2854 results about "Intelligent robots" patented technology

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.

The invention discloses an intelligent robot spraying method based on a spraying system. Firstly, three-dimensional contour data of a workpiece are obtained through three-dimensional scanning equipment, a model is constructed, and key information is marked. And then according to the model, the workpiece material, the spraying requirement and the environment condition, a spraying path is planned through an intelligent algorithm, coating parameters are set, a robot executes spraying in sequence, and real-time monitoring is conducted through a high-precision position and a visual sensor in the process. After spraying is completed, the quality is detected through detection equipment, and when the requirements are not met, the computer system adjusts parameters according to deviation and directs the robot to conduct supplementary spraying or re-spraying. Whole-course data are recorded, and technological parameters and a path algorithm are optimized through statistical analysis. According to the method, high-precision, intelligent and efficient spraying is achieved, the method adapts to various workpiece and process requirements, the spraying quality and the production efficiency are improved, good adaptability and expandability are achieved, and intelligent development of industrial spraying is powerfully promoted.

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 present invention provides an intelligent robot for power inspection and an inspection method thereof, comprising a quadruped robot, wherein a laser radar, a voiceprint sensor and an infrared sensor are provided at the front end of the quadruped robot; a pan-tilt base is provided at the middle of the quadruped robot, and a camera is provided on the pan-tilt base; a six-degree-of-freedom mechanical arm is also provided at the tail of the quadruped robot, and a mechanical claw is provided at the end of the mechanical arm. The robot flexibly plans tasks based on the layout of the substation, sets inspection cycles for different equipment according to their importance, and automatically generates accurate task plans, thereby avoiding the tediousness and omissions of manual planning and improving planning. The advanced path planning algorithm enables the robot to quickly plan the optimal path, integrates multiple positioning technologies to ensure accurate navigation, and effectively reduces travel time. Multiple inspection modes are carried out in parallel, non-stop inspections are dynamic screening, routine inspections are comprehensive inspections, and non-preset inspections are flexibly targeted at specific points, which greatly improves the coverage efficiency of the inspection and avoids detection blind spots.

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.

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.

The invention relates to a multi-modal intelligent robot system and an interaction method, and belongs to the technical field of robot digital data processing, and the method comprises the following steps: S1, multi-modal input collection; s2, carrying out multi-modal input preprocessing; s3, performing multi-modal information fusion, inputting the information into a pre-trained multi-modal feature fusion model, and performing feature fusion on each modal feature through a quantitative feature data fusion mechanism to obtain a user interaction intention vector and a user emotional state vector; s4, interaction response generation: matching a preset interaction response strategy library based on the user interaction intention vector to obtain basic response content, and performing emotion adaptation adjustment on the basic response content in combination with the user emotion state vector to generate multi-modal response content including text information, voice information and action information; s5, interactive feedback is executed; the method has the beneficial effects that the robot is fused with multi-modal information through the basic response content and the user emotional state vector, and interaction is dynamically optimized.

The invention discloses an information interaction method for an intelligent robot with a body, and the method comprises the steps: S1, receiving the information which is transmitted by a user and comprises a task instruction, S2, carrying out the analysis of the information based on a context understanding module, so as to recognize the specific meaning and environment conditions of the task instruction, s3, operation corresponding to the specific meaning is executed, and execution parameters are adjusted in a specific environment to ensure that the task instruction is accurately implemented; according to the information interaction method of the intelligent robot with the body, environment perception and adaptive capacity are enhanced, various perception information such as vision, hearing and touch can be integrated, comprehensive perception of the environment is achieved, instruction understanding and execution accuracy is improved, the intelligent robot with the body can analyze complex user instructions more accurately, and user experience is improved. Through real-time multi-modal data fusion and decision optimization, the robot can realize real-time integration of different data sources in a highly dynamic scene, and the task execution efficiency and the user experience are improved.

The invention discloses an intelligent robot control method based on multi-sensor cooperation, and relates to the field of intelligent robots, and the method comprises the following operation steps: S1, carrying out the architecture deployment of sensors; s2, establishing communication; s3, edge intelligent calculation enhancement; s4, a dynamic adaptive fusion strategy; s5, carrying out group intelligent decision making; and S6, carrying out sensor role dynamic conversion. According to the intelligent robot control method based on multi-sensor cooperation, the sensor data credibility can be adjusted in real time according to the environment condition, the fusion weight can be dynamically allocated, the data accuracy is ensured through the cross check of the adjacent sensor data, and meanwhile, the intelligent robot control method based on multi-sensor cooperation can be realized through the ant colony cooperation algorithm in combination with the self-adaptive pheromone volatilization regulation and control. The path exploration strategy can be flexibly adjusted according to task complexity and environment dynamic change, the search efficiency can be effectively improved, various environments can be more quickly adapted, and the optimal action path can be quickly found.

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

The invention discloses a method for dynamically establishing a knowledge graph based on multi-source heterogeneous data fusion, and belongs to the field of intelligent robot systems. Firstly, multi-source heterogeneous data fusion is carried out, a distributed Kalman filter network is constructed, filters are designed for different sensors, and data space-time alignment is realized; carrying out conflict detection and resolution by adopting an improved D-S evidence theory, and dynamically adjusting a fusion weight through a confidence evaluation model; secondly, constructing a dynamic knowledge graph, and establishing a three-layer graph structure comprising an environment entity, a space-time relationship and probabilistic reasoning; designing an event-triggered incremental updating algorithm, starting local reconstruction when the environmental change degree exceeds a threshold value, and introducing an LSTM memory network to enhance the time sequence reasoning capability; and finally, constructing a reinforcement learning model based on the dynamic knowledge graph, designing a multi-target reward function, and finally training until the model is converged. According to the method, the positioning error is reduced through EKF / UKF combined filtering, the multi-sensor data space alignment error is reduced through a conflict resolution mechanism, and the execution efficiency and the coping capacity of the omni-directional mobile robot in a complex dynamic environment can be improved.

The invention relates to the technical field of voice interaction, particularly discloses an AI intelligent robot voice interaction method and system, and aims to solve the problems of low voice interaction accuracy, insufficient reliability and lack of authority control in a complex noise environment. A dynamic noise feature library containing steady-state noise, impact noise and human voice interference features and a pre-stored gesture instruction library are constructed, audio signals are collected in real time, low-frequency-band, middle-frequency-band and high-frequency-band differential noise reduction is executed, Mel-frequency cepstral coefficient features are extracted, noise scenes are matched, corresponding voice recognition models are switched, and voice recognition is achieved. And calculating a confidence value of the voice instruction, outputting multi-modal verification data in combination with a dynamic confidence threshold, and outputting an authority control signal through voiceprint matching, authority verification and instruction consistency judgment. Through multi-modal fusion, dynamic adaptation and authority control, the voice recognition accuracy and interaction safety in a complex noise environment are remarkably improved, and the method is suitable for scenes such as factory intelligent inspection.

A submerged arc furnace intelligent inspection system based on cloud side-end cooperation belongs to the technical field of submerged arc furnace intelligent robot inspection, and comprises an end side device, an edge side platform, a cloud platform and a cross-layer cooperative processing module, the end side device is in communication connection with the edge side platform, and the edge side platform is in communication connection with the cloud platform; and the cross-layer cooperative processing module is used for intelligently deploying global resources of the end-side equipment, the edge-side platform and the cloud platform, predicting a future load trend by adopting a dynamic sparse LSTM model algorithm, inputting a prediction result into a security constraint PPO reinforcement learning algorithm to adaptively adjust a task and a computing power allocation proportion, and carrying out dynamic resource allocation according to an output result. According to the invention, the cross-layer cooperative processing module interacts and communicates with the cloud side, the edge side and the end side, so that a dynamic allocation strategy of resources is realized, and the problems of low resource utilization rate and poor real-time performance caused by a rigid resource allocation strategy during routing inspection are avoided.

The invention discloses an intelligent storage robot path planning method, and belongs to the technical field of intelligent robots, and the method comprises the steps: improving a bidirectional A-Star algorithm, and combining a Time Elasticic-Band (TEB) obstacle avoidance algorithm, and providing a new intelligent storage path planning method. The improved A-Star algorithm adopts bidirectional search to replace unidirectional search of a traditional A-Star algorithm; 5-neighborhood search is adopted to replace 8-neighborhood search; introducing a self-adaptive dynamic search factor to improve a heuristic function; and smoothing the path by adopting a redundant node removal and linear interpolation method. The improved bidirectional A-Star algorithm searches to obtain a global optimal path, and the TEB algorithm optimizes and adjusts a local path according to the global path to complete dynamic obstacle avoidance. According to the method, the speed and precision of path planning are effectively improved, the local path can be adjusted in real time to avoid dynamic obstacles, and the requirement for path planning of the intelligent storage robot is met.

The invention discloses a control method and equipment for an intelligent robot with a body and a storage medium, and belongs to the technical field of robots. The method comprises the steps of receiving a task instruction and environment perception data, performing fusion processing on the task instruction and the environment perception data, generating task semantic information associated with the task instruction, analyzing the task semantic information through an implicit planner, generating an implicit action mark, and based on the implicit action mark, generating an implicit action. And decomposing a to-be-executed task corresponding to the task instruction into a plurality of task sub-targets layer by layer, generating an action instruction sequence based on the task sub-targets, and executing a control action corresponding to the action instruction sequence. According to the method, based on the constraint of the implicit action mark and the action instruction sequence, the robot with the body can flexibly respond to environment parameter changes such as object position deviation or new task requirements in the task execution process, the coupling degree of the action instruction of the robot with the body and the predefined scene is reduced, and high stability of task execution is ensured.

The invention relates to the technical field of intelligent robots and old-age care, and discloses a target navigation method and device based on environmental context, a robot and a medium, which can be applied to a household auxiliary robot intelligent medicine delivery scene, and the method comprises the following steps: receiving multi-modal input of a user, preprocessing the multi-modal input, and generating preprocessed multi-modal data; reading the current time and the environment state, and performing perceptual analysis based on a pre-constructed tetrad map, the current time and the environment state to generate a candidate search area list; determining a target candidate area from the candidate search area list; generating a target navigation path based on the SLAM map and the target candidate area, and moving the robot to the target candidate area according to the target navigation path; and obtaining a target candidate region image, and performing visual semantic verification based on the target candidate region image and the target object label in the structured text instruction. According to the invention, the robot navigation accuracy and the user experience are improved.

The invention provides a building measurement method and system based on an intelligent robot, and relates to the technical field of intelligent robots, and the method comprises the steps: deploying the intelligent robot to a to-be-measured building area; determining a final driving path of the intelligent robot according to the layout of the to-be-detected building area; the intelligent robot automatically runs along the final running path, and acquires three-dimensional point cloud data, image information and geographic position information of the building environment by using an internal sensor assembly; and processing the three-dimensional point cloud data, the image information and the geographic position information of the building environment, including point cloud splicing, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building. By automatically collecting and processing data, the building three-dimensional model and the detailed measurement report are efficiently generated, the measurement precision and efficiency are improved, and the labor cost is reduced.

Co-manipulation robotic systems are described herein that may be used for assisting with laparoscopic surgical procedures. The co-manipulation robotic systems allow a surgeon to use commercially-available surgical instruments while providing benefits associated with surgical robotics. Further, the co-manipulation robotic system may operate in multiple modes to enhance usability and safety, while allowing the surgeon to position the instrument directly with the instrument handle and further maintain the desired position of the instrument or a desired force applied by the instrument using the robot arm. For example, the robot arm may be moved automatically to maintain a constant tension force on an anatomical structure via the instrument in a constant tension mode.

The invention relates to the technical field of intelligent robots, in particular to a mechanical arm-dexterous hand system tool intelligent grabbing strategy generation method oriented to a space microgravity environment. Based on installation configuration of a mechanical arm-dexterous hand system, gravity coefficients are set for a tool to be grabbed and an environment scene, and a simulation environment under space microgravity is constructed; on the basis of configuration of a tool to be grabbed and the dexterous hand, a grabbing configuration of the dexterous hand is generated, and a fixed grabbing test is carried out in the simulation environment; and on the basis of the simulation environment, the pose information of the tool to be grabbed and the real-time states of the mechanical arm and the dexterous hand serve as input of neural network training, actions of the mechanical arm and the dexterous hand serve as output of neural network training, and a grabbing strategy is obtained through neural network training. According to the scheme, an intelligent grabbing strategy of a mechanical arm-dexterous hand system tool can be generated for the space microgravity environment.

The invention relates to the technical field of body-equipped intelligent robots and intelligent equipment, in particular to a self-adaptive flexible tightening body-equipped intelligent robot which comprises a body-equipped moving platform, the top of the body-equipped moving platform is provided with a body-equipped lifting mechanism, and the top of the body-equipped moving platform is provided with a body-equipped articulated arm module. And a tightening actuator and a clamping jaw actuator are arranged at the tail end of the outer part of the body articulated arm module. According to the self-adaptive flexible tightening intelligent robot and the tightening process method, sensing and self-adaptive adjustment of external environment changes are achieved through multi-module integration, and the tightening process method based on a multi-mode sensing system and an intelligent control system is constructed; vision, force sense and touch sense data are fused, real-time monitoring and dynamic adjustment of the tightening process are achieved through a machine learning algorithm, and the intelligent level and quality stability of tightening operation are effectively improved.

The invention relates to the technical field of robot intelligent control, in particular to a concrete construction digital intelligent robot control system. Comprising a concrete data acquisition module, a construction path planning module, a robot control module, a quality monitoring module and an interactive feedback module. The data acquisition module acquires information such as concrete height and humidity of a construction area through a sensor; the path planning module generates an optimal construction path by using an artificial bee colony algorithm; the robot control module accurately executes a path instruction through sliding mode control and a fuzzy adaptive algorithm; the quality monitoring module adopts a support vector machine to evaluate construction quality and generate a report; and the interaction feedback module realizes path adjustment and information interaction through a visual page. Through combination of digital intelligence and algorithm optimization, integration of intelligent planning of a construction path, precise control of the robot and quality monitoring and evaluation is realized, and the intelligent level and engineering quality of concrete construction are remarkably improved.

The invention discloses an under-actuated robot finger with a rigid-flexible hybrid transmission mechanism, and relates to the technical field of intelligent robots. The connecting seat is fixed at the top end of the gear box, a rotating shaft is mounted in the middle of the connecting seat, a worm gear is sleeved on the rotating shaft, and the rotating shaft is meshed with the worm; the tail end of the knuckle shell is rotatably connected with the worm gear; the tail end of the swinging arm is hinged to the front side edge of the connecting seat; a limiting groove is formed in the top end of the swinging arm; the tail end of the driving arm is hinged to the rear side edge of the surface of the worm gear, the top end of the driving arm is hinged to the inner side end of the sliding rod, the fingertip assembly comprises a flexible base and a rigid framework and is provided with a spring hinged to the top end of the knuckle shell, and the outer side end of the sliding rod is hinged to a rigid supporting rod embedded into the fingertip assembly. The rigid-flexible hybrid transmission mechanism is combined with the under-actuated design, the method is simple and easy to implement, the requirements of the humanoid prosthetic finger on the aspects of size, weight, integration and grabbing envelope are met, the characteristic of coupling-adaptive movement is achieved, and the flexibility and comfort are improved.

The invention discloses an artificial intelligence robot path planning method and system, and the method comprises the steps: 1, carrying out the comprehensive perception of an environment geometric structure, object semantics and dynamic obstacles, eliminating scene differences, and further constructing the topological graph representation of an environment; 2, pre-training a path planning model, designing a scene context encoder, encoding a scene specific rule into a low-dimensional vector, and outputting a scene context code and updated planning model parameters to provide a planning capability adapted to a new scene for the step 3; 3, dynamically adjusting a track according to real-time sensor data by adopting a hierarchical decision-making architecture; step 2, recording success / failure path segments in the new scene, regularly updating a local planning module, regularly feeding back empirical data in the new scene to the step 2, and triggering a conservative obstacle avoidance mode when the confidence coefficient is lower than a threshold value; and 4, constructing a quantitative evaluation system, building an automatic test platform, simulating diversified scenes and dynamic interference, and automatically generating a test case.

The invention discloses a control method for a multi-modal information fusion intelligent robot with a body. The control method comprises the following steps: initializing a system, and collecting surrounding physical environment and object state information and a natural language instruction of a user; performing scene understanding and task analysis, processing data through a multi-modal information fusion mechanism and a cross-modal attention module, and generating unified multi-modal data; task planning and priority ranking are carried out, complex tasks are decomposed into subtask sequences, and a priority ranking layer dynamically adjusts the execution sequence; performing action execution and feedback adjustment, and generating a control instruction through a self-adaptive operation control algorithm; continuous learning and strategy verification are carried out, integrated execution is realized by using a hybrid AI system, and the robustness of an operation strategy is verified through a simulation environment; and closed-loop iteration is carried out to realize real-time response of the intelligent robot with the body. According to the method, the perception understanding precision and the task execution efficiency of the intelligent robot with the body are improved, the operation precision adaptability and the system robustness flexibility are guaranteed, and the method is suitable for multiple scenes.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

The invention provides an intelligent spraying composite robot system based on a large language model and a method thereof, and relates to the field of intelligent robots and automatic spraying, the intelligent spraying composite robot system comprises a chassis, a mechanical arm, a spraying gun, a camera, an instruction receiver and a controller; a mechanical arm and an instruction receiver are carried on the chassis; spray guns and cameras are mounted on the mechanical arms; the instruction receiver receives a natural language voice instruction, analyzes semantics through a large language model, and generates an action instruction; the controller comprises a motion control module, a three-dimensional point cloud data acquisition module, a feature point extraction module, a track planning module and a spraying operation module; the method improves the man-machine interaction, reduces the use threshold of the robot, is easy to operate, gives play to the spraying advantages of the composite robot, does not need to program in mainstream offline simulation software, can generate a spraying path with adjustable and controllable parameters only by collecting the point cloud template of the workpiece and selecting the feature points, and can be widely applied to furniture, furniture and the like. And spraying operation in the fields of automobiles, aerospace and the like.

The invention belongs to the technical field of intelligent control of robots, and discloses a self-adaptive adjustment system for behavior modes of an intelligent robot with a body, which comprises a neuromorphic sensing module for acquiring and fusing multi-mode environment sensing information, performing space-time compression on the multi-mode environment sensing information by referring to a brain visual cortex sparse coding principle, and generating a neural network; generating an environment state representation vector, and constructing a neuromorphic perception path; the cognitive constraint decision module is used for generating an action strategy by adopting a neural symbol hybrid architecture based on the environment state representation vector; through a cognitive momentum optimization mechanism, a strategy inertia item is introduced to improve an updating process of an action strategy parameter, and a behavior decision vector is obtained; the variable impedance execution module is used for mapping the behavior decision vector into a plurality of virtual muscle cooperation elements through a muscle cooperation mapping rule, and solving an expected movement track of each execution joint of the robot; and the intelligent robot with the body has higher adaptability, stability and execution capability.

The invention relates to an intelligent robot feeding and discharging method and system, and the method comprises the steps: obtaining the image information of a target material, carrying out the recognition and analysis of the image information of the target material, and obtaining an analysis result which comprises the position information and posture information of the target material; inputting the analysis result into a pre-trained operation decision model for analysis, and generating an action planning instruction corresponding to the target material; task scheduling operation is executed based on the current task state and the resource information, a task scheduling result is obtained, and the scheduling operation comprises task distribution optimization and path generation control; according to the task scheduling result and the action planning instruction, the target robot is scheduled to complete feeding and discharging operation; and in the loading and unloading execution process, the operation state is monitored in real time, the operation state is detected and analyzed, an interference detection result is obtained, and the action planning instruction is dynamically adjusted based on the interference detection result. The production line has the effect of improving the efficiency of the production line.

The invention discloses an intelligent machine construction method and system for ground concrete base troweling, and relates to the technical field of intelligent construction.The intelligent machine construction method comprises the steps that an intelligent robot collects sensing data of a concrete base in a construction area in real time; the sensing data is analyzed through a preset algorithm to generate a construction strategy, and troweling operation is automatically completed; in the troweling operation process, the intelligent robot transmits real-time sensing data to the cloud platform, and a closed-loop monitoring system of construction quality is formed. Through preset path planning, parameter adjustment and an image recognition algorithm, accurate matching of troweling operation with the base layer state is achieved, troweling uniformity is improved, manual dependence is reduced, perception data in the robot operation process are uploaded to the cloud platform in real time, comparative analysis and feedback control are carried out, and the troweling efficiency is improved. Quality problems can be recognized and corrected in real time, construction data in the whole process can be traced, and remote construction supervision is visualized.

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.