target perception can be provided in a complex water-air-land environment, and the detection reliability and the communication efficiency are significantly improved.

The invention provides an underwater robot obstacle avoidance system based on object capture. The method comprises the steps of collecting spatial visual information in a target area where the underwater robot is located in real time; acquiring multi-modal environment data in a target area where the underwater robot is located; constructing polarized light data in the multi-modal environment data into a water body scattering model by adopting a Mueller matrix to correct optical distortion in spatial visual information; acquiring a real-time motion state of the underwater robot through an inertial measurement unit; acquiring water flow disturbance data of the underwater robot, and correcting position drift caused by water flow disturbance in a real-time motion state by combining a water body flow model with the water flow disturbance data acquired in real time; constructing a dynamic risk field corresponding to the target area based on the spatial visual information, the corrected real-time motion state and sonar ranging data in the multi-modal environment data; and generating an obstacle avoidance strategy of the underwater robot in a future preset time period based on the dynamic risk field so as to realize dynamic obstacle avoidance.

The invention discloses an underwater robot target identification and tracking evaluation method based on deep learning, and the method comprises the steps: obtaining a target optical image, a sonar echo signal and a flow field parameter, constructing multi-modal data, and carrying out the preprocessing and space-time alignment; target apparent features and flow field features are extracted, dynamic weight fusion is carried out, and a target identification result is obtained through classifier processing; constructing a trajectory prediction model based on the target identification result and the flow field dynamic characteristics, and obtaining an initial trajectory prediction result by combining historical trajectory data optimization; constructing an implicit flow field model, and performing joint training with the trajectory prediction model to obtain an optimized trajectory prediction model; and a trajectory prediction result output by the optimized model is obtained, deviation is calculated in combination with actual trajectory data, the physical constraint weight and the target identification parameters in the trajectory prediction model are dynamically adjusted according to the deviation, and high precision and strong robustness of underwater target identification tracking of the underwater robot can be effectively improved.

The invention relates to the technical field of underwater robot cooperative control, and provides an elastic consistency control method and system of a multi-underwater robot system. The elastic consistency control method for the multi-AUV system comprises the following steps: discretizing and linearizing the multi-AUV system, introducing a performance index to describe the influence of communication topology on the dynamic behavior of the system, and calculating the elastic consistency of the multi-AUV system on the basis of a centralized optimal control method of a local decoupling system and a robust stability theory. And designing an elastic consistency controller and obtaining controller gain by solving a local algebraic Riccati equation so as to realize consistency control of multiple AUV system states. According to a sparse mode matrix spectrum of a distributed controller, a system stability condition is given, and efficient cooperation and stable operation of a multi-AUV system in a complex underwater environment are ensured. According to the method, the calculation and communication complexity is remarkably reduced, the method is suitable for a multi-AUV cooperative working environment, and a new technical solution is provided for cooperative control of the underwater unmanned system.

The invention relates to the technical field of underwater robots, in particular to an underwater robot cluster path planning method and system based on a dung beetle algorithm, and the method comprises the steps: obtaining a coordinate division region to construct an environment model, calculating and extracting an obstacle distance to fuse ocean current interference to generate a dynamic obstacle avoidance strategy, and evaluating the path speed similarity to generate a candidate path. And acquiring an offset angle difference, normalizing environmental factors to generate a correction path, and calculating a curvature fusion collision probability to generate a global path control instruction. According to the method, a dynamic model is constructed based on Cartesian coordinate division, the spatial analysis precision is improved, a sonar distance and ocean current interference are fused to generate a dynamic obstacle avoidance strategy, the trajectory smoothness is enhanced, candidate paths are screened by using vector similarity and Monte Carlo simulation, the collaboration is optimized, disturbance is reduced through coordinate offset and ocean current correction, and the accuracy of the dynamic obstacle avoidance strategy is improved. The curvature and collision probability are combined to balance safe energy consumption, and multi-dimensional parameter fusion improves cluster adaptability, precision, efficiency and energy utilization rate.

The invention discloses a variable-structure water-air amphibious robot and a motion control method. The robot comprises a water-air amphibious robot body, a variable-structure wing system and an electric control system. The robot body comprises a sealed cabin, a propeller fixing assembly, a fluid shell and a variable support tail cover. The variable-structure wing system comprises a driving gear transmission assembly, a two-section folding wing and a wing folding linkage assembly; the electric control system comprises a control core element, a visual module, a power supply module, a GPS module and an underwater depthometer module which are arranged in the cabin body; the motion control method is based on a deep reinforcement learning control method, control generalization ability is enhanced through teacher and student strategies and random domain, and the motion control method has more advantages in scenes such as inaccurate modeling and large external disturbance change. The structure of the variable-structure water-air amphibious robot integrates the structures of a rotorcraft, a fixed-wing aircraft, an underwater robot and the like, the modes of the wings can be switched, and efficient movement operation of the water-air amphibious robot is achieved.

The invention discloses an underwater robot with a splicing function and a method thereof, and belongs to the field of underwater robots, the underwater robot comprises a waterproof shell, a sensing module, a main control module, a power supply module and a rotary splicing module; the sensing module is used for environment sensing, the main control module is used for decision control, the power supply module provides energy support, the rotary splicing module is a core module of the robot, a rotary telescopic mechanism comprising a rotary shaft telescopic arm and a limiting rotor is creatively provided, and the rotary telescopic mechanism can be matched with limiting grooves in waterproof shells of other single robots. And splicing of the underwater robot is realized. According to the scheme of combining the underwater robot through the rotary splicing mechanism, independent operation or group operation can be selected according to the use scene, the combination shape can be switched during group operation to meet the task requirement, and the group task which cannot be completed by a traditional individual robot is achieved; therefore, the adaptability and execution capability of the underwater robot to complex tasks and environments are improved.

The high-reliability separating and combining mechanism is structurally composed of a driving module and a driven module, a rotating disc of the driving module is driven by a driving device to rotate, a center hole of the rotating disc is a first-stage guide groove, and a plurality of arc-shaped guide rails are evenly distributed on the periphery of the first-stage guide groove; a second-stage guide cone is slidably connected to the second-stage guide rod; a plurality of radial sliding grooves are formed in the base, and the bottom of the secondary guide cone slides along the sliding grooves; the passive module is of a cylinder structure, the top end of the cylinder is connected with a first-stage guide cone, and second-stage guide grooves are evenly distributed in the circumference of the cylinder. When the first-stage guide cone is inserted into the first-stage guide groove, the driving device drives the rotating disc to rotate, and the second-stage guide cone moves along the arc-shaped guide rail and the sliding groove and is inserted into the second-stage guide groove. According to the opening and closing mechanism, a complex sensing and control system is replaced with mechanical structure guiding and physical limiting, and the opening and closing mechanism has the advantages of being high in butt joint precision, reliable in opening and closing, simple in structure, low in cost, easy to maintain and the like.

The invention provides an underwater image enhancement method fusing multi-scale frequency domain and multi-color gamut features, and the method comprises the following steps: employing an underwater robot to collect an original image, carrying out the size normalization processing, and obtaining a standardized image; inputting the standardized image into a frequency domain feature enhancement network to generate an enhanced frequency domain feature; forming the standardized image into three paths of color features; splicing and fusing the three paths of color features to obtain enhanced color features; splicing and fusing the enhanced color representation and the frequency domain representation to obtain a final joint feature; and L1 loss and structural similarity loss are adopted for joint supervision, and network training is completed. According to the method, multi-scale frequency domain enhancement and multi-color gamut information enhancement are coupled in the same frame, so that collaborative repair of multiple degeneration such as color distortion, contrast reduction and detail blur of the underwater image is realized.

The invention provides an ocean current interference resistant fault-tolerant control method and system for a robot multi-motor propulsion system, and the method comprises the steps: inputting the actual and expected position error and speed error of an underwater robot into a self-adaptive backstepping sliding mode path tracking controller, and obtaining the thrust needed by tracking motion; introducing a propeller weight matrix representing the fault degree of the propeller into a weighted pseudo-inverse method, optimizing the propeller weight matrix by adopting an intelligent algorithm to obtain an optimal propeller weight matrix, and performing thrust distribution on thrust required by tracking motion through the weighted pseudo-inverse method to obtain a thrust distribution result; according to the operation and thrust distribution results of the multiple motors, the parameters of all the motors are adjusted in a self-adaptive mode through a multi-motor fault-tolerant control strategy; and each motor remotely controls the underwater robot based on the parameters after self-adaptive adjustment, and obtains the actual position and speed of the underwater robot at the current moment for loop closing.

The invention provides an LSTM underwater robot modeling method based on TPE hyper-parameter optimization, and relates to the technical field of underwater robot modeling, and the method comprises the steps: carrying out the time sequence feature extraction through a memory unit comprising a forgetting gate, an input gate and an output gate, and predicting the state change amount delta Yt'at a t + 1 moment through an output layer; performing iterative optimization on the number of layers and the number of units of the LSTM network and training the model to obtain an optimized LSTM model; evaluating the optimized LSTM model through a multi-step cumulative prediction error, inputting an initial real state into the model to carry out T-step recursive prediction, updating a current state by utilizing a prediction state increment in each step, and finally calculating an average position error and an attitude error in a T-step window on a verification set; and selecting the hyper-parameter combination with the minimum comprehensive error of the verification set as a final model parameter, and completing the dynamic modeling of the underwater robot. According to the method, the problem that the model is inaccurate due to excessive parameterization in a nonlinear dynamic model can be solved.

The invention relates to the technical field of water pollutant concentration detection, and discloses a water pollutant concentration detection system and method based on big data analysis, and the water pollutant concentration detection method based on big data analysis comprises the following steps: constructing a multi-level sensing network, the multi-layer sensing network comprises a fixed monitoring station layer, a water surface unmanned ship layer and an underwater robot layer; carrying out multi-modal data fusion by utilizing a knowledge constraint data assimilation algorithm to generate consistent water quality parameter estimation; a double-layer cognitive structure combining a knowledge graph and a causal model is constructed, and water pollutant propagation understanding and tracing are achieved; continuous optimization of system configuration is realized through a multi-objective reinforcement learning framework; through collaborative optimization of a multi-layer sensing network, the system realizes three-dimensional monitoring of a water body, the monitoring coverage range is expanded, and meanwhile, the high-precision detection capability is kept.

The invention discloses a construction method of a multi-underwater robot cooperative control system, which relates to the field of underwater robots, and establishes a multi-parameter coupled data acquisition and preprocessing mechanism by using a Doppler current profiler ADCP, an inertial measurement unit IMU and controller software. A combined algorithm of sliding median filtering and Kalman filtering is adopted, and data disturbance caused by environment noise and attitude drift is effectively removed; and through abnormal point triple standard deviation determination and double adjacent point interpolation completion, stable correction of sampling data is realized. A standardized data set obtained after normalization processing unifies parameter scales of different physical magnitudes, so that the local average flow velocity, the flow velocity fluctuation root mean square and the average acceleration can be directly compared in a feature space. Therefore, according to the method, the high-consistency expression of the water flow disturbance characteristics in time and space is realized, so that the synchronous measurement precision of multiple underwater robots in a non-uniform flow field is remarkably improved.

The invention provides an underwater robot sealing state evaluation method, and relates to the technical field of information, and the method comprises the steps: collecting depth change data and water pressure gradient fluctuation information during the operation of an underwater robot, carrying out the high-frequency sampling of the position of a sealing joint, obtaining the change trend of environment parameters around a sealing surface, and calculating the sealing state of the underwater robot; obtaining a preliminary environment dynamic distribution result; performing filtering and feature extraction on the water pressure gradient fluctuation data according to the preliminary environment dynamic distribution result to obtain dynamic response data of the stress state of the sealing surface, and analyzing the water pressure fluctuation frequency and fluctuation amplitude to determine periodic features of gap deformation of the sealing surface; generating a dynamic strategy adjustment instruction according to the abnormal state distribution of the sealing joint, and obtaining operation parameter configuration after the adjustment instruction is executed; and synchronously transmitting the adjusted operation parameter configuration and the sealing joint state monitoring data to an underwater robot control system, and updating the operation load distribution of the sealing joint in real time to obtain optimized equipment operation state data.

The invention provides an underwater robot self-positioning platform based on multi-feature and multi-modal information fusion, and the platform is installed on an underwater robot, and comprises a visual sensing module which is used for collecting image information of an underwater environment, and extracting visual point features and visual line features from the image information; the acoustic measurement module is used for providing three-axis speed information and low-frequency pose information of the underwater robot based on the Doppler principle; the pressure measuring module is used for measuring water depth information of the underwater robot on the basis of a fluid statics principle; and the data processing and optimizing module is used for fusing the visual point features, the visual line features, the three-axis speed information, the low-frequency pose information and the water depth information on the basis of a factor graph optimizing framework so as to realize positioning and mapping. Compared with a traditional system, the self-positioning platform provided by the invention realizes tight coupling joint optimization of point-line features and acoustics and pressure sensor information, can stably operate in extreme underwater environments with low texture, turbidity and the like, and realizes high-precision underwater synchronous positioning and map construction.

The invention discloses an underwater navigation positioning method and system based on supercontinuum laser intensity correlation. The system comprises a master control system, a transmitting end underwater base station and a receiving end underwater carrier. The method comprises the following steps: firstly, a main control system generates a super-continuum laser signal with pseudo-random code information, the super-continuum laser signal is separated into optical signals of different wavelength channels through a light splitting module, and the optical signals are respectively sent to underwater base stations uniquely corresponding to the wavelength channels and are emitted; then the underwater carrier receives optical signals from a plurality of base stations, identifies a signal source base station according to wavelength identification, performs correlation operation with a locally copied pseudo-random code sequence, determines signal arrival time TOA and calculates a pseudo-range; and finally, resolving the three-dimensional coordinate and local clock error result of the underwater carrier by combining the pseudo-ranges, absolute positions and transmission delays of the underwater carrier and the at least four base stations. According to the invention, high-precision and high-robustness absolute positioning of the underwater environment is realized, and the reliability of long-time positioning and navigation of the underwater robot is enhanced.

The invention belongs to the technical field of artificial intelligence, and relates to an underwater navigation method and system based on panoramic visual perception and language model reasoning. The method comprises the following steps: acquiring a 360-degree panoramic video sequence when an underwater robot runs, pre-processing the 360-degree panoramic video sequence, dividing the 360-degree panoramic video sequence into sub-views, extracting visual features, combining position and action embedding information, and aggregating the sub-views into a space-time trajectory feature flow through a Transform network. An adaptive module is embedded in a language model, and visual track features are injected into a language hidden space by using a cross attention mechanism, so that visual-language information fusion and content reasoning are realized. Meanwhile, semantic scene representation is constructed through three-dimensional scene modeling, illusion landmarks are inhibited by means of a landmark uncertainty evaluation module, and finally reasoning output of a language model is mapped into a structured control instruction. According to the method, autonomous semantic navigation based on content understanding in an underwater environment is realized.

The invention discloses a current ripple suppression method for an underwater propulsion motor, relates to the technical field of motor control, and reduces overshoot and adjustment time when the rotating speed of the motor changes suddenly by improving a traditional field-oriented control framework and adopting a differential tracker to smooth an input reference rotating speed. Dead-beat model-free predictive control and an ESO observer are introduced into a current loop, and current ripples caused by parameter mismatch and external interference are effectively restrained. Model-free predictive control is adopted, the number of mathematical model parameters is reduced, the model accuracy is improved, and the control precision is improved. The current ripple is further reduced by identifying the inductance parameter and adjusting the PWM frequency according to the actual inductance value. According to the invention, the overshoot phenomenon in the rotating speed adjusting process is obviously reduced, the stability and efficiency of motor operation are effectively improved, and the service life of the motor is prolonged. The invention provides a new solution for improving the performance of the underwater robot propulsion system.

The invention provides a synchronous positioning and mapping method for underwater pipeline detection, and belongs to the technical field of underwater robot navigation and positioning. In order to solve the problems of limited illumination, strong echo interference, high structural repeatability and the like in a seawater pipeline environment, the invention provides a factor graph optimization-based tight coupling pose estimation framework fusing a two-dimensional imaging sonar, a Doppler velocity meter DVL and an inertial measurement unit IMU. Through joint constraint and optimization of multi-source sensing information, the method can realize high-precision positioning and continuous mapping of the ROV in an environment with extremely low visibility and a complex pipeline structure, and effectively improves autonomy and data reliability of an underwater inspection process. The method is clear in structure, easy and convenient to implement, high in calculation efficiency, capable of stably running on a conventional ROS platform and suitable for various types of underwater pipeline detection and inspection tasks.

An underwater robot application scene-oriented sodium battery residual life prediction method comprises the following steps of: obtaining normalized features after input features pass through a double-path feature normalization module; processing the input features through a multi-local sensing path module, and fusing and splicing the processed input features with the normalized features to obtain spliced features; performing 1 * 1 convolution operation on the input features, adding the input features and the splicing features element by element, and performing processing through a lightweight residual connection module to obtain multi-scale features; enabling the coder-decoder to predict an initial capacity prediction value according to characteristics rich in global time sequence information in the multi-scale characteristic data; performing element-by-element addition on the initial capacity prediction value and the normalized feature, and calculating a final sodium battery residual life prediction value; the coder-decoder can capture the long-range dependency relationship of the battery capacity sequence through a self-attention mechanism, the gradient disappearance problem of the recurrent neural network in the long sequence is solved, and the prediction precision is remarkably improved.

The invention discloses a navigation method fusing an improved artificial potential field method and model prediction path integration, and relates to the technical field of navigation, the method comprises the following steps: constructing an attraction field, an improved repulsive force field and a vortex potential field based on the improved artificial potential field method, and further generating a global guide vector; with a global guide vector as a reference, generating a plurality of discrete control sequences based on a control quantity by using model prediction path integration; converting the control quantity into an actual physical control quantity through linear mapping, and performing multi-step trajectory prediction on each discrete control sequence based on a dynamic model; then evaluating the quality index of each track by a resultant cost function, fusing the weight of each track through weighted iteration, and further outputting an optimized discrete control sequence; mapping the optimized discrete control sequence into an execution instruction; and controlling the underwater robot according to the execution instruction. By fusing the improved artificial potential field method and the model prediction path integral, the navigation precision can be improved.

The application provides a weak fault feature extraction method for an autonomous underwater vehicle propeller, and belongs to the technical field of underwater vehicle fault diagnosis, and comprises two parts: fault feature enhancement and feature fusion. First, the application optimizes parameters by judging the Gaussianity of all modes of multi-source state signals and control signals through negative entropy, completes noise reduction, and extracts and enhances fault features based on a modified Bayesian algorithm. Then, the feature signals are divided into multiple time intervals, the faults occurring in each interval are taken as focal elements, all signals except the longitudinal velocity are subjected to first feature fusion, the first fusion result is subjected to second fusion with the feature signal of the longitudinal velocity, the fault features are further enhanced, and the monotonicity between the fault features and the fault degree is presented. The application can provide a basis for subsequent fault detection and identification, and is particularly suitable for state monitoring of autonomous underwater vehicle propellers.

The invention relates to the technical field of robots, and discloses an underwater robot control system for detection, which comprises a carrier platform, a multi-sensor fusion module and a core controller, the multi-sensor fusion module collects and fuses multi-source data, and outputs high-precision state estimation through Kalman filtering; the core controller processes a sonar image by using a pulse YOLO model constructed based on a pulse neural network and multistage denoising convolution, extracts image features, performs target detection and three-dimensional positioning, and generates spatial semantic information; in combination with a semantic map, the system carries out strategy optimization by expanding a PPO model, a control instruction is generated, and path planning and dynamic obstacle avoidance are realized; the system is suitable for a complex underwater environment, has high precision, high robustness and high autonomy, and is widely suitable for the fields of ocean monitoring, underwater detection, engineering operation and the like.

The invention discloses an underwater robot path tracking control method and device, terminal equipment and a storage medium, and belongs to the technical field of path tracking, and the method comprises the steps: obtaining the current pose data of an underwater robot and the path parameters of a to-be-tracked path; then, according to the current pose data and the path parameters, current operation state parameters of the underwater robot are obtained through calculation; according to the current operation state parameter and a preset path tracking model, determining a current target operation action of the underwater robot; and finally, adjusting the running state of the underwater robot according to the current target running action, so that the underwater robot advances according to the to-be-tracked path. By implementing the method and the device, the problem that the control precision is reduced due to the fact that a PID controller based on fixed parameters is adopted to realize path tracking in the prior art and cannot adapt to the constantly changing running state of the underwater robot in real time can be solved.

The invention discloses an acoustic optical communication positioning method for an autonomous adjustment channel of an underwater robot, and the method comprises the steps: constructing an underwater dynamic acousto-optic fusion repeater, achieving the wide-area guaranteed-bottom connection with the underwater robot through an acoustic link, and providing meter-scale precision coarse positioning covering a whole operation region; the method provides prior information for rapid establishment of a subsequent optical link, further dynamically compresses an optical search area to realize rapid and accurate link establishment, and performs autonomous and smooth switching among acoustic, optical and acousto-optic hybrid transmission modes according to real-time link quality assessment (signal-to-noise ratio), thereby realizing acousto-optic deep coupling and cooperative scheduling, and improving the reliability of the system. The system has the advantages of long-distance reliable connection, short-distance high-speed data return, high-precision positioning and the like, and solves the core technical problems that the distance and the speed cannot be obtained at the same time and the positioning precision and the coverage range are mutually restricted in the prior art.