Method and System for Compensating Positioning Errors of Surgical Robots via Optoelectronic Fusion

Abstract

This invention belongs to the field of medical robot technology and discloses a method and system for compensating for positioning errors in surgical robots through optoelectronic fusion. The system comprises an optical positioning acquisition module, an electromagnetic interference detection and auxiliary positioning module, an optoelectronic dynamic fusion and weight adaptive adjustment module, a real-time error dynamic modeling and feedback module, and a motion compensation control and path adjustment module. The optical positioning acquisition module uses a multi-angle camera and a three-dimensional distributed infrared marker design to capture actuator pose information from all angles. The electromagnetic interference detection and auxiliary positioning module monitors environmental interference in real time and calibrates electromagnetic positioning data by establishing an interference model. The optoelectronic dynamic fusion and weight adaptive adjustment module utilizes extended Kalman filtering and dynamic weighting algorithms to effectively fuse the two types of data, comprehensively eliminating adverse factors such as blind spots and environmental interference, accurately acquiring actuator pose, and significantly improving the accuracy and reliability of surgical positioning.

Description

Technical Field

[0001] This invention belongs to the field of medical robot technology, specifically a method and system for compensating for the photoelectric fusion positioning error of surgical robots. Background Technology

[0002] In the current clinical application of surgical robots, positioning accuracy is an important foundation for ensuring the safety and effectiveness of surgery. However, existing surgical robot positioning technology still has many technical bottlenecks, which seriously restrict its application effect and development prospects. First, at the data acquisition level, existing optical positioning technology generally relies on monocular cameras or image acquisition methods in a single direction. The acquisition perspective is limited, making it difficult to achieve accurate capture of the surgical robot actuator from multiple angles and all directions in three-dimensional space. This easily creates blind spots in the field of view, resulting in deviations in pose information.

[0003] Secondly, although electromagnetic positioning technology has strong real-time tracking capabilities, it is easily affected by metal instruments and complex electromagnetic interference signals in actual surgical environments. If the interference signals are not effectively identified and calibrated, significant errors will occur in the electromagnetic positioning data, reducing the accuracy and stability of the positioning. In addition, in the data processing and error correction stages, existing technologies mostly use data fusion algorithms with fixed parameters, which cannot dynamically adapt to real-time changes in the surgical environment and cannot adjust data weights according to the intensity of environmental interference and the movement state of the equipment, resulting in a lack of robustness and reliability in the fusion results.

[0004] Meanwhile, traditional error modeling mostly only compensates for static pose deviations, ignoring dynamic influencing factors such as motion speed and electromagnetic environment changes. This fails to fully reflect the actual error state, causing the compensation model to deviate from the actual operating conditions. Existing path correction methods mostly adopt open-loop control structures, lacking real-time closed-loop feedback, and cannot continuously correct the motion path during surgery, leading to continuous error accumulation and seriously affecting the motion accuracy and operational safety of the surgical robot. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for compensating for positioning errors by optoelectronic fusion in surgical robots, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a surgical robot optoelectronic fusion positioning error compensation system, which consists of an optical positioning acquisition module, an electromagnetic interference detection and auxiliary positioning module, an optoelectronic dynamic fusion and weight adaptive adjustment module, a real-time error dynamic modeling and feedback module, and a motion compensation control and path adjustment module;

[0007] Optical positioning and acquisition module: It uses cameras arranged at multiple angles to acquire real-time images of infrared marker points distributed in three dimensions on the actuator of the surgical robot, and outputs raw optical pose data to provide basic information for subsequent modules. Its multi-angle acquisition is the beginning of achieving accurate positioning.

[0008] Electromagnetic Interference Detection and Auxiliary Positioning Module: Receives data from the optical module, uses a high-sensitivity sensor array to detect environmental interference, models and calibrates electromagnetic positioning data to eliminate interference effects, ensures the reliability of positioning data, and lays a solid foundation for data fusion;

[0009] The photoelectric dynamic fusion and weight adaptive adjustment module: Based on the data from the first two modules, it uses extended Kalman filtering and adaptive algorithms to dynamically fuse the data, adjust the weights according to the environment, and output a high-confidence pose. It is the key link in the conversion from raw data to reliable pose data.

[0010] Real-time error dynamic modeling and feedback module: Receives fused pose, fused speed, environment and other factors to build a dynamic model, outputs error feedback parameters, accurately analyzes errors, provides a basis for path correction, and realizes precise control of error compensation;

[0011] Motion compensation control and path adjustment module: Based on error parameters, it uses motion path correction control formulas to transmit the control back to the execution system in a closed loop, adjusts the path in real time, and forms a complete compensation control closed loop to ensure the precise movement of the surgical robot. It is the final execution guarantee for error compensation.

[0012] Preferably, the optical positioning and acquisition module includes:

[0013] (1) Multi-angle optical image acquisition architecture: The optical positioning acquisition module achieves precise acquisition through innovative design. On the surgical robot actuator, the characteristic infrared markers are distributed in three dimensions to ensure that they can be captured from different perspectives. The camera array is arranged in multiple angles, covering multiple horizontal and vertical angles, forming an all-round visual acquisition area. Multiple cameras work together to acquire images of the infrared markers from different perspectives. Compared with monocular or unidirectional acquisition, it can obtain richer image information, laying a solid foundation for subsequent positioning;

[0014] (2) Optical Data Processing and Pose Calculation: The acquired image information needs to be professionally processed to obtain pose data. Using image processing algorithms, the position information of the marker points in the image can be accurately extracted. Combined with the camera calibration parameters, the three-dimensional spatial pose calculation formula is used to back-project the optical image coordinates to three-dimensional space, thereby obtaining the preliminary position of the robot actuator in space. This formula serves as the basic conversion tool of the optical system, establishing a connection between image information and actual spatial position, and finally outputting the original optical data of the target pose, significantly improving the accuracy and reliability of positioning.

[0015] Formula for calculating 3D spatial pose:

[0016]

[0017] In the formula: The target spatial pose coordinates (unit: mm) are measured optically. This is the camera intrinsic parameter matrix (including parameters such as focal length and optical center); The pixel coordinate matrix of infrared markers in the image plane.

[0018] Preferably, the electromagnetic interference detection and assisted positioning module includes:

[0019] (1) Electromagnetic Interference Monitoring and Data Acquisition Architecture: The electromagnetic interference detection and auxiliary positioning module is closely connected with the optical positioning and acquisition module. While receiving raw optical data, it undertakes the important task of monitoring the surgical environment. The module is equipped with a high-sensitivity electromagnetic sensor array, which can capture parameters such as electromagnetic signal intensity and frequency in the environment in real time, and conduct detection and analysis on the position and characteristics of metal equipment, accurately identify interference sources, and provide a detailed data basis for subsequent processing, thus creating a new paradigm that combines real-time electromagnetic interference monitoring and auxiliary positioning.

[0020] (2) Electromagnetic Positioning Data Calibration and Output: Based on the collected data, this module uses an electromagnetic interference model to separate useful electromagnetic positioning signals from interference signals. Using the electromagnetic positioning spatial transformation formula, the electromagnetic sensor data is converted to a spatial coordinate system consistent with the optical system through coordinate transformation, ensuring spatial consistency between the two types of data. Combined with the original optical data, the electromagnetic positioning data undergoes in-depth calibration and correction, ultimately outputting high-precision electromagnetic-assisted positioning data, effectively eliminating the influence of environmental interference and significantly improving the accuracy and stability of electromagnetic positioning.

[0021] Electromagnetic positioning space transformation formula:

[0022]

[0023] In the formula: Spatial pose coordinates calculated by the electromagnetic eye tracking system (unit: mm); The transformation matrix from electromagnetic beacon to global coordinate system; Relative pose data measured by electromagnetic sensors.

[0024] Preferably, the photoelectric dynamic fusion and weight adaptive adjustment module includes:

[0025] (1) Data fusion architecture based on extended Kalman filter: The photoelectric dynamic fusion and weight adaptive adjustment module is connected to the electromagnetic interference detection and auxiliary positioning module, taking optical positioning and electromagnetic positioning data as input. With the help of extended Kalman filter, a filtering algorithm suitable for nonlinear systems, the two types of data are dynamically fused. It can effectively handle the uncertainty in the data, and through prediction and update steps, integrate optical and electromagnetic positioning information, laying the foundation for obtaining accurate pose data and building the core framework of data fusion;

[0026] (2) Dynamic Weight Adjustment and High-Confidence Pose Output: In the data fusion process, the environmental interference adaptive weight adjustment algorithm plays a crucial role. Based on changes in the surgical environment, such as the strength of electromagnetic interference and the optical acquisition status, the extended Kalman filter fusion formula (including dynamic weights) is used to dynamically adjust the weights of optical and electromagnetic data in real time. When electromagnetic interference increases, the formula reduces the weight of electromagnetic data; when optical acquisition is limited, the weight ratio of optical data is reduced. By continuously monitoring environmental parameters and positioning accuracy, adaptive data fusion is achieved, and a high-confidence fused pose that accurately reflects the actual pose of the actuator is output.

[0027] Extended Kalman filter fusion formula (including dynamic weights):

[0028] State prediction:

[0029]

[0030] Status Update:

[0031]

[0032] Dynamic weight adjustment:

[0033]

[0034] In the formula: For predicted state variables (unit: mm); To control the input at the previous moment; The overall Kalman gain at the current moment; The current observation; For observation model functions; For optical data weighting coefficients; For electromagnetic data weighting coefficients; , For optical and electromagnetic sub-Kalman gain.

[0035] Preferably, the real-time error dynamic modeling and feedback module includes:

[0036] (1) Multi-source data integration and error influencing factor analysis: The real-time error dynamic modeling and feedback module is connected to the photoelectric dynamic fusion module to receive high-precision fused pose data. During the operation of the surgical robot, its motion speed, pose state, and complex surgical environment will have a significant impact on the positioning error. This module collects the actuator motion speed information in real time through high-precision sensors, and combines it with the electromagnetic environment data obtained by the electromagnetic interference detection and auxiliary positioning module to comprehensively integrate multi-source information and deeply analyze the mechanism of various factors on the positioning error, providing rich and accurate data support for subsequent error modeling;

[0037] (2) Dynamic Error Modeling and Feedback Parameter Output: Based on integrated multi-source data, the module utilizes a velocity-pose-environment joint feedback mechanism to construct an innovative dynamic error compensation system. The core velocity-pose-environment coupled error dynamic equation breaks through by coupling velocity error and electromagnetic interference factors into the dynamic error feedback, abandoning the traditional static compensation mode. Through this equation, combined with fused pose and other data, the error is dynamically calculated, and the real-time motion error feedback parameters are accurately output. These parameters provide a reliable basis for subsequent motion compensation control and path adjustment, realizing dynamic and accurate compensation for the motion error of the surgical robot.

[0038] Dynamic equation for velocity-pose-environment coupling error:

[0039]

[0040]

[0041] The positioning error at the current moment (unit: mm); Fusion target pose (unit: mm); Desired speed (unit: mm / s); Actual speed (unit: mm / s); Real-time detection value of electromagnetic interference field strength; This is the speed error weighting coefficient; Environmental interference error weighting coefficient.

[0042] Preferably, the motion compensation control and path adjustment module includes:

[0043] (1) Error Feedback-Driven Path Correction Architecture: The motion compensation control and path adjustment module is closely connected to the error dynamic modeling and feedback module, receiving motion error feedback parameters as the basis for path correction. This module uses the motion path correction control formula to automatically correct the robot's control path based on the error feedback information. By adjusting the motion angles and speeds of the robot's joints, the robot can move along the corrected path, achieving initial control from error feedback to path correction, laying the foundation for subsequent closed-loop optimization;

[0044] (2) Dynamic Closed-Loop Control and Precise Motion Output: After completing the initial path correction, the motion compensation control and path adjustment module transmits the correction command back to the surgical robot execution system in a closed loop, while simultaneously monitoring the robot's actual motion in real time. The motion path correction control formula is then used again to incorporate the deviation between the actual motion and the corrected path into the calculation. Through error feedback, the motion control path is dynamically corrected, and precise correction commands are output again, forming a full-process closed-loop control. This formula is continuously iterated and optimized to ensure that the robot can adjust its path promptly based on real-time errors during surgical operations, effectively compensating for motion errors, significantly improving the motion control precision and accuracy of the surgical robot, and ensuring the smooth progress of the surgical operation.

[0045] Motion path correction control formula:

[0046]

[0047] In the formula: For control input (motion command) after motion compensation; Original motion control commands; Path gain adjustment matrix; The motion error (unit: mm) output by the error dynamic modeling module.

[0048] This invention also provides a method for compensating for the photoelectric fusion positioning error of a surgical robot, which uses the aforementioned photoelectric fusion positioning error compensation system for surgical robots. The specific steps of this method are as follows:

[0049] S1. Real-time acquisition of multi-source data: The optical positioning acquisition module acquires the pose optical data of the surgical robot actuator in real time, and the electromagnetic interference detection and auxiliary positioning module acquires the environmental electromagnetic interference data in real time to ensure data reliability and lay a solid foundation for subsequent processing.

[0050] S2. Optoelectronic Fusion and Error Modeling: Based on the data collected in the previous step, the optoelectronic dynamic fusion module dynamically fuses the data to generate a high-confidence pose. Then, the error modeling module constructs a coupled model and outputs error feedback parameters to provide a basis for path correction.

[0051] S3. Motion Compensation Path Correction: Based on the error feedback parameters, the motion compensation module corrects the robot path in real time and transmits the instructions back to the execution system in a closed loop. By comparing the actual path with the corrected path, it continuously adjusts to achieve precise motion control and ensure the accuracy of surgical operations.

[0052] Preferably, the specific steps for real-time acquisition of multi-source data in step S1 are as follows:

[0053] S11. Dual-module collaborative data acquisition mechanism: The optical positioning acquisition module and the electromagnetic interference detection module work together to achieve real-time acquisition of multi-source data. The optical module captures images of characteristic infrared markers distributed in three dimensions on the surgical robot actuator at a preset frequency using multi-angle cameras. After image processing and calculation, it outputs raw optical data. The electromagnetic module uses a high-sensitivity sensor array to monitor metal equipment and electromagnetic signals in the surgical environment in real time. After interference model analysis and processing, it outputs electromagnetic-assisted positioning data. The parallel acquisition by the two modules provides a foundation for subsequent processing.

[0054] S12. Data Acquisition Reliability Assurance: This synchronous acquisition method, for the first time, combines real-time electromagnetic interference detection with assisted positioning. Optical data provides actuator pose information, while electromagnetic data takes into account environmental interference. The two complement each other, ensuring the dynamic reliability of the data source. This provides a reliable data foundation for subsequent optoelectronic fusion, error modeling, and motion compensation, fundamentally improving the overall reliability of surgical robot positioning and control.

[0055] Preferably, the specific steps of photoelectric fusion and error modeling in step S2 are as follows:

[0056] S21. Dynamic Fusion Processing of Optical and Electromagnetic Data: Optical and electromagnetic data acquired in real time from multiple sources are input into the dynamic fusion and adaptive weight adjustment module. Using the extended Kalman filter algorithm, the two types of data are deeply fused. This algorithm is suitable for nonlinear systems and can effectively handle uncertainties in the data. Simultaneously, the environmental interference adaptive weight adjustment algorithm plays a role, dynamically adjusting the weights of optical and electromagnetic data in the fusion process in real time based on changes in the surgical environment, such as electromagnetic interference intensity and optical acquisition conditions. This generates a high-confidence fused pose in real time, providing an accurate data foundation for subsequent error modeling.

[0057] S22. Dynamic Error Modeling and Parameter Output: The high-confidence fused pose obtained from photoelectric dynamic fusion is transmitted to the error dynamic modeling and feedback module. This module, based on a velocity-pose-environment joint feedback mechanism, introduces velocity feedback and electromagnetic environment factors to construct a dynamic error compensation mathematical model. Combining real-time acquired motion velocity information and electromagnetic environment information, it comprehensively considers the influence of multiple factors on the error and outputs motion error feedback parameters through mathematical calculation and analysis. This dual closed-loop structure of environment-adaptive weight adjustment and dynamic error modeling significantly improves the adaptability and accuracy of error compensation in complex surgical environments.

[0058] Preferably, the specific steps for motion compensation path correction in step S3 are as follows:

[0059] S31. Error-Driven Real-Time Path Correction: The motion compensation path correction stage is driven by the motion error feedback parameters output in step 2. The motion compensation control and path adjustment module is equipped with an advanced path planning algorithm. Upon receiving the error parameters, it immediately corrects the robot control path in real time. By precisely adjusting the motion angles and speed parameters of each joint of the robot, it generates control commands that meet the correction requirements and transmits them to the surgical robot execution system to drive the robot to move along the new path, completing the initial error compensation.

[0060] S32. Closed-Loop Feedback Optimized Motion Control: When the robot executes corrected path motion, the motion compensation control and path adjustment module continuously monitors its actual motion trajectory in real time. By frequently comparing the actual motion with the corrected target path, the deviation data between the two is captured. Based on the motion path correction control logic, the path planning is adjusted again and the control commands are updated, forming a closed-loop compensation mechanism of "monitoring-comparison-correction-feedback" throughout the entire process. Compared with traditional offline correction methods, this closed-loop control can dynamically respond to error changes, significantly improving the motion control accuracy of the surgical robot and the reliability of surgical operations.

[0061] The beneficial effects of this invention are as follows:

[0062] 1. This invention significantly improves the positioning accuracy of surgical robots through multi-module collaborative innovation. The optical positioning and acquisition module breaks through the limitations of traditional monocular or unidirectional acquisition by adopting a multi-angle camera and a three-dimensional distributed infrared marker design to capture actuator pose information from all directions. The electromagnetic interference detection and auxiliary positioning module monitors environmental interference in real time and calibrates electromagnetic positioning data by establishing an interference model. The photoelectric dynamic fusion and weight adaptive adjustment module utilizes extended Kalman filtering and dynamic weighting algorithms to effectively fuse the two types of data, which can eliminate adverse factors such as blind spots and environmental interference from all directions, accurately obtain actuator pose, and significantly improve the accuracy and reliability of surgical positioning.

[0063] 2. This invention constructs a dual environmental adaptation mechanism. The electromagnetic interference detection and assisted positioning module, with its high-sensitivity sensor array, monitors metal equipment and electromagnetic signals in the surgical environment in real time. By separating useful signals through an interference model, it reduces the impact of environmental interference on electromagnetic positioning. The photoelectric dynamic fusion and weight adaptive adjustment module can adjust the weights of optical and electromagnetic data in real time according to environmental changes, ensuring the accuracy of the fused pose. It can operate stably in complex electromagnetic environments and under conditions of obstructed optical acquisition, greatly enhancing the environmental adaptability of the surgical robot in different surgical scenarios.

[0064] 3. This invention achieves a leap in the accuracy of surgical robot motion control through a dynamic error modeling and closed-loop control system. The real-time error dynamic modeling and feedback module integrates multiple factors such as speed, pose, and environment to construct a dynamic error compensation model and accurately calculate motion error feedback parameters. After receiving the parameters, the motion compensation control and path adjustment module uses an advanced path planning algorithm to correct the path in real time and transmits the command closed loop back to the execution system, forming a dynamic closed-loop control of "monitoring-feedback-correction". This can respond to motion errors in real time, continuously optimize the robot's motion path, significantly improve the motion control accuracy of the surgical robot, and effectively reduce the risks of surgical operations. Attached Figure Description

[0065] Figure 1 This is a flowchart of the optoelectronic fusion positioning error compensation system for the surgical robot of the present invention;

[0066] Figure 2 This is a flowchart of the optoelectronic fusion positioning error compensation method for the surgical robot of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] like Figures 1 to 2 As shown in the figure, the present invention provides a surgical robot optoelectronic fusion positioning error compensation system, which consists of an optical positioning acquisition module, an electromagnetic interference detection and auxiliary positioning module, an optoelectronic dynamic fusion and weight adaptive adjustment module, a real-time error dynamic modeling and feedback module, and a motion compensation control and path adjustment module.

[0069] Optical positioning and acquisition module: It acquires infrared marker point images by arranging cameras at multiple angles, and outputs raw optical data by combining a specific spatial distribution design, providing basic information for subsequent modules. Its multi-angle acquisition is the starting point for achieving accurate positioning.

[0070] Electromagnetic Interference Detection and Auxiliary Positioning Module: Receives data from the optical module, uses a high-sensitivity sensor array to detect environmental interference, models and calibrates electromagnetic positioning data to eliminate interference effects, ensures the reliability of positioning data, and lays a solid foundation for data fusion;

[0071] The photoelectric dynamic fusion and weight adaptive adjustment module: Based on the data from the first two modules, it uses extended Kalman filtering and adaptive algorithms to dynamically fuse the data, adjust the weights according to the environment, and output a high-confidence pose. It is the key link in the conversion from raw data to reliable pose data.

[0072] Real-time error dynamic modeling and feedback module: Receives fused pose, fused speed, environment and other factors to build a dynamic model, outputs error feedback parameters, accurately analyzes errors, provides a basis for path correction, and realizes precise control of error compensation;

[0073] Motion compensation control and path adjustment module: Based on error parameters, it uses motion path correction control formulas to transmit the control back to the execution system in a closed loop, adjusts the path in real time, and forms a complete compensation control closed loop to ensure the precise movement of the surgical robot. It is the final execution guarantee for error compensation.

[0074] This invention significantly improves the performance of surgical robots through multi-module collaboration and dual-mechanism innovation. Regarding positioning accuracy, the optical positioning acquisition module, using multi-angle cameras and 3D infrared markers, overcomes the limitations of traditional acquisition methods, comprehensively capturing the actuator's pose. The electromagnetic interference detection and auxiliary positioning module monitors environmental interference in real time and calibrates electromagnetic positioning data. The photoelectric dynamic fusion and weight adaptive adjustment module utilizes extended Kalman filtering and dynamic weighting algorithms to effectively fuse data, eliminate perspective and environmental interference, and achieve precise positioning. In terms of environmental adaptability, the electromagnetic interference detection and auxiliary positioning module, with its high-sensitivity sensor array and interference model, reduces the impact of the environment on electromagnetic positioning. The photoelectric dynamic fusion module adjusts data weights in real time according to environmental changes, ensuring accurate fused pose, enabling the surgical robot to operate stably in complex electromagnetic and optically obstructed environments, thus broadening its application range.

[0075] Among them, the optical positioning and acquisition module adopts a multi-angle optical image acquisition mode in terms of acquisition architecture. It sets characteristic infrared markers distributed in three dimensions on the surgical robot actuator, and works with a multi-angle camera array to construct an all-round visual acquisition area from different horizontal and vertical angles. The collaborative operation of multiple cameras greatly improves the richness of image information acquisition, which is more advantageous than traditional monocular or unidirectional acquisition.

[0076] Image processing algorithms are used to accurately extract the position information of marker points in the image. Combined with camera calibration parameters and three-dimensional spatial pose calculation formula, the optical image coordinates are back-projected into three-dimensional space to achieve effective correlation between image information and actual spatial position. This results in the output of high-precision target pose raw optical data, which significantly enhances the accuracy and reliability of positioning and provides a solid data foundation for subsequent data fusion and error compensation.

[0077] Among them, the electromagnetic interference detection and auxiliary positioning module is deeply connected to the optical positioning and acquisition module in the electromagnetic interference monitoring and data acquisition architecture. While receiving raw optical data, it uses a high-sensitivity electromagnetic sensor array to monitor parameters such as electromagnetic signal strength and frequency in the surgical environment in real time, and analyzes the position and characteristics of metal equipment to accurately identify interference sources, providing rich and accurate data for subsequent processing.

[0078] In the electromagnetic positioning data calibration and output stage, the module uses an electromagnetic interference model to process the collected data, separating the useful electromagnetic positioning signal from the interference signal. Using the electromagnetic positioning spatial transformation formula, the electromagnetic sensor data is transformed to a spatial coordinate system consistent with the optical system, ensuring spatial consistency between the two types of data. Then, combined with the original optical data, the electromagnetic positioning data undergoes in-depth calibration and correction, ultimately outputting high-precision electromagnetic-assisted positioning data. This effectively eliminates the influence of environmental interference on electromagnetic positioning, significantly improving the accuracy and stability of electromagnetic positioning.

[0079] The optoelectronic dynamic fusion and weight adaptive adjustment module is deeply integrated with the electromagnetic interference detection and assisted positioning module. Using optical and electromagnetic positioning data as input, it employs an extended Kalman filter algorithm to construct the core data fusion framework. This algorithm, addressing the nonlinear characteristics of surgical scenarios, effectively eliminates data uncertainty through iterative prediction and update steps, achieving the initial integration of optical and electromagnetic positioning information.

[0080] Building upon this foundation, the environmental interference adaptive weight adjustment algorithm dynamically adjusts the weights based on changes in the surgical environment. Utilizing an extended Kalman filter fusion formula (including dynamic weights), it senses parameters such as electromagnetic interference intensity and optical acquisition stability in real time. When the number of electromagnetic devices in the operating room surges, the system automatically reduces the weight of electromagnetic data; when optical obstruction occurs, it dynamically increases the reliability of optical data. Through this dual-mechanism collaborative operation, the module continuously optimizes the data fusion strategy, ultimately outputting a high-confidence fusion result that closely matches the actual actuator pose, providing accurate data support for subsequent error compensation.

[0081] The real-time error dynamic modeling and feedback module, through a two-stage innovative architecture, achieves high-precision error analysis and parameter output. At the level of multi-source data integration and error influencing factor analysis, this module is deeply integrated with the photoelectric dynamic fusion module, receiving high-confidence fused pose data and relying on high-precision sensors to collect actuator motion speed information in real time, while simultaneously integrating electromagnetic environment data acquired by the electromagnetic interference detection and auxiliary positioning modules. Through systematic analysis of multi-dimensional factors such as motion speed, pose state, and surgical environment, the mechanism by which each factor affects positioning error is deeply explored.

[0082] In the dynamic error modeling and feedback parameter output stage, the module is based on a velocity-pose-environment joint feedback mechanism. It innovatively integrates velocity error and electromagnetic interference factors into the error dynamic feedback system using a core velocity-pose-environment coupled error dynamic equation. This equation dynamically calculates and outputs real-time motion error feedback parameters from fused pose and other data, providing a reliable basis for subsequent motion compensation control and path adjustment. This achieves dynamic and precise compensation for the surgical robot's motion errors, significantly improving the timeliness and accuracy of error compensation.

[0083] The motion compensation control and path adjustment module achieves precise control of the motion path through a two-layer innovative architecture. This module is deeply interconnected with the error dynamic modeling and feedback module, using the received motion error feedback parameters as the core driver for path correction. Based on the motion path correction control formula, the module automatically corrects the robot's control path. By precisely adjusting key parameters such as the motion angle and speed of each joint, it generates appropriate control commands and transmits them to the execution system, driving the robot to move along the corrected path, completing the initial compensation of errors, and laying the foundation for subsequent closed-loop optimization.

[0084] Based on the initial corrections, the module constructs a dynamic closed-loop control system. While transmitting correction commands back to the execution system, it continuously monitors the robot's actual motion trajectory in real time. Utilizing the motion path correction control formula again, the system incorporates the deviation between the actual motion and the target path into iterative calculations, dynamically optimizing the motion control path through an error feedback mechanism. This formula, through continuous iterative updates, achieves real-time optimization and output of control commands, forming a complete closed-loop control process of "monitoring-feedback-correction-execution." Compared to traditional open-loop correction techniques, this dynamic closed-loop architecture can rapidly and accurately adjust the robot's motion path based on real-time errors generated during the surgical procedure, significantly improving the motion control accuracy of the surgical robot and providing a solid guarantee for the safety and effectiveness of surgical operations.

[0085] This invention also provides a method for compensating for the photoelectric fusion positioning error of a surgical robot, using the aforementioned photoelectric fusion positioning error compensation system for surgical robots. The specific steps of this method are as follows:

[0086] S1. Real-time acquisition of multi-source data: The optical positioning acquisition module and the electromagnetic interference detection module operate synchronously. The former acquires the actuator's pose optical data from multiple angles, while the latter monitors the environment to obtain electromagnetic positioning data, ensuring data reliability and laying a solid foundation for subsequent processing.

[0087] S2. Optoelectronic Fusion and Error Modeling: Based on the data collected in the previous step, the optoelectronic dynamic fusion module dynamically fuses the data to generate a high-confidence pose. Then, the error modeling module constructs a coupled model and outputs error feedback parameters to provide a basis for path correction.

[0088] S3. Motion Compensation Path Correction: Based on the error feedback parameters, the motion compensation module corrects the robot path in real time and transmits the instructions back to the execution system in a closed loop. By comparing the actual path with the corrected path, it continuously adjusts to achieve precise motion control and ensure the accuracy of surgical operations.

[0089] This invention achieves a breakthrough improvement in the motion control accuracy of surgical robots by relying on a dynamic error modeling and closed-loop control system. The real-time error dynamic modeling and feedback module integrates multiple factors such as speed, pose, and environment to construct a dynamic error compensation model, accurately outputting motion error feedback parameters. After receiving the parameters, the motion compensation control and path adjustment module uses advanced path planning algorithms to correct the path in real time and transmits the command closed loop back to the execution system, forming a dynamic closed loop of "monitoring-feedback-correction." This system can respond to errors in real time and continuously optimize the path, significantly improving motion control accuracy and significantly reducing surgical operation risks compared to traditional technologies.

[0090] In step S1, real-time multi-source data acquisition refers to the collaborative operation of the optical positioning acquisition module and the electromagnetic interference detection module during the real-time acquisition process. The former, using cameras arranged at multiple angles, acquires images of characteristic infrared markers distributed in three dimensions on the surgical robot actuator at a preset frequency. After image processing algorithms and calculations, it outputs accurate raw optical data to obtain the actuator's pose information. The latter utilizes a high-sensitivity electromagnetic sensor array to monitor the status of metal equipment and electromagnetic signals in the surgical environment in real time. Through electromagnetic interference model analysis and processing, it outputs electromagnetic-assisted positioning data to understand the environmental interference situation.

[0091] This synchronous acquisition method achieves an innovative combination of real-time electromagnetic interference detection and assisted positioning. The two types of data complement and corroborate each other, ensuring the dynamic reliability of the acquired data source. This provides solid and reliable data support for key aspects such as subsequent photoelectric data fusion, error modeling and analysis, and motion compensation control, thereby improving the overall reliability of surgical robot positioning and control from the source of data acquisition.

[0092] In step S2, optoelectronic fusion and error modeling refers to constructing an environment-adaptive dual-closed-loop structure through two main stages: dynamic fusion processing of optoelectronic data and dynamic error modeling. First, optical and electromagnetic data acquired in real-time from multiple sources are input into the optoelectronic dynamic fusion and weight adaptive adjustment module. Using the extended Kalman filter algorithm, the uncertainty in the data is effectively handled to address the nonlinear characteristics of the surgical scenario, achieving deep fusion of the two types of data. Simultaneously, the environmental interference adaptive weight adjustment algorithm dynamically adjusts the weight ratio of optical and electromagnetic data in the fusion process in real-time based on changes in electromagnetic interference intensity and whether optical acquisition is obstructed in the surgical environment, thereby generating a high-confidence fusion pose and laying a solid data foundation for subsequent error modeling.

[0093] Then, the high-confidence fused pose is transmitted to the error dynamic modeling and feedback module. This module, based on a velocity-pose-environment joint feedback mechanism, innovatively introduces velocity feedback and electromagnetic environment factors to construct a dynamic error compensation mathematical model. Combining real-time acquired motion velocity information and electromagnetic environment information, it comprehensively analyzes the impact of multiple factors on the error, and outputs motion error feedback parameters through precise mathematical calculations and analysis. This dual closed-loop structure can significantly improve the adaptability and accuracy of error compensation to complex environments, ensuring the reliability of the surgical robot's positioning, based on dynamic changes in the surgical environment.

[0094] In step S3, motion compensation path correction refers to building an efficient and precise motion control system through two core components: error-driven real-time correction and closed-loop feedback optimization. First, using the motion error feedback parameters output in step S2 as the driving source, the motion compensation control and path adjustment module, relying on advanced path planning algorithms, rapidly corrects the robot's control path in real time the moment the error parameters are acquired. By precisely adjusting the motion angles and speed parameters of each joint of the robot, precise control commands are generated and transmitted to the surgical robot execution system, driving the robot to move along the corrected path, completing the initial compensation for motion errors and laying the foundation for subsequent optimization.

[0095] After initial corrections, the system enters the closed-loop feedback optimization phase. The motion compensation control and path adjustment module continuously monitors the robot's actual motion trajectory in real time, comparing it frequently with the corrected target path to accurately capture any deviations. Based on the motion path correction control logic, the path planning is readjusted and control commands are updated, forming a complete closed-loop compensation mechanism of "monitoring-comparison-correction-feedback." This mechanism can dynamically respond to error changes that occur at any time during the surgery, significantly improving the motion control accuracy of the surgical robot compared to traditional offline correction methods, and providing a solid guarantee for the reliability of surgical operations.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A surgical robot photoelectric fusion positioning error compensation system, characterized in that: The system consists of an optical positioning and acquisition module, an electromagnetic interference detection and assisted positioning module, an optoelectronic dynamic fusion and weight adaptive adjustment module, a real-time error dynamic modeling and feedback module, and a motion compensation control and path adjustment module. Optical positioning and acquisition module: Real-time acquisition of three-dimensional infrared marker point images on the actuator of the surgical robot through cameras arranged at multiple angles, and output of raw optical pose data; Electromagnetic interference detection and assisted positioning module: Upon receiving the raw optical data, it uses a high-sensitivity sensor array to detect environmental interference, and then models and calibrates the electromagnetic positioning data to eliminate the effects of interference; Photoelectric dynamic fusion and weight adaptive adjustment module: Based on the output data of the optical positioning acquisition module and the electromagnetic interference detection and auxiliary positioning module, the module uses extended Kalman filtering and adaptive algorithm to dynamically fuse the data, adjust the weights according to the environment, and output a high-confidence fused pose. Real-time error dynamic modeling and feedback module: Receives fused pose, fused speed and environmental factors to build a dynamic model, and outputs error feedback parameters; Motion compensation control and path adjustment module: Based on error feedback parameters, it uses motion path correction control formulas to transmit the control back to the execution system in a closed loop, adjusts the path in real time, and forms a compensation control closed loop.

2. The optoelectronic fusion positioning error compensation system for surgical robots according to claim 1, characterized in that: The optical positioning and acquisition module includes: (1) Multi-angle optical image acquisition architecture: On the surgical robot actuator, the feature infrared markers are distributed in three dimensions, and the camera array is arranged in multiple angles to form an all-round visual acquisition area; (2) Optical data processing and pose calculation: Using image processing algorithms, the position information of the marker points in the image is accurately extracted. Combined with the camera calibration parameters, the pose calculation formula in three-dimensional space is used to back-project the optical image coordinates to three-dimensional space, thereby obtaining the preliminary position of the robot actuator in space. Formula for calculating 3D spatial pose: In the formula: The target spatial pose coordinates are used for optical measurements. This is the camera intrinsic parameter matrix; The pixel coordinate matrix of infrared markers in the image plane.

3. The optoelectronic fusion positioning error compensation system for surgical robots according to claim 1, characterized in that: The electromagnetic interference detection and assisted positioning module includes: (1) Electromagnetic interference monitoring and data acquisition architecture: Equipped with a high-sensitivity electromagnetic sensor array, it captures the electromagnetic signal strength and frequency parameters in the environment in real time, and conducts detection and analysis on the position and characteristics of metal equipment to accurately identify the interference source; (2) Electromagnetic positioning data calibration and output: The useful electromagnetic positioning signal and the interference signal are separated by the electromagnetic interference model. With the help of the electromagnetic positioning space transformation formula, the electromagnetic sensor data is transformed into a spatial coordinate system that is consistent with the optical system through coordinate transformation. Combined with the original optical data, the electromagnetic positioning data is deeply calibrated and corrected. Electromagnetic positioning space transformation formula: In the formula: Spatial pose coordinates calculated by the electromagnetic eye-tracking system; The transformation matrix from electromagnetic beacon to global coordinate system; Relative pose data measured by electromagnetic sensors.

4. The optoelectronic fusion positioning error compensation system for surgical robots according to claim 1, characterized in that: The photoelectric dynamic fusion and weight adaptive adjustment module includes: (1) Data fusion architecture based on extended Kalman filter: Based on optical positioning and electromagnetic positioning data, the two types of data are dynamically fused by using the filtering algorithm of the extended Kalman filter nonlinear system. Through prediction and update steps, the optical and electromagnetic positioning information are integrated. (2) Dynamic weight adjustment and high confidence pose output: Based on the changes in the surgical environment, the weights of optical and electromagnetic data are dynamically adjusted in real time using the extended Kalman filter fusion formula. By continuously monitoring environmental parameters and positioning accuracy, a high confidence fused pose reflecting the actual pose of the actuator is output. Extended Kalman filter fusion formula: State prediction: Status Update: Dynamic weight adjustment: In the formula: To predict state variables; To control the input at the previous moment; The overall Kalman gain at the current moment; The current observation; For the observation model function; For optical data weighting coefficients; For electromagnetic data weighting coefficients; , For optical and electromagnetic sub-Kalman gain.

5. The optoelectronic fusion positioning error compensation system for surgical robots according to claim 1, characterized in that: The real-time error dynamic modeling and feedback module includes: (1) Multi-source data integration and error influencing factor analysis: During the operation of the surgical robot, the motion speed information of the actuator is collected in real time by high-precision sensors. Combined with the electromagnetic environment data obtained by the electromagnetic interference detection and auxiliary positioning module, the multi-source information is fully integrated and the mechanism of various factors on the positioning error is analyzed in depth. (2) Dynamic error modeling and feedback parameter output: Based on integrated multi-source data, an innovative dynamic error compensation system is constructed by using the velocity-pose-environment joint feedback mechanism. The core velocity-pose-environment coupled error dynamic equation is used to dynamically calculate the error and accurately output the motion error feedback parameters by combining the fused pose data. Dynamic equation for velocity-pose-environment coupling error: This represents the positioning error at the current moment. Fuse target pose; Expected speed; Actual speed; Real-time detection value of electromagnetic interference field strength; This is the speed error weighting coefficient; Environmental interference error weighting coefficient.

6. The optoelectronic fusion positioning error compensation system for surgical robots according to claim 1, characterized in that: The motion compensation control and path adjustment module includes: (1) Error feedback driven path correction architecture: After receiving motion error feedback parameters, the motion path correction control formula is used to automatically correct the robot's control path based on the error feedback information, and the motion angle and speed parameters of each joint of the robot are adjusted. (2) Dynamic closed-loop control and precise motion output: After completing the initial path correction, the correction command is sent back to the surgical robot execution system in a closed loop. At the same time, the actual motion of the robot is monitored in real time. The motion path correction control formula is used again to incorporate the deviation between the actual motion and the correction path into the calculation. The motion control path is dynamically corrected through error feedback. Motion path correction control formula: In the formula: The control input after motion compensation; Original motion control commands; Path gain adjustment matrix; The motion error output by the error dynamic modeling module.

7. A method for compensating for the photoelectric fusion positioning error of a surgical robot, employing the photoelectric fusion positioning error compensation system for a surgical robot as described in any one of claims 1-6, characterized in that: The specific steps of this method are as follows: S1. Real-time acquisition of multi-source data: The optical positioning acquisition module acquires the pose optical data of the surgical robot actuator in real time, and the electromagnetic interference data of the environment is acquired in real time through the electromagnetic interference detection and auxiliary positioning module. S2, Optoelectronic Fusion and Error Modeling: Based on the data collected in step S1, the optoelectronic dynamic fusion and weight adaptive adjustment module realizes dynamic data fusion to generate a high-confidence pose. Then, the real-time error dynamic modeling and feedback module constructs a coupled model and outputs error feedback parameters. S3. Motion Compensation Path Correction: Based on the error feedback parameters, the motion compensation module corrects the robot path in real time and transmits the instructions back to the execution system in a closed loop. By comparing the actual path with the corrected path, it continuously adjusts and achieves precise motion control.

8. The method for compensating for the photoelectric fusion positioning error of a surgical robot according to claim 7, characterized in that: The specific steps for real-time acquisition of multi-source data in step S1 are as follows: S11, Dual-module collaborative data acquisition mechanism: The optical positioning acquisition module captures images of the characteristic infrared markers distributed in three dimensions on the surgical robot actuator at a preset frequency, and outputs the raw optical data after image processing and calculation. The electromagnetic interference detection and auxiliary positioning module monitors metal devices and electromagnetic signals in the surgical environment in real time, and outputs electromagnetic auxiliary positioning data after interference model analysis and processing. S12. Reliability assurance of data acquisition: For the first time, real-time electromagnetic interference detection is combined with assisted positioning. Optical data provides actuator pose information, while electromagnetic assisted positioning data takes into account environmental interference. The two complement each other.

9. The method for compensating for the photoelectric fusion positioning error of a surgical robot according to claim 8, characterized in that: The specific steps for photoelectric fusion and error modeling in step S2 are as follows: S21. Dynamic fusion processing of optoelectronic data: After receiving optical data and electromagnetic assisted positioning data acquired in real time from multiple sources, the two types of data are deeply fused using the extended Kalman filter algorithm to handle the uncertainty in the data. At the same time, the environmental interference adaptive weight adjustment algorithm dynamically adjusts the weights of optical and electromagnetic data in the fusion in real time according to changes in the surgical environment, thereby generating a high-confidence fused pose in real time. S22. Dynamic Error Modeling and Parameter Output: After receiving the high-confidence fused pose obtained by photoelectric dynamic fusion, a dynamic error compensation mathematical model is constructed based on the velocity-pose-environment joint feedback mechanism, introducing velocity feedback and electromagnetic environment factors. Through mathematical calculation and analysis, motion error feedback parameters are output.

10. The method for compensating for the photoelectric fusion positioning error of a surgical robot according to claim 9, characterized in that: The specific steps for motion compensation path correction in step S3 are as follows: S31. Error-driven real-time path correction: After taking the motion error feedback parameters output in step S2 as the driving core, the robot control path is immediately corrected in real time. By precisely adjusting the motion angle and speed parameters of each joint of the robot, control commands that meet the correction requirements are generated and transmitted to the surgical robot execution system. S32. Closed-loop feedback optimized motion control: When the robot executes the corrected path motion, the deviation data between the two is captured by comparing the actual motion with the corrected target path at high frequency. Based on the motion path correction control logic, the path planning is adjusted again and the control command is updated, forming a closed-loop compensation mechanism for the entire process of monitoring-comparison-correction-feedback.

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