29 results about "Mobile agent" patented technology

The present disclosure relates to a mobile agent in an RFID system with reduced power consumption and increased localization capabilities. In one aspect, a relay circuit attached to a mobility mechanism such as a drone or robot is in communication with an RFID reader and a backend host computer. The mobile agent moves around a warehouse, for instance, in which landmark RFID tags are arranged. The mobile agent can transmit with an adjustable power to identify a single landmark tag and associate it with object tags of one or more objects within the range of the mobile agent. The host computer can communicate with the mobile agent to instruct it to adjust its power when multiple landmark tags are detected.

The invention discloses a multi-agent priority obstacle avoidance path planning method for a dynamic obstacle, and relates to the technical field of mobile agent path planning, the method comprises the following steps: a global planning stage: optimizing an A * algorithm by introducing a heuristic scaling factor, an obstacle and direction penalty term and a neighborhood adaptive extension mechanism, a path is smoothed by combining broken line reconstruction and a Bezier curve; in the local planning stage, the weight of a DWA evaluation function is dynamically adjusted through fuzzy control, and the real-time obstacle avoidance robustness is improved. Meanwhile, a priority obstacle avoidance strategy and a dynamic look-ahead mechanism are designed, multi-agent conflicts are coordinated, and collective stagnation is avoided. Simulation results show that the method shows better path length, search efficiency and obstacle avoidance performance in static, unknown static and dynamic obstacle environments, effectively improves the multi-agent coordination stability, and is suitable for actual scenes such as warehouse logistics and the like.

A method of predicting trajectories for agents of a scenario, the method comprising, for each agent generating an agent feature vector based on one or more observed past states of the agent, computing a set of pairwise feature vectors, each computed as a combination of the agent feature vector for that agent with a respective agent feature vector generated for each other agent of the scenario, processing the pairwise feature vectors as independent inputs to one or more interaction layers of a trajectory prediction neural network to generate a pairwise output for each pairwise feature vector, aggregating the pairwise outputs over the other agents of the scenario to generate an interaction-based feature representation for each agent, processing the interaction-based feature representation in one or more prediction layers of the trajectory prediction neural network, and generating, based on the output of the one or more prediction layers, at least one predicted trajectory for each agent.

The application discloses an identity management method and system, which is suitable for mobile agents or other devices. The method comprises the following steps: generating a plurality of public-private key pairs for each mobile agent before the mobile agent performs a target task, wherein each mobile agent corresponds to an actual identity identifier, and each public-private key pair corresponds to a public-private key pair identifier; determining a plurality of temporary identity identifiers corresponding to the mobile agent according to the actual identity identifier and the public-private key pair identifier; generating a plurality of digital certificates corresponding to the mobile agent according to the temporary identity identifier and the public-private key pair; signing the digital certificate by using the private key of the mobile agent device owner, and storing the signed digital certificate, the temporary private key in the public-private key pair and the public key of the mobile agent device owner into the mobile agent. The application solves the technical problem that the mobile agent cannot indicate that it belongs to the same owner in a privacy protection manner in the related art.

The application discloses a kind of dynamic threshold point cloud matching mobile intelligent agent relative pose cooperative acquisition method, utilize shared laser point cloud data and autonomous positioning result, using improved iterative nearest neighbor point (ICP) registration algorithm, the relative pose between mobile intelligent agent is calculated, and the reliability of calculation result is evaluated, further improve the observation reliability and application value of relative pose.The method of the present application only needs to reuse lidar data on the basis of autonomous positioning, without additional enhancement mark, the observation calculation of relative pose can be completed, the original system is changed little, does not affect the original layout of system, does not need to rely on other feature enhancement hardware, no additional influence, low cost;It has better accuracy and stronger robustness, and the effectiveness evaluation of point cloud registration result makes the application more suitable for the application scene of relative pose, and enhances the reliability of result utilization.

A data processing apparatus (100) and method for efficiently and accurately determining a relative pose (160) of a mobile agent such as a vehicle are disclosed. The apparatus (100) comprises processing circuitry (110) configured to determine a feature vector and a score for each point in a source point cloud (140) and a feature vector for each point in a target point cloud (150), and to transform a plurality of key points of the source point cloud (140) from a first pose to an unknown second pose for a plurality of relative pose candidates. The processing circuitry is further configured to pair each transformed key point with a target point in the target point cloud (150) based on distance, and to determine the relative pose of the mobile agent based on the plurality of feature vectors and positions of the plurality of transformed key points and target points.

The invention particularly relates to a difficulty adaptive reinforcement learning method, device and equipment for a mobile agent, and the method comprises the steps: determining a sampling range in an interaction environment, and carrying out the multi-round sampling in the interaction environment based on the sampling range, and obtaining a multi-round sampling track; on the basis of a difficulty self-adaptive positive sample playback mechanism, calculating a dominant score of each round of sampling trajectory, sorting multiple rounds of sampling trajectories according to the dominant scores, screening trajectories corresponding to the dominant scores meeting a preset buffer condition from a sorting result to obtain multiple successful trajectories, and forming a high-quality playback pool on the basis of the multiple successful trajectories; and extracting a key track in the high-quality playback pool, and updating a strategy network of the mobile agent by utilizing the key track to mix with the online data according to a preset proportion, so as to optimize a pre-established reasoning warm start model according to the updated strategy network. Therefore, the problems of low exploration efficiency, poor generalization and the like of the mobile intelligent agent in an interaction environment are solved, and efficient generalization is realized.

The application discloses a multi-level feature multicast transmission method and device, equipment, storage medium and product, and relates to the technical field of semantic communication. The method comprises the following steps: acquiring multi-level features according to a target picture, the multi-level features comprising semantic features; determining semantic errors of different levels according to the semantic features; and determining the communication resources of data receiving equipment in the current level according to target features of the target picture. The receiving capabilities of different users are acquired, so that reasonable resource allocation can be performed when subsequent semantic communication is performed for multiple users. By establishing an optimization problem based on the communication resources of the data receiving equipment of different levels and the semantic errors of different levels, an optimal solution is obtained. Furthermore, the target features are multicast transmitted based on the optimal solution, the communication resource overhead in the multi-user scenario is reduced, the requirements of different mobile agents are met in information transmission, and the transmission delay and resource consumption are reduced as a whole.

The invention relates to the technical field of passive optical network (PON), is applied to fiber to room (FTTR), enterprise-level fiber to room-Bus (FTTR-B) and broadband fusion terminal products, and particularly relates to an OUN gateway optimization method, device, equipment and medium based on a PON network, by obtaining a spatial radio frequency map and a resource availability state of a mobile agent node in a target area, the OUN gateway optimization method, device and equipment based on the PON network can be used for optimizing an OUN gateway, and the OUN gateway optimization method, device and medium based on the PON network can be used for optimizing the OUN gateway according to the spatial radio frequency map and the resource availability state of the mobile agent node in the target area. And determining whether to trigger a network coverage optimization process according to the first network quality parameter and the resource availability state and the first network quality parameter and the real-time space coordinate of the target terminal device, if so, determining an optimal moving path of the mobile agent node based on the spatial radio frequency map and the real-time space coordinate, and if not, determining the optimal moving path of the mobile agent node. And the mobile agent node is controlled to move to the target deployment position according to the optimal moving path, and the network access connection is completed by using the preset protocol, so that the intelligent path planning and dynamic network optimization of the mobile agent node are realized, and the network coverage can actively adapt to the actual position and demand of the user.

The utility model relates to the technical field of intelligent agent charging and discharging, and discloses a power supply system and a mobile intelligent agent, which comprise a battery, an automatic charging protection unit, a manual charging protection unit, a discharging protection unit, a control unit and an infrared sensing unit, the output end of the automatic charging protection unit is connected with the output end of the manual charging protection unit, the charging end of the battery and the power supply end of the control unit; the input end of the manual charging protection unit inputs a second power supply voltage; the input end of the discharge protection unit is connected with the discharge end of the battery, and the output end of the discharge protection unit is connected with the power supply end of the control unit and outputs third power supply voltage; the input end of the infrared sensing unit inputs an external infrared signal, and the output end of the infrared sensing unit is connected with the input end of the control unit. According to the utility model, charging and discharging protection and infrared positioning functions are integrated, and the power supply reliability can be improved.

The invention discloses an online alliance construction method for mobile agent group calculation, and belongs to the technical field of mobile edge calculation. The method comprises the steps that firstly, modeling is conducted on tasks, equipment resources and communication, and effectiveness is quantitatively calculated to serve as an optimization target; secondly, respectively constructing a dynamically updated resource hypergraph and a dynamically updated task hypergraph by utilizing a hypergraph theory so as to describe a high-order coupling relationship among equipment, links and tasks; and finally, task allocation is converted into a three-order hypergraph matching problem, and optimal matching is quickly solved through a low-complexity tensor matching algorithm fusing CUR decomposition, Fiber-CUR screening and probability relaxation marking, so that an equipment alliance meeting communication, computing power and type requirements is constructed for each subtask. According to the method, the problems of poor adaptability, difficulty in relation description and low construction efficiency in an online scene are effectively solved, accurate and rapid matching of tasks and resources is realized, and the overall utility of an edge computing system is remarkably improved.

The invention discloses a hidden jail break attack method and device for a visual language mobile agent, and belongs to the field of terminal equipment safety. The hidden jailbreak attack method comprises the following steps: firstly, optimizing an original malicious instruction by utilizing a detoxication algorithm to obtain a current malicious instruction, and embedding the current malicious instruction into a UI (User Interface) of a third-party mobile application; secondly, designing a trigger based on the physical attribute of the touch event, and judging whether the current user operation is a preset operation driven by an intelligent agent or not by utilizing the trigger based on the physical attribute of the touch event; and finally, only when the intelligent agent operation is judged, the hidden malicious instruction is triggered to be displayed temporarily, so that the intelligent agent is induced to execute malicious planning. The method has the advantages that only application layer permission is needed, attack concealment is high, an existing security mechanism can be effectively avoided, jailbreak with high success rate is achieved in single-round interaction, and an effective tool is provided for evaluating and improving the security of the visual language mobile agent.

Provides a method for transferring items of personal data. [Solution] A process flow between a party and a blockchain for the initial user request to retrieve personal data stored in another party's wallet, wherein the client logs into MYHI and requests a health wallet. MYHI identifies the client's identity. The MyCO agent issues an invitation to the client in QR code (registered trademark) format to the client who is obtaining a health wallet for the first time. The client scans the invitation in the client mobile agent, accepts the invitation, and requests credentials from their reports using the connection already established between the client mobile agent and the health wallet. MyCO issues health credentials to the client mobile agent and stores them in the personal wallet within it.

The present disclosure provides a system and a method for controlling an ego vehicle (EV) in an environment surrounding the EV and including at least one other agent (OA) representing a moving object. The method includes determining an independent trajectory for the EV independent from motion of the at least one OA based on a state of the EV, and an independent trajectory for the at least one OA independent from motion of the EV based on a state of the at least one OA. The method further includes determining jointly and interdependently joint trajectories of the EV and the at least one OA by optimizing a cost function of a difference between the joint trajectories and the independent trajectories. The method further includes controlling the motion of the EV based on the joint trajectory of the EV.

This invention provides a terahertz massive MIMO wireless simultaneous localization and mapping method to address the difficulties in decoupling multidimensional parameters due to the dual-wideband effect and the data association ambiguity caused by the lack of Doppler information in dynamic scenarios. The implementation steps are as follows: 1) Construct a third-order tensor model of the received signal containing spatial, frequency, and time dimensions, and expand the tensor using a time smoothing strategy to overcome the rank deficiency problem; 2) Iteratively decompose the observation tensor in the compressed domain using a trilinear alternating least squares algorithm to achieve high-precision channel parameter decoupling and communication signal detection; 3) Encapsulate the estimated Doppler, angle, and time delay into measurement vectors, calculate the joint posterior probability based on the confidence propagation algorithm, eliminate data association ambiguity, and infer the trajectory of the mobile agent and environmental characteristics. This invention effectively overcomes the dual-wideband effect, achieving accurate tracking and robust mapping.

The invention provides a forestry operation intelligent control method based on a laser point cloud, and relates to the technical field of multi-mobile agent cooperative control and task scheduling, and the method comprises the steps: constructing and initializing cumulative data containing a first cumulative coverage state quantity and a second cumulative coverage state quantity, and employing a unified state representation framework as a cooperative reference, the task progress of heterogeneous operation units (such as a ground platform and an air platform) is subjected to independent and separable quantitative expression under the same reference system, so that the problems that in the prior art, operation states of a multi-source heterogeneous platform are mixed, contribution progresses of the multi-source heterogeneous platform cannot be distinguished, and dynamic comparison and collaboration cannot be achieved are solved; and the demand urgency and the contribution complementarity of the opposite side are fused into a unique cooperative control logic quantity, so that the problem that the decision conflict between the real-time task demand change and the information value cannot be dynamically responded due to the dependence on global path planning or fixed priority in the existing cooperative operation is solved.

A method for managing moving agents is provided. The method comprises identifying goods agents, moving agents, and a plurality of requests within a predetermined area, generating a nodal graph including the moving agents, requests, and goods as vertices and edges defining relations between two vertices, obtaining actions for the moving agents by inputting the nodal graph to a reinforcement-learning based graphical neural network model stored in the moving agents, the reinforcement-learning based graphical neural network outputs the action for the moving agent in response to receiving the nodal graph, and instructing the moving agents to operate based on the actions to satisfy at least one of the plurality of requests.

The invention relates to a computer-implemented method for operating a mobile agent (1) in an environment based on a pose of the mobile agent (1), wherein for operating the mobile agent (1) a localization of the mobile agent (1) is performed with the following steps: - detecting (S1) sensor data about walls (2) and / or objects (3) located in the environment, wherein the sensor data describes an alignment and a distance of the walls (2) and / or objects (3) in an agent coordinate system (A) fixed relative to the agent; - determining (S11-S18) a pose of the mobile agent (1) in the environment by means of a SLAM algorithm, taking into account a Manhattan orientation.

The embodiment of the invention discloses a behavior simulation method of a mobile agent applied to an emergency rescue scene. A specific embodiment of the method comprises the following steps: generating an initial simulation environment according to scene environment information and analogue simulation configuration information; according to the agent description information corresponding to the mobile agent, environment data local extraction is carried out in the initial simulation environment, and a real-time environment information set is obtained; generating environment state characteristics and agent state characteristics according to the real-time environment information set and the agent real-time state corresponding to the mobile agent; generating behavior planning information for the mobile intelligent agent according to the environment state characteristics and the intelligent agent state characteristics; and performing behavior simulation on the mobile intelligent agent according to the behavior planning information and the real-time orientation of the intelligent agent. According to the embodiment, the robustness and accuracy of behavior planning of the mobile robot in different emergency rescue scenes are improved.

The application discloses a remote video face signing method and device, a terminal and a storage medium. The method comprises the following steps: determining face signing input configuration information according to a face signing operation request, returning the face signing input configuration information, analyzing and rendering the face signing input configuration information by a face signing client to form a face signing input control, collecting face signing input data through the face signing input control and feeding back the face signing input data to a mobile agent terminal, and completing a face signing operation. If the face signing operation request meets a pre-set automatic face signing rule, an automatic face signing invitation is sent, user audio and video information is collected after the invitation is responded, and the face signing operation is completed. The application greatly enriches the auditing scene of a remote face signing auditor. The configurable and dynamic data processing method used in the method meets most face signing business requirements, so that each financial institution can quickly integrate and use.

This invention discloses a multi-agent networked cooperative localization and tracking method based on pose robust association, belonging to the field of multi-agent cooperative localization technology. The system includes an autonomous localization module, a wireless transmission module, a relative pose acquisition module, an observation selectivity module, and a cooperative localization optimization module. Different mobile agents with autonomous localization capabilities share data, including real-time autonomous localization results and laser point cloud data, through wireless network communication. Multi-agent networked cooperative localization is modeled by constructing a multi-agent pose factor graph. Robust pose association of multiple agents is achieved using improved point cloud registration and observation selectivity methods, thereby realizing efficient, accurate, robust, and deployable multi-agent cooperative localization and tracking.

A method for generating training data of a mobile agent relating to the technical field of artificial intelligence is provided. The method includes: collecting multiple data triples representing interaction behaviors in an application; each of the data triples including a first user interface state, an action, and a second user interface state; constructing a state transition graph based on the multiple data triples; obtaining an interaction trajectory based on the state transition graph; and generating training data of the mobile agent based on the interaction trajectory.

1. Name of the product in this design: Graphical User Interface for Managing Agency Matters in Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of the graphical user interface: For managing proxy matters, such as accessing residential proxies, data center proxies, ISP proxies, private data center proxies, mobile proxies, and network unblockers. 6. Human-computer interaction method of graphical user interface: Interaction can be achieved by touching or clicking the graphical user interface displayed on the screen of the electronic device or by operating the buttons of the electronic device. 7. Other situations requiring explanation: The portion shown by the dashed line in the view does not constitute part of the design for which protection is sought.

Embodiments of the present invention include a collaborative manufacturing system utilizing a plurality of mobile agents. The mobile agents operate dual robotic arms to improve single and multi-material builds' efficiency. In some embodiments, the dual robotic arms work together in the same area to create multi-functional components. In addition, the mobile agents can change tool heads on the arms to allow for hybrid manufacturing such as pick and place, additive, and subtractive manufacturing. One or more mobile agents interact with other mobile agents, thereby increasing the end product's efficiency and quality. Mobile agents utilize swarm manufacturing techniques to improve the manufactured product's time efficiency further and use machine learning to adjust and re-assign mobile agents constantly.

The invention discloses a mobile agent closed-loop dynamic positioning optimization method based on digital twinning. The method is suitable for dynamic positioning of unmanned vehicles, mobile robots and the like in complex and high-noise environments. The method comprises the following steps of: simulating signal multi-path propagation characteristics through a digital twin system, extracting characteristic coefficients such as path propagation time delay, attenuation and frequency shift, calculating a dynamic difference degree of a virtual signal and an actually measured signal, and establishing a joint posterior probability model in combination with a node motion state, so as to realize dynamic position estimation based on Bayesian reasoning. According to computing resource conditions, the method supports three optimization modes of convex optimization relaxation, a nonlinear Levenberg-Marquardt algorithm and a Kalman filtering fusion mechanism, real-time high-precision positioning is realized, recursive correction is performed on a prediction state and an observation error, and time sequence continuity and positioning precision are improved. According to the method, the problem of poor indoor static positioning precision caused by multi-path interference and non-line-of-sight propagation in a complex industrial environment is solved, and the anti-fluctuation capability and the estimation stability of the system are remarkably improved.

This invention provides an inertial navigation error compensation method and system for target tracking using multiple mobile agents. The method includes: first, an agent equipped with a camera acquires a target image, and then processes the image data to obtain a target angle measurement; simultaneously, an agent equipped with radar acquires a target point cloud, and then processes the point cloud data to obtain target distance and angle measurements; further, pseudo-distance estimation of the angle measurement is performed using the least squares method, leveraging the cooperation of multiple agents to address the problem of insufficient distance information in camera angle measurements; finally, target tracking is achieved using distributed Kalman filtering, and the target state is consistently fused. This invention can be applied to inertial navigation multi-mobile agent target tracking systems with a variable number of agents and two types of sensors, achieving compensation for multi-agent inertial navigation positioning errors and stable target tracking solely through the tracking of the target by the agent cluster.

The application discloses a kind of fixed node assisted mobile intelligent agent SLAM absolute positioning method, according to the pose transformation information of mobile robot, obtain factor graph, and determine covariance;According to the bias of the factor graph estimation IMU, update the trajectory and map of mobile robot;According to the bias of the IMU, the positioning state of mobile robot is determined.The application can use the fixed node with known absolute position to assist when mobile robot is mapping and positioning, so as to improve the accuracy of mapping and positioning;Using the method of fixed node assistance, the positioning of mobile robot is monitored in real time, error correction can be timely, improve the robustness and reliability of mobile robot;It can be integrated in a variety of functional sensor networks, easy to deploy.

The application relates to a mobile agent multi-dimensional semantic intelligent analysis method and system supporting multi-scene adaptation. The method comprises the following steps: firstly, collecting multi-modal environment information in real time and extracting scene-level global attributes, inputting the scene-level global attributes into a preset machine learning classifier to obtain a scene category probability vector, selecting an optimal target scene through matching degree calculation, and updating a configuration repository by creating a new configuration item in combination with user feedback for a new scene; secondly, calling semantic analysis settings associated with the optimal target scene from the configuration repository, and constructing semantic analysis execution logic suitable for the target scene; then, generating a structured reasoning result by hierarchically analyzing the multi-modal environment information according to the execution logic, and constructing a global semantic network graph; finally, extracting updated scene-level global attributes from the graph, inputting the updated scene-level global attributes into the machine learning classifier again to obtain a probability vector, and refining a final result by fusing historical matching records. The method can improve the efficiency and accuracy of scene analysis and matching.