512 results about "Robotic navigation" 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 relates to the technical field of visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention discloses an inspection robot navigation method, system and device based on Beidou and binocular vision fusion and a storage medium, and relates to the field of robot navigation and obstacle avoidance, and the method comprises the following steps: carrying out multi-modal data fusion processing based on an initial coordinate of an inspection robot and an image captured by a binocular vision system to obtain an environment sensing result; carrying out global planning and local optimization based on an environment perception result, and carrying out cooperation to generate a trajectory planning scheme; based on the environment sensing result and the trajectory planning scheme, intelligent navigation cooperative regulation and control are carried out, and an inspection robot motion control strategy is obtained; by improving the environmental perception accuracy, optimizing the path planning and performing intelligent navigation regulation and control, the navigation problem of the inspection robot in complex environments such as a transformer substation can be effectively solved, the inspection safety and efficiency are improved, and the method has remarkable practical application value.

The invention provides a robot navigation method and system based on visual identification, and relates to the technical field of computer vision, firstly, a continuous visual information set of the surrounding environment of a robot is obtained, the continuous visual information set comprises environment visual images of different directions of the advancing direction of the robot, and the object appearance features and the spatial arrangement relation are recorded; then establishing association mapping between the continuous visual information set and a preset navigation area to obtain a double-association mapping result; generating a semantic anchor point navigation path based on a double-correlation mapping result, wherein the semantic anchor point navigation path comprises a semantic anchor point sequence from the current position to the target position; real-time visual semantic features in the navigation process are obtained through a real-time visual collection module, and a semantic anchor navigation path is dynamically updated; finally, a final navigation execution path is output according to the updated path, the robot is driven to complete navigation operation, and the navigation capability and the intelligent level of the robot in a complex dynamic environment are improved.

The invention belongs to the technical field of robot navigation, and discloses a robot path planning method based on a graph neural network, through causal perception dynamic graph construction and AST-GNN training, a causal relationship, such as pedestrian steering-path change, of obstacle movement is excavated, extreme scene features are learned in combination with adversarial training, and the path planning precision is improved. Therefore, when the robot is in an emergency scene (such as object falling and pedestrian sharp turning), the obstacle movement pre-judgment accuracy is improved, the path re-planning response speed is increased, the collision risk is greatly reduced, and the operation safety in a complex dynamic environment is ensured; a block chain alliance chain and an intelligent contract mechanism are adopted, path data credibility is guaranteed through ECC encryption, a PBFT algorithm rapidly reaches a consensus, an intelligent contract automatically allocates priorities, such as emergency task priority, and a path optimized through differential geometry is combined, so that the path conflict rate of multiple robots in dense scenes such as storage and venues is remarkably reduced.

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 a robot environment perception method based on single-view three-dimensional scene generation, and the method comprises the steps: collecting a two-dimensional image containing target environment information through employing a monocular camera, generating multi-view information through combining depth estimation, normal prediction and a two-stage semantic guidance diffusion model, and constructing a high-quality three-dimensional scene. And reconstructing three-dimensional point cloud data through the neural radiation field, and performing texture rendering optimization on the point cloud data. A Point Net + + network is used for carrying out semantic analysis on point clouds, a multi-frame time sequence point cloud registration and Kalman filtering tracking method is introduced, modeling is carried out on a dynamic target, and a dynamic semantic map with a motion state is constructed. And finally, structured output environment information is used for robot navigation, path planning and task execution. The problems of high cost, high complexity and insufficient real-time performance and robustness of a three-dimensional scene generation technology in the field of robot environment perception are solved, and the method is high in structuring degree, standard and unified in output format and high in universality and engineering adaptation capacity.

The invention discloses a safety-enhanced trajectory planning method based on an Astar algorithm, and belongs to the technical field of intelligent robot navigation and path planning, and the method comprises the following steps: 1, constructing a grid map, and marking an obstacle region; 2, establishing a security evaluation mechanism, evaluating the surrounding environment of the path node, and quantifying a security index; 3, in a node expansion process, combining a heuristic item and a security index in a cost function to realize priority search for a security region during path selection; 4, key nodes of the obtained initial safety path are extracted based on a Douglas-Peucker algorithm to generate a rectangular safety corridor, and smoothing processing of the initial path is achieved based on a segmented Bezier curve. On the basis of guaranteeing path optimality, safety is used as an auxiliary guiding basis in the path searching process, and the path searching efficiency is improved. Therefore, consideration of the path length and the environmental risk is realized, and the navigation path with higher execution safety is generated.

The invention relates to the technical field of robot navigation, and discloses an automatic path-finding intelligent robot and a path-finding method thereof. According to the method, multi-dimensional spatial data including obstacle distribution, ground features and dynamic target motion information is acquired through an environment sensing device, spatial discretization processing is performed on the data, and a topological map which has a hierarchical structure and includes a node connection relation and a region passing weight is generated. Then, according to the node connection relation of the topological map, a path planning model based on manifold learning is established, and an optimal path candidate set is determined by calculating the manifold distance between adjacent nodes; then extracting a key node sequence in the candidate set, dynamically optimizing a path in combination with a region passing weight, and generating a preliminary navigation track; and finally, inputting the initial track into a motion control model, carrying out smooth processing according to kinematics constraint of the robot, and outputting a final execution path. According to the invention, the robot can efficiently find the way in a complex environment.

The invention relates to a robot navigation method and system based on a visual language large model and experience memory. The method comprises the following steps: firstly, generating a semantic map with labels by utilizing an SLAM technology through multi-modal environment data sensed by a robot; receiving an instruction, and analyzing the instruction through a large language model to obtain a structured task representation; constructing an experience library based on historical data, and generating an experience package in combination with task representation; planning a path based on a semantic map, a structured representation and an experience package, obtaining an alternative path set, evaluating confidence, determining whether to trigger a clarification action, and outputting an optimal path; and finally, robot navigation is controlled according to the optimal path, and the experience library is updated. Compared with the prior art, the method has the advantages of improving the accuracy and interpretability of autonomous navigation of the robot, improving the navigation success rate of the robot in a complex environment and the like.

The invention belongs to the technical field of robot navigation, and particularly relates to a humanoid robot navigation method based on visual semantic segmentation and radar obstacle detection, which comprises the following steps: synchronously acquiring an RGB image and a depth image of a current environment of a humanoid robot by using a visual sensor, and acquiring point cloud data by using a laser radar; performing semantic segmentation on the preprocessed RGB image; radar point cloud obstacle detection; fusing the semantic segmentation map and the laser point cloud map, introducing a Bayesian decision to judge whether the laser point cloud map is passable or not, and then calculating a fusion cost value to obtain a fusion cost map; adopting an RRT * / TEB algorithm to output an optimal path; using a nonlinear optimization solver to generate a foothold sequence accurate to each step; according to the method, a semantic-geometric two-dimensional navigation cost model is constructed; bayesian reasoning is deeply bound with a navigation scene, so that the navigation adaptability of an unstructured environment is improved; the navigation method is suitable for the humanoid robot, and is low in energy consumption, low in navigation deviation and high in safety.

The invention provides a unicompartmental knee arthroplasty robot navigation and positioning system and method. The system comprises a preoperative planning module, a positioning and navigation module and a mechanical arm control module. The preoperative planning module is used for carrying out knee joint segmentation and reconstruction according to the obtained knee joint medical image, carrying out preoperative planning and determining a unicompartmental knee arthroplasty scheme; the positioning navigation module is used for registering the intraoperative solid knee joint and the preoperative knee joint three-dimensional model according to the navigator and the tracer, and tracking the poses of the osteotomy tool and the solid knee joint in real time; and the mechanical arm control module is used for planning a motion path of the mechanical arm and controlling the mechanical arm to execute the motion path. In this way, the mechanical arm can be controlled to assist in completing the osteotomy operation of unicompartmental knee arthroplasty in a preoperative planning and intraoperative optical navigation mode, and the problems that the operation level is uneven and the operation effect is not expected due to different individuals in the current manual operation are solved.

The invention relates to the technical field of robot path planning, and discloses an improved robot path planning method based on an RRT algorithm, and the method comprises the steps: carrying out the global path searching through an improved fast exploration random tree (RRT) algorithm, and employing a special random sampling strategy and a KD-Tree to accelerate the nearest neighbor searching, and improving the searching efficiency; a greedy strategy is utilized to optimize a path, and redundant nodes are reduced; a smooth trajectory is generated by means of cubic spline interpolation, and control points are dynamically adjusted through collision detection. The method effectively solves the problems of path redundancy and poor smoothness of a traditional RRT algorithm, is suitable for the fields of robot navigation, logistics transportation and the like, and can remarkably improve the path planning efficiency and quality.

The invention belongs to the field of artificial intelligence and robot navigation, and discloses a visual language navigation method and system based on a cross-task incremental semantic memory graph, and the method comprises the steps that an intelligent agent executes a zero-sample visual language navigation task in a continuous environment; performing cross-modal alignment on the natural language instruction and environment observation based on a multi-modal large language model, selecting candidate waypoints and updating task progress; a semantic memory graph is constructed and dynamically updated, wherein the semantic memory graph is used for structured storage and cross-task multiplexing of scene semantic information and a spatial topological relation sensed by an intelligent agent in historical tasks; and performing global path planning and local dynamic fine tuning based on the semantic memory graph. According to the method, the problem of task-by-task forgetting in a traditional method is solved, environment understanding and task reasoning capabilities are improved by constructing structured long-term memory, and navigation precision and robustness are optimized through a global-local collaborative strategy.

The invention relates to the technical field of robot navigation, and discloses a multi-sensor fusion navigation method and system of a robot. The method comprises the following steps: collecting point cloud data of a laser radar sensor and motion data of an IMU six-axis sensor; executing generalized likelihood ratio test and sliding window interval search on the point cloud data and the motion data to obtain a zero-speed abnormal interval, and creating a detection threshold value for the current ground environment according to the zero-speed abnormal interval; performing spatial superposition on the position coordinates of the zero-speed abnormal interval and a laser radar scanning blind area to obtain an obstacle distribution map; cleaning control parameters are adjusted based on the detection threshold value, a re-planning cleaning path is generated based on the obstacle distribution map and the cleaning control parameters, the method can actively identify the potential trapped area and re-plan the cleaning path, and the autonomous navigation ability and cleaning efficiency of the intelligent cleaning robot are improved.

The invention discloses a multi-module collaborative navigation system for an intelligent robot, which relates to the field of robot navigation and comprises a multi-mode perception decision module, a semantic visual navigation module, a path memory fusion module and a rolling predictive control module which are electrically connected in sequence and work cooperatively. The multi-modal perception decision module performs weighted fusion on heterogeneous information, outputs scene representation and decision, and provides data support; the semantic visual navigation module analyzes the natural language instruction and combines visual data to complete path planning; the path memory fusion module carries out backtracking correction in an abnormal environment of semantic ambiguity and perception mismatch; and the rolling predictive control module realizes trajectory tracking and dynamic obstacle avoidance. According to the system, dependence on a prior map is reduced, instruction performability and dynamic obstacle avoidance capability are improved, collision risks are reduced, navigation real-time performance, stability and robustness are enhanced, and the system is compatible with multiple chassis and is suitable for complex dynamic scenes.

The invention discloses a bionic joint humanoid robot navigation positioning method, device and equipment, and the method comprises the steps: monitoring a landing shock wave signal and reflection echo data of a robot, carrying out the time domain expansion of the shock wave signal to form a pulse characteristic spectrum, extracting multipath propagation components, and carrying out the time difference correlation to generate an environment response value. Screening a direct wave component by using an environmental response value, positioning a sound wave propagation trajectory, and detecting impedance mutation to construct an acoustic matrix. A strong reflection zone is identified through node analysis, a sound pressure gradient field is extracted to determine an obstacle boundary, and a landmark feature bit is determined in combination with an environmental response value. And adjusting a dynamic stride frequency interval based on a landmark feature bit, forming an adaptive detection sequence, and performing sparse processing to construct path configuration. The mechanical vibration component of the shock wave signal is used for piezoelectric conversion to generate recycled electric quantity, the recycled electric quantity is matched with path configuration power to determine a sensing period, intermittent sensing is executed to obtain detection data to construct a detection convergence field, and finally an accurate positioning window is determined and converted into position coordinates.

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.

The invention provides a path planning method and system based on HER-ACO, and belongs to the technical field of robot path planning. The method comprises the following steps: S1, acquiring an environment grid map, wherein the grid map comprises a starting point, a target point and obstacle distribution information; s2, introducing an HER algorithm to improve a traditional ant colony algorithm ACO, and searching a feasible path from a starting point to a target point in the grid map by using the improved ant colony algorithm as an initial planning path; the improvement comprises the following steps: introducing an HER thought and a dynamic pheromone volatilization mechanism; increasing a direction weight factor, a neighborhood obstacle factor and a future obstacle factor; and S3, smoothing the initial planning path based on the feasibility check of the linear path, removing redundant intermediate nodes, and outputting a final planning path. A simulation result shows that the scheme has remarkable advantages in a complex environment, particularly has excellent performance in the aspects of path quality and convergence speed, and is suitable for scenes such as robot navigation and concrete robot path planning.

The invention relates to the field of robot navigation, and discloses a logistics robot navigation method based on vision and laser fusion navigation, which is used for solving the problem of echo energy attenuation or signal loss during robot navigation, and comprises the following steps: acquiring information reliability parameters of each space sub-region in a current laser scanning period; a reflection attenuation index is obtained through evaluation according to the information reliability parameter, whether signal attenuation occurs in the space sub-region or not is judged, if it is judged that signal attenuation occurs in a certain space sub-region, the space sub-region is marked as a low-confidence region, the initial depth information is corrected according to the corresponding reflection attenuation index, and actual depth information is obtained; the corrected environment model is obtained according to the actual environment depth information, the geometric structure of the corrected environment model is optimized and adjusted according to the reflection attenuation index change rate and the depth correction residual error of each space subarea, and the navigation precision and the operation safety of the robot in the storage environment are effectively improved.

The invention relates to the field of robot navigation, and particularly discloses a spatial perception observation information quality evaluation method and system, a terminal and a medium, and the method comprises the steps: converting the measurement information of each dimension into a normalization probability according to the measurement target of each dimension; according to the concept of entropy in the information theory, the normalized probability of each dimension is converted into a computable entropy value; a downstream task weight coefficient is introduced for the entropy value of each dimension, the multi-dimensional entropy value after the downstream task weight coefficient is introduced is mapped into a physical interpretable confidence coefficient in a [0, 1] interval through an exponential attenuation function, and the confidence coefficient explicitly represents the uncertainty of observation information of each dimension. According to the method, a space perception quality evaluation system with mathematics interpretability, scene adaptation and real-time calculation is formed, multi-source uncertainty is mapped into executable confidence, and multi-dimensional accurate evaluation of intelligent agent space perception observation information is achieved.

The invention discloses an indoor dynamic environment three-dimensional reconstruction method and system based on deep learning, and the method comprises the steps: employing SuperPoint and LightGlue to carry out the extraction and matching of feature points, carrying out the precise recognition of a dynamic target in combination with YOLOv11 and SAM technologies, and carrying out the implementation of an image pyramid feature selection strategy, and can uniformly reserve key feature points layer by layer, thereby achieving the precise recognition of a dynamic target. The feature points of the dynamic object are effectively eliminated, so that the efficiency is further improved, and finally, a high-precision, high-robustness and high-real-time visual synchronous localization and mapping (VSLAM) system for high-precision localization and mapping is realized in an indoor dynamic environment. The method is suitable for the fields of robot navigation, unmanned vehicles, indoor positioning and navigation and the like in indoor environments with weak GPS signals and more dynamic objects, such as large underground parking lots, shopping malls, office buildings and the like.

The invention provides a welding track planning method for a four-footed humanoid welding robot. The welding track planning method comprises the steps that the welding robot is navigated to a welding station, and welding seam information is recognized and extracted; motion trail planning is conducted on the double mechanical arms in an interpolation mode, and a mechanical arm tail end trail sequence generated through interpolation is obtained; the inverse kinematics of the double mechanical arms is solved for each pose, multiple sets of joint angular solutions are obtained, and the optimal solution which is coherent in motion and minimum in deviation is screened out; the condition number of the Jacobian matrix of each point of the joint track sequence is calculated, and the potential singular pose of the mechanical arm is analyzed and recognized; needed damping factors are calculated in real time according to the singular degree, and the joint speed of the mechanical arm is smoothly adjusted when a singular point is approached; and collision avoidance analysis is carried out on the tracks of the double mechanical arms. According to the method, by combining inverse kinematics multi-solution optimization selection, trajectory time re-parameterization, a damping least square control strategy and a double-arm space coordination collision avoidance mechanism, the stability, safety and consistency in the welding task execution process are ensured.

The invention relates to the technical field of robot navigation, and particularly provides a multi-layer map construction method for substation inspection navigation, which comprises the following steps: generating a point cloud map according to three-dimensional point cloud data of a substation, and marking passable areas, obstacle positions and road topological structures to obtain a geometric information layer; identifying the type of the equipment according to the image data of the equipment of the transformer substation, and generating a priority label of the equipment based on the type of the equipment to obtain an equipment information layer; according to the topographic relief degree matrix of the transformer substation, dividing the transformer substation environment into a flat area, a non-flat area and an obstacle area to obtain a topographic information layer; and carrying out space alignment on the geometric information layer, the equipment information layer and the topographic information layer to obtain a multi-layer map with a unified coordinate system. The problems that in the prior art, a transformer substation inspection map is single in information dimension, poor in reliability and not beneficial to inspection are solved.

The invention discloses a robot multi-floor composite navigation method and equipment, and belongs to the technical field of robot navigation. According to an existing robot navigation scheme, in the multi-floor navigation process, the information storage degree of a constructed grid map is low, and an optimal planning path cannot be obtained. According to the robot multi-floor composite navigation method, the multi-floor navigation characteristics are fully considered, the three-dimensional point cloud map is segmented according to the floors to obtain the three-dimensional point cloud maps of different floors, so that the information storage degree can be greatly improved, the three-dimensional point cloud map of each floor is subjected to projection and rasterization processing, and the navigation efficiency is improved. Generating a plurality of two-dimensional same-layer maps; analyzing the connection relationship between the floors and the structural characteristics between the floors to obtain an interlayer connection map; and based on the inter-floor connection map and the two-dimensional same-floor map, topological path search is carried out to obtain an optimal planning path, so that accurate navigation of the robot in the same-building multi-floor environment can be realized.

The invention discloses a non-flat terrain autonomous navigation method based on path pruning and trajectory smoothing. The method comprises the following steps: constructing an elevation map with three-dimensional map information as a navigation map according to robot navigation space information; based on the navigation map, performing global path search by adopting a path pruning strategy to obtain an initial path from the starting point to the target point; based on the initial path, track optimization is carried out by using a B spline curve, and a smooth track meeting robot dynamics constraints is generated; in response to an obstacle which is detected on the smooth trajectory and cannot be avoided, performing three-dimensional obstacle crossing trajectory planning based on terrain elevation information of the elevation map, and generating an obstacle crossing trajectory capable of crossing the obstacle; wherein the robot performs autonomous navigation based on the smooth track or the obstacle crossing track. According to the invention, efficient global search, smooth trajectory optimization and intelligent obstacle crossing decision of the robot in unknown and non-flat terrains can be realized.

The invention discloses a green cutting robot navigation method and system, relates to the field of green cutting robot navigation, is used for improving the navigation precision of a green cutting robot, and comprises the following steps: acquiring different types of environmental information in front; the different types of environment information comprise image data and radar point cloud data; determining target crop path information independently identified according to the respective environment information; the reliability scores of the target crop path information independently identified according to the respective environment information are evaluated respectively; comparing the spatial geometric consistency of the target crop path information independently identified according to the respective environment information and the respective reliability score, and quantifying the overall credibility of the current environment information; when the total credibility is greater than or equal to a credibility threshold value, navigation is performed according to different types of environment information; when the total credibility is smaller than a credibility threshold value, navigation is carried out according to internal dead reckoning.

The invention provides a robot environment sensing method and device based on semantic completion, which are used for improving the accurate sensing ability of a robot with a body to obstacles in a complex environment. The method comprises the following steps: S1, preprocessing collected original three-dimensional environment data to obtain standardized input data; s2, inputting the standardized input data into a semantic completion model for reasoning, and generating a three-dimensional semantic voxel graph; s3, extracting a voxel region representing a barrier category according to the semantic tag, and fusing the voxel region with historical perception information to generate barrier region representation; and S4, mapping the obstacle region representation to a robot sensing structure, and constructing an occupation graph or an obstacle code for supporting path planning and obstacle avoidance control. According to the method, multi-scale sparse voxel representation and a semantic completion strategy are combined, the continuity, robustness and scene adaptability of obstacle recognition are remarkably improved, and the method is suitable for various application scenes such as navigation of a robot with a body, obstacle avoidance of a service robot and the like.

A comprehensive spatial AI platform for neural 3D reconstruction and built environment analysis integrates foundation models, deep learning methods, and spatial reasoning capabilities to provide expert knowledge of the physical world. The platform combines symbolic AI and machine learning to facilitate 3D semantics for insurance, real estate, construction, robotics, and other business applications. The system processes captured images, videos, or point clouds through neural networks with low compute requirements, incorporating device-agnostic advanced spatial intelligence that delivers geometric, semantic, and relational data. The platform includes proprietary training innovations, comprehensive measurement and semantic understanding, external sensor integration, human-in-the-loop quality assurance, and API integration for programmatic access. Advanced spatial reasoning capabilities enable property damage assessments, construction progress tracking, robotics navigation, and real-time applications including room dimension validation and automated repair estimates, supporting enterprise workflows across various industry segments while enabling productivity improvements and new value-added spatial AI use cases.

The invention relates to the technical field of robot navigation, in particular to a hydraulic structure inspection robot self-adaptive navigation method based on multi-source data fusion. Comprising the following steps: acquiring visual image data, three-dimensional point cloud data, I MU data, ultrasonic data, historical hydrological data and current hydrological data; performing fusion processing on the visual image data, the three-dimensional point cloud data, the I MU data, the ultrasonic data and the historical hydrological data to obtain fusion state data and an environment map; determining an initial positioning result based on the fusion state data, the environmental map, the current hydrological data and a preset obstacle avoidance model; based on the initial positioning result and a preset verification model, determining a navigation positioning result; and obtaining a path navigation result based on the navigation positioning result, the environment map and the current hydrological data. The invention aims to solve the problem that the existing inspection robot is easy to collide in a sheltered environment or a dynamic scene, so that the inspection task is interrupted or the inspection efficiency is low.