19 results about "Navigation model" patented technology

A device may determine that transaction account information, associated with a transaction account associated with a user, is to be updated. The device may identify a merchant account that is configured to use the transaction account for a transaction associated with the user and a merchant, and navigate, using a navigation model, a merchant portal associated with the merchant, to access the merchant account. The device may perform, using the navigation model and login credentials for the merchant account, a login operation to access the merchant account via the merchant portal, wherein the login credentials are stored in a credential mapping that indicates an authorization to update the transaction account information to permit the transaction account information to be used in association with the merchant account. The device may update, using the navigation model, a transaction setting of the merchant account to include updated transaction account information for the transaction account.

The application provides an electric vehicle fast charging navigation method and device, electronic equipment and storage medium, constructs a coupling network model of a traffic network-power grid in a region where a target electric vehicle user is located, a coupling edge is composed of a road node containing a charging station and a power grid node of the charging station, and the weight of the road node is defined as an average travel time, the weight of the power grid node is defined as a voltage variation, and both of them jointly determine a charging cost of the charging station; a charging navigation model is constructed with a target function of a comprehensive cost of the target electric vehicle user and constraint conditions of minimum residual allowable capacity of a battery and queue tolerance time; and through processing of the charging navigation model, a charging station with the lowest comprehensive cost is taken as a target charging station, and an optimal path to the target charging station is output. The application can realize best charging station recommendation and optimal path planning of the electric vehicle user by using multi-source information such as the traffic network-power grid, and promote benign collaborative operation of the traffic network-power grid coupling network.

This invention relates to an unmanned vehicle navigation method based on variational inference and hierarchical memory. First, the dynamic equations of the unmanned ground vehicle system are established. Then, the historical state sequence of the unmanned ground vehicle system is obtained. A navigation model based on variational inference and hierarchical memory is constructed and trained using the historical state sequence of the unmanned ground vehicle system. The navigation strategy is optimized through the navigation model based on variational inference and hierarchical memory to predict the future potential state of the unmanned ground vehicle system, obtain the optimal action for navigation, and decode the motion velocity parameters of the vehicle body used for navigation control. These motion velocity parameters are then input into the dynamic equations of the unmanned ground vehicle system to calculate the control input variables used to control the vehicle body's operation, thereby controlling the vehicle body to execute the optimal action for navigation and achieving navigation control of the vehicle body. This method improves the robustness of unmanned vehicle navigation in environments where precise modeling is difficult.

The present disclosure provides a method, system and computer readable medium of generating a road navigation model. The method generates a road navigation model for autonomous vehicle navigation and includes receiving, by a server, navigation information from a plurality of vehicles, storing, by the server, the navigation information associated with the common road segment, generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from a plurality of vehicles, the autonomous vehicle road navigation model for the common road segment including at least one line representation of a road surface feature extending along the common road segment, and distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

The present application relates to the technical field of natural language processing and human-computer interaction, and provides a menu navigation method, device, equipment and medium, comprising obtaining a user query and a current system state; extracting a local subgraph in a menu knowledge graph based on this; inputting the user query, the current system state and the local subgraph into a menu navigation model to obtain a navigation decision action with a topological association relationship of an action space limited to the local subgraph; verifying and executing the navigation decision action to update the state and output a menu navigation result; wherein the menu navigation model is obtained by reinforcement learning training of a rule reward function based on system business rules and graph topology constraints. The present application strictly constrains the reasoning action space of a large model through a local subgraph, and guides the model to make a compliant decision in combination with a rule reward function, effectively reducing the reasoning illusion of the model, and thereby improving the accuracy and controllability of menu navigation in a complex system.

Embodiments of the present application provide a robot navigation method and device and electronic equipment, and relate to the technical field of robot navigation. The electronic equipment can obtain a description text and a map. In the process of the robot traveling, a scene image collected at a current traveling position is obtained. The map, the description text and the scene image are taken as inputs of a pre-trained navigation model. The navigation model determines a map position of the current traveling position in the map according to visual semantics represented by the scene image, and generates a plurality of trajectory points conforming to a traveling direction of the map position described by the description text in the scene image. The robot is controlled to travel according to a ground point corresponding to the trajectory point. The step of obtaining the scene image collected at the current traveling position is returned until the robot reaches a navigation end point. In this way, robot navigation can be realized by combining multi-modal information through a model framework.

The application discloses a minimally invasive spinal fusion system based on artificial intelligence, which comprises the following steps: after the deviation warning signal is obtained, the instrument trajectory data is subjected to smoothing filtering processing, the filtered trajectory sequence is obtained, and the filtered trajectory sequence is matched with the updated model grid; the dynamic path adjustment parameter is determined and is built into the surgical robot to adjust the vibration frequency parameter and improve the control experience level; the subsequent image data is processed by using the iterated model version, the temporary deviation caused by the instrument shaking is determined, and the temporary deviation is corrected through the path planning algorithm; the corrected navigation instruction sequence is generated, and the real-time feedback mechanism is applied to transfer the intuitive deviation information; the surgical instrument is driven to adjust the movement through the navigation instruction sequence, the adjusted trajectory data is obtained, and the deviation of the optimized path is compared and optimized in a cycle; if the deviation continuously falls below the threshold value, the current model is maintained, and the final navigation model is output to ensure reliable consistency.

The application relates to an indoor navigation method and device, a computer device, a storage medium and a program product. The method comprises the following steps: acquiring environment data and positioning data of an autonomous mobile device in a real environment; the environment data comprises a depth image collected by a depth sensor of the autonomous mobile device; the positioning data comprises position data of the autonomous mobile device, a heading angle and position data of a target point; the environment data and the positioning data are input into a pre-trained navigation model to generate an action control instruction for the autonomous mobile device; wherein the pre-trained navigation model is a reinforcement learning model trained according to a reward function, and an output value of the reward function is obtained according to a target completion reward value, a collision reward value, a direction reward value and an obstacle depth distance reward value; and the action indicated by the action control instruction is executed, and the target point is navigated to. According to the method, indoor navigation can be accurately performed in the case that prior information about a scene map is lacking.

The application relates to a visual language navigation data enhancement method and device for an indoor mobile service robot and a storage medium. An instruction generation model is trained by using a visual language navigation data set; a first navigation path is collected in a new environment according to a simulation environment; a first navigation instruction is generated by using the trained instruction generation model according to the first navigation path, a simulation environment data set is expanded, and a preset navigation model is trained; synchronous positioning and map construction are performed in an actual deployment environment of the indoor mobile service robot, and a topological navigation map is constructed according to the positioning and map construction results; a panoramic map is collected in the actual deployment environment according to the topological navigation map; a second navigation path is collected in the actual deployment environment, and a second navigation instruction is generated by using the trained instruction generation model, a deployment environment data set is expanded, and the trained navigation model is fine-tuned. Compared with the prior art, the application has the advantages of improving the generalization of the navigation model and the performance of the navigation model in the deployment environment.

The invention relates to the technical field of train integrated navigation and positioning, in particular to a multi-source fusion self-adaptive fault-tolerant integrated navigation system and method for a high-speed train. The system synchronously acquires multi-source data through the multi-source information acquisition module; high-frequency vibration energy features and nonlinear signal features are extracted through a dynamic error modeling module, and adaptive adjustment of environmental perception and IMU error model parameters is achieved; the dynamic credibility coefficient is evaluated through a fusion decision module, and the information fusion weight of the sub-filter is dynamically adjusted; executing three-stage fault diagnosis and isolation through a hierarchical fault-tolerant module; and the safety output module calculates the protection level and packages and outputs the protection level. According to the invention, the key technical problems of navigation model mismatch, unintelligent fusion strategy, weak fault-tolerant capability and the like in a high-dynamic and strong-vibration train operation environment in the prior art are effectively solved, and continuous, reliable and verifiable high-precision positioning service is provided for a train operation control system.

This application provides a method, apparatus, electronic device, and storage medium for generating training data. The method involves acquiring a preset number of popular locations related to a target city; for each popular location, constructing multiple incorrect names based on the target Chinese characters within the location and a preset list of pronunciation errors; and generating multiple sets of two-round navigation correction dialogues based on the multiple incorrect names and the popular locations themselves, which are then applied to the training data of a navigation model. Applying this technical solution can avoid the technical problem of inaccurate navigation applications in vehicle systems due to missing relevant training data.

This invention discloses a context-aware UAV visual language navigation model and a UAV navigation planning and trajectory generation method, relating to the field of UAV navigation technology. It includes: a visual encoding module; a memory module for storing historical frames and visual tokens of the current frame in chronological order, forming an accumulative historical context of arbitrary length, and configuring an index key for each visual token; a memory retrieval module, a memory retrieval Transformer, for retrieving and fusing relevant historical visual tokens from the memory module using the visual tokens of the current frame as a query, and outputting memory-enhanced visual tokens with a length of N equal to the original visual tokens; a planning module, a planning LLM, outputting planning tokens representing the UAV's high-level decision-making intent; and an action generation module. This invention can improve the navigation stability and accuracy of UAVs in long-range, complex environments, and facilitates real-time deployment on UAV platforms with limited edge computing power.

The application discloses a kind of visual language navigation methods based on topological map pretraining, belong to computer vision field, including the following steps: pretraining stage, baseline method utilizes mask language modeling task, mask region classification task and single-step action prediction task to pretrain the model of baseline network.Model of baseline method is pre-trained by three pre-training tasks respectively using MLM prediction head, MRC prediction head, SAP prediction head realization.Three new pre-training tasks are used to pretrain baseline network in pretraining stage;Topological map navigation order prediction task, topological map node orientation prediction task and topological map-instruction matching prediction task are realized by NOP prediction head, NSP prediction head and MIP prediction head respectively.The method shows the optimal performance in success rate, and the pre-training task proposed in the application can further improve the navigation performance of visual language navigation model.

This application relates to the field of UAV visual language navigation technology, and in particular to a method, electronic device, and storage medium for generating visual language navigation model training data. The processor can automatically generate multiple target trajectories and corresponding tracking trajectories based on 3D scene map data; construct a simulation scenario of the UAV tracking targets within a 3D scene based on the 3D scene map data, each target trajectories, and their corresponding tracking trajectories; and collect raw UAV perception data based on the simulation scenario; and generate a labeled visual language navigation model training dataset based on the raw UAV perception data. Therefore, the embodiments of this application can automatically generate target trajectories, tracking trajectories, and labeled visual language navigation model training datasets, which is beneficial for batch generation of large-scale, high-quality visual language navigation training datasets and reduces the cost of manual annotation.

This invention relates to the field of mapless navigation technology, specifically to a mapless autonomous navigation method and system for mobile robots based on a PID structural memory module. The method includes: acquiring the robot's local perception stream data, constructing a dataset and dividing it into training, validation, and test sets; constructing an autonomous navigation model, which includes an encoder module, a supervision module, a sharing module, a dual-value module, and a policy module, with the encoder module including a PIDM encoder module; training the autonomous navigation model using the training and validation sets, optimizing the encoder module using the temporal reachability loss and future expected reward loss output by the supervision module during training, calculating the value loss using the dual-value module combined with a value pruning mechanism, and guiding the policy gradient update of the model based on the value loss, temporal reachability loss, and future expected reward loss; and inputting the test set into the trained autonomous navigation model to generate motion commands. This invention overcomes local optimum deadlock and achieves elastic escape.

The application discloses a method and system for locating and delimiting a cortical pathological focus based on an intracranial electroencephalogram signal. The method comprises the following steps: constructing an individualized three-dimensional navigation model; obtaining an original intracranial electroencephalogram signal; generating a plurality of representative segments according to the original intracranial electroencephalogram signal; analyzing the original intracranial electroencephalogram signal and the plurality of representative segments to obtain network analysis core indexes; generating a fusion three-dimensional brain model according to the network analysis core indexes and the individualized three-dimensional navigation model; generating a space-time three-dimensional electroencephalogram model and an electrophysiological functional area distribution map according to the fusion three-dimensional brain model and the network analysis core indexes; and generating accurate electrophysiological locating and delimiting information of the cortical pathological focus according to the space-time three-dimensional electroencephalogram model and the electrophysiological functional area distribution map. The application realizes accurate electrophysiological delimiting of an intraoperative cortical pathological focus (glioma, hemangioma, focal cortical dysplasia, etc.).

This application provides a tracking control method and system for an underwater robot that integrates reinforcement learning navigation and model predictive control, relating to the field of robot control technology. The method includes: based on a top-level reinforcement learning navigation model, treating the underwater robot as a point mass and taking the target path as input, outputting desired velocity and angular velocity; the top-level reinforcement learning navigation model is trained through a first Markov decision process and a reinforcement learning algorithm; based on an intermediate-level nonlinear model predictive tracking controller, taking the desired velocity and angular velocity as tracking targets, outputting desired force and torque; the intermediate-level nonlinear model predictive tracking controller combines the underwater robot's dynamics model to perform state prediction, and performs optimization control by solving a constrained optimization problem in each control cycle; the desired force and torque are applied to the underwater robot's thrusters to drive the underwater robot to perform path tracking control tasks.

This invention provides a method, apparatus, computer device, and storage medium for robot navigation, belonging to the field of robot navigation. The method includes: real-time acquisition of the target distance between the robot and its destination position, and the obstacle distance to the nearest obstacle; constructing two algorithms to control the robot's discrete and continuous actions respectively; determining the attractive and repulsive potential fields respectively, and constructing an artificial potential field based on the attractive and repulsive potential fields; combining the two algorithms to construct a unified navigation model based on the artificial potential field; determining the robot's potential energy change based on the artificial potential field, using the potential energy change of the robot at each step as a reward function to guide the robot to move in the direction of decreasing potential energy; and determining that the robot has completed navigation when the target distance is less than a preset threshold. Thus, this discrete-continuous action hybrid control mode can adapt to complex environments and provide the robot with a precise navigation path.

This invention discloses an end-to-end navigation model training data generation and closed-loop simulation verification system for unmanned aerial vehicles (UAVs), comprising: a data acquisition module; a 3D reconstruction and 3D Gaussian splash modeling module; a coordinate unification module; a trajectory task and expert trajectory generation module, which generates expert trajectories or planned trajectories for corresponding tasks in a unified coordinate system and imposes visibility constraints on the initial point and orientation to ensure that the target is within the field of view; a 3D Gaussian splash rendering module; a command generation and dataset construction module; a strategy model training module; and a closed-loop inference and service-oriented deployment module. This invention can utilize the high-fidelity new perspective rendering capability of 3DGS 3D Gaussian splash to reduce the economic cost, time cost, and flight safety risks of data acquisition. It can also enable training data, navigation models, and simulation trajectories to be directly mapped to real geographical scenes, significantly improving the transferability and adaptability of the model in real-world scenarios such as parks and cities.