838 results about "Navigation path" patented technology

A system and method for latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses spatiotemporal media into navigable latent representations while preserving geometric and semantic relationships through tensor structure maintenance. A latent hyperspace manager organizes compressed representations as geodesic trajectories within a geometric manifold structure based on differential geometry principles. A geodesic trajectory mapper computes optimal navigation paths through the high-dimensional space, while symbolic anchors positioned at semantically significant locations serve as persistent reference points. Spatiotemporal routing protocols manage navigation decisions across multiple temporal scales. A strategy caching system preserves successful navigation patterns for reuse, enabling continuous learning. The system generates synthetic content during navigation to support infinite zoom capability, allowing exploration beyond original media boundaries. Cross-modal fusion combines diverse input modalities into unified representations, applicable to immersive media exploration, scientific visualization, and surveillance analysis.

The invention relates to the technical field of robot navigation, and discloses a safety inspection robot multi-mode navigation system fused with subconscious learning. The system obtains a visual image, a depth distance, an inertial measurement unit and other multi-dimensional perception data of an inspection environment, performs environment feature extraction to generate an environment semantic topological graph, and constructs a dynamic navigation cost map and a preliminary navigation path according to the environment semantic topological graph. During path execution, the system detects abnormal navigation behaviors in real time, extracts the features of the abnormal navigation behaviors and obtains a first correction coefficient. And the system judges whether the implicit environment semantic identifier is identified, and if the implicit environment semantic identifier is identified, the corresponding path key node is taken as a semantic navigation reference point, a final navigation path is generated through fusion, and a time stamp is recorded. And if the time stamp exceeds the preset tolerance, extracting a corresponding second correction coefficient. And the system combines the basic navigation performance evaluation value, the first correction coefficient and the second correction coefficient to output a multi-modal navigation decision result.

The invention relates to the technical field of navigation planning, and discloses an unmanned aerial vehicle automatic inspection nest path planning method and system, and the method comprises the steps: carrying out the multi-source data fusion of a target inspection region, and obtaining space measurement environment data; analyzing an airspace limiting condition in the spatial measurement environment data, and determining a navigation path measurement constraint condition in combination with an inspection task demand of the target inspection area; performing gridding measurement on the target inspection area to identify candidate waypoints; according to the spatial position information, analyzing a spatial relationship of the candidate waypoints to construct an initial navigation network; performing multi-constraint evaluation on the initial navigation network to obtain constraint weights corresponding to the candidate waypoints, and integrating the candidate waypoints with the constraint weights higher than a preset weight threshold into a global optimal waypoint set; carrying out passable connection on adjacent waypoints in the global optimal waypoint set to obtain a navigation path; according to the invention, the efficiency of automatic inspection nest path planning of the unmanned aerial vehicle can be improved.

The invention provides an agent visual language navigation method and system based on task completion prediction. The method comprises the step of constructing a dual-drive structure composed of a self-adaptive mixed pooling mechanism and a task completion analysis module. Firstly, in the visual information processing process, a dynamic weight distribution strategy is adopted to carry out multi-scale adaptive mixed pooling on panoramic features, so that the fusion effect of local and global semantic information is optimized, and the retention capability and semantic integrity of navigation historical information in a dynamic topological map are improved. And then, inspired by a human navigation cognitive behavior mechanism, a task completion analysis module is designed and introduced, and the task execution progress is dynamically estimated based on the recognition condition of a key landmark in a navigation path, so that an intelligent agent is driven to preferentially select a key path node and invalid exploration is reduced. And finally, realizing efficient understanding and execution of the natural language instruction by the intelligent agent through a multi-round cyclic cross-modal reasoning and action prediction mechanism.

The invention provides a scene reconstruction and semantic understanding method and system based on a visual large model, and relates to the field of computer vision and three-dimensional reconstruction, and the method comprises the steps: obtaining multi-view image data of a target scene, inputting a pre-training visual large model, and outputting a joint feature representation and attention weight matrix; generating three-dimensional coordinate values and semantic probability distribution of spatial sampling points in a three-dimensional space according to the joint feature representation, and constructing a spatial semantic field; clustering the spatial sampling points by using the attention weight matrix, performing semantic consistency enhancement, and converting the spatial sampling points into deterministic semantic tags; constructing a geometric optimization objective function, and adjusting three-dimensional coordinate values; and extracting continuous space sampling points with the same semantic tag to form an object boundary, constructing a scene topological graph, deducing a scene functional structure, and generating a navigation path. According to the method, the unification of accurate geometric reconstruction and deep semantic understanding of the scene is realized, the three-dimensional reconstruction precision and semantic analysis accuracy are improved, and reliable support is provided for intelligent navigation.

The invention discloses a photovoltaic cleaning control method fusing RTK positioning and visual compensation, and belongs to the technical field of photovoltaic module cleaning. According to the invention, through the RTK track points acquired offline, the least square method is combined with the RANSAC algorithm to carry out segmented straight line fitting, and the original points are projected to the corresponding straight lines to realize collinear correction, so that the measurement error is eliminated, and a continuous and collimated navigation path can be generated; edge lines (photovoltaic module grid lines) with geometrical characteristics in the environment are detected in real time through a visual sensor, the line segment direction is extracted through Hough transform, the line segment direction is compared with the target course calculated through RTK, course deviation is estimated, dynamic correction is conducted through a control strategy, and it can be ensured that the photovoltaic cleaning robot operates along the expected path with high precision; and on the basis of a lightweight YOLOv7 model, the photovoltaic panel is subjected to pollution category division and mapped to optimal water pressure and brush speed parameters, cleaning parameters are dynamically modified, and then cleaning strategy control is completed.

The invention discloses an intelligent parking method and device based on multi-source data fusion and a medium, and relates to the technical field of big data technologies. The method comprises the following steps: collecting multi-source parking data, and analyzing the multi-source parking data to generate a parking lot label library; generating a user tag based on historical search records and residence time preferences of the user, and constructing a user information database; when it is detected that a user triggers a parking request, performing multi-dimensional matching calculation on a user tag and a parking lot tag library to obtain a recommended parking space sequence; and optimizing the recommended parking space sequence based on the real-time traffic state and the user credit score to generate a final navigation path. According to the method, a dynamic parking recommendation mechanism is constructed by collecting and analyzing the multi-source parking data and the user preference data.

The invention discloses a robot automatic navigation method and system based on depth vision fusion, and relates to the technical field of intelligent navigation, and the method comprises the steps: carrying out the registration of a point cloud subset of continuous frames through employing an ICP algorithm, distributing weights for a static obstacle and a dynamic obstacle, and carrying out the fusion, and obtaining a fused environment perception data set. The method comprises the following steps: updating a dynamic environment in real time, generating a dynamic environment map, carrying out trajectory tracking on moving targets by using binocular vision equipment, calculating a real-time motion trajectory of each moving target, obtaining an environment hotspot map, generating a global navigation path for a robot by using an A * algorithm, planning a detouring path at the periphery of a hotspot area, and outputting a navigation path sequence. According to the method, intelligent optimization of the motion path of the robot is realized through path planning and an obstacle avoidance strategy, the safety is ensured, the path efficiency is maximized, and unnecessary detour and pause time is reduced.

The invention relates to the technical field of unmanned aerial vehicle path planning, in particular to an unmanned aerial vehicle automatic navigation method and system for power transmission line inspection. Comprising the steps of collecting terrain elevation data and a wind speed fluctuation signal, generating a line corridor boundary elevation distribution diagram, analyzing the influence of a terrain gradient on a conductor sag, and determining a curvature limitation adjustment demand; re-planning the flight path in combination with the obstacle position information, and generating a detection path sequence; calculating a height abrupt change value and an attitude angle deviation value, judging whether an acceleration impact suppression requirement exists or not, and generating an inspection risk score of the vegetation invasion area; determining a cross span distance risk quantitative index, and generating a flight control instruction sequence; and verifying whether the path meets a boundary integrity standard, and generating an automatic navigation path sequence. According to the method, the problems of instability, insufficient coverage and poor safety of an unmanned aerial vehicle routing inspection path under a complex terrain condition are solved, and stable, continuous and efficient automatic generation of the power transmission line routing inspection path is realized.

A system and method for generation-augmented latent hyperspace navigation in spatiotemporal media using hierarchical and Lorentzian autoencoders. The system compresses media into latent representations while preserving geometric, temporal, and semantic relationships. A latent hyperspace manager organizes compressed data as geodesic trajectories, and a geodesic trajectory mapper computes navigation paths. Symbolic anchors provide persistent reference points, while spatiotemporal routing coordinates decisions across multiple scales. A strategy caching system preserves successful navigation patterns for reuse as procedural memory. A synthetic content generator including latent diffusion models, neural radiance fields, and context-aware refinement produces augmentation for continuous zoom, bidirectional traversal, and rotational reorientation. A user input interface and zoom controller enable interactive exploration and reconstruction, supporting applications in immersive media, visualization, and surveillance.

The invention relates to the technical field of blind guiding robot interactive navigation, and particularly discloses a blind guiding robot interactive navigation system combining vision, inertial navigation and voice, which is characterized in that environmental vision, movement and voice data are synchronously acquired through a multi-modal perception data acquisition module, and are analyzed through a situation-user joint state feature extraction module; a user potential intention deduction module is combined to predict a user intention and generate a self-adaptive guide strategy; a self-adaptive guiding path optimization module adjusts a navigation path according to a strategy to ensure safety and high efficiency; finally, the situational multi-mode interaction instruction output module provides voice and tactile feedback, and interaction experience and navigation autonomy are enhanced. According to the method, the travel problem of the visually impaired person in a complex environment is solved, the individuation, context perception capability and safety of blind guiding are improved, and more natural and efficient navigation assistance is brought to the visually impaired person.

The invention discloses an intelligent robot navigation system with a body based on a multi-modal large model, and the system comprises an RGB-D camera module which captures a color image and a depth image of an environment in real time, and carries out the preprocessing of the color image and the depth image, and outputs the preprocessed image; the laser radar SLAM module provides a basis for subsequent coordinate calculation and navigation path planning; the multi-modal VLM module is used for calculating the three-dimensional coordinates of the target location in the map; and the navigation control module controls the robot to move. The beneficial effects of the invention lie in that the system realizes efficient environmental perception and natural language understanding through multi-modal fusion, and can accurately identify a target location, calculate a three-dimensional coordinate and plan a safe path, thereby improving the autonomous navigation precision and reliability of the robot, and being suitable for intelligent movement control in a complex scene.

The invention relates to the technical field of visual navigation, in particular to a blind person navigation path planning method combining visual SLAM and semantic segmentation. The method comprises the following steps: acquiring an environment image and pose data; performing front-end tracking by using the pose data, and determining a key frame sequence; generating a depth map based on the environment image, and identifying a passable area according to the depth map; constructing a three-dimensional point cloud map according to the key frame sequence; semantic segmentation is carried out according to the three-dimensional point cloud map, and obstacle type labels are recorded; according to the obstacle type label, carrying out safety level layering on the passable area, and determining a path cost weight; performing global path planning based on the path cost weight, and generating a semantic enhancement navigation trajectory; and driving real-time voice guidance by using the semantic enhancement navigation trajectory, and calculating a path execution deviation corresponding to the real-time voice guidance. According to the method, obstacle types are distinguished through semantic segmentation based on a visual navigation technology, a semantic enhancement path is generated, semantic hierarchical navigation is realized, and the intelligence of path decision is improved.

The invention belongs to the technical field of robot navigation and path planning, and particularly discloses and provides a dynamic navigation planning method and system for a service robot, which overcomes the limitation of single-dimensional analysis in a complex dynamic scene by constructing a dynamic social thermodynamic diagram and deducing group dynamic intention data. The perception precision of the environment structure and the people stream situation is improved; a linkage mechanism among a dynamic social thermodynamic diagram, individual behavior characteristics and path planning is established, and a social cost function is generated to optimize a navigation path according to the traffic efficiency and social etiquette of group future dynamic vectorization evaluation; the actual influence of the navigation behavior of the robot on the environment is monitored in real time, and a planning strategy is adjusted in combination with feedback data, so that the adaptability of the navigation decision and the continuous improvement of the environment acceptance degree are realized.

Disclosed is an intelligent ship navigation path generation method based on visual and spatial semantic enhancement for an aid to navigation, including: identifying the aid to navigation around a ship and determining a current position of the ship; obtaining a navigable area of the ship based on semantic information of the aid to navigation; removing portions that overlap with high risk areas from the navigable area; selecting a next position of the ship from the navigable area; and repeating the above steps to make real-time corrections to the ship's navigation path.

The invention relates to a robot autonomous navigation method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a plurality of sensing data sets from different sensors; wherein the sensing data sets carry the same timestamp; generating a semantic map and a geometric map of the target area according to the sensing data sets; acquiring voice control information, and matching at least one semantic tag in the semantic map according to the voice control information; and generating a navigation path according to the matched semantic tag and the geometric map, and completing an autonomous navigation task of the robot according to the navigation path. The method can improve the autonomous navigation stability of the robot.

The invention discloses an agricultural machine navigation method and device, equipment and a storage medium. The method comprises the following steps: determining a visual path point of an agricultural machine at the current moment, acquiring an original farmland image of the agricultural machine looking forward at the visual path point through a camera, and acquiring farmland point cloud data of the agricultural machine looking forward at the visual path point through a laser radar; determining a fitting path line of a farmland furrow according to the farmland original image and the farmland point cloud data; determining the steering angle and the speed of the agricultural machine according to the fitting path line and the visual path point; and adjusting the steering and speed of the agricultural machine according to the steering angle and speed of the agricultural machine to realize navigation driving of the agricultural machine along the fitting path line. According to the technical scheme of the invention, vision is taken as a dominant, the navigation path of the agricultural machinery in the furrow environment is accurately extracted by fusing the camera and the laser radar sensor, and meanwhile, the navigation performance of the agricultural machinery is enhanced according to the navigation path and agricultural machinery motion control, so that the accurate navigation method for the agricultural machinery in the furrow layout environment is realized, and the robustness is high.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line unmanned aerial vehicle autonomous charging method and system. The method comprises the steps of monitoring electric quantity level and position information of the unmanned aerial vehicle in real time to obtain state data; based on the state data, when the electric quantity level is lower than a preset threshold value, generating a charging request signal; in response to the charging request signal, calculating a navigation path according to the position information and the position of the charging nest to obtain path data; controlling the unmanned aerial vehicle to fly to the charging nest according to the path data, and adjusting the flight trajectory in real time by using the environment sensing data in the flight process to obtain an updated trajectory; when the unmanned aerial vehicle arrives at the charging nest based on the updated track, docking operation is executed; and after the connection is completed, starting a charging process to charge a battery of the unmanned aerial vehicle. The method effectively solves the core problems that the autonomous charging opportunity of the unmanned aerial vehicle is not accurately judged, the return path lacks the dynamic adjustment capability and the docking reliability is low.

A hybrid augmented navigation system for a vehicle includes a windshield including a primary viewing envelope, the primary viewing envelope including a tactical region and a strategic region, an imaging system including a tracking camera, and a navigation system including navigation data. The hybrid augmented navigation system also includes a controller that is communicatively coupled with the imaging system and the navigation system. The controller is configured to generate a hybrid augmented navigation at the windshield via a hybrid augmented navigation application. The hybrid augmented navigation application includes tactical navigation graphics projected at the tactical region of the primary viewing envelope and strategic navigation graphics projected at the strategic region of the primary viewing envelope. The tactical navigation graphics include a navigation path, and the strategic navigation graphics include landmark indicia based on the navigation data.

The invention relates to the technical field of mobile robot autonomous navigation path planning, and discloses a complex orchard scene-oriented hybrid sampling global path planning method, which comprises the steps of adopting a Gaussian hybrid sampling strategy, switching between global uniform sampling and Gaussian offset sampling through a probability threshold, and considering global exploration and key area guidance; a dynamic node expansion strategy is adopted, the expansion step length is adaptively adjusted according to the distance between the expansion step length and a target point, and the expansion direction is optimized by fusing vectors facing a sampling point and the target point and introducing an obstacle influence factor; a layered collision detection strategy is adopted, and step-by-step filtering is carried out through rough detection based on a quadtree, secondary screening based on an axis alignment bounding box and accurate geometric judgment in sequence so as to improve the efficiency; and a dynamic iteration termination mechanism is adopted, and the minimum number of iterations and the path quality convergence state are combined as composite termination conditions. The method can quickly generate a smooth and safe path, and is especially suitable for structured scenes such as orchards.

The invention relates to an AI image navigation method, device and equipment for prostatic hyperplasia minimally invasive surgery and a medium thereof. The method comprises the following steps: acquiring a prostate three-dimensional image and an intraoperative real-time image sequence, and fusing the three-dimensional image after preliminary filtering to obtain an optimized and enhanced image; based on the enhanced image and the preoperative three-dimensional image, a three-dimensional offset vector is obtained through a multi-modal registration algorithm combined with an anatomical structure reference, and optimized prostate three-dimensional attitude data is generated in combination with anatomical constraints; combining the attitude data and the three-dimensional image, determining boundary area distribution characteristics, calculating a risk assessment value, mapping a risk level, and generating a path correction instruction to obtain an instrument navigation path if the risk level exceeds a threshold value; and based on the navigation path, constructing a clinical recovery model in combination with the anatomical structure, generating postoperative recovery index simulation data, and performing adjustment and verification until preset requirements are met to obtain a final operation execution scheme. According to the method, precision and individuation of surgical navigation are achieved, and comprehensive technical support is provided for precise implementation of minimally invasive surgery.

The invention discloses a smart traffic-oriented refined navigation and electronic fence route planning method and system, and relates to the technical field of smart traffic, the method is executed by a dynamic collaborative optimization engine, and the method comprises the following steps: in response to an electronic fence strategy setting or adjusting instruction, starting a collaborative optimization process; in the first stage, the current traffic state of the whole road network and a plurality of candidate electronic fence strategies are input into a macroscopic traffic flow simulator, and the traffic state change of the whole road network in a period of time in the future under each strategy is deduced. According to the smart traffic-oriented refined navigation and electronic fence route planning method and system, global collaborative optimization of an electronic fence strategy and a multi-vehicle navigation route is realized, and through three-stage processing of macroscopic deduction, microcosmic verification and closed-loop feedback, the navigation route planning efficiency is improved; the problems of secondary congestion and reduced road network operation efficiency caused by local traffic control are effectively avoided, and traffic management decisions are changed from passive avoidance to active guidance.

The invention provides a blind person navigation assisting method, device and equipment based on AI glasses and a storage medium. The method comprises the following steps: constructing a navigation path between a current position of a user and a destination; according to the image data and the sensing data, determining environment information in a preset range of the navigation path and a real-time pose of the user; generating navigation guidance information according to the environment information and the real-time pose of the user; and converting the navigation guidance information into a spatial audio signal, and carrying out traveling navigation based on spatial audio prompt on the user through the spatial audio signal. According to the method, the navigation guidance information related to the real-time pose of the user is generated by integrating the visual sensing data of the AI glasses, the pose information of the inertial sensor and the navigation path information, and the navigation guidance information is converted into the spatial audio signal for navigation; when the user is separated from the navigation path, the user can be guided to accurately judge the advancing direction through spatial audio prompt, and the appearance safety of the user is improved.

The invention relates to the technical field of intelligent wearing, and discloses an intelligent interaction system and method based on AR glasses. According to the method, an environment scene is intelligently divided, a plurality of interaction areas are generated, then an initial interaction task instruction is received through a user interface, AR glasses are started to execute a task, initial environment data are collected in the execution process, and historical interaction records are obtained. Then, the interaction priority and the attention level of each interaction area are calculated by using the data, so that a customized navigation path is generated. When the AR glasses interact according to the path, position information of the AR glasses is collected in real time, interference source characteristics are obtained, whether the AR glasses enter an interference influence range is detected, if yes, an offset alarm is triggered, whether the AR glasses deviate from a preset path is judged based on the position information, and if not, a path correction command is generated. In addition, real-time visual data can be captured, an abnormal mode or a specific target is analyzed and recognized, and if the abnormal mode or the specific target is recognized, the position information and the corresponding visual data are stored.

The invention belongs to the technical field of autonomous navigation, and particularly relates to an autonomous navigation method, system and device based on a multi-modal large model and a medium. According to the invention, the initial driving path is planned by importing the scene map and receiving the task instruction, the image data is acquired in real time through the image acquisition equipment in the driving process and identified, the pose conversion is carried out when the target object is identified, and the path planning algorithm is carried out on the converted target point to obtain the navigation path. If an object outside the scene map is detected or a target object is not recognized when reaching a target point, the robot rotates to collect image data, binds the image data with the direction, and performs multi-modal large model analysis on the bound image data to obtain scene description of each piece of image data; and inputting all scene descriptions, task instructions, scene maps and current positioning of the robot into a large language model, speculating a target position point, and performing a path planning algorithm by taking a speculated target as a fulcrum to obtain a navigation path.

The invention discloses a trajectory planning model training method and device, equipment and a storage medium, and relates to the technical field of computers. The method comprises the steps of obtaining pre-training sample data; the pre-training sample data comprises driving track data of a driver; performing iterative training processing on a to-be-trained pre-training model based on the pre-training sample data to obtain a hidden variable embedding layer and a decoder in the trained pre-training model; constructing a trajectory planning model to be trained based on the feature encoder, the strategy network, the evaluation network, the hidden variable embedding layer and the decoder; obtaining training sample data, wherein the training sample data comprises sample vehicle driving data, sample obstacle data, sample map data, sample signal lamp data and sample navigation path data; and based on the training sample data, performing reinforcement learning training processing on the to-be-trained trajectory planning model to obtain a trained trajectory planning model.

The invention provides an AGV (Automatic Guided Vehicle) visual texture navigation path map real-time generation method, which comprises the following steps: collecting ground glossiness and a gray scale saturation region area, comparing the brightness difference between the gray scale saturation region and a normal region, and identifying the distribution position and the coverage range of a high-reflection region; according to the distribution position and the coverage range of the high-reflection area, extracting a texture missing part covered by the specular reflection patches, and determining boundary coordinates of the texture missing part; according to the optimized light incident angle distribution, the gray jump amplitude when the AGV drives into the polished tile area from the rough cement ground is collected, and the transition intensity of material change is evaluated; and filling a texture missing region in the path map by using the reconstructed complete texture features, and integrating the texture information of the path map to generate a complete AGV navigation path map.

The invention relates to the technical field of emergency rescue, and discloses a cooperative control system for disaster sensing and emergency rescue. According to the system, a disaster situation sensing unit collects field data through a multi-mode sensor network and generates a real-time situation map; a rescue resource scheduling unit performs hierarchical coding on rescue resources according to the situation map, establishes a resource distribution thermodynamic diagram and performs superimposed analysis on the resource distribution thermodynamic diagram and disaster partitions; the multi-target decision-making unit receives the thermodynamic diagram and the situation map, generates a rescue task sequence through a priority algorithm and outputs a decision-making instruction set; the dynamic path planning unit performs topology reconstruction on a rescue path based on an instruction set, and generates an obstacle avoidance navigation path set in combination with terrain obstacle data; and the emergency response execution unit drives the rescue equipment to execute standardized operation according to the path set, and feeds back execution state data to the sensing unit to form closed-loop updating. According to the system, the whole-process cooperative control of disaster sensing, resource scheduling and rescue execution is realized.

The invention discloses an automatic driving path planning method based on fractional order particle swarm optimization, and the method comprises the steps: collecting scenic spot roads, obstacles, tourist density and scenic spot stop point information, and generating a multilayer semantic map; gridding the multi-layer semantic map, taking a starting point, an ending point and a stop point as control nodes, and performing weighting according to road geometry and tourist density to form a path search space; distributing a candidate path for each particle in the path search space, and introducing a fractional order memory factor in a particle updating process; a comprehensive evaluation rule is established according to driving safety, sightseeing comfort, energy consumption cost and arrival time, and the particle swarm is driven to be iteratively updated until convergence conditions are met; and outputting the navigation path with the optimal comprehensive evaluation score, performing spline smoothing processing on the coherent nodes, and issuing the processed coherent nodes to an automatic driving vehicle controller. According to the invention, the automatic driving path planning realizes the beneficial effects of accurate path planning, safety, comfort and high energy consumption economy in a complex scenic area environment.

The invention provides an unmanned surface ship obstacle avoidance navigation path generation and tracking method and device. The method provided by the invention comprises the following steps: constructing a ship kinematics model according to navigation motion parameters of an unmanned surface ship; recognizing obstacles on the initial navigation path, and constructing a dangerous area; calculating a geometrical relationship between the initial navigation path and the dangerous area based on a ship kinematics model, and generating an obstacle avoidance navigation path; adjusting the obstacle avoidance navigation path based on the interaction relationship between the obstacle avoidance navigation path and the plurality of dangerous areas; detecting the curvature of the obstacle avoidance navigation path at the access port, and generating a tangent circle optimization path tangent to the boundary of the dangerous area at the access port based on the curvature to obtain an obstacle avoidance navigation optimization path; on the basis of a finite time theory and a ship kinematics model, an expected navigation heading angle is generated according to an obstacle avoidance navigation optimization path; and constructing a navigation tracking adjustment module, and driving the unmanned surface ship to track the obstacle avoidance navigation optimization path based on a navigation tracking instruction output by the ship kinematics model.