918 results about "Visual feedback" patented technology

The invention relates to the technical field of automatic workpiece grabbing and visual servo control, and discloses a workpiece grabbing method and system based on visual feedback, and the method comprises the steps: obtaining a real-time image through a front-view camera, a side-view camera and a top-view camera, and extracting surface features through a feature fusion network; inputting the features into a pose estimation model based on a particle filtering framework, and iteratively updating the pose in combination with a multi-dimensional observation likelihood function to generate prediction parameters; constructing a multi-target path planning model based on the parameters, and optimizing the path by adopting a dynamic planning algorithm with the shortest path and the minimum joint movement as targets; a hierarchical control model is established, a strategy layer performs global planning, an adjustment layer corrects a local path, and an execution layer realizes trajectory tracking through visual servo control and outputs a control instruction to complete grabbing. Through multi-view perception, robust pose estimation, global optimization path planning and hierarchical control, the precision, efficiency and stability of workpiece grabbing in a complex environment are improved.

The invention relates to the technical field of AI intelligent customer service, in particular to an AI intelligent customer service multi-channel interaction and data analysis system which comprises a multi-channel access module, an interaction data preprocessing module, a multi-modal intention analysis module, a dynamic strategy generation module, a data capitalization management module and a visual feedback module. The multi-channel access module is used for receiving multi-source heterogeneous interaction data; the interaction data preprocessing module generates standardized interaction data; the multi-modal intention analysis module generates a multi-dimensional user portrait and a real-time session path; the dynamic strategy generation module outputs adjustment parameters of a dynamic service strategy; the data asset management module generates a data asset packet with a timestamp; and the visual feedback module is used for generating an interactive decision graph. According to the invention, the data security, the customer service intelligence level and the decision optimization capability are improved, and finally an efficient, accurate and safe intelligent customer service system is realized.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The invention relates to the technical field of welding automation and robot visual perception, and discloses a robot intelligent welding method and system.The method comprises the steps that a three-dimensional point cloud image is constructed through a laser triangulation method; filtering and enhancing the point cloud image, and extracting a weld point cloud set by adopting a U-Net semantic segmentation model; carrying out space path identification and attitude regression based on the set to obtain a track element of a position-attitude pair; fitting a path by adopting a cubic B-spline curve and interpolating to generate a continuous welding track; and in the welding process, visual feedback is combined, and proportional differential control logic is introduced for closed-loop track error compensation. Compared with the technical problem that in the prior art, weld joint path recognition and accurate tracking cannot be stably achieved under complex structures such as curved surfaces and variable cross sections, a perception control closed loop based on visual servo is constructed, high-precision automatic welding of the complex weld joint structures is achieved, and the track fitting precision is improved.

The invention discloses a code spraying identification optimal path planning method based on an intelligent optimization algorithm, and relates to the technical field of code spraying control, and the method comprises the steps: dispersing a code spraying region into a three-dimensional space, and constructing a dynamic environment model of marking parameter constraint; based on the code spraying task priority queue, a hybrid optimization algorithm is adopted to carry out collaborative optimization of marking parameters and path nodes, and an optimal path candidate set is generated; an obstacle avoidance model is constructed based on the optimal path candidate set, a collision-free path is generated, the pose of a nozzle is adjusted in combination with visual feedback, and an anti-interference code spraying action sequence is output; monitoring the operation feedback of the code spraying equipment, and carrying out the iterative updating of the marking parameter library and the algorithm rule. According to the method, the core advantages of multiple intelligent optimization algorithms are fused, a mixed collaborative optimization architecture is constructed, the limitation of a single algorithm in a complex scene is effectively broken through, and a traditional path planning algorithm is easily troubled by a local optimal solution and is difficult to meet multi-dimensional constraint conditions at the same time.

The invention provides a picking mechanical arm operation method and system based on visual feedback. The picking mechanical arm operation method comprises the steps that firstly, a multi-dimensional visual perception set including the shape contour, surface physical characteristic visual representation, connection part space distribution, growth environment dynamic interference and the like of a picked object is obtained; a mechanical arm collaborative decision-making model is constructed based on the multi-dimensional visual perception set, stress distribution characteristics of connection parts are analyzed, and optimal contact area parameters and mechanical arm posture collaborative parameters are determined; according to the parameters, generating a sub-time sequence action instruction set containing action information in the contact stage, the clamping stage and the separation stage; when the mechanical arm is controlled to execute the instruction set, a dynamic visual feedback set of the picking scene is obtained in real time; and finally, input parameters of the collaborative decision model are updated based on feedback, and instruction set parameters are adjusted, so that the mechanical arm completes picking operation on the premise of keeping the integrity of the picked object, and the intelligent level and the picking operation quality of the picking mechanical arm are improved.

The invention relates to the technical field of ward air purification systems, and discloses a ward air purification system which comprises an environment sensing module, a purification path planning module, a filter unit configuration module, a purification execution module, a dynamic adjustment module, a state monitoring module and a feedback control module. The environment sensing module obtains ward space information to construct an air flow topology; the purification path planning module calls air quality data to label the topology; the filter unit configuration module sets purification nodes and adjustment intervals; the purification execution module implements basic purification; the state monitoring module analyzes data to determine operation state characteristics, and the feedback control module generates monitoring records and performs visual feedback. The system can accurately perceive the air quality of the ward, scientifically plan the purification path, dynamically adjust the purification strategy, effectively improve the air quality of the ward and guarantee the health of patients and medical staff, and has high practical value.

The invention discloses an edge spraying compensation control method based on intelligent visual feedback. The method comprises the following steps: performing space scanning on a to-be-sprayed workpiece to obtain spraying image data, and establishing a space corresponding relation between a spray gun motion coordinate and a workpiece surface coordinate; registering the real-time spraying image by using the corresponding relation, identifying a geometric boundary line, brightness gradient change and a coating adhesion area of the edge of the workpiece, and generating edge feature data; according to the difference between the edge feature data and the target spraying image, the spraying coverage deviation and the boundary overlapping error are calculated, the coating thickness change trend, the optical density change rate and the boundary direction offset are extracted, and the spraying gun posture adjustment amount, the spraying distance correction amount, the spraying pressure correction amount and the path speed correction amount are generated; the angle, the spraying distance, the pressure and the movement speed of the spray gun are dynamically adjusted according to the parameters; according to the method, real-time visual feedback and self-adaptive compensation control in the spraying process are achieved, and the uniformity and consistency of the coating on the edge of the workpiece are effectively improved.

The invention discloses a seeding unit integrating visual feedback and calibration functions and a control method, and relates to the technical field of agricultural machinery, the seeding unit comprises a control system, a row spacing adjusting mechanism and a seeding depth control mechanism, the seeding depth control mechanism is connected with the row spacing adjusting mechanism, and the row spacing adjusting mechanism is connected with the control system; the control system is composed of an industrial camera and an image recognition module, the image recognition module comprises a broken ridge prediction control mechanism and a ridge center line fitting and sowing offset correction mechanism, and the ridge center line fitting mechanism recognizes ridge line boundaries and fits center line tracks. The broken ridge prediction control mechanism detects a ridge line interruption area and delays ridges according to the advancing speed and response of the seeding monomers. According to the seeding monomer integrated with the visual feedback and calibration functions and the control method, the visual identification technology is integrated to monitor the earth surface condition in real time, the response is quick, the control is accurate, and the seeding monomer is suitable for high-speed seeding operation under different terrain and soil conditions.

The invention discloses an AI data scheduling distribution system and method based on cloud computing, and relates to the technical field of intelligent cloud computing, and the system comprises an AI data collection module, a feature extraction module, a decision parameter generation module, a scheduling distribution module and a visual feedback module. The AI data acquisition module acquires equipment video data and temperature data, the feature extraction module extracts video temperature features, the decision parameter generation module outputs equipment vibration intensity and temperature rise rate, and the scheduling distribution module calculates equipment shutdown probability and node load score and triggers a shutdown instruction and a task migration decision. And the visual feedback module maps the equipment health state and node load distribution, and optimizes the feature weight. Through multi-modal data fusion and a closed-loop feedback mechanism, accurate early warning of equipment abnormity and dynamic optimal allocation of resources are realized, visual decision assistance and adaptive weight adjustment are combined, equipment safety and cluster load balancing efficiency in a cloud computing environment are improved, and manual intervention requirements and operation and maintenance cost are reduced.

The invention discloses a robot-oriented article recognition and grabbing system and method, and belongs to the technical field of artificial intelligence, and the robot-oriented article recognition and grabbing system comprises a multi-source data input module, an image recognition module, a semantic generation module, a task guiding evaluation module and a visual feedback module, the multi-source data input module comprises an RGB-D camera for obtaining target 3D point cloud data and a natural language instruction interface for receiving a natural language instruction FI; the image recognition module comprises a double-flow convolutional neural network and an improved Transform network; the semantic generation module comprises a large language model LLM; the task-oriented evaluation module comprises a task-oriented capture evaluator TGE and a point cloud encoder; and the visual feedback module is used for judging whether grabbing succeeds or not after the grabbing task is finished. The target object can be accurately recognized, and the grabbing performance of the mechanical arm on the target object is optimized.

The invention relates to the field of navigation signal processing, and discloses a radiation therapy bolus dose feedback and tracking system based on image registration, the system comprises an image acquisition module, an image registration module, a bolus identification module, a dose recalculation module, a dose deviation analysis module and a feedback and adjustment module, space registration is carried out through an FBCT image acquired every day and an original plan CT, and the radiation therapy bolus dose feedback and tracking system based on image registration is obtained. The fitting state between the bolus and the skin is automatically identified, the position of an air gap is detected, and dose recalculation is carried out based on an actual structure. Through comparison with an original planned dose, the system can output a deviation index of a key dose parameter and provide visual feedback, if the deviation exceeds a set threshold value, a prompt is automatically sent out, and a radiotherapy technician and a physician are supported to carry out bolus adjustment or call an adaptive planning engine to reconstruct a treatment plan. Quantitative analysis of bolus fitting quality and closed-loop control of dose delivery consistency are realized, radiotherapy precision and safety are effectively improved, and the method is suitable for superficial radiotherapy scenes containing bolus design.

The invention relates to the technical field of robot control, in particular to a robot object grabbing adaptive learning system based on visual and tactile feedback. The robot object grabbing adaptive learning system based on visual and tactile feedback comprises a data acquisition module used for acquiring adaptive control data of a robot; the joint adjusting module is used for obtaining the joint compensation amount of the robot according to the adaptive control data of the robot and adjusting the grabbing motion of the robot to obtain the final joint adjustment degree; the visual feedback module is used for obtaining a rough grabbing force estimation value of the visual information by using a visual feedback formula according to the visual data of the robot; and the tactile feedback module is used for obtaining correction force of tactile feedback by using a tactile feedback formula according to the tactile data of the robot. Through multi-modal information fusion and modular design, high-precision and self-adaptive grabbing of the thermochromic object by the robot is realized.

The invention provides a humanoid robot hand-eye coordination intelligent calibration control system and a humanoid robot hand-eye coordination intelligent calibration control method, and the humanoid robot hand-eye coordination intelligent calibration control system integrates high-precision contour extraction, visual feedback closed-loop control, multi-sensor fusion and dynamic path planning. The robot hand-eye calibration method has the beneficial effects that the calibration efficiency and precision can be remarkably improved, the problem of calibration parameter drift caused by system time-varying characteristics and environmental interference is effectively solved through continuous tracking of a target contour and real-time online compensation of pose deviation, and the calibration accuracy is improved. Therefore, the reliability and the absolute positioning precision of the robot in long-term complex operation tasks are guaranteed, and the self-adaptive capability of a robot system is enhanced.

The invention discloses a CAD drawing instant positioning method and device based on spatial index analysis, and relates to the related technical field of CAD drawing positioning, and the method comprises the steps: carrying out the multi-stage splitting identification of a multi-version target CAD drawing; performing associated indexing on the drawing version information, the drawing layer information and the drawing component set; semantic analysis and spatial association are carried out on the drawing text annotations; embedding and associating the drawing component space semantic tag set into a component-layer-drawing three-level association graph; performing matching indexing on the drawing semantic query request based on the drawing spatial index mapping table; and inquiring a topology path based on the component to carry out instant positioning and visual feedback. The technical problems that in the prior art, complex spatial relations and semantic information in the drawings are difficult to effectively process, and multi-version CAD drawings are low in retrieval efficiency, insufficient in instantaneity and inaccurate in positioning are solved, and the technical effect of improving the retrieval efficiency, query instantaneity and positioning accuracy of the multi-version drawings is achieved.

The invention, which relates to the technical field of agricultural intelligent equipment, discloses an agricultural plant protection unmanned aerial vehicle sowing control system comprising a sowing flow field dynamic modeling and prediction module, an attitude regulation and control module, a speed matching module, a path compensation module, a visual monitoring module and a control strategy module. A flow field dynamic mapping model based on the flight height, the downward pressing airflow speed and the particle size and density of the scattered particles is constructed, particle drop point deviation is predicted in combination with flight real-time data, and a pneumatic coupling risk area is identified. Through flow field modeling prediction and attitude adjustment, trajectory compensation, speed control and visual feedback, adaptive regulation and control in the sowing process are realized, air scattering and nonlinear collapse are effectively prevented, seedling burning, germination failure and pesticide residue risks caused by over-dense sowing are avoided, the operation uniformity and the agricultural effect are improved, and the method has practical value and popularization prospects.

The invention discloses a digital twin intelligent rendering method and system based on GPU load optimization and camera visual angle self-adaption, and the method comprises the steps: obtaining the basic information of a GPU, and evaluating the actual rendering capability of the GPU through a performance test; pre-defining a plurality of surface number grades according to the complexity of the digital twin model; performing LOD calculation based on the visual angle of the camera; the system performance is monitored in real time; on the basis of LOD calculation of the view angle of the camera, the GPU load condition is combined, a self-adaptive adjustment or predictive adjustment strategy is adopted, and the rendering complexity of a non-key area is optimized; user manual adjustment options are provided, allowing the user to adjust the rendering quality according to personal preferences, and providing visual feedback at the time of adjustment. According to the method, the GPU load is reduced while high-precision rendering of the key area is guaranteed, LOD switching is optimized in combination with historical inspection data, and the rendering efficiency and the system stability are improved.

The invention relates to an unmanned aerial vehicle simulation training method for video guidance and track deviation evaluation based on deep learning. The method specifically comprises the following steps: S1, data acquisition and input; s2, generating and prompting a path visible area; s3, track deviation evaluation of image and flight control fusion; s4, trajectory behavior modeling and score analysis; s5, training a feedback output mechanism; and S6, carrying out personalized training recommendation and playback analysis. According to the invention, space-level deviation evaluation is realized by fusing the image and the flight control data, and the judgment is more accurate; the real-time visual feedback and voice prompt system can improve the training instantaneity and the interaction experience; a Transform-based trajectory scoring mechanism is close to the nature of an operation behavior, and multi-dimension evaluation is carried out; differentiated task recommendation is supported, and teaching pertinence and training efficiency are improved; the whole process is visual and can be repeated, and the teaching closed-loop design is optimized.

The invention, which belongs to the technical field of mine safety monitoring, provides a multi-source information fusion-based roadway surrounding rock roof separation tour-inspection early-warning system comprising a data acquisition module, an edge calculation module, a cloud analysis module and an early-warning feedback module. The data acquisition module acquires multi-source data of surrounding rock of a coal mine tunnel in an automatic mode; the edge calculation module performs spatio-temporal data synchronization on the collected multi-source data and extracts data features; the cloud analysis module performs feature weighted fusion based on the multi-source data to construct a risk early warning model; and the early warning feedback module triggers graded early warning according to prediction data of the risk early warning model, and provides visual feedback, so that a set of automatic, full-coverage and multi-source data fused roadway roof separation monitoring system is constructed.

The embodiment of the invention provides a ceramic diaphragm automatic positioning system based on high-precision visual feedback and closed-loop control, and the system comprises a parameter reference setting module which is used for setting a theoretical reference point and a visual detection parameter; the real-time visual detection module is used for collecting an image of the to-be-stacked film and outputting an image coordinate of the mark point; the core operation module is used for converting the image coordinates of the mark points into actual coordinates based on the visual detection parameters, reading a theoretical reference point of the current to-be-stacked diaphragm and calculating a position error vector between the actual coordinates and the theoretical reference point, and the position error vector comprises a translation compensation amount and a rotation compensation amount; and the motion control and closed loop feedback module is used for receiving the position error vector and generating a control instruction to drive the workbench to move. According to the system, through parameterized reference setting, real-time visual detection, intelligent error calculation and real-time motion compensation, the technical limitation is effectively overcome, and the lamination alignment precision and efficiency are remarkably improved.

The invention relates to the field of artificial intelligence, in particular to a reflective interference workpiece visual identification positioning and grabbing method and system based on artificial intelligence, and the method comprises the steps: obtaining two-dimensional image data containing a reflective workpiece, and carrying out the image enhancement and filtering preprocessing; training and reasoning the two-dimensional image by using a deep learning model, and automatically identifying and extracting a non-reflective region in the image to form a region of interest; acquiring three-dimensional point cloud data of the scene through a binocular linear scanning laser vision sensor, and extracting a point cloud area corresponding to the non-reflective area from the three-dimensional point cloud data; on the basis of a point cloud matching algorithm, registering the extracted point cloud of the region of interest with a standard workpiece model, and calculating pose information; planning a robot grabbing path according to the calculated workpiece pose data; feedback and optimization are carried out, the grabbing process is monitored in real time, a grabbing strategy is adjusted by combining a sensor or visual feedback, and the grabbing success rate and stability are improved; according to the invention, accurate identification, positioning and stable grabbing of the reflective workpiece are realized.

The invention relates to the technical field of digital twinning, in particular to a real-time interactive visualization system and method based on digital twinning, and the system comprises a data collection module which is used for configuring a multi-protocol adapter deployed on a physical equipment layer to collect the operation data of physical equipment, and transmitting the operation data of the physical equipment to an edge computing node. According to the method, the general data adapter is used for driving the three-dimensional rendering engine to execute real-time rendering based on physics, so that the virtual scene can dynamically reflect details such as illumination change and material aging of a physical entity, and high-fidelity visual feedback is provided. According to the method, a closed-loop control mechanism is established in combination with a multi-modal interaction framework, pure data monitoring is converted into operable bidirectional interaction, operation of a user on a visual interface can be converted into feedback instructions in real time to reversely adjust physical equipment, and the response efficiency of remote operation and maintenance is improved.

The invention provides a self-adaptive accurate rust removal method and system based on unmanned aerial vehicle visual feedback, and relates to the technical field of large metal structure surface maintenance and anticorrosion treatment.The rust removal method comprises the steps that a global path is planned, and a global flight waypoint sequence is generated; a local image of the current to-be-derusted area is collected; the corrosion characteristics of the current to-be-derusted area are analyzed, and initial derusting parameters and initial derusting time are set; after each derusting operation is finished, a frame of intermediate result image is collected; based on the difference between the intermediate result image and the historical image, performing quantitative evaluation on the effect of the previous rust removal operation, and performing feature analysis on the residual rust region; self-adaptively adjusting and generating rust removal parameters of the next stage; and the steps of derusting operation, image collection, evaluation and parameter adjustment are repeatedly executed, and after the termination condition is met, the unmanned aerial vehicle is switched to the next to-be-derusted area.

The invention provides a robot complex task dynamic arrangement method, belongs to the field of robot task arrangement, and is used for solving the problems of weak dynamic environment adaptation capability, poor multi-robot collaboration and insufficient risk pre-judgment of traditional static task arrangement in related technologies. According to the method, after double matrixes are constructed and fused into tensors, low-rank decomposition is performed, a threshold value is dynamically generated and optimized, task priorities are calculated through multiple factors, resources are allocated in combination with resource constraint and federated learning, synchronous cross-scene adaptation during execution, risk pre-planning and visual feedback optimization are performed, and dynamic adaptation, efficient collaboration and stable execution of complex tasks are achieved.

The invention relates to the technical field of sentiment analysis, in particular to a sentiment analysis method and system. The sentiment analysis method comprises the steps of collecting a dialogue voice signal, extracting an acoustic feature and a semantic feature from the dialogue voice signal, performing double-flow extraction of the acoustic feature and the semantic feature on the collected dialogue voice signal, performing normalization on the acoustic feature and the semantic feature respectively, and performing sentiment analysis on the acquired dialogue voice signal. The normalized acoustic features and semantic features are spliced to form a joint feature vector, the joint feature vector is mapped into a high-dimensional intermediate emotion vector through linear transformation, controllable Laplace noise is added to the intermediate emotion vector, and the intermediate emotion vector is mapped into a high-dimensional intermediate emotion vector; and outputting an encrypted intermediate emotion vector, inputting the encrypted intermediate emotion vector into a pre-trained emotion analysis neural network, outputting a nine-dimensional emotion vector spectrum, and generating visual feedback on a local device by using the nine-dimensional emotion vector spectrum. According to the invention, emotion understanding and communication optimization in a dialogue scene are greatly improved.

Embodiments of a balance device can include one, two or three of a medial-lateral curved track assembly, an anterior-posterior curved track assembly and a linear slide assembly. The balance device can be employed as part of a system further including, a camera-based motion tracking system communicably coupled to the balance device and a visual subsystem communicably coupled to the motion tracking system and operable to manipulate visual feedback to the user during operation of the balance device. Embodiments of a method according to the present disclosure include receiving measurements of a plurality of forces and a plurality of moments acting on a balance platform of a balance device, estimating a plurality of non-user force measurements and a plurality of non-user moment measurements caused by gravity and inertial accelerations, determining a compensation adjustment and implementing the compensation adjustment to the balance platform.

The invention relates to a personalized exercise rehabilitation risk early warning method and device based on IMU dynamic instability assessment, and the method comprises the steps: collecting multi-dimensional exercise data of a subject, carrying out the posture fusion, and obtaining the limb posture, joint angle change rate and angular momentum of the subject; performing time sequence mode decomposition to extract a single gait cycle and a specific jump landing attitude; calculating a biomechanical feature vector of each motion primitive; inputting the single gait cycle, the specific jump landing attitude and the biomechanical feature vector into a hybrid injury induced motion model, and outputting a multi-dimensional injury risk index vector; and comparing the multi-dimensional injury risk index vector with historical health data and a moving target dynamic adjustment threshold value of the subject, and if the threshold value is exceeded, providing real-time auditory, tactile or visual feedback to the subject. According to the method, the influence of movement speed and rhythm change is overcome, dynamic instability in movement can be more sensitively reflected, and personalized risk assessment in a real sense is realized.

One or more light sources may be integrated with a medication delivery device, and states of the light sources may be changed. For example, a first lighting state change may be performed, such as based on a detection that an applied force meets or exceeds a threshold break force or an interaction of a movable component with a proximal sensor integrated with the medication delivery device. The first lighting state change may provide visual feedback indicating that an injection has begun. Additionally, a second lighting state change may be performed, such as based on a detection that a resistive force meets or exceeds a threshold back force or an interaction of the movable component with a distal sensor integrated with the medication delivery device. The second lighting state change may indicate that a complete dosage of the medication has been delivered from the medication delivery device.

The invention discloses a multi-size wafer pre-positioning method and system based on visual identification, and belongs to the technical field of wafer pre-positioning, and the method specifically comprises the steps: collecting a to-be-positioned wafer image of a stage region, employing a visual feature extraction model to recognize the outer edge features of a to-be-positioned wafer, constructing an outer edge candidate curve, and carrying out the recognition of the outer edge candidate curve; performing multi-circle fitting on the outer edge candidate curve, determining a size parameter of a to-be-positioned wafer by adopting a hierarchical confidence rule, generating an eccentric correction path of the minimum rotation amount based on the size parameter of the to-be-positioned wafer, predicting the offset of the wafer relative to the center of the carrying table, and establishing a rotation compensation sequence; performing segmented rotation and micro-displacement adjustment on the carrying table according to the eccentric correction path, and performing pre-positioning on the wafer to be positioned under the condition of multiple sizes; the method does not need to depend on a template with a fixed size or a mechanical positioning structure, can automatically analyze the outer edge features of wafers with different sizes, and completes eccentric prediction and rotation compensation based on visual feedback.

The invention relates to the technical field of building automation and robots, and discloses a floor robot cutting system based on self-adaptive visual guidance. The technical problems that an existing floor cutting robot excessively depends on a preset path, and the adaptability is poor and the cutting precision is low when the actual working conditions that errors exist between on-site marking lines and design drawings and the ground is uneven exist are solved. An actual cutting datum line is captured and recognized in real time through a visual perception module carried on a robot body, and structured path information is generated; and then the self-adaptive control module dynamically plans and corrects the motion trail of the robot according to the deviation between the visual feedback information and the current pose of the robot so as to guide the cutting execution mechanism to accurately work, so that the accuracy loss caused by environment change and construction errors in a traditional scheme is effectively overcome, and the cutting efficiency is improved. And the operation precision, the field environment adaptability and the automation level of automatic floor cutting are remarkably improved.