289 results about "Robotic navigation" patented technology

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 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 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 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 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.

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 discloses a magnetic positioning resolving method and system, and relates to the technical field of magnetic positioning and sensor resolving, the method successfully separates an attitude error from magnetic field drift by explicitly introducing a rotation quaternion and bias modeling, has good physical interpretability and environmental adaptability, and can be applied to the field of magnetic positioning and sensor resolving. The precision, the robustness and the generalization ability of the magnetic positioning system in a complex environment can be remarkably improved, and the method is suitable for various application scenes such as robot navigation, industrial detection and medical equipment.

The invention discloses an intelligent mobile robot navigation method for a dynamic environment, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting the dynamic environment of a robot in real time through a laser radar, an RGB-D camera and a UWB module, building a robot real-time environment perception model, and carrying out the real-time environment perception of the robot; the robot real-time environment sensing model comprises parameterized description of metal reflection, dynamic obstacles and narrow terrains; s02, determining a plurality of key design parameters of navigation of the intelligent mobile robot; and S03, based on the plurality of key design parameters, constructing an environment perception accuracy objective function of the intelligent mobile robot and a real-time response and scene understanding depth objective function. According to the invention, high-precision environment perception, low-delay path re-planning and safe and efficient navigation are synchronously realized in a dynamic environment through metal reflection interference filtering, dynamic obstacle acceleration pre-judgment and narrow terrain adaptive passing strategies.

The invention discloses a robot navigation and positioning method based on machine vision. The robot navigation and positioning method comprises the following steps: acquiring and preprocessing an environment image sequence; performing feature point detection and feature description by using an improved DELF model to generate a visual feature point set; obtaining a visual pose estimation result; obtaining fusion pose information; constructing a local environment map by adopting a sparse point cloud reconstruction method; generating a global environment map; generating an optimal advancing path by using a path planning method; and a robot motion control instruction is generated, path adjustment and navigation correction are performed, and high-precision autonomous positioning and navigation of the robot in a complex environment are realized.

The embodiment of the invention provides a robot navigation obstacle avoidance method and a robot. Relates to the technical field of robot control. The method is applied to a robot and comprises the following steps: in response to a navigation instruction, determining a global path remaining distance between a current pose and a global end point pose of the robot; if the global path residual distance is smaller than a second preset distance threshold value, obstacle detection is executed on a target area where the global end point position is located, and an obstacle detection result of the target area is obtained; if no obstacle exists in the target area, the robot is controlled to continue to walk along the current navigation path until the robot reaches the global end point pose; if the obstacle exists in the target area, acquiring attribute information of the obstacle; and generating a corresponding obstacle avoidance strategy based on the attribute information, and controlling the robot to execute the obstacle avoidance strategy so as to reach the global end point pose. According to the method, the technical effect of improving the safety and flexibility of the robot in the navigation process is achieved.

The invention relates to the field of robot navigation, in particular to a robot dog formation collaborative operation method and system in a disaster environment. The method comprises the following steps: acquiring future intention information of a pilot dog and a local environment model sensed by a relay dog, and fusing to construct a prospective environment model; calculating a link quality score of a relay dog to the intention of a pilot dog based on the model, constructing a potential field, and solving a gradient to obtain a communication gradient impetus; switching the operation mode of the relay dog according to the operation state of the pilot dog; in the corresponding mode, the communication gradient driving force, the communication attraction pointing to the pilot dog and the obstacle repulsive force are fused, the navigation resultant force is calculated, and the relay dog is controlled to work cooperatively. Through the prospective communication quality gradient field, the relay dog prejudges and avoids the communication blind area, the modal switching adaptation requirement is combined, the problem that formation communication is prone to being interrupted in the disaster environment is solved, the communication continuity and robustness are improved, and support is provided for efficient collaborative operation of the robot dog.

The invention relates to a robot navigation method and system based on hierarchical attention, and belongs to the technical field of path planning. Comprising the following steps: acquiring pose of a robot and position information of a target point, and acquiring an environment image and pedestrian data in front of a current moving path of the robot; extracting a spatial semantic feature vector of the image by using a hierarchical attention architecture; utilizing a multilayer perceptron to extract dynamic pedestrian feature vectors; preprocessing the position information to obtain target point feature vectors, splicing the three feature vectors, dynamically fusing the three feature vectors, and inputting the three feature vectors into a strategy network and a value network to obtain the action and state value of the robot at the next moment; obtaining a new pose, position information and environment state according to the new execution action, and calculating a reward value in combination with a reward function; and continuously updating the two network parameters according to the reward value and the new state value, obtaining an optimal reinforcement learning model after multiple iterations, and completing robot navigation by using the reinforcement learning model. According to the invention, the accuracy and stability of navigation are improved.

The invention provides a robot dynamic obstacle trajectory prediction navigation method based on a visual model, and relates to the field of power equipment maintenance, the method comprises the following steps: collecting original data of dynamic scene multi-modal perception, carrying out space-time alignment, extracting thermal infrared dynamic feature vectors and visual dynamic feature vectors, and carrying out dynamic scene multi-modal perception on the thermal infrared dynamic feature vectors and the visual dynamic feature vectors; simultaneously calculating the fuzzy degree of the visual image and the dynamic complexity of the scene; calculating a fusion weight of the thermal infrared feature and the visual feature through a dynamic modal weight algorithm, and outputting a fusion feature vector; inputting the fusion feature vector into a visual model, and calculating the trajectory probability distribution of the dynamic obstacle; calculating comprehensive uncertainty and risk coefficients based on the trajectory probability distribution; and based on the fusion feature vector and the trajectory probability distribution, integrating the uncertainty and the risk coefficient, and planning an obstacle avoidance path and a decision report in combination with an A algorithm, so as to realize the dynamic navigation of the robot. According to the technical scheme, the reliability and intelligence of robot navigation can be improved, and the navigation requirement of a complex environment is met.

The invention discloses an SLAM (Simultaneous Localization and Mapping) optimization mapping method and device combining geometric verification and constraint of a reflector, relates to the technical field of industrial-grade mobile robot navigation, and solves the problem that in the prior art, a continuous constraint mechanism cannot be established in a mapping process to correct accumulative errors in motion. The method comprises the following steps: acquiring an original laser radar point cloud containing a reflector, screening out reflection points with the distance smaller than a preset distance, and carrying out clustering and quintuple geometric verification on the screened points by adopting a density clustering algorithm, so as to obtain a multi-dimensional geometric verification mechanism based on PCA (Principal Component Analysis); local coordinates of the center of the reflector are calculated and converted into global coordinates, the reflector is used as a stable Landmark depth to be fused into an SLAM back-end graph optimization framework, and optimization nodes including a timestamp, a robot pose, a reflector ID and an observation pose are constructed based on a global coordinate system to participate in graph optimization; and the matching problem caused by insufficient natural characteristics or environment change in a long corridor scene can be solved in a targeted manner.

The invention belongs to the technical field of mobile robot autonomous navigation, and particularly relates to a robot navigation method and system based on a large model, and the method comprises the steps: collecting an environment RGB image, laser point cloud and user voice; based on a pre-constructed scene semantic graph, respectively extracting image and point cloud features, and fusing the image and point cloud features through a self-attention and cross attention mechanism to obtain visual point cloud composite features; further fusing the composite feature and a scene semantic graph feature into a scene feature, and aligning the scene feature and the encoded speech feature to a language space of a large language model; and the aligned features are input into a large language model based on Transform, and a motion control signal of the robot is directly output, so that end-to-end autonomous navigation is realized. Through multi-modal deep fusion and unified semantic alignment, the ability of the robot to understand natural language instructions and perform intelligent obstacle avoidance and path planning in a dynamic complex environment is significantly improved, and the environmental adaptability and deployment convenience of the system are enhanced.

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 discloses a waypoint prediction method fusing instruction landmark features in visual language navigation, and belongs to the crossing field of artificial intelligence and computer vision. The method is realized through the following steps: firstly, extracting landmarks and co-occurrence landmarks thereof from a natural language instruction by utilizing a large language model, and generating a candidate landmark sequence; then, the occurrence probability of the candidate landmarks in actual observation is calculated based on a visual large model CLIP, and noise interference is dynamically corrected and suppressed through a learnable co-occurrence scoring module; secondly, fusing the corrected landmark features with the depth visual features, inputting a double-layer Transform model to model a spatial relationship, generating a waypoint probability heat map, and outputting adjacent waypoints through non-maximum suppression (NMS); and finally, topological mapping is carried out on the predicted waypoints, an optimal path is obtained through cross-modal path planning, and an intelligent agent is guided to execute low-level actions. The method does not need to depend on a predefined environment map or manually annotate data, navigation errors in a continuous environment are remarkably reduced, the generalization ability of unseen scenes is improved, and the method is suitable for open world navigation scenes such as robot navigation and automatic driving.

The invention discloses a tunnel construction robot path planning method and system based on deep reinforcement learning, and relates to the technical field of tunnel construction automation and robot navigation, and the method comprises the steps: pre-defining a state space and an action space of robot path planning in a tunnel construction environment, and building a dynamic reward function; constructing a deep reinforcement learning architecture based on a lightweight action network and a comment network, and training an action-comment network based on state-action pair data in an experience pool by taking the sum of an accumulated reward and an entropy regularization item of a maximization strategy as a target; and on the basis of the current state information of the tunnel construction robot, the optimal moving action of the next step is obtained by utilizing the trained action network, and path planning of the robot is realized. According to the method, the deep reinforcement learning technology is applied to the tunnel construction scene, and the autonomous decision-making ability and the path planning precision of the robot in the dynamic construction environment are optimized and improved in combination with real-time data acquisition and processing of the complex tunnel environment.

The invention provides a quadruped robot autonomous navigation method and system based on multi-sensor fusion, and relates to the technical field of robot navigation, and the method comprises the steps: carrying out the calibration, feature extraction and alignment of multi-sensor data, and constructing a three-dimensional environment semantic map; generating a navigation path by considering kinematics and dynamics constraints; dynamically selecting foot end drop points and tracks according to topographic features; and feeding back and correcting the map model in real time. According to the method, the sensing precision, the path planning rationality and the motion stability of the quadruped robot in a complex environment are improved, and the self-adaptive navigation capability of the robot is enhanced.

The invention discloses a multi-source fusion deep learning visual navigation method, system and device and a storage medium, and the method comprises the steps: collecting a visual image sequence and a Beidou positioning signal obtained by a robot in an operation process, carrying out the preprocessing, and generating a fusion input sequence with a quality score; performing two-channel feature coding on the visual image sequence and the Beidou positioning signal, mapping the features to a unified joint representation space, and performing dynamic modeling on the reliability of each mode in the current environment; based on the confidence coefficient of each mode and the cross-mode correlation, generating a double-branch dynamic mask of vision and Beidou, and combining a confidence coefficient modulation vector to fuse the two types of features to obtain a fused feature sequence; and performing context modeling on the fused feature sequence, constructing a joint optimization objective function for cooperative training, and outputting a robot navigation trajectory. According to the invention, the navigation precision, reliability and autonomy of the robot in a complex environment can be improved.

The invention provides an implicit instruction visual language navigation method and system based on a multi-modal large model, and relates to the technical field of computer vision, artificial intelligence and robots. The method comprises the following steps: generating a semantic map and an occupation map based on visual RGB image and depth image data; then an implicit instruction input by a user is analyzed, a semantic navigation reasoning token is generated in combination with a local scene map and the current observation state of the robot, and a next navigation action is predicted by fusing multi-source information through a cross-modal attention mechanism; real-time obstacle detection is carried out, and collision and deadlock in navigation are avoided through a collision punishment loss item; and finally, optimizing the training process of robot navigation through two stages of imitation learning and reinforcement learning by adopting a hierarchical joint optimization strategy and combining with the implicit instruction data set. According to the method, a scene semantic graph and a real-time observation state are uniformly coded by defining a special token, so that efficient reasoning and navigation decision-making of implicit semantics are realized.

1. Name of the designed product: Wearable navigation device (humanoid robot). 2. Use of the designed product: For robot navigation. 3. Design points of the designed product: In shape. 4. Picture or photo best indicating the design points: Fig. 1.

The invention provides a robot navigation method and device, electronic equipment, a storage medium and a program product, and belongs to the technical field of robot visual language navigation, and the robot navigation method comprises the following steps: in response to a navigation instruction, obtaining at least one atomic action obtained by analyzing the navigation instruction; screening from a knowledge database based on the current atomic action to obtain target knowledge data; updating node features associated with the current atomic action in the navigation map based on the target knowledge data; and executing the current atomic action based on the updated node features, and performing feature injection on the node features associated with the current atomic action in the navigation map through the target knowledge data so as to relieve a semantic gap between an instruction and a low-level action, so that the path success rate and the cross-environment generalization ability are remarkably improved, and the success probability of navigation is further improved.

The present application belongs to the field of automatic driving and robot navigation technology, and particularly relates to a real-time map construction method and system for a monorail crane in a downhole roadway environment, which method comprises: acquiring vehicle-mounted sensor data and correcting motion distortion using an inertial measurement unit to generate standardized laser ranging data; constructing a discretized spherical sensitivity field, identifying a longitudinal degeneration vector through nonlinear mapping optimization, and generating a degeneration risk coefficient; mapping point cloud data to the longitudinal degeneration vector direction to generate a real-time density waveform, and calculating the best waveform offset by aligning with a reference density waveform; constructing a target function containing a longitudinal waveform locking constraint term weighted by the degeneration risk coefficient, solving the globally optimal pose, and completing map construction. The present application solves the problem of longitudinal positioning drift in a weak geometric structure roadway and achieves high-precision map construction.

The invention discloses a robot navigation method based on reinforcement learning security constraint and danger perception, and relates to the technical field of robots and artificial intelligence, and the robot navigation method comprises the following steps: when a robot is controlled to execute a navigation task in a target area, using a security filter SF to perform security constraint on the execution action of the robot; and when the robot executes the navigation task, the danger perception virtual robot HAVR is utilized to obtain experience in the dangerous area so as to enhance the ability of the robot to explore in the dangerous area. The action generated by reinforcement learning can be dynamically adjusted, so that the action meets the safety constraint, and the safety of the robot in the reinforcement learning training process is further enhanced; by introducing the HAVR with risk-driven rewards, the robot can still obtain experience in a dangerous area while introducing security constraints, and the exploration security of the robot is further enhanced while the performance of the model after convergence is ensured.

The present disclosure relates to a robot movement operation method and device, electronic equipment, storage medium and computer program product, the method comprising: obtaining a semantic segmentation result based on an RGB image; generating a three-dimensional point cloud based on a depth map; associating the semantic segmentation result corresponding to each pixel point and the three-dimensional space coordinates, and projecting the obtained association result to a bird's eye view perspective; obtaining a target position of a task-related object based on a semantic map and the name of the task-related object; evaluating the operability of the nearby position of the target position through an operable map prediction model; and controlling the robot to perform an operation on the task-related object based on the local operable map. In this way, by evaluating the operability of the nearby position of the target position of the task-related object through the operable map prediction model, the robot can be navigated to an operable position that avoids obstacles, thereby ensuring that the robot can accurately operate on the task-related object, and the operation success rate can be improved.

The invention discloses a robot navigation method based on a plane layout. The method comprises the following steps: S1, based on a building plane layout map, analyzing an image by using a multi-modal large model, extracting topological and geometric features in an environment, and constructing a semantic topological structure; s2, acquiring a navigation task instruction, and analyzing corresponding initial topological node and target topological node information; s3, inputting the semantic topological structure and the node information into a large language model, and generating a global path node sequence by utilizing the spatial reasoning capability of the large language model; s4, on the basis of real-time data acquired by the environment sensing equipment and prior information of the plane layout, the real-time pose of the robot is determined in a mode of fusing semantic observation matching and geometric observation matching; and S5, based on the node sequence and the real-time pose, planning a global geometric path, and using a local planner and a controller to drive the robot to move to a target point along the path. The method does not need to construct an SLAM map in advance, and is low in cost and good in flexibility.

The invention particularly relates to an unknown environment zero sample object target navigation method based on common sense reasoning, which comprises the following steps of: firstly, extracting a scene semantic tag by utilizing a pre-trained open vocabulary target detection model, and constructing a global spatial memory map to realize spatial representation and memory; and meanwhile, extracting global context information in the global spatial memory map based on a large language model so as to infer the position of a target object in an unknown area, and finally outputting a robot navigation action through path planning until the robot finds the target object. According to the method, the robot can adapt to a new object and a new environment without additional training, and the robot can understand semantic information of a target object category in an unknown environment and find an instance of the target object category without seeing a specific instance of the target object, so that the target object category can be identified. The problems that an existing object target navigation method depends on a large amount of labeled training data for training, the sample efficiency is low, and the generalization ability is poor are solved.

The invention relates to the technical field of underwater robot navigation and environment perception, and discloses a positioning method and system for an underwater environment, and the positioning method comprises the steps: obtaining flow velocity profile data, temperature data, salinity data and depth data at a sampling point; obtaining a normalized flow field feature vector and a physical feature vector corresponding to each sampling point; calculating the cosine similarity of the normalized flow field feature vector and the physical feature similarity of the normalized three-dimensional physical feature vector; performing weighted fusion on the cosine similarity and the physical feature similarity to obtain fusion similarity; performing screening by combining the fusion similarity and a preset constraint rule to obtain a position revisit constraint factor; adding the position revisit constraint factors into the factor graph to construct a complete factor graph model; and solving the factor graph by using an iSAM2 algorithm to obtain a globally optimized position sequence of the underwater vehicle. According to the method, the accumulated drift error can be corrected, and the reliability of the track of the AUV in a video-sound limited scene is effectively guaranteed.