15212results about "Instruments for road network navigation" patented technology

A system and method of calculating an estimated time of arrival for a traveler is provided, along with a notification of this estimated time that is sent to a third party. The traveler is equipped with a navigation unit having a global positioning sensor and a wireless communications device. The navigation unit communicates with a service center equipped with a server capable of generating a preferred navigational route for the traveler. The service center also includes a traffic database for maintaining current traffic information. Once the traveler has selected a predetermined time and frequency of notifications based upon a selected criterion, the navigation unit transmits the traveler's position and velocity to the service center. The service center then calculates the estimated time of arrival based upon the traveler's position, velocity, distance to be traveled and traffic and weather conditions. Once this time is calculated, the service center sends a notification to the third party.

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

The invention discloses a vehicle-mounted Beidou positioning deviation self-calibration system fused with inertial navigation information, and relates to the technical field of navigation and positioning. The method is used for solving the problems of positioning deviation accumulation and signal failure in a complex environment. The system realizes space-time alignment of inertial navigation and Beidou observation data through timestamp interpolation and coordinate system conversion, and constructs a multi-source synchronous data stream. A difference value between an inertial navigation calculation speed and a Beidou Doppler speed is analyzed based on a vehicle incomplete constraint model, and a weight factor is generated in combination with a confidence threshold to suppress abnormal observation. A geometric matching error is calculated through a high-precision lane map curvature and a vehicle steering angle, and candidate tracks are screened to generate transverse calibration parameters. The noise covariance is dynamically adjusted through a tight coupling filtering algorithm, the mode is switched to a lane constraint calibration mode when a Beidou signal is interrupted, inertial navigation errors are compensated through error boundary constraint, and stable output is achieved. Multi-source data deep fusion and adaptive robust calibration are realized, and system robustness and positioning continuity are improved.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

Aspects presented herein relate to perception data collection and curation. In one aspect, a network entity receives, from a first set of user equipments (UEs), a first set of perception data collected by the first set of UEs. The network entity configures, for a second set of UEs based on the first set of perception data, at least one of: a set of planned routes and time plans for collecting a second set of perception data or a set of embeddings centroids. The network entity receives, from the second set of UEs, the second set of perception data based on at least one of the configured set of planned routes and time plans or the configured set of embeddings centroids.

The invention belongs to the technical field of positioning and navigation, and discloses a high-reliability positioning and navigation method for an air-ground cooperative unmanned system. According to the method, a negative obstacle is accurately extracted by using an RANSAC algorithm and a region growing method, a two-dimensional grid map is generated through three-dimensional point cloud re-projection, a negative obstacle region is marked on the map, and a prior map is provided for an unmanned vehicle. The unmanned vehicle constructs a local map in combination with a sensor of the unmanned vehicle, and the local map is matched with a prior map through an ICP algorithm to accurately position a global pose. According to a priori map and real-time sensor data, a grid cost map containing static obstacles, dynamic obstacles and negative obstacles is generated, a TSCNN predicts dynamic obstacle tracks according to time sequence data, map cost values are updated, an A * algorithm carries out optimal path planning based on the updated dynamic cost map, and the optimal path planning is carried out based on the updated dynamic cost map. And dynamic obstacles and negative obstacles are avoided in real time. According to the invention, efficient, accurate and safe navigation and obstacle avoidance can be realized in a dynamic environment and a complex terrain.

The invention relates to a mobile robot visual language navigation method based on a diffusion strategy. The method comprises the following steps: S1, constructing an RGB and deep space-time fusion coding module; s2, constructing a natural language instruction key semantic query coding module, and effectively condensing and compressing a natural language instruction; s3, fusing vision, language, local historical displacement and global displacement information, and constructing a multi-modal condition cross attention coding module; s4, constructing a conditional diffusion Transform decoding module, obtaining a noise vector based on conditional vector decoding, acting on a multi-step denoising process, and outputting a plurality of candidate multi-step navigation trajectories; s5, constructing a navigation trajectory discriminator based on a comparative learning method, wherein the navigation trajectory discriminator is used for efficiently and autonomously discriminating the matching score of the candidate trajectory and the target point; and the like. The method has the advantages that the anti-jamming capability is high, the task completion rate is high, the method can adapt to various complex environments and dynamic changes, the generalization capability and the expandability are high, and the method can stably run in diversified application scenes.

An Applicant Assistant System may receive, from a user device, a user input indicating a query or task associated with information displayed in a user interface. The device may determine one or more components associated with the user interface and access component metadata of the determined one or more components, the component metadata for each of the one or more components indicating one or more of a fact, a data link, or an action. A prompt comprising at least some of the component metadata, at least some context information, and an indication of one or more available response elements may be provided to a large language model (LLM). The LLM may response with one or more response elements that may be processed by the System, such as to cause updates to the user interface on the user device.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of building reconnaissance, in particular to a high-precision reconnaissance lofting intelligent positioning system and method based on multi-source data fusion, and the system comprises a data collection module, a fusion processing module, an intelligent positioning calculation module, a lofting path generation module, a wireless communication module and a visual terminal module. The data acquisition module synchronously acquires multi-source data; the data fusion processing module carries out space-time alignment and feature level fusion and detects the state of the sensor; the intelligent positioning resolving module outputs three-dimensional coordinates by adopting various algorithms; the lofting path generation module generates an optimized path according to the BIM model; the wireless communication module realizes high-speed data transmission and equipment communication; the visual terminal module provides a visual interaction interface. Multi-source data fusion is utilized, the positioning precision is improved, the reliability of the system is guaranteed through sensor failure detection, the system adapts to the complex environment, the lofting path is optimized, the overall performance of the system is enhanced, and an efficient and accurate solution is provided for building investigation lofting.

The invention discloses an AR navigation system and method based on a visual language model, and the method comprises the steps: firstly carrying out video data collection, and constructing a memory database; secondly, querying a navigation target most related to a natural language request of a user in the constructed memory database to obtain a current AR equipment pose and a target pose; and then solving the shortest path from the current pose to the target pose by using the current AR equipment pose and the target pose according to the point cloud map, and optimizing the path direction to finally obtain an optimized path. And finally, guiding a user to move along the planned path through view superposition path indication and voice prompt in AR equipment by utilizing the optimized path, and updating the point cloud map and the memory database. According to the invention, high-precision real-time positioning and sparse point cloud map construction can be realized only by camera input in indoor and outdoor complex environments with low GPS precision, accurate navigation is carried out, and the flexibility and intelligent level of navigation are improved.

The invention relates to a path planning method for a mobile charging robot in an unstructured environment, which combines global path planning and local dynamic obstacle avoidance, performs global path planning through an improved A * algorithm, and performs real-time local obstacle avoidance by using a dynamic window method. The global path and the local path are fused through dynamic weight adjustment, and it is ensured that the robot keeps the continuity of the global path while avoiding obstacles. In the path planning process, factors such as terrain complexity and robot advancing speed are considered, and the energy consumption of the path is minimized by adopting an energy consumption optimization algorithm, so that the working time of the robot is prolonged, and the execution efficiency is improved. The path planning method is suitable for storage, industry, field inspection and other complex dynamic environments, has high adaptability, real-time performance and robustness, and provides an effective solution for efficient application of the mobile charging robot in an unstructured environment.

The invention relates to the technical field of intelligent warehousing and logistics automation, in particular to a forklift dynamic path planning method based on deep reinforcement learning, and the method comprises the steps: deploying multi-view vision, geomagnetism and other multi-source sensors, achieving data calibration and fusion through employing an insect compound eye-imitating vision model, and constructing a high-dimensional state space vector; heuristic models such as migrant bird navigation and biological stress response are adopted, deep reinforcement learning is combined, decision instructions are generated from the three aspects of path planning, dynamic obstacle avoidance and energy efficiency management, actions are executed through a control system, and deviation is fed back; a reward function is used for evaluating the decision effect, rewards are formed by weighting path efficiency, obstacle avoidance success and energy consumption penalty, the weight can be updated in a self-adaptive mode, and therefore the deep reinforcement learning model is optimized. According to the method, the accuracy, safety and efficiency of forklift path planning are effectively improved, the method can adapt to complex dynamic environments, and the requirements of intelligent logistics and industrial automation for forklift intelligent operation are met.

The invention discloses a dynamic weight correction and path deviation probability prediction method for a vehicle track, which comprises the following steps of: acquiring vehicle data and multi-source dynamic data of the vehicle track in real time through an optical sensor, a radio wave sensor and an inertial navigation sensor, carrying out space-time calibration, extracting obstacle characteristics and road structure characteristics, and predicting the path deviation probability of the vehicle track. The obstacle movement trend is quickly captured through a space-time diagram sequence and a diagram convolutional neural network, real-time obstacle avoidance is realized in combination with dynamic weight adjustment, a driving intention is predicted by using a Bayesian neural network, a trajectory planning strategy is adjusted through weight correction, and uncertainty is reduced by using multi-source data fusion and probabilistic prediction. A closed-loop feedback mechanism continuously optimizes the model, and efficient operation is kept in complex scenes such as intersections and roundabout through real-time weight adjustment and closed-loop feedback, so that the purpose of quickly responding to dynamic obstacles or driving behavior changes can be achieved, and the precision of the path deviation prediction probability is improved.

The invention provides a multi-AGV distributed path planning method based on graph reinforcement learning, which is used for solving the technical problem of low path planning efficiency of a multi-AGV system in an information limited dynamic environment. The method comprises the following steps: establishing a dynamic heterogeneous graph model in multi-AGV collaborative path planning; constructing a distributed partially observable Markov decision model based on a graph structure in combination with path planning and collaborative decision; on the basis of a multi-agent deep reinforcement learning framework of a graph neural network and a multi-head attention mechanism, a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is designed, information aggregation is realized on the basis of local perception and neighborhood interaction by adjusting weights of nodes and edges in a dynamic heterogeneous graph, and a multi-AGV cooperative path planning algorithm for coping with an information-limited dynamic environment is realized. And the environment change is updated and adapted in real time. According to the method, the path planning efficiency can be improved through an information aggregation mechanism in an information-limited dynamic environment, multi-AGV cooperative path planning is optimized through the graph neural network, and efficient and stable operation of the system is ensured.

The invention provides an all-terrain autonomous navigation and task planning method and system for a photovoltaic inspection robot, and relates to the technical field of photovoltaic inspection, and the method comprises the steps: constructing a three-dimensional grid map and generating an initial inspection path, dividing hexagonal sub-regions and calculating a passing cost matrix, and optimizing a global inspection path based on the cost matrix. The motion state is predicted in real time, the motion track is corrected, thermal imaging and visible light data are collected to generate an inspection report, autonomous navigation and efficient inspection under complex terrains can be achieved, the inspection efficiency and accuracy are improved, and the labor cost is reduced.

To inhibit a thief from taking away a vehicle to a distant place.SOLUTION: A vehicle 100 includes: an on-vehicle navigation device 10 for repeatedly obtaining a location of the vehicle 100 through a GPS 200; and an on-vehicle immobilizer device 20. The on-vehicle immobilizer device 20 turns on a remote immobilizer on the basis of the fact that the location of the vehicle 100, obtained from the on-vehicle navigation device 10, is outside a preset geographical region. The on-vehicle immobilizer device 20 prohibits operation of a drive system of the vehicle 100 while the remote immobilizer is in an on-state. Notification that the remote immobilizer has been turned on is provided to a terminal 300 of a legitimate user of the vehicle 100 via a server 400 or directly.SELECTED DRAWING: Figure 1

The invention belongs to the technical field of electric vehicles, and discloses an electric vehicle intelligent connection system based on Beidou positioning. Comprising the following steps: acquiring Beidou positioning data and driving state data; performing multi-source fusion correction on the Beidou positioning data, and performing trajectory optimization processing to obtain a two-wheeled electric vehicle position trajectory; performing feature extraction on the two-wheeled electric vehicle position track to generate a feature vector; performing space-time correlation analysis on the feature vectors to generate a driving behavior model; performing safety risk assessment on the driving behavior model to obtain a risk early warning index; performing grading processing on the risk early warning index to obtain a grading safety early warning strategy; acquiring environment data; performing semantic analysis on the environment data, and constructing a knowledge graph; performing dynamic obstacle recognition on the knowledge graph, performing path planning decision, and constructing an intelligent navigation strategy; the two-wheeled electric vehicle is controlled based on the graded safety early warning strategy and the intelligent navigation strategy, and the riding safety and the travel efficiency of the electric vehicle are remarkably improved.

The invention discloses a polling path dynamic planning method, medium and equipment, and the method comprises the steps: obtaining environment information in real time, dynamically switching an RTK-GPS mode, a visual inertial navigation mode and a laser SLAM positioning mode, generating the current positioning information of a robot in combination with a global map, and constructing a local environment map; and planning an adaptive second preset inspection path based on the first preset inspection path. Environmental feature information is actively collected in an outdoor environment, a real-time obstacle avoidance path is generated by dynamically updating a local environment map, and the real-time obstacle avoidance path is fused to a second preset inspection path, so that smooth switching and dynamic obstacle avoidance cooperation of a positioning mode in an indoor and outdoor scene are realized; the problem of planning failure caused by multiple positioning source conflicts, local path oscillation and environment sudden change in a complex scene is solved, and the continuity of the inspection process and the path planning robustness are guaranteed.

An apparatus and method relate to analyzing metrics relating to EV charging points and facilitating selection of an EV charge point based on metrics associated with the EV charge points. Methods may include: determining locations of one or more electric vehicle charging stations; determining a total power available at the one or more electric vehicle charging stations; determining, for the one or more electric vehicle charging stations, if dynamic load balancing is used; determining occupancy of charge points at each of the one or more electric vehicle charging stations; calculating an available power at a charge point of the one or more electric vehicle charging stations based on the total power available and the occupancy of charge points for the one or more electric vehicle charging stations; and calculating an estimated time to charge a vehicle at the one or more electric vehicle charging stations based on the available power.

The invention provides a working method of a blind guiding robot based on a laser radar and a camera, and belongs to the technical field of mobile service robots. Accurate space structure information is provided through three-dimensional point cloud data, dynamic traffic characteristics such as traffic lights and zebra crossings are captured through visual images, and comprehensiveness of environmental perception is achieved through complementation of the space structure information and the visual images; through combination of environment information and traffic characteristics, passable areas and barrier distribution in a map are dynamically adjusted, dynamic changes such as traffic light switching and pedestrian movement are adapted, and a navigation route can be updated in real time; the characteristics of traffic light states, zebra crossing and the like are fused into path planning, it is ensured that the robot complies with traffic rules, real-time position and speed constraint, dynamic adjustment of motion trails and efficient obstacle avoidance can be carried out on the robot, and the robot can make a decision and optimize a path autonomously according to human social rules.

Systems and methods are provided for providing package delivery assistance. The systems and methods detect, by a wearable device, an identifier associated with a package and retrieve package delivery information from a package delivery device using the identifier of the package. The systems and methods display, by the wearable device, a portion of the package delivery information and generate, by the wearable device, visual navigational assistance to guide a courier to a delivery location associated with the package.

In various examples, embodiments are directed to navigation-road and perception-lane matching for autonomous and semi-autonomous systems and applications. In this regard, lane-road matching is performed using geometric similarity to generate effective lane-road mappings for use in lane planning and decision making, among other things. In some embodiments, road data representing at least one road section and lane data representing a lane associated with a location of an ego-machine are received. Thereafter, a determination is made that one or more consecutive road sections match a lane based at least on a geometric similarity between the lane and the one or more consecutive road sections. A representation of the lane being mapped to the one or more consecutive road sections is generated based at least on the determining that the one or more consecutive road sections matched the lane.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention provides a storage environment deep reinforcement learning multi-AGV conflict-free path planning method, and the method comprises the steps: creating a virtual storage simulation training environment, and extracting virtual storage simulation training environment information from the virtual storage simulation training environment; determining a state observation value code of each AGV according to the virtual storage simulation training environment information; setting an action space of the AGV, and constructing a reward function of deep reinforcement learning; and constructing a distributed conflict-free multi-AGV path planner introducing a dynamic grouping mechanism, carrying out iterative training on the distributed conflict-free multi-AGV path planner through a deep reinforcement learning network according to the reward function and the state observation value code and the action space of each AGV, updating network parameters of the distributed conflict-free multi-AGV path planner, and carrying out path planning on the distributed conflict-free multi-AGV path planner according to the network parameters of the distributed conflict-free multi-AGV path planner. A trained distributed conflict-free multi-AGV path planner is obtained; and carrying out conflict-free path planning on a plurality of AGVs in an actual storage environment by using the trained distributed conflict-free multi-AGV path planner to obtain a conflict-free path of each AGV.

The invention provides an implementation method of a vehicle-mounted six-axis gyroscope. The implementation method comprises a gyroscope data initialization and zero offset calibration step; a data processing step; a six-axis data fusion and attitude estimation step: using a Kalman filtering algorithm to receive the three-axis acceleration value and the three-axis angular velocity value provided by the data processing step as input, and continuously updating and optimizing state estimation of the system by combining prediction based on a known motion model and observation based on a sensor measurement value; fusing the filtered acceleration and angular velocity data through a six-axis fusion algorithm, and calculating attitude information of the object by adopting a quaternion representation method; after the attitude information represented by the quaternion is obtained, a three-axis Euler angle is further solved, and attitude data is provided for attitude monitoring of the vehicle and an intelligent driving system; and a serial port data output step. The invention aims to improve the navigation precision and reliability, improve the safety of vehicle driving, accurately judge the vehicle state and update the vehicle body posture and the driving condition in real time.

The invention provides a reinforcement learning path planning method for multiple unmanned vehicles in an intelligent factory. The method comprises the following steps: building a feeding path planning simulation environment; taking each unmanned vehicle as an intelligent agent, interacting the intelligent agent with the environment to extract information, and constructing a state space, an action space and a reward function of the Markov decision model according to the information; building an IPPO algorithm network, and training the strategy of the Markov decision model based on the IPPO algorithm network and the reward function; during training, a strategy network loss function with a rollback strategy is obtained through improvement by combining cutting loss with KL divergence in a mode of introducing hyper-parameters; and determining the current state at each moment according to the actual current position of the unmanned vehicle, inputting the current state into the strategy network, and outputting the current action to be executed by the unmanned vehicle. The method is high in convergence speed, stable in strategy updating, high in exploration capacity, capable of effectively reducing path blockage and high in environment adaptability.

The invention discloses a vision-guided robot active path detection method, and relates to the technical field of robots, and the method comprises the steps: obtaining visual modal data, laser radar modal data and depth modal data of a detection area of a robot in real time through a multi-modal sensor; preprocessing the visual modal data, the laser radar modal data and the depth modal data by using a convolutional neural network; a plurality of terrain type regions are separated from the preprocessed visual modal data, the preprocessed laser radar modal data and the preprocessed depth modal data by applying a color space conversion strategy; performing target detection on the plurality of terrain type regions through a deep learning model, and extracting candidate regions of terrain obstacles; pixel segmentation is carried out on the candidate region of the terrain obstacle, and the position and the region boundary of the terrain obstacle are determined; and adjusting the real-time advancing path of the robot according to the constructed cost map.

In various examples, systems and methods of the present disclosure correspond to speed limit fusion for automotive systems and applications. Systems and methods are disclosed that fuse speed limit data from both one or more map sources (e.g., nav-maps, HD maps, SD maps, etc.) and a perception system, and that employ rules to help resolve conflicts in speed limits from the perception system and the map(s). For example, if a conflict occurs, the rules may indicate whether to use the speed limit data from the perception data, the speed limit data from the map(s), and / or whether to use another type of speed limit data, such as cached speed limit data.