314 results about "Service robot" patented technology

The invention relates to the technical field of multi-modal posture recognition, and relates to a multi-modal posture recognition system based on Transform fusion, which fully excavates the complementary advantages of visual information in spatial detail representation and inertial information in time sequence dynamic capture through space-time alignment of multi-modal data and deep fusion based on an attention mechanism. The accuracy and the stability of attitude estimation under the conditions of visual occlusion, rapid movement and complex illumination are obviously improved; posture optimization is carried out by introducing physical constraints such as bone length constancy and joint movement range limitation, and time domain smoothing and contact state correction are applied, so that the precision of the generated three-dimensional posture sequence is ensured, a solid technical foundation is laid for improving the naturalness, safety and intelligent level of man-machine interaction, and the method is suitable for popularization and application. And meanwhile, reliable application and deep development of the intelligent robot in key fields of service robots, virtual reality, remote cooperation and the like are powerfully promoted.

The invention relates to the technical field of service robot navigation and motion control, in particular to a service robot dynamic obstacle pre-judgment obstacle avoidance system, which comprises an environment sensing unit for outputting sensing data; the target state estimation unit outputs a target state sequence; the future evolution prediction unit is used for outputting a target future evolution result; the risk quantification unit outputs the current collision risk degree; operating the strategy management unit, and outputting strategy parameters; the evasion decision and path generation unit is used for outputting candidate evasion tracks and speed curves; the execution and control interface unit is used for outputting a chassis control instruction and implementing trajectory tracking; and the device communication and calculation platform is used for outputting a time sequence synchronization and data channel management result according to operation requirements and providing fault monitoring. According to the method, target state estimation, future evolution prediction, collision risk quantification and strategy linkage are completed within real-time constraints, consistent input and constraints are provided for planning and execution on a unified data channel, and safety and traceability are improved.

The invention provides a service robot cross-modal user intention analysis method and system. The method comprises the following steps: firstly, acquiring and analyzing a voice instruction sequence and a visual flow containing a gesture, fusing a task target object and a gesture pointing direction to generate an intention hypothesis, and then projecting the intention hypothesis to a knowledge graph of an environment with a body; the method comprises the following steps: acquiring an intent hypothesis of a user, querying a candidate object set simultaneously matched with spatial constraints and semantic constraints of the intent hypothesis, when ambiguity exists in the candidate object set, generating an inquiry instruction for eliminating the ambiguity according to a relation path in a knowledge graph of the body environment, and finally analyzing and confirming a final user intent according to the response of the user to the inquiry instruction; according to the technical scheme provided by the invention, the ability of the robot to cope with uncertainties such as unknown reference and environment shielding in a real scene is enhanced, the interactive experience closer to the human cognitive level is realized, and the interactive efficiency and satisfaction of the user in a complex environment are also improved.

The invention provides a customer service robot response method and system based on a knowledge graph, and relates to the field of natural language processing. The method comprises the following steps: obtaining a user input query; identifying a query intention input and queried by a user and slot position information through an intention identification model; generating a knowledge graph query statement based on the query intention and the slot position information, and retrieving related entities in a knowledge graph and associated entity relationships; executing path-based multi-hop semantic reasoning and rule-based logical reasoning on the basis of entities and associated entity relationships thereof, and determining an optimal response result; and generating a natural language response based on the optimal response result. The method is used in the response process of the customer service robot based on the knowledge graph, and the technical problem that an existing customer service robot is inaccurate in response to complex problems is solved.

The invention discloses a mechanical arm teleoperation control method and system based on incremental mapping, and the method comprises the steps: carrying out the noise reduction processing of the position and posture of a handle through adaptive low-pass filtering, and achieving the position and posture consistency mapping from a handle space to a mechanical arm working space after coordinate system axis rearrangement and similar transformation mapping; the system provides two kinds of control output of a pose tracking mode and a speed tracking mode, real-time inverse kinematics solution is carried out through a Jacobian matrix and a damping least square method in the speed mode, and resolution scaling and exponential moving average / spherical linear interpolation smooth filtering are carried out on a target pose in the pose mode. And then the handle motion instruction is mapped into a Cartesian instruction or a joint space instruction at the tail end of the mechanical arm, closed-loop execution is conducted through a real-time state feedback link, and the method can be used for scenes such as robot service, teaching, remote operation and data collection.

The invention relates to the technical field of artificial intelligence, intelligent and robot control, and discloses an emotion service robot system based on a multi-mode mental theory, which comprises a hierarchical mental reasoning module, a connection module and an action semantic execution module, the hierarchical mental reasoning module is used for generating a decision text containing a high-level strategy according to the multi-modal environment information collected by the robot; the connection module is used for constructing a semantic instruction according to the decision text generated by the hierarchical mental reasoning module; and the action semantic execution module is used for generating a control action of the robot according to the semantic instruction constructed by the connection module and the real-time image acquired by the robot. According to the method, a control architecture fusing a vision-language-action model and a robot center view angle mental reasoning hierarchy is constructed, so that the robot can carry out multi-order belief reasoning and implicit target inference from the view angle of the robot. According to the system, the robot is no longer a pure instruction follower and has an active service capability.

A serving robot comprises: a traveling unit; a vertical frame positioned on the upper part of the traveling unit and extending in the vertical direction; a plurality of coupling units positioned in the vertical frame so as to be spaced in the vertical direction; a support bracket fixed to one of the plurality of coupling units; a tray plate comprising a flat upper surface; a first coupling bracket positioned on the lower part of the tray plate; and a second coupling bracket positioned on the upper surface of the support bracket and being formed so as to be coupled to the first coupling bracket. The serving robot enables a user to freely change the position of a tray as necessary, thereby enabling carrying foods of various sizes.

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

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a service robot behavior recognition method based on multi-mode sensing fusion, which comprises the following steps of: establishing a calculation model which is switched between a conventional monitoring mode and an active inquiry mode, a continuous fuzzy state with a low amount of information in a period of time is identified as a specific risk signal, a system is driven to forcibly switch from passive data analysis to an active challenge process, key new evidence is obtained by executing mild interaction related to a scene, and final behavior identification is performed according to the new evidence, so that the behavior identification accuracy is improved. By changing the operation philosophy of the calculation model, the service robot is evolved into an intelligent agent capable of proposing inquiry actively when the information is indefinite, so that the problem that danger silence is difficult to deal with in the traditional technology is solved.

The invention discloses a long-time multi-mode target navigation method, and relates to the field of autonomous service robots. Historical observation is continuously integrated into a semantic skeleton memory graph, a space-anchored reusable memory is constructed, the memory can be reused among multiple sub-tasks, the sample utilization efficiency is improved, and the task time is shortened; and diversified user instructions such as a multi-modal target, a multi-modal prompt mechanism adaptive text, a reference image or an object instance and the like are supported. The long-sight-distance planner aims at minimizing the expected travel, the long-time task efficiency is improved, and the energy consumption is reduced.

According to the man-machine interaction-oriented multi-modal fine-grained emotion recognition method based on the large model provided by the invention, cross-modal alignment from coarse granularity to fine granularity is realized through an attention pairing interaction module (APIM) on the basis of an aspect-driven vision-text alignment and fusion network (AVTAF); emotion-related visual features (such as facial expressions and gestures) in a robot scene can be accurately captured, and environmental noise is inhibited; meanwhile, the RD-GAT is enhanced, and the reasoning ability of a large model on multi-modal emotion semantics is improved by integrating external emotion knowledge (such as SenticNet). The technology provides a new normal form for intelligent upgrading of robot emotion interaction and multi-modal understanding of a large model, and is expected to promote breakthrough application in the fields of family service robots, medical accompanying assistants, multi-modal content generation and the like.

The invention discloses a service robot fine-grained task planning method based on hierarchical collaboration. Firstly, a scene graph and an article knowledge graph are constructed; then, the task instruction is converted into an abstract high-level action sequence through high-level task planning, and the abstract high-level action sequence is converted into a fine-grained low-level action sequence through low-level task planning; and finally, converting the low-layer action into a plurality of action primitives, executing the action primitives in a simulation environment, selecting feasible action primitives, executing the feasible action primitives in a physical world, and synchronously monitoring the running state of the robot. If the operation state is stable, selecting an optimal action element for execution; if the operation state is not stable, the operation state is fed back to a low-layer task planner; if the comprehensive scores of all the action elements are smaller than a score threshold value, feeding back to a low-layer task planner; and repeating the steps until the task is completed. Accurate mapping from task instructions to physical actions is achieved, and the fine granularity and reliability of task execution of the robot are improved.

The invention belongs to the technical field of intelligent robot control, particularly relates to a multi-target balance reinforcement learning robot flexible control strategy optimization method, and aims to solve the problem that operation efficiency, energy consumption, safety boundary and man-machine interaction flexibility are difficult to synchronously consider in complex tasks in traditional reinforcement learning. And a fixed weight multi-target reward mechanism lacks a state sensing capability, so that the strategy adaptability and robustness are poor. The method comprises the following steps: constructing a robot dynamic environment model; designing a state-aware nonlinear multi-target reward function; introducing a security constraint guided exploration mechanism; constructing a dual-path policy network architecture; implementing an online self-adaptive tradeoff adjustment mechanism; and end-to-end strategy training and deployment are completed. According to the technical scheme, task execution efficiency is remarkably improved, energy consumption is reduced, safety and environmental adaptability are enhanced, and the method is suitable for high-flexibility control scenes such as industrial cooperation, medical assistance and service robots.

The invention relates to the technical field of insurance business processing, and particularly discloses an automatic car insurance business handling method based on an AI customer service robot. The method comprises the following steps: receiving and standardizing business related information through an information acquisition unit, screening effective information through an information quality evaluation model, extracting key fields by means of OCR text recognition, and generating structured insurance data in combination with multi-modal consistency comparison; a vehicle information system is called to verify data integrity and accuracy in real time and automatically complement and correct errors, and compliance verification is completed through an underwriting rule engine according to a preset rule of an insurance company to generate standardized insurance data; a rate engine is called based on the data to generate a quotation scheme, the quotation scheme is submitted to an underwriting system to generate an underwriting conclusion after being confirmed by the user, a payment two-dimensional code is automatically generated after underwriting is passed, an electronic insurance policy is pushed when the user pays successfully, and meanwhile, whole-process data is stored persistently, and a service ledger is generated.

The invention specifically discloses a target perception type dynamic visual SLAM method fusing point and line features, and relates to the technical field of visual SLAM. According to the method, a YOLOv8-seg model is combined with TensorRT reasoning to realize target detection and segmentation, and a DeepSORT algorithm is reconstructed to complete multi-target stable tracking; carrying out motion consistency check based on a luminosity consistency principle and an optical flow method; calculating a three-dimensional center of the target according to the detection frame, the mask depth and camera parameters to realize three-dimensional reconstruction of the target; point and line features are extracted and processed, and the line features are represented by Plcarbon coordinates; and constructing a nonlinear optimization model based on an ORB-SLAM3 framework, and jointly optimizing camera pose, map points and map line parameters. The method effectively improves the positioning precision and the system robustness in a dynamic scene, achieves the precise modeling of a static structure and the reliable generation of a dynamic target track, and is suitable for autonomous navigation scenes such as an unmanned aerial vehicle and a service robot.

This invention belongs to the field of service robot visual scene recognition technology, and provides a knowledge-enhanced method and system for object category recognition in multi-service robots. The method acquires scene images of the robot's operation, extracts visual information feature maps from these images, obtains preliminary predictions of target detection boxes and their object categories, and converts these visual information feature maps into scene information vectors. Trusted target label nodes from a pre-constructed knowledge graph are selected, and a matching vector is formed based on the converted scene information vector and target detection box information. The similarity between the matching vector and edges starting from the trusted target label nodes is calculated, and the query prediction result for the object category in the scene image is determined based on the similarity. Finally, the query prediction result for the object category in the scene image is fused with the preliminary prediction result to obtain the final object category recognition result.

A robotic insurance platform is disclosed that transforms an autonomous service robot into a tamper-resistant “digital witness” capable of supplying legally probative evidence without human intervention. The robot is equipped with surround video cameras, a spatial microphone array, an on-board processor, a cryptographically isolated secure enclave, a wireless communication module, and a rolling-buffer memory that retains encrypted audio-video data for a configurable period such as forty days. A companion mobile application enables an insured user to register, perform know-your-customer identity verification, and pair the robot with an insurance policy stored on a cloud server that hosts an claim-decision engine and policy database. Following explicit verbal consent from the user, the robot records continuously while performing ordinary tasks. When an incident is detected—either by on-board heuristics or by a user-initiated claim request—the processor extracts a time window surrounding the event, computes a cryptographic hash of the clip inside the secure enclave, and commits that hash as an immutable anchor to a permissioned or public blockchain ledger. Only after blockchain confirmation is the encrypted clip transmitted to the insurance server, where the claim-decision engine verifies integrity, applies machine-learning analytics to determine causation, and issues a coverage determination. Approved claims trigger repair dispatch, replacement shipment, or direct monetary reimbursement, while unclaimed data exceeding the retention interval are securely erased. The platform delivers objective, bias-free evidence, virtually eliminates false claims, and reduces end-to-end settlement time from weeks to minutes, thereby lowering operational costs for insurers and increasing transparency for policyholders.

The application discloses a ship-boat coupling type inspection robot for offshore wind turbine generator units, which comprises a drone and an unmanned ship, and a ship body monitoring module for assisting the drone in monitoring tasks is arranged on the unmanned ship; the body of the drone is in a cylindrical frame structure, can conveniently pass through inside a wind turbine tower drum, and the body is provided with a drone monitoring module; the drone monitoring module comprises an image acquisition device, a sound and image acquisition device and a vibration acquisition device, and can effectively acquire various data inside the tower drum of the wind turbine generator unit. The ship-boat coupling type inspection robot can realize all-around state monitoring and fault diagnosis of offshore wind turbine generator units by adopting the combined operation mode of the unmanned ship and the drone, can timely master the operation state of the wind turbine generator unit, early discover potential fault signs, reduce the fault rate, ensure safe and efficient power generation operation of the wind turbine generator unit, simultaneously changes the previous manual inspection mode, greatly saves the operation and maintenance cost, and ensures personnel safety.

The invention discloses an under-driven dexterous hand grabbing pose generation method based on deep learning, and belongs to the technical field of robot operation and intelligent grabbing. According to the method, the prior knowledge of a large language model LLM to real capture is introduced into a data set, and a reasonable capture pose is realized by using object semantic-geometric joint features of a point cloud encoder Point Transform and the LLM. According to the method, three types of grabbing strategy classification models for the under-actuated five-finger dexterous hand are constructed, including two-finger kneading, three-finger kneading and five-finger grabbing, and are used for restraining and guiding generation of subsequent fine grabbing postures. According to the selected grabbing strategy, a network is generated to predict six-dimensional poses and angles of all joints of the dexterous hand, and a grabbing posture matched with the shape of the object is obtained. According to the method, two constraint loss items are additionally added to realize grabbing quality optimization. The method can be suitable for intelligent grabbing tasks of industrial robots, service robots and precision operation equipment under the condition that negative grabbing of samples and complex post-processing optimization are not needed.

The application provides an IMU assisted deep SLAM method and system fusing language-visual multi-modal perception, comprising initial calibration and semantic map initialization, pre-integration prediction and key frame determination, dense point cloud reconstruction and relative pose estimation, semantic embedding extraction, semantic guided loop detection and semantic three-dimensional map incremental updating and the like function modules, IMU motion prior, depth geometric constraint and language model semantic factor are jointly modeled through a graph optimization framework, high-precision positioning and labeled map construction in a complex dynamic environment are realized. Compared with the prior art which only depends on geometric or inertial information, the application reduces the loop mismatching rate, improves the loop convergence efficiency and long-time repositioning robustness, and provides a semantic interface for natural language navigation, target retrieval and the like upper layer tasks. The application can be widely applied in fields of service robots, security inspection, intelligent driving and post-disaster search and rescue and the like.

The invention discloses an ethical decision chip for a home-based care service robot, the robot and an ethical risk prevention and control method. The chip comprises a scene identification module which identifies a service scene and a risk level; the hierarchical ethical rule storage module is used for storing an ethical rule system expressed in a quantization parameter form; the ethical rule engine is used for calling quantitative parameters to carry out multi-target ethical tradeoff calculation and blocking the highest emergency risk starting instruction filtering feature library; and the ethical auditing module is used for encrypting and storing decision process data. The invention further provides a robot comprising the chip and a prevention and control method based on the chip. Physical isolation of ethical constraint and task processing is achieved through independent hardware, abstract ethical values are converted into quantifiable technical parameters, scene self-adaption grading prevention and control are achieved, the technical problems that ethical rules cannot be quantified, the decision making process is opaque and the like are solved, and the ethical safety level of the robot is improved.

1. The name of the design product: scenic area service robot. 2. The use of the design product: a robot for scenic area service. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention discloses a semantic planning and behavior generation system driven by autonomous consciousness. According to the system, a DIKWP mesh cognitive model is adopted, an intention / purpose semantic layer is embedded into an AI internal architecture, and autonomous decision making is achieved through five-layer semantic representation of data, information, knowledge, wisdom and intention. The system comprises a perception module, an intention generation module, a semantic planning module, a meta-cognition monitoring module, an execution module and the like. The AI can set a task target by itself and dynamically plan an action scheme based on a semantic state. Under the action of a double-circulation element cognitive mechanism, the AI can monitor the deviation between a behavior and a target in real time and automatically correct a plan. Compared with a traditional black box model, each step of decision of the AI has a traceable semantic basis and interpretation which can be understood by human beings, and the interpretability and controllability of the system are improved. The autonomous planning system can automatically adjust strategies according to environmental changes, ensures that AI behaviors always accord with expected targets and ethical safety constraints, is widely applicable to the fields of service robots, automatic driving, intelligent assistants and the like, and has important application values and commercial prospects.

1. Name of the designed product: Robot. 2. Use of the designed product: Service robot for home, office or public place. 3. Design points of the designed product: The shape of the product. 4. Picture or photo best indicating the design points: Perspective view.

The invention discloses a power grid marketing self-service method based on a face recognition self-service robot, and relates to the technical field of power grid marketing. According to the power grid marketing self-service method based on the face recognition self-service robot, the self-service robot recognizes the face of a user, obtains the age range of the user, inputs the age range into a marketing model, generates a personalized marketing scheme, feeds back the personalized marketing scheme to the marketing model according to the evaluation score after the user business is autonomously transacted, and sends the personalized marketing scheme to the marketing model according to the age range tag generated by the user. And dynamically adjusting the marketing scheme of the age range. According to the method, high-precision age identification is realized through feature extraction, the age interval label is generated, the accuracy of user feature analysis is effectively improved, and a marketing scheme according with user preferences of different age groups is dynamically generated based on a matching score mechanism of the age feature vector and the product feature.

The application discloses a kind of super-high flexible robot cables, belong to robot cable technical field.Symmetrical layered structure is used, from inside to outside in turn: cable core, insulating layer, shielding layer and sheath layer.Cable core uses layered structure of stranding, significantly improve the fatigue resistance of cable, prolongs service life;Insulating layer uses ethylene-tetrafluoroethylene copolymer, with good flexibility, and can adapt to complex working environment;Shielding layer uses aluminum foil-tin coated copper wire, provides good electromagnetic shielding effect, reduces signal interference, improves the operation accuracy and reliability of robot system;Sheath layer uses thermoplastic polyurethane elastomer as matrix, and introduces the modifier of independent research and development, substantially improves the flame retardance and flexibility of cable;Therefore, the cable prepared by the application has high flexibility and flame retardance, and can be widely used in industrial robots, medical manipulators, service robots and other high-demand scenarios.

The application belongs to the technical field of intelligent service robots, and relates to a home maintenance robot with omni-directional movement and electromechanical cooperative posture control, which comprises an omni-directional chassis, a multi-degree-of-freedom mechanical arm module, a capsule type modular intelligent material supply cabin, an environment sensing and interaction head module, a sensor group and a main controller. The chassis is driven by Mecanum wheels and motors; the material supply cabin comprises an annular cabin body assembly, a plurality of capsule cabins, a central feeding channel, a lifting feeding platform, a platform lifting driving mechanism, a cabin door opening mechanism, a discharging channel and a magnetic guide mechanism. The device can realize standardized classified storage of small standard parts, automatic cabin selection, single capsule release, central receiving and magnetic discharging, thereby reducing the risk of standard part scattering, mixing and jamming; effectively improving the passability of the robot in unstructured terrains such as carpets, doorsteps and slopes, avoiding the imbalance of the center of gravity when the mechanical arm extends for operation, and being compact in structure and suitable for daily maintenance of household items in single-family homes.

The application relates to the technical field of artificial intelligence, and provides an interactive method, device, equipment, medium and program product of a customer service robot, the method comprising the following steps: determining a target operation process matched with a user demand indicated by a conversation context based on the conversation context; the conversation context is determined based on a user query to be replied and historical interaction data belonging to the same conversation as the user query; inputting process content corresponding to the conversation context and the target operation process into a node detection model to obtain a target process node to be executed output by the node detection model; the node detection model is used for determining a target process node most matched with the conversation context from a plurality of process nodes through semantic understanding capability; and generating reply information of the user query based on the target process node. The application solves the problem that a process execution is rigid and cannot adapt to user jump expression, realizes nonlinear and dynamic execution of an operation process, and further improves the interactive experience of the customer service robot.

The utility model discloses a security check service robot, which comprises a movable chassis, a height adjusting module arranged at the top of the movable chassis, a mechanical arm arranged on the height adjusting module, a connecting module connected between the height adjusting module and the mechanical arm, and a metal detector arranged at the execution tail end of the mechanical arm, the visual inspection module is used for capturing a security inspection process image; the height adjusting module is used for driving the mechanical arm through the connecting module to drive the metal detector to ascend and descend so as to adjust the height. And the visual inspection module is used for collecting an image in a security inspection process, so that the mobile chassis, the height adjustment module and the mechanical arm act in a coordinated manner. According to the utility model, the action of a security inspector during manual security inspection can be simulated, so that the operation that the security inspection is carried out by manually holding a metal detector is replaced, a person to be inspected can be flexibly, safely and rapidly scanned, the labor cost and the working intensity of the security inspector are reduced, and the privacy protection of the person to be inspected is enhanced.

The invention relates to a system comprising at least one autonomous mobile robot (service robot) and at least one mobile terminal for operating the robot. According to an embodiment of the invention, the robot and the mobile terminal are configured to communicate via at least one first wireless connection. The robot is configured to detect information about a stay position of a user (e.g. based on a stay position of the terminal) and, depending on the detected information about the stay position, to decide whether to send information to the mobile terminal and, if so, which information, and / or to decide whether to execute, interrupt, continue or terminate activities by the robot and, if so, which activities.