49918 results about "Roboty" patented technology

The invention relates to the technical field of industrial automation control, and discloses a multi-machine collaborative industrial robot intelligent scheduling system and an application method.The system comprises a data acquisition module for acquiring real-time motion data through a multi-modal sensor; the feature fusion module is used for generating environment dynamic feature data based on graph neural network fusion data; the strategy generation module is used for generating collaborative strategy parameters by utilizing a collaborative reinforcement learning model; the trajectory optimization module is used for constructing a multi-target trajectory optimization model to plan an optimal synchronous trajectory; the motion control module is used for establishing a layered motion control model to realize cooperative motion control; a dynamic resource allocation module can also be included to optimize resource allocation. The application method sequentially executes corresponding steps according to the modules of the system. According to the method, the environment can be accurately sensed, the cooperation strategy can be optimized, the efficient track can be planned, accurate motion control and reasonable resource allocation can be realized, and the cooperative motion control capability and the production efficiency of the industrial robot in the complex environment can be effectively improved.

The invention discloses a system and a method for task planning of an intelligent robot with a body based on multi-dimensional situation awareness, and particularly relates to the technical field of task planning of the intelligent robot with the body, space-time alignment is carried out on asynchronous heterogeneous data generated by a multi-modal sensor channel, and cross-modal space-time features are extracted through a cross-modal feature fusion network; a fusion situation matrix is generated, dynamic causal modeling is used to update association strength among the multi-modal data, an anti-factual reasoning engine is used to identify and trace abnormities, and an abnormities traceability result is output; dynamically adjusting the reliability weight of each sensing channel through a multi-modal credibility evaluation model by using the fusion situation matrix and an abnormal traceability result; and on the basis of the reliability weight, inputting the fusion situation matrix into a real robot dynamic model and a digital twin virtual model, executing collaborative predictive control, and starting an adaptive rule evolution mechanism when a safety score is lower than a threshold value, thereby solving the problem of fusion matrix distortion in dynamic obstacle avoidance and precise grabbing tasks.

A Generative Artificial Intelligence (Gen. AI) based chatbot apparatus implements classical AI models and Generative AI models to answer user queries with results retrieved from relational databases and unstructured knowledge bases. When a user query is received, it is determined if the intent of the user query can be determined with a confidence greater than a configured confidence limit. If yes, a structured query mapped to the intent is employed to answer the user query. If the intent determination confidence is less than the configured confidence limit then Gen. AI-based techniques using a plurality of LLMs are used to respond to the query. A Gen. AI switch is also implemented to switch between the plurality of LLMs to answer user queries with greater accuracy.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention discloses a target recognition and grabbing posture planning system for a collaborative robot, and belongs to the technical field of mechanical arms. According to material posture information provided by a sensing module, the optimal grabbing posture is intelligently matched from a preset grabbing strategy library by fusing mechanical arm movement track parameters and target material physical characteristics; the initial accuracy of the grabbing scheme is ensured, in the grabbing process, the camera continuously collects the material poses in real time, and the offset is accurately calculated by comparing the real-time poses with the expected poses. Once material deviation is detected, a visual and force closed-loop adjusting mechanism is triggered immediately, a force-position hybrid control model is utilized, position deviation is synchronously corrected, and grabbing force parameters are dynamically adjusted, so that the grabbing stability and precision are remarkably improved, the grabbing failure rate is effectively reduced, manual intervention and reset time are greatly shortened, and the grabbing efficiency is improved. Therefore, the production efficiency is comprehensively improved.

The invention provides an industrial robot autonomous collaborative decision-making method and system based on multi-modal sensing and a medium, and belongs to the technical field of industrial robot intelligent control. The method comprises the steps of performing cross-modal space-time alignment to eliminate data space-time differences by collecting visual, tactile and auditory information, realizing multi-modal feature fusion in combination with a dynamic weight adjustment mechanism and an attention calculation model, and generating a joint decision strategy through a deep learning optimization model. The system dynamically allocates sensor weights according to task types, introduces a multi-objective optimization mechanism of energy consumption, precision and safety, and sets a fault-tolerant rule to automatically recover the weights or recalibrate the sensors. According to the method, the problems of rigid data fusion and single optimization dimension in traditional multi-modal decision making are solved, and the precision, the response speed and the environmental adaptability of collaborative operation of the industrial robot are remarkably improved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an illumination assembly. The illumination assembly is configured to illuminate a region that: (i) extends between a rear edge of the frontal shell and an extent of the frontal edge of the rear shell, (ii) is positioned adjacent to the extent of the rear edge of the frontal shell, and (iii) is positioned adjacent to the extent of the frontal edge of the rear shell.

An autonomous robotic vehicle with dynamic path planning capabilities, including: a dynamic path planning module configured to generate and adapt navigation paths in real time based on environmental changes and mission objectives; a sensor fusion module that integrates data from multiple sensors, including LiDAR, radar, cameras, ultrasonic sensors and GPS, to create a comprehensive environmental model; an obstacle avoidance module capable of detecting and predicting both stationary and moving obstacles and modifying the navigation path to avoid collisions; an artificial intelligence-based decision-making module for analyzing environmental data and learning from past navigation scenarios for improved performance; a decentralized control module that enables the independent operation of the vehicle in areas with interrupted or no connection to a central server; an energy management module configured to optimize battery usage by dynamically adjusting operating parameters based on real-time data and mission requirements; a cooperation module that enables communication and coordination between multiple autonomous robot vehicles for fleet management or the synchronized execution of tasks; a fail-safe module to ensure uninterrupted operation and data security in the event of communication failures or hardware malfunctions.

The invention provides an intelligent robot inspection system in a power distribution and transformation intelligent auxiliary system, and relates to the technical field of intelligent sensing and control, and the system comprises a real-time operation collection module which is used for planning a global path of an intelligent inspection robot, generating an initial inspection path, carrying out real-time operation collection, and obtaining a multi-modal detection data set; the fault feature recognition module is used for performing fault feature recognition, generating an abnormal feature set and constructing a dynamic risk map; the simulation maintenance feedback module is used for carrying out simulation maintenance feedback according to the inspection correction path in combination with the equipment maintenance suggestion and generating a maintenance feedback result; and the inspection control module is used for generating an optimized inspection path to perform inspection control on the intelligent inspection robot. According to the invention, the technical problem that the routing inspection efficiency and the fault diagnosis accuracy are influenced because routing inspection path planning often depends on a preset environment map and the intelligent routing inspection robot cannot automatically adjust the routing inspection path according to the change of the equipment state in the prior art is solved.

The invention discloses an underwater robot autonomous obstacle avoidance and path planning system based on a multi-modal sensor, particularly relates to the field of underwater robot navigation, solves the problems of multi-modal sensing and path planning in a complex environment, and unifies the feature space of a multi-source heterogeneous sensor based on a physical field correction mode of an acousto-optic fluctuation rule. Fusion deviation caused by physical property difference of sonar and visual data is overcome, and obstacle characterization precision and dynamic environment adaptability are remarkably improved; a dynamic confidence decision-making mechanism is combined with a path curvature-moment constraint model, the environmental perception credibility and the robot motion performance are synchronously fused into a path generation process, the feasibility of motion control is ensured while the geometric collision risk is avoided, and the contradiction between the safety and the performability in path planning is solved; the global path optimization eliminates the hidden danger of local path sudden change through iterative smoothing under double constraints of physical field and dynamics, and forms an optimal navigation scheme considering obstacle avoidance efficiency, energy consumption and motion stability.

The invention provides an autonomous navigation path planning method, device and equipment for a humanoid robot in a complex environment. A navigation task is determined by obtaining the current position and path requirement of the robot; constructing a constraint preposition map based on body size constraint, joint motion constraint and dynamic balance constraint, and calculating a feasible motion space of the robot in advance; acquiring environment sensing data for variation trend analysis, and predicting a future movement track of the dynamic obstacle to generate a dynamic environment prediction sequence; establishing a bidirectional planning mechanism, performing forward and reverse path search at the same time, and generating a plurality of candidate path hypotheses through meeting point detection and path fusion; analyzing a human behavior pattern to identify a potential passage conflict area, establishing a social negotiation strategy, and taking an intermediate meeting point as a social key node to adaptively adjust a candidate path; the optimal safety path is determined through multi-dimensional safety evaluation of the falling risk, the collision risk and the energy consumption risk, and intelligent autonomous navigation of the humanoid robot in a complex environment is achieved.

The invention discloses a three-degree-of-freedom parallel robot trajectory tracking method and system, and relates to the technical field of industry-university-research, and the method comprises the steps: obtaining motion data related to the execution trajectory of a three-degree-of-freedom parallel robot, and carrying out the data preprocessing; based on historical operation data, an intelligent prediction model is constructed to predict trajectory errors and is used for optimizing feedforward compensation of trajectory tracking control parameters; acquiring actual execution track information of the robot based on a visual sensor, and performing error calculation on the actual execution track information and an expected track to perform real-time track correction; dynamically adjusting control parameters in combination with error prediction data and visual feedback data; in the operation process of the robot, the control strategy is continuously optimized, and the intelligent prediction model is updated by using real-time data. Through the intelligent prediction model, visual feedback and adaptive control optimization, high-precision trajectory tracking, rapid error correction, energy consumption reduction and environmental adaptability enhancement are realized.

The invention discloses a multi-door-lock cooperative processing method and system, and the method comprises the steps: generating an assistance request IGMP multicast message according to a preset triggering operation detected by a first door lock B and read RFID identification information, and transmitting the assistance request IGMP multicast message to a router; according to the assistance request IGMP multicast message, recording interface information and validity period associated with the RFID identification information in a multicast forwarding table entry, and caching the multicast message; according to the preset triggering operation detected by the second door lock A and the same read RFID identification information, an assisted request multicast message containing the RFID identification information is generated, and the assisted request multicast message is sent to the router; according to the RFID identification information and the multicast forwarding table item, after the validity period is verified to be not expired, the message is directionally forwarded to the first door lock B, the first door lock B sends an assistance confirmation message to the second door lock A, and a robot calling instruction is triggered. According to the embodiment of the invention, low-delay and high-reliability door lock cooperative control can be realized.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a frontal shell having a rear edge, a rear shell having a frontal edge, and an electronics assembly. The electronics assembly includes various components and devices used in the operation of the humanoid robot.

The invention includes systems and methods for relating to improved body structures for robots. A robot assembly includes a base member (e.g., a pelvis base). The robot assembly includes a first hip member rotatably connected to the pelvis base. The first hip member is connected to a first electric actuator configured to rotate the first hip member about a first hip axis relative to the pelvis base. A first intermediate member is rotatably connected to the first hip member. The first intermediate member is connected to a second electric actuator configured to rotate the first intermediate member about a second hip axis relative to the first hip member. A first leg member is rotatably connected to the first intermediate member. The first leg member is connected to a third electric actuator configured to rotate the first leg member about a third hip axis relative to the first intermediate member.

The invention provides a slope deformation monitoring method and device based on multi-source data fusion, and relates to the technical field of dam slope safety monitoring. The method comprises the steps that a multi-source monitoring system composed of a global satellite signal receiver, a measuring robot and an interference synthetic aperture radar is arranged in a dam slope area; according to the collected slope deformation disturbance parameters, performing monitoring area selection division on a dam slope area; determining a data fusion weight corresponding to each data fusion observation area; collecting multi-source monitoring data in each data fusion observation area; and based on the determined data fusion weight, performing weighted fusion processing on the multi-source monitoring data in the selected area to generate a slope deformation monitoring result. According to the technical scheme, fusion of multi-source monitoring data is realized through region division, the reliability, comprehensiveness and environment adaptability of slope deformation monitoring data are improved, and the stability, accuracy and precision of a slope deformation monitoring result are improved.

The invention provides an intelligent mechanical arm control system scheme based on multi-model cooperation, and belongs to the field of automation and intelligent control. The system is divided into four parts of man-machine interaction, detection and perception, task decoupling planning and program generation, and robot execution operation. Man-machine interaction is carried out, a user inputs an instruction in a text or voice format, and instruction processing is carried out through voice recognition and a natural language translation model; detecting and sensing, and identifying and positioning a target captured by the camera by using a multi-modal large model; task decoupling planning and program generation: analyzing the user instruction and the perception information into an executable program by applying a task planning model and a program translation model; and checking and correcting the program by using the program checking model, and driving the mechanical arm to complete a task. According to the system scheme, variable environments can be dealt with, complex language instructions can be understood, intelligent control over the mechanical arm is achieved, and the multi-task execution capacity and the intelligent level of the mechanical arm are remarkably improved.

A humanoid robot includes an upper region includes a head and neck assembly having a neck portion and a head portion coupled to the neck portion. Said head portion includes: a rear shell, and a frontal shell coupled to the rear shell to define a head volume between the frontal shell and the rear shell, and an electronics assembly with a display located in the head volume between the frontal shell and the rear shell. The frontal shell of the head housing assembly includes: a first arc length at a first location below the display, a second arc length at a second location aligned with a portion of the display, and wherein said second arc length is greater than the first arc length, and a third arc length at a third location above the display, and wherein said third arc length is greater than both the first arc length and the second arc length.

The invention discloses a multi-robot collaborative scheduling system in an automatic warehousing system, which relates to the technical field of robot collaborative scheduling and comprises a task management module, a path planning module, a communication collaborative module, an exception handling module, a warehousing space dynamic partition module and a task fusion scheduling module. The task management module comprises a task priority calculation unit, a task distribution unit and a dynamic energy consumption evaluation unit, the path planning module comprises a global path optimization unit, a local path adjustment unit and an obstacle avoidance path optimization unit, and by arranging the task management module, the dynamic task priority calculation and distribution function is achieved, and the dynamic energy consumption is evaluated. The problem of adaptation of dynamic task requirements and complex environments is solved, and the completion speed of task allocation and path planning is increased; by arranging the path planning module, the function of dynamically optimizing the path according to the real-time environment is realized, the problem of path conflict optimization in multi-robot scheduling is solved, and the path obstacle avoidance and execution efficiency is ensured.

The embodiment of the invention relates to robot path planning, in particular to a self-adaptive robot trajectory planning method and system based on deep reinforcement learning, and the method can completely present the motion condition of a robot by obtaining a real-time trajectory data set covering an environment interaction state and a motion attitude sequence; key features can be accurately extracted through dynamic track feature extraction processing; a self-adaptive trajectory optimization strategy is generated based on a pre-trained deep reinforcement learning strategy network, so that the strategy can be flexibly adjusted according to dynamic characteristics; incremental trajectory correction processing is carried out on the robot motion posture sequence, the trajectory can be optimized step by step, and an optimized trajectory sequence conforming to dynamic environment adaptability is generated; and the optimized trajectory sequence is synchronized to the motion control system, so that the robot can quickly and accurately execute the optimized trajectory, and the motion planning capability and execution efficiency of the robot in a complex dynamic environment are remarkably enhanced.

This invention disclosed a full-space intelligent detection method and system for underground drainage networks, as well as storage media, including the following steps: image acquisition, intelligent image denoising, internal pipe defect segmentation, concealed defect detection around the pipe, 3D reconstruction with volume quantification, and pipeline life prediction. This invention introduced a bionic four-wheel-drive, all-terrain detection robot that can effectively navigate through mud and flowing water-challenges that hinder traditional detection devices. By leveraging deep learning algorithms as well as various techniques of computing vision, 3D reconstruction, and point cloud processing, the system thoroughly analyzed collected data to determine defect types and precise locations. Utilizing this analysis, precise location of different defect type and quantitative measurement of their dimensions can be realized. Based on the data analysis results, a deep-learning driven model was developed for predicting pipeline longevity to support maintenance staff with timely information on pipe defects and operational lifespan.

The invention relates to the technical field of industrial robots, in particular to an industrial robot disordered grabbing system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module, a pose estimation and correction module, a grabbing task planning module and a mechanical arm execution module. The multi-modal sensing module is used for acquiring RGB image data, depth image data, point cloud data and force / torque data in a working environment, and the pose estimation and correction module performs feature extraction and fusion on multi-modal data based on topology invariant mapping and performs adaptive pose estimation on a target object. The grabbing task planning module generates an optimal grabbing strategy based on deep reinforcement learning and outputs a grabbing control instruction, and the mechanical arm execution module receives the grabbing control instruction, controls a mechanical arm to execute a grabbing action and monitors a grabbing state through force / torque feedback. And the environmental adaptability and the perception robustness are improved.

The invention relates to the technical field of man-machine interaction, in particular to a robot behavior mode dynamic adjustment method based on multi-mode perception, which comprises the following steps: S1, collecting a visual image, a voice signal and an environment parameter of a target scene in real time; s2, extracting each modal feature; s3, dynamically allocating weights, and generating a fusion feature vector; s4, identifying a current scene type and a user attribute; s5, matching a corresponding interaction mode from a preset strategy library based on the scene type and the user attribute identified in the step S4; and S6, executing the matched interaction mode in the S5, and updating the weight distribution rule according to the feedback data. According to the method, vision, voice and environment characteristics are fused through a multi-mode perception technology, the behavior mode of the robot is dynamically adjusted based on a self-adaptive weighted fusion algorithm, and the interaction strategy is optimized in combination with user feedback, so that the interaction accuracy and the intelligent level of the robot under different scenes and user attributes are improved.

The invention relates to the technical field of equipment control, and provides a vision-based collaborative robot end control method and related equipment thereof. Multi-modal dynamic anti-interference filtering and semantic fusion are carried out on original visual data to obtain three-dimensional semantic point cloud data, and coordinate system alignment is carried out on the three-dimensional semantic point cloud data according to a robot-based coordinate system to obtain a target pose parameter set; performing dynamic path planning on the target pose parameter set to obtain a joint space motion track, and performing inverse kinematics solution and motor instruction synthesis on the joint space motion track according to robot D-H model parameters to obtain a motor control instruction, and performing closed-loop feedback optimization on the motor control instruction according to the state data of the end effector to obtain a dynamic correction control instruction. According to the method, through full-link optimization from sensing, planning to execution, the operation precision and safety of the collaborative robot in an uncertain environment are remarkably improved.

The invention discloses a mobile storage and charging robot remote scheduling and path planning system based on the Internet of Things, and relates to the technical field of robot control. Comprising a path dependency modeling module, a path conflict analysis module, a resource dependency graph construction module, a decoupling rearrangement scheduling module, a time sequence offset evaluation module and a path weight regulation and control module, and obtaining a path node sequence and an access time period of a current to-be-executed task of each mobile storage and charging robot, and generating a task path pre-occupation graph. By constructing the path dependence model, the conflict prediction mechanism and the task decoupling rearrangement strategy, accurate identification and effective intervention of path conflicts and resource deadlocks in the multi-robot scheduling process are realized, the stability of the scheduling system and the task execution continuity are improved, efficient completion of energy supply is ensured, and the scheduling efficiency is improved. And the operation efficiency and safety of the system are obviously optimized.

Techniques are disclosed for enabling creators to create multimodal chatbots that are based on or simulate / model characters. The characters may be from audiovisual (AV) media such as films and TV shows or real people. The application leverages a combination of Visual Interpretation AI, Retrieval-Augmented Generation (RAG), Low-Rank Adaptation (LoRA), and function calling to provide rich, interactive experiences. The inferencing performed by an instant multimodal chatbot utilizes base weights, character weights, relationship weights, experience weights as we as environmental inputs. An instant chatbot uses a number of AI models including a video to text model, an image to text model, a sensory to text model, a large language model, a text to video model, a text to image model and a text to voice model. A user can interact with the chatbot in a variety of ways including text, audio and video.

The invention relates to the technical field of robot path planning, and discloses a robot path planning method based on reinforcement learning. The method comprises the following steps: acquiring environment depth information and an obstacle movement track through a multi-sensor array, constructing a dynamic environment sensing network, and generating an environment state tensor under space-time constraint; building a hierarchical reinforcement learning framework, and optimizing the motion track of the robot in stages by adopting a strategy gradient algorithm to obtain an initial path strategy; designing a reward function calculation model based on an attention mechanism, and accounting an action value in real time according to an environment state tensor; deploying a distributed experience playback buffer pool, and performing priority sampling and track fragment recombination on historical decision data; and establishing a strategy iterative optimization mechanism, and searching and dynamically correcting an initial path strategy by utilizing a Monte Carlo tree. The method can accurately adapt to the dynamic environment, optimize the path decision efficiency, enhance the adaptability and reliability of robot path planning, and is suitable for various robot autonomous operation scenes.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

Disclosed in the present invention are a multi-modal shared teleoperation system and method for a three-arm space robot. The system at least comprises a master-side teleoperation system, a communication module and a slave-side robot system, wherein the master-side teleoperation system at least comprises two force feedback hand controllers, a microphone array and upper computer software, and the slave-side robot system comprises two operating arms each equipped with a gripper at the end, an observation arm having a binocular camera mounted at the end, a vision unit, a force sensor and lower computer software. The method comprises: an operator controlling two operating arms of an extravehicular robot to execute a task, and controlling an observation arm to acquire a better local field of view. In the method, a multi-modal teleoperation method comprising pose control, voice control and force control is fused with autonomous control of a robot by means of a shared control algorithm, and thus, human-robot collaborative control over the position, orientation and contact force of a robotic arm can be realized on the basis of the requirements of the operator, and the robot autonomously executes other relatively simple tasks, thereby reducing the operation burden of operators, and improving the control efficiency.