Fuel assembly handling robot motion control system and method

Abstract

The invention belongs to the technical field of nuclear power, and particularly relates to a motion control system and method for a fuel assembly operation robot. According to the invention, through a strategy of dynamically generating the matching template, the generation of the template is based on the real-time sensed fuel assembly state information (such as rough position, attitude estimation and key feature points) in cooperation with the preset assembly feature knowledge base (such as geometric structure, key contour feature and grabbing interface position relation); the special packaging tool realizes real-time template generation on the fitted contour surface, and meets the high-precision positioning requirement of motion control of an operation robot. In addition, a special data assembly algorithm set and a matched information warehouse are packaged, so that the whole fuel assembly operation robot device has task configuration and full-automatic task implementation capabilities.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of nuclear power, and particularly relates to a fuel assembly operating robot motion control system and method. BACKGROUND

[0002] In the fuel assembly production process, the fuel assembly operating robot device needs to accurately complete the grabbing, lifting, transporting and placing operations of the fuel assembly in the specified area, and needs to avoid damage to the fuel assembly due to extrusion and collision during the operation. Generally, problems exist in the fuel assembly operation process as follows:

[0003] (1) Work environment interference: the light reflection phenomenon caused by the refraction / scattering of lighting facilities in the working environment affects the imaging quality and recognition accuracy of traditional vision (such as a camera) or optical sensors;

[0004] (2) Fuel assembly state: the fuel assembly may have conditions such as inclination, slight deformation, grid obstruction, surface attachments (such as oxide layer, debris) before being grabbed, resulting in a large difference between the profile features presented and the ideal template;

[0005] (3) Operation precision requirement: the alignment precision requirement of the fuel assembly grabbing interface (such as the gripper and the assembly top grid plate) is usually not less than millimeter level, and any deviation may cause grabbing failure, assembly damage or even safety accidents;

[0006] (4) Limited pre-stored template type: the traditional matching method based on pre-stored fixed templates (such as templates generated based on three-dimensional models or standard pictures) is difficult to cover all complex and variable environments and assembly states, and has insufficient robustness and high mis-matching rate. SUMMARY

[0007] In order to overcome the problems in the related art, a fuel assembly operating robot motion control system and method are provided.

[0008] According to an aspect of an embodiment of the present disclosure, a fuel assembly operating robot motion control system is provided, which comprises:

[0009] a task planning module for loading basic data and generating assembly operation task information;

[0010] a sensor data integration module for obtaining preliminary data of a target fuel assembly from a robot sensor, and generating a trajectory planning data set required for rough positioning guidance;

[0011] a fuel assembly profile feature template generation tool for dynamically generating an ideal profile template based on the estimated assembly pose; the fuel assembly profile feature template generation tool comprises:

[0012] a fuel assembly pose estimation sub-module configured to estimate a position, an orientation, and an inclination angle of the fuel assembly relative to a predefined reference coordinate system based on the preliminarily extracted features, and generate a pose data vector by using a geometric constraint or a low-complexity matching algorithm;

[0013] a feature calculation sub-module configured to perform a parsing process on the input fuel assembly rough profile acquisition data, and obtain key feature points, edge segments, and profile data by using at least one of an edge detection operator, a feature point detection operator, and a profile extraction operator, and form a feature data vector;

[0014] a template assembly sub-module configured to generate an ideal profile template that should be observed in a current view angle and an inclined state according to the estimated inclination angle by using a perspective projection transformation method, and generate a template data vector;

[0015] a fuel assembly feature and profile processing module configured to call the fuel assembly profile feature template generation tool, parse and obtain component feature and profile information, match the obtained accurate feature profile with the dynamically generated template, and calculate a spatial position of a fuel assembly grasping position relative to a coordinate system of a robot grasping device by using a high-precision matching algorithm;

[0016] a motion trajectory planning and control module configured to perform a pose adjustment of an operating robot device according to the calculated spatial position, and form a target code information vector of each motion axis;

[0017] a fuel assembly operation comprehensive data warehouse configured to store and interact with basic information and calculation and verification information required in a whole operation process, and provide data support for the modules.

[0018] In a possible implementation, the fuel assembly pose estimation sub-module comprises a process data warehouse and a pose parsing operator; and / or

[0019] The feature calculation sub-module comprises a process data warehouse, a data parsing operator, and a feature generation operator.

[0020] In a possible implementation, the template assembly sub-module is configured to obtain an expected profile frame of a fuel assembly body and a top grid by fitting calculation based on the preliminarily detected key corner points, and perform detail filling based on a component feature knowledge base; and / or

[0021] confidence weights of different regions are set in the template; and / or

[0022] when it is estimated that there is serious occlusion in a certain region of the component, the region is shielded in the template.

[0023] In a possible implementation, the entity files of the fuel assembly operation comprehensive data warehouse comprise:

[0024] Data warehouse index, used to record the registration information of all data sets;

[0025] Task data set, used to record the information related to the operation of the component;

[0026] Robot information set, used to record the information related to the robot device;

[0027] Fuel assembly feature data set, used to record the information of the pre-set fuel assembly model and the corresponding features and key structure information;

[0028] Sensor data set, used to record the information related to the sensor data collection;

[0029] Operation point data set, used to record the information of the operation point calculation result;

[0030] Trajectory planning information set, used to record the information of the trajectory planning strategy calculation result.

[0031] In a possible implementation, the fuel assembly feature and contour processing module comprises:

[0032] Fuel assembly contour feature template generation tool loading interface;

[0033] Template comparison sub-module;

[0034] Calculation process information base;

[0035] Data interaction interface.

[0036] According to another aspect of the embodiments of the present disclosure, a fuel assembly operation robot motion control method is provided, which comprises:

[0037] Step 1: loading the basic data through the task planning module, and generating the component operation task information;

[0038] Step 2: obtaining the preliminary data of the target fuel assembly from the robot sensor through the sensor data integration module, generating the trajectory planning data set required for rough positioning guidance after information analysis and data assembly, and guiding the robot to move to the approximate front area of the fuel assembly;

[0039] Step 3: performing the following operations through the fuel assembly contour feature template generation tool:

[0040] Step 3.1: based on the preliminary extracted features, using geometric constraints or conventional low complexity matching algorithms, estimating the current position, direction and inclination angle of the fuel assembly relative to the pre-defined reference coordinate system, and generating the pose data vector;

[0041] Step 3.2: Perform a parsing process and feature data generation on the input fuel assembly rough profile data, including applying at least one of an edge detection, a feature point detection and a contour extraction operator to obtain key feature points, edge segments and main contour data of the fuel assembly, and form a feature data vector;

[0042] Step 3.3: Obtain a standard assembly profile set in the assembly feature knowledge base, and according to the estimated fuel assembly tilt angle, apply a perspective projection transformation method to generate an ideal profile template that should be observed under the current view angle and tilt state, and finally generate a template data vector;

[0043] Step 4: Match the obtained accurate feature profile with the dynamically generated template through the fuel assembly feature and contour processing module, and through a high-precision matching algorithm, calculate the spatial position of the fuel assembly grabbing position relative to the coordinate system of the robot grabbing device;

[0044] Step 5: Through the motion trajectory planning and control module, according to the calculated spatial position, perform the pose adjustment of the operating robot device, form a target encoding information vector of each motion axis, and supply the robot with the pose adjustment to perform the fuel assembly operation.

[0045] In a possible implementation, in the step 3.3, the process of generating the template data vector further includes:

[0046] Based on the key corner points preliminarily detected, an expected profile framework of the fuel assembly body and the top grid plate is obtained through fitting calculation;

[0047] Based on the assembly feature knowledge base, detail filling is performed, and the details include the standard shape and relative position of the grabbing hole.

[0048] In a possible implementation, in the step 3.3, the process of generating the template data vector further includes:

[0049] In the template, the confidence weight of different regions is set, wherein the weight of the top grid plate edge is the highest, and the weight of the secondary edge of the side surface is lower.

[0050] According to another aspect of the embodiments of the present disclosure, a non-volatile computer readable storage medium is provided, which stores computer program instructions, and the computer program instructions are executed by a processor to implement the above method.

[0051] The beneficial effects of the present disclosure are that the present disclosure discards the traditional pre-stored fixed template, generates a matching template through a dynamic generation strategy, so that the generation of the template is based on the real-time perceived fuel assembly state information (such as rough position, attitude estimation, key feature points) combined with the pre-set assembly feature knowledge base (such as geometric structure, key contour feature, and grasping interface position relationship), encapsulates a special tool to realize real-time template generation of the fitted contour surface, and meets the high-precision positioning requirements of the motion control of the operation robot. In addition, by encapsulating a special data assembly algorithm set and a supporting information warehouse, the whole set of fuel assembly operation robot device has the task configuration and full-automatic task implementation capability.

[0052] The generative strategy of the system of the present disclosure can automatically adapt to different assembly states and harsh environments, does not need to pre-store a large number of fixed templates for various possible working conditions, overcomes the poor adaptability of pre-stored fixed templates to complex working conditions, improves the matching accuracy of fuel assemblies under real-time working conditions, and generates a robot motion trajectory based on the obtained high-precision contour surface data according to target point coordinate information, greatly reduces the risk of grabbing failure, assembly collision or damage, and ensures the safe operation of nuclear facilities. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 FIG. 1 is a schematic diagram of a fuel assembly operation robot motion control system according to an exemplary embodiment.

[0054] Figure 2 FIG. 2 is a schematic diagram of a fuel assembly contour feature template generation tool according to an exemplary embodiment.

[0055] Figure 3 FIG. 3 is a schematic diagram of a fuel assembly operation comprehensive data warehouse according to an exemplary embodiment.

[0056] Figure 4 FIG. 4 is a schematic diagram of a fuel assembly operation robot motion control method according to an exemplary embodiment. DETAILED DESCRIPTION

[0057] The present disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] The system of the present disclosure is designed for the characteristics of nuclear fuel assembly operation business in the production environment of the factory assembly link, and designs a task planning module, a sensor data integration module, a fuel assembly feature and contour processing module, and a motion trajectory planning and control module covering the whole process around the assembly contour feature template tool and the fuel assembly operation comprehensive data warehouse.

[0059] Reference is made to Figures 1 to 3, fuel assembly profile feature template generation tool (FASFT) is a special tool for performing the generation of fuel assembly profile feature template. The assembly profile feature template generation tool (FASFT) comprises: assembly pose estimation sub-module, feature calculation sub-module, template assembly sub-module and data interaction interface;

[0060] Fuel assembly pose estimation sub-module: based on the preliminary extracted features, using geometric constraints or conventional low complexity matching algorithm, estimate the current approximate position, direction and tilt angle of the fuel assembly relative to the predefined reference coordinate system, generate pose data vector V fa-loc ; the fuel assembly pose estimation sub-module comprises: process data repository, pose analysis operator;

[0061] Feature calculation sub-module: through the data interaction interface, perform analysis processing and feature data generation on the input fuel assembly rough profile acquisition data, including using edge detection (such as Canny), feature point detection (FAST, ORB) and contour extraction operators, obtain the key feature points, edge segments and main contour data of the fuel assembly, form feature data vector V fa-f ; comprising process data repository, data analysis operator, feature generation operator;

[0062] Template assembly sub-module: through the data interaction interface, obtain the standard assembly profile set in the component feature knowledge base, according to the estimated tilt angle of the fuel assembly, apply the built-in perspective projection transformation method in the module to generate an ideal top grid plate profile template that should be observed under the current viewing angle and tilt state; in addition, based on several key corner points detected preliminarily, the expected profile framework of the fuel assembly body and the top grid plate can be obtained through fitting calculation, and finally the component feature knowledge base is used to perform detailed filling (such as the standard shape and relative position of the grab hole), the confidence weight of different regions in the template can be set synchronously during the calculation process (such as the top grid plate edge weight is the highest, the side edge weight is lower), if there is serious occlusion in a certain region of the estimated assembly, the region can be shielded in the template to avoid interference with the matching calculation; through the above calculation, the template data vector V fa-t is finally generated; comprising process data repository, profile template generation operator;

[0063] Data interaction interface: used for bidirectional data transmission with other functional modules in the system;

[0064] Fuel assembly operation comprehensive data warehouse (FAMSD) is used to record the basic information required for the whole process of fuel assembly operation and the related information for subsequent calculation and verification. Each FAMSD standard structure entity file contains single or multiple records; a FAMSD structure entity contains: data warehouse index I s-i , task data set C t , robot information set C r, fuel assembly feature dataset C fa-s , sensor dataset C s , operating point dataset C d , trajectory planning information dataset C pl ; structure see Figure 2 ; each part is described as follows:

[0065] Data warehouse index I s-i : record all dataset registration information in the current FAMSD entity file, including entry number subset SC n , data association relationship subset SC r , key field subset SC k , data update state subset SC s , structure I s-i <SC n |SC r |SC k |SC s >;

[0066] Task dataset C t : record all loaded component operation task related information in the current FAMSD entity file, including task type subset ST c , associated component object subset ST o , task information subset ST i , task state subset ST s , structure C t <ST c |ST o |ST i |ST s >;

[0067] Robot information dataset C d : record all loaded robot device related information in the current FAMSD entity file, including device type subset SD c , associated task type subset SD t , device state subset SD s , structure C d <SD c |SD t |SD s >;

[0068] Fuel assembly feature dataset C fa-s : record all loaded preset fuel assembly model and corresponding feature and key structure related information in the current FAMSD entity file, including summary information subset SF a , identification information subset SF i , operating forbidden zone information subset SF h , structure Cfa-s <SF a |SF i |SF h >;

[0069] Sensor dataset C s : record all loaded sensor collection data related information in the current FAMSD entity file, including location information subset SS l , type information subset SS c , collection data information subset SS d , structured as C s <SS l |SS c |SS d >;

[0070] Operation point data set C d : record updated location calculation result related information in the current FAMSD entity file, including positioning basic data subset SL i , positioning related task data subset SL t , positioning related device data subset SL d , structured as C d <SL i |SL t |SL d >;

[0071] Trajectory planning information set C pl : record updated trajectory planning strategy calculation result related information in the current FAMSD entity file, including strategy basic information subset SP i , strategy related task information subset SP t , strategy related device information subset SP d , task state information subset SP t-s , structured as C pl <SP i |SP t |SP d |SP t-s >.

[0072] Task planning module (TP): used to realize basic data loading and component operation task information generation; the task planning module includes: task data loading interface, task detailed information library, fuel assembly information library, data interaction interface; the structure is shown in part (1) Figure 1 of the figure

[0073] Sensor data integration module (SDI): through the device access interface, the preliminary data of the mounted sensor (distance sensor, laser or structured light module, high-resolution camera, etc.) in the target fuel assembly area is obtained from the internal sensors of the robot (such as distance sensors), and after information analysis and data assembly, the trajectory planning data set required for rough positioning guidance is generated according to the preset program, and the data set is transmitted to the sensor data set C of the assembly positioning data warehouse through the data transmission interface s , which can support the robot to move to the approximate front area of the fuel assembly; the sensor data integration module includes a sensor data acquisition data loading interface, a sensor detailed parameter library, a calculation result information library, and a data interaction interface Figure 3 part (2);

[0074] Fuel assembly feature and profile processing module (FAFSM): through the data transmission interface, the assembly profile feature template tool is called to analyze and obtain the assembly feature and profile information, the obtained accurate feature profile (especially the top grid plate area) is matched with the dynamically generated template, and through the built-in high-precision matching algorithm, the spatial position (three-dimensional coordinates and attitude angle) of the fuel assembly grabbing position relative to the coordinate system of the robot grabbing device is calculated. The fuel assembly feature and profile processing module includes an FASFT loading interface, a template comparison submodule, a calculation process information library, and a data interaction interface; the structure is shown in part (5) Figure 3 ;

[0075] Motion trajectory planning and control module (MPC): performs the pose adjustment of the operating robot device to form a target encoding information vector V mc for the robot to adjust the pose to perform fuel assembly operation; the motion trajectory planning and control module (MPC) includes an FASFT loading interface, a matching result analysis submodule, an axis encoding generation submodule, a calculation process information library, and a data interaction interface; the structure is shown in part (6) Figure 3 ;

[0076] The system of the present disclosure has high precision and high robustness: the dynamically generated template closely fits the current actual scene (state + environment), significantly improves the matching accuracy and reliability under strong interference and assembly state changes (tilt, deformation, occlusion), and improves the millimeter-level grabbing positioning accuracy;

[0077] The "generative" strategy of the system of the present disclosure enables the system to automatically adapt to different component states and harsh environments without pre-storing a large number of fixed templates for various possible working conditions; reduces the time-consuming caused by mis-matching, positioning failure or repeated adjustment, and speeds up the refueling operation process; precise positioning and control greatly reduce the risk of grabbing failure, component collision or damage, and ensure the safe operation of nuclear facilities; significantly reduces the dependence on manual intervention, and promotes the development of fuel operation to a higher degree of automation and intelligence.

[0078] Figure 4 is a schematic diagram of a fuel assembly operation robot motion control method according to an exemplary embodiment, as shown in Figure 4 The method is based on the above-mentioned system, and the method is as follows:

[0079] Step 1 initialization and rough positioning, including:

[0080] Step 1.1 task loading: used for performing batch acquisition of original task information from the outside of the system; calling the task data loading interface in the task planning module (TP), performing task data reading operation;

[0081] Step 1.2 information filling: used for performing information preprocessing; performing structured matching on the read task data and batch storing to the task planning module task detailed information library;

[0082] Step 1.3 information screening: used for performing key task information matching retrieval and extraction; based on the current task requirements, extracting key information such as fuel assembly number, fuel assembly associated area information, fuel assembly adaptive robot device and working time / period window according to the task priority requirements, and storing to the task planning module fuel assembly information library;

[0083] Step 1.4 FAMPD information transmission: used for performing FAMPD structure entity file update and transmission data to the fuel assembly operation planning data warehouse; calling the fuel assembly outline feature template generation tool (FASFT) through the data transmission interface, performing data transmission operation to the FAMPD structure entity file of the fuel assembly operation planning data warehouse; after the data transmission is completed, updating the warehouse index vector of the FAMPD structure entity file through the fuel assembly outline feature template generation tool (FASFT);

[0084] Step 1.5 preliminary motion: the robot moves to the approximate area above the fuel assembly according to the information obtained in the information screening step 1.3 through the preset program and the distance sensor carried on the body;

[0085] Step 1.6 sensing data acquisition: through the robot carrying multiple sensors (distance sensor, laser or structured light module, high-resolution camera, etc.), collecting the preliminary outline data of the target fuel assembly in the current area.

[0086] Step 2 Feature extraction and state estimation, including:

[0087] Step 2.1 Raw data parsing: using the component feature and contour processing module, through the FASFT loading interface, call the component contour feature template tool, load the component feature and contour data obtained in step 1.6, and the pre-stored fuel assembly standard template data;

[0088] Step 2.2 Feature data acquisition: using edge detection algorithm (such as Canny), feature point detection algorithm (such as FAST, ORB) and other shape extraction algorithm, the key feature points, edge segments and main contour data of the fuel assembly are obtained; The generated data is stored in the calculation process information base;

[0089] Step 2.3 Fuel assembly state estimation: based on the feature data extracted in step 2.2, the geometric constraint or simple matching algorithm is used to calculate the current approximate spatial position, direction (Yaw) and possible tilt angle (rotation angle, pitch angle) of the fuel assembly; The generated data is stored in the fuel assembly operation planning data warehouse FAMPD structure entity file through the FASFT loading interface of the component feature and contour processing module, and the warehouse index vector of the FAMPD structure entity file is updated.

[0090] Step 3 Dynamic template generation, including:

[0091] Step 3.1 Data loading: using the component feature and contour processing module, through the FASFT loading interface, call the component contour feature template tool to obtain the current fuel assembly standard geometric model and the feature data generated in step 2.2;

[0092] Step 3.2 Template frame generation: based on the data loaded in step 3.1, combined with the fuel assembly state information generated in step 2.3, the corresponding fuel assembly contour template under the current view angle and tilt state is generated by using the perspective projection transformation algorithm;

[0093] Step 3.3 Template refinement: based on the fuel assembly standard set model loaded in step 3.1, the generated fuel assembly template is executed for geometric model detail filling, including grabbing the shape and relative position of the operation hole, shielding the current operation view angle shielding area, and updating the final generated fuel assembly template data;

[0094] Step 4 Real-time contour matching and pose calculation

[0095] Step 4.1 Data loading: using the component feature and contour processing module, through the FASFT loading interface, call the component contour feature template tool to obtain the current fuel assembly standard geometric model and the feature data generated in step 2.2;

[0096] Step 4.2 Template Matching: Using high-precision matching algorithms, based on the feature data extracted in Step 2 and the real-time template data generated in Step 3, perform matching operations, including:

[0097] Step 4.2.1 Optimized Normalized Cross Correlation (NCC): Perform similarity calculation between the preset template and the real-time image under illumination change conditions;

[0098] Step 4.2.2 Use improved SIFT / SURF / ORB algorithms to extract feature descriptors and perform feature point matching, and use RANSAC algorithms to eliminate false matches;

[0099] Step 4.2.3 Calculate the shortest distance set of real-time contour points to the template contour, and find the transformation parameter set that minimizes the distance sum;

[0100] Step 4.3 Pose Generation: According to the best matching position and transformation parameters (translation, rotation, and scaling) found in Step 4.2, calculate the three-dimensional coordinates (X, Y, Z) and attitude angles (Roll, Pitch, Yaw) of the fuel assembly grabbing point geometric center position relative to the robot gripper tool coordinate system, and generate robot pose data;

[0101] Step 5 Robot Motion Planning and Control

[0102] Step 5.1 Information Analysis: Use the motion trajectory planning and control module (MPC) to call the matching result analysis submodule, and according to the template matching and pose information fed back in Step 4, if the current robot real-time pose does not meet the fuel assembly operation requirements, the robot pose adjustment needs to be performed, forming a target encoding information vector for each motion axis, to adjust the robot pose for the next operation;

[0103] Step 5.2 Motion Axis Encoding Conversion Generation: Based on the results of Step 5.1, assemble the motion parameters such as axis motion direction and axis motion rate to form a motion axis encoding information vector;

[0104] Step 5.3 Motion Axis Encoding Information Export: Through the data transmission submodule, complete the real-time transmission of the motion axis encoding information vector for the robot to perform pose adjustment;

[0105] Step 5.4 Position Confirmation and Grabbing: When the robot gripper moves to the target pose (satisfies the preset precision threshold) and the pose is stable after multiple fine adjustments, the system confirms the position and sends instructions to drive the gripper to perform grabbing actions (such as mechanical jaw closing and pin insertion into the grabbing hole). After grabbing is completed, subsequent operations such as lifting and transporting can be performed.

[0106] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0107] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, a

[0108] The computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0109] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0110] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0111] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0112] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0113] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0114] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative of the embodiments and not restrictive. Many modifications and variations of the described embodiments are possible and are within the scope of the disclosure. The selection of terms is intended to best describe the principles of the embodiments, practical application, or improvement over the technology in the art, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A fuel assembly handler robot motion control system characterized by, The system comprises: a task planning module for loading basic data and generating component operation task information; a sensor data integration module for obtaining preliminary data of a target fuel assembly from a robot sensor, and generating a trajectory planning data set required for coarse positioning guidance; a fuel assembly contour feature template generation tool for dynamically generating an ideal contour template based on an estimated component pose; the fuel assembly contour feature template generation tool comprises: a fuel assembly pose estimation sub-module for estimating the position, orientation and tilt angle of a fuel assembly relative to a predefined reference coordinate system based on preliminarily extracted features, using a geometric constraint or a low-complexity matching algorithm, and generating a pose data vector; a feature calculation sub-module for performing analytical processing on input fuel assembly coarse contour acquisition data, obtaining key feature points, edge segments and contour data through at least one of edge detection, feature point detection and contour extraction, and forming a feature data vector; a template assembly sub-module for generating an ideal contour template that should be observed under a current viewing angle and tilt state by applying a perspective projection transformation method according to the estimated tilt angle, and generating a template data vector; a fuel assembly feature and contour processing module for calling the fuel assembly contour feature template generation tool, analyzing and obtaining component feature and contour information, matching the obtained accurate feature contour with the dynamically generated template, and calculating the spatial position of the fuel assembly gripping position relative to the robot gripping device coordinate system through a high-precision matching algorithm; a motion trajectory planning and control module for performing operation robot device pose adjustment according to the calculated spatial position, and forming a target encoding information vector of each motion axis; a fuel assembly operation comprehensive data warehouse for storing and interacting with basic information and calculation verification information required in the whole operation process, and providing data support for the above modules.

2. The system of claim 1, wherein, The fuel assembly pose estimation sub-module comprises a process data storage and a pose analysis operator; and / or The feature calculation sub-module comprises a process data storage, a data analysis operator and a feature generation operator.

3. The system of claim 1, wherein, The template assembly sub-module is configured to: obtain the expected contour framework of the fuel assembly body and the top grid based on the preliminarily detected key corner points through fitting calculation, and perform detail filling based on the component feature knowledge base; and / or confidence weights of different regions are set in the template; and / or when there is serious occlusion in a certain region of the estimated component, the region is shielded in the template.

4. The system of claim 1, wherein, The entity files of the fuel assembly operation comprehensive data warehouse comprise: a data warehouse index for recording the registration information of all data sets; a task data set for recording component operation task related information; a robot information set for recording robot device related information; a fuel assembly feature structure data set for recording preloaded fuel assembly model and corresponding feature and key structure information; a sensor data set for recording sensor acquisition data related information; an operation point position data set for recording operation point position calculation result information; a trajectory planning information set for recording trajectory planning strategy calculation result information.

5. The system of claim 1, wherein, The fuel assembly feature and contour processing module comprises: Fuel assembly profile feature template generation tool loading interface; Template comparison sub-module; Computational process information base; Data interaction interface.

6. A method of controlling motion of a fuel assembly handling robot, comprising: The method is implemented based on the system of any one of claims 1-5, and the method comprises: Step 1: loading basic data through a task planning module, and generating component operation task information; Step 2: obtaining preliminary data of a target fuel assembly from a robot sensor through a sensor data integration module, generating a trajectory planning data set required for rough positioning guidance after information analysis and data assembly, and guiding the robot to move to a region in front of the fuel assembly; Step 3: performing the following operations through a fuel assembly profile feature template generation tool: Step 3.1: based on the preliminarily extracted features, using geometric constraints or conventional low-complexity matching algorithms to estimate the current position, direction and tilt angle of the fuel assembly relative to a predefined reference coordinate system, and generating a pose data vector; Step 3.2: performing analysis processing and feature data generation on the input fuel assembly rough profile acquisition data, including using at least one operator in edge detection, feature point detection and contour extraction to obtain key feature points, edge segments and main contour data of the fuel assembly, and forming a feature data vector; Step 3.3: obtaining a standard assembly profile set in the component feature knowledge base, applying a perspective projection transformation method according to the estimated tilt angle of the fuel assembly, generating an ideal profile template that should be observed under the current viewing angle and tilt state, and finally generating a template data vector; Step 4: matching the obtained accurate feature profile with the dynamically generated template through a fuel assembly feature and profile processing module, calculating the spatial position of the fuel assembly gripping position relative to the robot gripping device coordinate system through a high-precision matching algorithm; Step 5: performing pose adjustment of the operating robot device according to the calculated spatial position through a motion trajectory planning and control module, forming a target encoding information vector of each motion axis, and providing the robot with pose adjustment to perform fuel assembly operation.

7. The method of claim 6, wherein, In the step 3.3, the process of generating the template data vector further comprises: Based on the preliminarily detected key corner points, obtaining the expected profile framework of the fuel assembly body and the top grid plate through fitting calculation; Based on the component feature knowledge base, performing detail filling, the details including the standard shape and relative position of the gripping hole.

8. The method of claim 6, wherein, In the step 3.3, the process of generating the template data vector further comprises: Setting confidence weight of different regions in the template, wherein the weight of the top grid plate edge is the highest, and the weight of the secondary edge of the side surface is lower.

9. A non-transitory computer readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions are executed by the processor to implement the method of any one of claims 6-8.

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