Machine vision AI wheel robot based on artificial intelligence algorithm and method

Abstract

The invention relates to a machine vision AI wheel robot based on an artificial intelligence algorithm and a method. The wheel robot comprises a working area scanning module, a force field distribution analysis module, a grabbing strategy adjustment module, a tire dynamic response capture module, a target placement area scanning module and a transplanting data recording module. According to the wheel robot, the state and the physical characteristics of the tire can be comprehensively mastered by analyzing the surface patterns and the force field distribution of the tire. The tire state is accurately identified and analyzed, so that the adaptability and the operation accuracy of the tire under different working conditions are improved, the grabbing strategy can be adjusted in real time, and the grabbing and placing tasks can be stably and effectively completed under various conditions. The intelligent decision-making ability significantly improves the operation efficiency, and reduces the operation risk caused by environmental changes.

Description

Technical Field

[0001] The present invention relates to the field of wheel transportation, and in particular to a machine vision AI wheel robot and method based on an artificial intelligence algorithm. Background Art

[0002] Wheel robots used for wheel transplantation belong to the application direction of the intersection of industrial robots and artificial intelligence technology, and mainly focus on automated tire handling, grasping, moving and precise placement tasks. This field aims to achieve efficient, accurate and safe tire transplantation operations through machine vision, mechanical perception and intelligent control technology. Compared with the traditional method that relies on manual or simple mechanical equipment, robots in this field can perceive the working environment in real time, analyze the tire status, and dynamically adjust the operation strategy through artificial intelligence algorithms and sensor networks to adapt to complex industrial scenarios, while greatly improving operational efficiency, reducing labor intensity and cost investment. The core technologies in this field include three-dimensional environment modeling, dynamic analysis, autonomous navigation, grasping mechanics optimization, and real-time monitoring and feedback control.

[0003] The lack of dynamic adaptability in existing technologies for tire grabbing and transplanting tasks is an obvious shortcoming. Existing technologies usually have difficulty in real-time sensing the dynamic response of tires during grabbing and moving, and lack the ability to make immediate adjustments based on sensor feedback. As a result, operational errors are prone to occur in complex environments, reducing the success rate of the task. In the target placement link, traditional technologies often make simple and rough assessments of the placement area, lacking a comprehensive analysis of key factors such as the surface flatness, tilt angle, and obstacle interference of the area, resulting in inaccurate selection of the optimal placement position, which increases the possibility of tire slippage or unstable placement. In addition, existing technologies generally lack a complete adaptive control mechanism, and are unable to respond to abnormal situations in a timely manner during operation, such as the weightlessness or posture deviation of the tire, which limits the stability and safety of the operation. Summary of the invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a machine vision AI wheel robot and method based on artificial intelligence algorithm.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: a machine vision AI wheel robot based on artificial intelligence algorithm, the wheel robot comprising: A work area scanning module is used to scan the work area, build a three-dimensional map of the area, identify the tire area and the target area, identify obstacles in the map, and plan a navigation path for the wheel robot; The force field distribution analysis module is used to scan the surface pattern of the target tire, generate a pattern feature map, analyze the pattern type, detect the tire surface information, and record dynamic parameters. Based on the tire shape, material and stacking method, it generates a tire force field distribution map; The grasping strategy adjustment module is used to establish a behavior prediction model for the tire to be transplanted based on the tire force field distribution map, predict the dynamic performance of the tire under different grasping angles and grasping forces, and generate the dynamic adjustment range of the grasping strategy; The tire dynamic response capture module is used to capture the dynamic response of the tire in real time during the tire transplanting process, and sense the weightlessness of the tire based on the clamping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; The target placement area scanning module is used to scan the state of the target placement area, predict the stability of the tire after placement through the behavior prediction model, and adjust the tire angle during the tire transplantation process based on the stability prediction result; The transplanting data recording module is used to record the tire data and transplanting data of the transplanting after the transplanting task is completed.

[0006] Another object of the present invention is to provide a working method of a machine vision AI wheel robot based on an artificial intelligence algorithm, the working method comprising: Scan the working area, build a three-dimensional map of the area, identify the tire area and the target area, identify obstacles in the map, and plan a navigation path for the wheel robot; Scan the surface pattern of the target tire, generate a pattern feature map, analyze the pattern type, detect the tire surface information, and record dynamic parameters. Based on the tire shape, material and stacking method, generate a tire force field distribution map; For tires that need to be transplanted, a behavior prediction model is established based on the tire force field distribution map to predict the dynamic performance of the tire under different gripping angles and gripping forces, and generate a dynamic adjustment range for the gripping strategy; During the tire transplanting process, the dynamic response of the tire is captured in real time, and the tire is sensed to lose weight based on the clamping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range. Scan the status of the target placement area, predict the stability of the tire after placement through the behavior prediction model, and adjust the tire angle during the tire transplantation process based on the stability prediction results; After the transplanting task is completed, record the tire data and transplanting data of this transplanting.

[0007] As a further solution of the present invention, the scanning of the working area, building a three-dimensional map of the area, identifying the area where the tire is located and the target area, identifying obstacles in the map, and planning a navigation path for the wheel robot specifically includes: Collect data on the work area, capture the contour information of the work area, identify the tire stacking position and target placement position, obtain complete spatial data of the work area, and generate a depth map; Based on the generated depth map, the environmental data is converted into point cloud data, and the multi-view data is integrated through point cloud stitching technology. The pose calibration and overlapping area alignment technology are used to establish a three-dimensional work area map containing static objects and dynamic objects. Mark the tire stacking area and target placement area in the three-dimensional work area map, extract the geometric features of the tires, including size, position and relative stacking relationship, and record the boundary information of the target placement area; Analyze the obstacles in the 3D work area map, and generate the shortest obstacle avoidance path based on the tire position and target placement area combined with the marked obstacle range.

[0008] As a further solution of the present invention, the surface pattern of the target tire is scanned, a pattern feature map is generated, the pattern type is analyzed, the tire surface information is detected, and dynamic parameters are recorded, and a tire force field distribution map is generated based on the tire shape, material and stacking method, specifically including: Scan the target tire to capture the tire's surface pattern, material properties, geometric features, and texture features, and obtain the tire's image information and depth information; Preprocess the captured tire images, extract the tire pattern features from the preprocessed images, generate a pattern feature map, analyze the pattern type, depth and distribution, and identify the tire wear status based on the tire type; Based on the geometric features, material properties and pattern characteristics of the tire, a force field distribution model of the tire is constructed, including the tire force area, friction force distribution and center of gravity position; Combining all tire characteristics and the force field distribution model, a complete tire force field distribution map is generated.

[0009] As a further solution of the present invention, the behavior prediction model is established based on the tire force field distribution diagram to predict the dynamic performance of the tire under different gripping angles and gripping forces, and to generate a dynamic adjustment range of the gripping strategy, specifically including: Based on the collected tire features, the generated tire force field distribution map is integrated to build a behavior prediction model. The tire's geometric features and force field distribution data are input to predict the dynamic performance of the tire under changes in gripping points, angles, and forces, including slip angles, tilt trends, and stability after gripping. Based on the behavior prediction model, a gripping strategy for the current tire is generated, including gripping point position, gripping force range, and gripping angle range.

[0010] As a further solution of the present invention, the predicted dynamic performance of the tire under changes in gripping point, angle and force, including slip angle, tilt trend and stability after gripping, is specifically: Calculate the slip angle: ; in, represents the slip angle, which is the angle at which the tire slides after grabbing. is the tangential force, i.e. the force parallel to the contact surface, is the normal force, i.e. the force perpendicular to the contact surface; Calculate the slope trend: ; in, is the change in tilt angle, that is, the degree of tilt of the tire during the gripping process. is the tire weight, is the center of gravity offset distance, calculated from the force field distribution diagram, is the moment of inertia of the tire; Calculate the stability after grabbing: ; in, To obtain the stability index, When , it means the grasping is in a stable state. , indicating that the crawling failed. is the friction coefficient, which is calculated from the tire material properties and texture characteristics.

[0011] As a further solution of the present invention, the generation of the gripping strategy for the current tire includes gripping points, gripping force range and gripping angle range, specifically: Calculate the comprehensive score of each candidate grab point: ; in, To grab the point The overall rating of is the weight factor; Determine the grip strength scope: The friction force generated by the clamping force is greater than the tangential force plus a safety margin ,Right now: ; The gripping force does not exceed the maximum load allowed by the tire ,Right now: ; Get the final grip strength scope: ; Determine the grip angle scope: ; in: ; ; and Indicates the minimum and maximum values ​​of the grab angle, Indicates the allowed tilt trend threshold, Indicates the grab angle is Grasping stability.

[0012] As a further solution of the present invention, the dynamic response of the tire is captured in real time during the tire transplanting process, and the tire is sensed for weightlessness based on the clamping feedback data. If a weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; Real-time monitoring of the dynamic state of the tire during the transplanting process, including tire displacement, posture change, tilt angle and slip trend, and comparing the monitoring data with the prediction results of the behavioral model; The force sensor and tactile sensor installed on the gripper collect gripping force information in real time to ensure that the gripping force is within the predetermined range, and calculate the instant changes in normal force and tangential force based on the feedback data; Combine real-time monitoring data and clamping feedback data to determine whether to trigger a weightlessness signal, including: ; If the weightlessness signal is triggered, the range is dynamically adjusted according to the pre-generated grasping strategy, the grasping force is recalculated, and the normal force applied by the gripper is maximized based on the force field distribution map. ; ; ; in, is the current tangential force, For additional safety margin; The dynamically adjusted grasping strategy is executed in real time, and the grasping action is closed-loop controlled by combining monitoring data and feedback data.

[0013] As a further solution of the present invention, the state of the target placement area is scanned, the stability of the tire after placement is predicted by a behavior prediction model, and the tire angle is adjusted during the tire transplanting process based on the stability prediction result, specifically including: Extract the target placement area from the 3D work area map, and partition the surface of the target placement area into suitable placement areas and high-risk areas. At the same time, mark the area priority score by calculating the surface flatness, tilt angle and obstacle interference degree; Based on the geometric characteristics of the target placement area and the force field distribution map of the tire, combined with the center of gravity position, the stability index of the tire after placement is calculated ; ; Set the index threshold. If the stability index is lower than the index threshold, recalculate the placement angle and position of the tire and adjust the direction of the tire during the transplanting process. During tire transplantation, the tire gripper angle and posture are adjusted in real time to make the tire conform to the updated placement strategy and ensure that the final placement position and angle meet the stability requirements; After the tire is successfully placed, the force sensor and vision system confirm whether the tire is in a stable state, and record the status data of the placement area and the final placement parameters of the tire.

[0014] As a further solution of the present invention, if the stability index is lower than the index threshold, the placement angle and position of the tire are recalculated, specifically: ; ; in, For the new placement angle, is the placement angle, Indicates the stability index Maximum placement angle , Represents the new placement coordinates, represents the regional priority score for target placement, Indicates the region priority score for target placement The placement position that reaches the maximum value .

[0015] The beneficial effects of the present invention are: The wheel robot can fully understand the state and physical characteristics of the tire by analyzing the surface pattern and force field distribution of the tire. This precise identification and analysis of the tire state improves its adaptability and operational accuracy under different working conditions, and can adjust the grasping strategy in real time to ensure that grasping and placement tasks can be completed stably and effectively under various conditions. This intelligent decision-making ability significantly improves operational efficiency and reduces operational risks caused by environmental changes.

[0016] In addition, the dynamic response of the tire is monitored in real time during the transplanting process, and weightlessness is sensed based on the clamping feedback data. This function ensures that the robot can quickly respond to abnormal conditions during operation and form an adaptive control mechanism to optimize the force and angle adjustment during the grasping and placement process, making the operation safer and more reliable.

[0017] In terms of target placement area selection and stability prediction, the robot can efficiently determine the best placement location through detailed regional analysis, avoiding the waste of time and resources caused by blind attempts. This data-driven decision-making process not only improves the success rate of tire placement, but also effectively reduces the complexity of subsequent operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A structural block diagram of a machine vision AI wheel robot based on an artificial intelligence algorithm provided by an embodiment of the present invention; Figure 2 A flowchart of a working method of a machine vision AI wheel robot based on an artificial intelligence algorithm provided by an embodiment of the present invention; Figure 3 A flowchart for planning a navigation path for a wheel robot provided by an embodiment of the present invention; Figure 4 A flow chart for generating a tire force field distribution diagram provided by an embodiment of the present invention; Figure 5 A flowchart of generating a dynamic adjustment range of a crawling strategy provided by an embodiment of the present invention; Figure 6 A flow chart of performing weightlessness sensing on a tire based on clamping feedback data provided by an embodiment of the present invention; Figure 7 A flow chart for predicting the stability of a tire after placement provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0020] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0021] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0022] Figure 1 The structural block diagram of the machine vision AI wheel robot based on artificial intelligence algorithm provided by the embodiment of the present invention is as follows: Figure 1 As shown, the wheel robot comprises: A working area scanning module 100 is used to scan the working area, construct a three-dimensional map of the area, identify the area where the tire is located and the target area, identify obstacles in the map, and plan a navigation path for the wheel robot; The force field distribution analysis module 200 is used to scan the surface pattern of the target tire, generate a pattern feature map, analyze the pattern type, detect the tire surface information, and record dynamic parameters, and generate a tire force field distribution map based on the tire shape, material and stacking method; The grasping strategy adjustment module 300 is used to establish a behavior prediction model for the tire to be transplanted based on the tire force field distribution map, predict the dynamic performance of the tire under different grasping angles and grasping forces, and generate a dynamic adjustment range of the grasping strategy; The tire dynamic response capture module 400 is used to capture the dynamic response of the tire in real time during the tire transplanting process, and to sense the weightlessness of the tire based on the clamping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; The target placement area scanning module 500 is used to scan the state of the target placement area, predict the stability of the tire after placement through the behavior prediction model, and adjust the tire angle during the tire transplantation process based on the stability prediction result; The transplanting data recording module 600 is used to record the tire data and transplanting data of the transplanting after the transplanting task is completed.

[0023] Figure 2 A flowchart of a machine vision AI wheel robot working method based on an artificial intelligence algorithm provided by an embodiment of the present invention, such as Figure 2 As shown, the working method includes: S100, scanning the working area, constructing a three-dimensional map of the area, identifying the area where the tire is located and the target area, identifying obstacles in the map, and planning a navigation path for the wheel robot; During the scanning process, the robot will focus on identifying the stacking position and target placement of the tires, generate spatial data of the entire work area, and generate a depth map through algorithm processing. The depth map can clearly show the various levels of the three-dimensional space, laying the foundation for the subsequent point cloud data integration.

[0024] Next, the robot converts the depth map into point cloud data. Point cloud data is a high-precision three-dimensional representation method that can accurately depict every detail in the environment through the distribution of points. Through point cloud stitching technology, the robot can use data collected from multiple angles to integrate into a seamless and complete three-dimensional work area map. In this process, posture calibration and overlapping area alignment technology are particularly critical. It can calculate the relative position relationship of multi-view point cloud data through algorithms and accurately match overlapping areas, thereby ensuring the accuracy of the stitching data and avoiding data deviations from affecting the quality of the final three-dimensional map.

[0025] In the generated 3D work area map, the robot further uses a geometric feature extraction algorithm to mark the tire stacking area and target placement area. This step not only identifies the location of the tire, but also analyzes its size, stacking method and hierarchical relationship, as well as the boundary information of the target placement area. With this data, the robot has a comprehensive understanding of the structure of the work area and the geometric characteristics of the tire. This step also combines obstacle data to analyze the location, shape and range of static and dynamic obstacles in the 3D map.

[0026] Finally, based on the above data, the robot combines the path planning algorithm to generate an obstacle avoidance strategy. The core goal of this strategy is to find the shortest path from the tire stacking area to the target placement area while ensuring that all obstacles are avoided on the path. The robot will comprehensively consider the length, complexity and safety of the path to generate the optimal navigation path, laying the foundation for subsequent accurate execution.

[0027] like Figure 3 As shown, the scanning of the working area, building a three-dimensional map of the area, identifying the area where the tire is located and the target area, identifying obstacles in the map, and planning a navigation path for the wheel robot specifically includes: S110, collecting data on the working area, capturing contour information of the working area, identifying tire stacking positions and target placement positions, obtaining complete spatial data of the working area, and generating a depth map; S120, based on the generated depth map, converts the environmental data into point cloud data, integrates the multi-view data through point cloud stitching technology, and uses posture calibration and overlapping area alignment technology to establish a three-dimensional work area map including static objects and dynamic objects; S130, marking the stacking area and the target placement area of ​​the tires in the three-dimensional working area map, extracting geometric features of the tires, including size, position, and relative stacking relationship, and recording boundary information of the target placement area; S140, analyzing obstacles in the three-dimensional work area map, and generating the shortest obstacle avoidance path based on the position of the tire and the target placement area in combination with the marked obstacle range.

[0028] S200, scanning the surface pattern of the target tire, generating a pattern feature map, analyzing the pattern type, detecting tire surface information, and recording dynamic parameters, and generating a tire force field distribution map based on the tire shape, material, and stacking method; This step uses high-resolution cameras and laser scanning equipment to scan the target tire in detail to capture multi-dimensional data such as its surface pattern, material properties, geometric features, and texture features. The robot can fully perceive the surface information of the tire, including the tire's pattern, subtle changes in surface texture, material characteristics (such as rubber type and hardness), and tire geometry parameters (such as diameter, thickness, and curvature). This data collection process lays a solid foundation for subsequent analysis.

[0029] After acquiring the data, the robot will pre-process the captured tire images. This usually includes noise reduction, contrast enhancement, edge detection and image segmentation to improve the quality and usability of the data. The pre-processed images will be used to extract the tire's tread features and generate a tread feature map. This tread feature map can clearly show the type, depth, distribution form and directionality of the pattern. At the same time, the robot will further analyze the wear status of the tire based on the distribution of the pattern type and surface texture. For example, by comparing the changes in the tread depth, the robot can assess whether there are obvious wear areas or abnormal damage on the tire.

[0030] Then, based on the geometric features, material properties, pattern characteristics, and wear status of the tire, the robot will begin to build a force field distribution model of the tire. The force field distribution model is a highly complex and dynamic description that integrates key mechanical information such as the tire's force area, friction distribution, and center of gravity position. For example, the robot will use algorithms to calculate the force magnitude of the tire at different contact points, analyze which areas have higher friction, and even predict the possible behavior of the tire during grasping and moving through changes in the center of gravity position. This model provides the robot with an accurate description of the physical properties of the tire.

[0031] Ultimately, the robot will integrate all the characteristic data of the tire and its corresponding force field distribution model to generate a complete tire force field distribution map. This distribution map not only shows the physical characteristics and mechanical properties of the tire, but also provides rich information support for subsequent grasping and moving operations. For example, through the force field distribution map, the robot can quickly determine the most suitable area for grasping, the grasping force to be applied, and how to adjust the grasping angle to keep the tire balanced and stable.

[0032] The generation of pattern feature maps and force field distribution maps enables the robot to clearly identify the status of the tire, such as the degree of wear and material changes. This capability greatly enhances the robot's adaptability in different mission scenarios. Secondly, the construction of the force field distribution model provides a scientific basis for the robot in the process of grasping and moving, reducing the risk of tire slippage or deformation caused by improper grasping. In addition, a comprehensive analysis of the dynamic parameters of the tire (such as friction and center of gravity position) enables the robot to adjust the grasping strategy in real time to ensure the accuracy and safety of the operation.

[0033] like Figure 4 As shown, the surface pattern of the target tire is scanned, a pattern feature map is generated, the pattern type is analyzed, the tire surface information is detected, and dynamic parameters are recorded. Based on the tire shape, material and stacking method, a tire force field distribution map is generated, which specifically includes: S210, scanning the target tire, capturing the surface pattern, material properties, geometric features, and texture features of the tire, and obtaining image information and depth information of the tire; S220, preprocessing the captured tire image, extracting tire pattern features from the preprocessed image, generating a pattern feature map, analyzing the pattern type, depth and distribution, and identifying the tire wear state in combination with the tire type; S230, constructing a force field distribution model of the tire based on the geometric features, material properties and pattern features of the tire, including the tire force area, friction force distribution and center of gravity position; S240 combines all tire characteristics and the force field distribution model to generate a complete tire force field distribution map.

[0034] S300, for tires that need to be transplanted, establishes a behavior prediction model based on the tire force field distribution map, predicts the dynamic performance of the tire under different gripping angles and gripping forces, and generates a dynamic adjustment range for the gripping strategy; This step first requires combining the geometric characteristics of the tire (such as size, shape and structure) with the force field distribution data (such as friction, center of gravity position and force conditions) to form a comprehensive description of the tire characteristics. Through in-depth analysis of these data, the robot can identify the dynamic performance of the tire under different gripping conditions. This includes key parameters such as the possible slip angle and tilt tendency of the tire under different gripping points, gripping angles and gripping forces, as well as the stability of the tire after gripping.

[0035] In the process of building a behavior prediction model, the robot uses machine learning and physical simulation technology to train algorithms to identify and predict the impact of different operating conditions on tire behavior. Such a model can not only make predictions based on static data, but also update in real time to adapt to rapidly changing environments. For example, when the robot collects tire status data in real time during the grasping process, the model can quickly analyze and predict changes, and even make corresponding adjustments during the grasping process, which can improve the success rate of grasping and reduce the risk of errors.

[0036] Based on the behavior prediction model, the robot will generate a grasping strategy for the current tire, which includes detailed information such as grasping points, force range, and angle range. The selection of grasping points is based on the force field distribution map to ensure that the tire is evenly stressed during the grasping process to avoid deformation or damage caused by excessive local force. The grasping force range and angle range are generated through an in-depth analysis of the tire's characteristics to ensure that the robot can effectively prevent sliding and tilting when grasping. Especially in a complex stacking environment, the rationality of the grasping strategy directly affects the success rate of tire movement and placement.

[0037] This step greatly improves the intelligence and autonomy of the wheel robot when performing tire transplanting tasks through an accurate behavior prediction model. First, the establishment of the model enables the robot to dynamically adjust its grasping strategy according to real-time data, enhancing its adaptability to environmental changes, thereby improving the success rate and efficiency of grasping. Secondly, through accurate prediction of the dynamic performance of the tire, the robot can effectively avoid losses caused by improper grasping, such as tire sliding, tilting or instability. In addition, the refinement of the grasping strategy not only improves the safety of operation, but also optimizes the overall workflow and reduces the time waste and labor costs caused by incorrect operations.

[0038] like Figure 5 As shown, based on the tire force field distribution diagram, a behavior prediction model is established to predict the dynamic performance of the tire under different gripping angles and gripping forces, and to generate a dynamic adjustment range of the gripping strategy, specifically including: S310, based on the collected tire features, the generated tire force field distribution map is integrated to construct a behavior prediction model, and the geometric features and force field distribution data of the tire are input to predict the dynamic performance of the tire under changes in gripping point, angle and force, including slip angle, tilt trend and stability after gripping; S320, based on the behavior prediction model, generating a gripping strategy for the current tire, including gripping points, gripping force range, and gripping angle range.

[0039] In this step, the dynamic performance of the tire under the change of gripping point, angle and force is predicted, including slip angle, tilt trend and stability after gripping, specifically: Calculate the slip angle: ; in, represents the slip angle, which is the angle at which the tire slides after grabbing. is the tangential force, i.e. the force parallel to the contact surface, is the normal force, i.e. the force perpendicular to the contact surface; Calculate the slope trend: ; in, is the change in tilt angle, that is, the degree of tilt of the tire during the gripping process. is the tire weight, is the center of gravity offset distance, calculated from the force field distribution diagram, is the moment of inertia of the tire; Calculate the stability after grabbing: ; in, To obtain the stability index, When , it means the grasping is in a stable state. , indicating that the crawling failed. is the friction coefficient, which is calculated from the tire material properties and texture characteristics.

[0040] Furthermore, the generation of the grabbing strategy for the current tire includes grabbing points, grabbing force range, and grabbing angle range, specifically: Calculate the comprehensive score of each candidate grab point: ; in, To grab the point The overall rating of is the weight factor; Determine the grip strength scope: The friction force generated by the clamping force is greater than the tangential force plus a safety margin ,Right now: ; The gripping force does not exceed the maximum load allowed by the tire ,Right now: ; Get the final grip strength scope: ; Determine the grip angle scope: ; in: ; ; and Indicates the minimum and maximum values ​​of the grab angle, Indicates the allowed tilt trend threshold, Indicates the grab angle is Grasping stability.

[0041] S400, which captures the dynamic response of the tire in real time during the tire transplanting process, and senses the weightlessness of the tire based on the gripping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; Real-time monitoring of the dynamic state of the tire during the transplanting process, including comprehensive tracking of tire displacement, posture changes, tilt angles, and slip trends. These data are carefully recorded and compared with the previously established behavior prediction model to verify whether the actual behavior is in line with expectations. This real-time comparison can help the robot quickly identify potential problems, such as tilting or slipping of the tire due to improper gripping.

[0042] At the same time, the force sensors and tactile sensors installed on the grippers play a vital role in this process. These sensors collect gripping force information in real time to ensure that the applied gripping force is always maintained within the predetermined range. Through these sensors, the robot can accurately calculate the instant changes in normal force (force perpendicular to the contact surface) and tangential force (force along the contact surface), which is crucial to maintaining the stability of the tire and avoiding slippage. When the force applied by the grippers is too large or too small, the robot can immediately adjust its operation to avoid damage to the tire or loss of control.

[0043] If there are signs in the system monitoring data and gripping feedback data that a weightlessness signal has been triggered (for example, the tire slips or tilts), the system will dynamically adjust the range according to the pre-generated grasping strategy. At this time, the robot will recalculate the grasping force and maximize the normal force applied by the gripper based on the force field distribution map to ensure that the tire can be firmly grasped. In this way, the robot can not only respond quickly to emergencies, but also effectively adjust the grasping strategy to ensure the stability and safety of the tire.

[0044] When implementing the dynamically adjusted grasping strategy, the robot combines monitoring data and feedback data for closed-loop control. This means that throughout the grasping process, the system will constantly compare real-time data with preset targets and adjust the grasping action at any time to ensure the accuracy and safety of the operation. This closed-loop control mechanism gives the robot greater flexibility and adaptability in complex environments, and can respond to potential unexpected situations in real time.

[0045] Real-time dynamic monitoring can ensure that the robot can provide immediate feedback on the tire status. This immediacy enables the system to quickly adjust operations when any abnormality occurs, thereby improving the success rate and safety of grasping. The combination of clamping feedback data and real-time monitoring forms an adaptive control mechanism, which reduces errors caused by improper human operation and avoids losses and waste. In addition, the ability to dynamically adjust the grasping strategy enables the robot to respond flexibly in complex and uncertain environments, greatly enhancing the breadth and practicality of its application scenarios.

[0046] like Figure 6 As shown, the dynamic response of the tire is captured in real time during the tire transplanting process, and the tire is sensed to lose weight based on the clamping feedback data. If a weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; S410, real-time monitoring of the dynamic state of the tire during the transplanting process, including displacement, posture change, tilt angle, and slip trend of the tire, and comparing the monitoring data with the prediction result of the behavior model; S420, the force sensor and tactile sensor installed on the gripper collect gripping force information in real time to ensure that the gripping force is within a predetermined range, and calculate the instantaneous changes in normal force and tangential force based on the feedback data; S430, combining the real-time monitoring data and the clamping feedback data, determines whether to trigger a weightlessness signal, including: ; S440, if the weightlessness signal is triggered, the range is dynamically adjusted according to the pre-generated grasping strategy, the grasping force is recalculated, and the normal force applied by the gripper is maximized based on the force field distribution diagram ; ; ; in, is the current tangential force, For additional safety margin; S450 executes the dynamically adjusted grasping strategy in real time, and combines monitoring data and feedback data to perform closed-loop control of the grasping action.

[0047] S500, scanning the state of the target placement area, predicting the stability of the tire after placement through a behavior prediction model, and adjusting the tire angle during the tire transplanting process based on the stability prediction result; This step first extracts the target placement area from the 3D work area map, and divides it into suitable placement areas and high-risk areas by deeply analyzing the surface characteristics of the area. This division is completed based on a comprehensive calculation of the surface flatness, inclination angle, and obstacle interference of the area. At the same time, the system will mark each sub-area with a priority score to evaluate whether it is suitable as the final placement location of the tire. The priority score reflects the placement safety and feasibility of the area, providing a key basis for the subsequent placement strategy generation.

[0048] Next, the robot combines the geometric features of the target placement area and the force field distribution map of the tire to further optimize the placement position and angle by calculating the stability index of the tire after placement. The stability index is calculated based on physical models and mathematical methods to quantify the balance and force conditions of the tire after placement. For example, the robot will consider the impact of the tilt angle of the target area on the center of gravity of the tire, and predict whether the tire is likely to slip or tilt through simulation analysis. If the stability index is lower than the preset index threshold, the system will automatically recalculate the placement angle and specific position of the tire, and dynamically adjust the direction and posture of the tire during the transplant process to ensure that the ideal stability can be achieved after placement.

[0049] During the actual tire transplanting process, the robot will adjust the gripper angle and posture in real time to gradually bring the tire closer to the updated placement strategy. Through monitoring and control, the robot can adjust the operation path according to the real-time status of the target placement area to ensure that the tire is accurately placed in the optimal position while maintaining the angle and direction of the tire in line with stability requirements. This real-time adjustment capability is particularly important because in a dynamic environment, the original plan may be affected by external interference and become no longer applicable. The robot's strategy correction based on real-time data can effectively solve this problem.

[0050] After the tire is successfully placed, the robot uses force sensors and a visual system to make a final confirmation of the tire's status. This step ensures that the tire is indeed in a stable state, such as no tilting, slipping, or uneven force. At the same time, the system records the status data of the placement area and the final placement parameters of the tire. This data can be used for subsequent task optimization and model improvement, and also provide a reference for the next operation.

[0051] Through surface analysis and priority scoring of the target area, the robot can efficiently select the most suitable location for tire placement, avoiding the time waste and operational risks that may be caused by blind attempts. The calculation of stability indicators and the introduction of dynamic adjustment strategies enable the robot to be more adaptable when facing complex and changing external environments, which can not only ensure the success rate of tire placement, but also effectively avoid potential risks. In addition, the ability to adjust the gripper angle and posture in real time, combined with the closed-loop control mechanism, further improves the accuracy and robustness of the placement task. Finally, the confirmation and data recording of the tire status after placement not only provides a guarantee for the completion of the task, but also lays the foundation for the optimization of subsequent tasks by accumulating experience data.

[0052] like Figure 7 As shown, the state of the target placement area is scanned, the stability of the tire after placement is predicted by a behavior prediction model, and the tire angle is adjusted during the tire transplanting process based on the stability prediction result, specifically including: S510, extracting the target placement area from the three-dimensional work area map, and partitioning the surface of the target placement area into a suitable placement area and a high-risk area, and marking the area priority score by calculating the surface flatness, tilt angle, and obstacle interference degree; S520, based on the geometric characteristics of the target placement area and the force field distribution map of the tire, combined with the center of gravity position, calculate the stability index of the tire after placement ; ; S530, setting an index threshold, if the stability index is lower than the index threshold, recalculating the placement angle and position of the tire, and adjusting the direction of the tire during the transplanting process; S540, during the tire transplanting process, adjusts the tire gripper angle and posture in real time to make the tire conform to the updated placement strategy and ensure that the final placement position and angle meet the stability requirements; S550, after the tire is successfully placed, confirm whether the tire is in a stable state through the force sensor and the visual system, and record the status data of the placement area and the final placement parameters of the tire.

[0053] In this step, if the stability index is lower than the index threshold, the placement angle and position of the tire are recalculated, specifically: ; ; in, For the new placement angle, is the placement angle, Indicates the stability index Maximum placement angle , Represents the new placement coordinates, represents the regional priority score for target placement, Indicates the region priority score for target placement The placement position that reaches the maximum value .

[0054] S600: After the transplanting task is completed, the tire data and transplanting data of the transplanting are recorded.

[0055] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0056] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0057] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0061] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A machine vision AI wheel robot based on artificial intelligence algorithm, characterized by: The wheel robot comprises: A work area scanning module is used to scan the work area, build a three-dimensional map of the area, identify the area where the tire is located and the target area, identify obstacles in the map, and plan a navigation path for the wheel robot; The force field distribution analysis module is used to scan the surface pattern of the target tire, generate a pattern feature map, analyze the pattern type, detect the tire surface information, and record dynamic parameters. Based on the tire shape, material and stacking method, it generates a tire force field distribution map; The grasping strategy adjustment module is used to establish a behavior prediction model for the tire to be transplanted based on the tire force field distribution map, predict the dynamic performance of the tire under different grasping angles and grasping forces, and generate the dynamic adjustment range of the grasping strategy; The tire dynamic response capture module is used to capture the dynamic response of the tire in real time during the tire transplanting process, and sense the weightlessness of the tire based on the clamping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; The target placement area scanning module is used to scan the state of the target placement area, predict the stability of the tire after placement through the behavior prediction model, and adjust the tire angle during the tire transplantation process based on the stability prediction result; The transplanting data recording module is used to record the tire data and transplanting data of the transplanting after the transplanting task is completed.

2. The working method of the machine vision AI wheel robot based on artificial intelligence algorithm as claimed in claim 1, characterized in that: The working method comprises: Scan the working area, build a three-dimensional map of the area, identify the tire area and the target area, identify obstacles in the map, and plan a navigation path for the wheel robot; Scan the surface pattern of the target tire, generate a pattern feature map, analyze the pattern type, detect the tire surface information, and record dynamic parameters. Based on the tire shape, material and stacking method, generate a tire force field distribution map; For tires that need to be transplanted, a behavior prediction model is established based on the tire force field distribution map to predict the dynamic performance of the tire under different gripping angles and gripping forces, and generate a dynamic adjustment range for the gripping strategy; During the tire transplanting process, the dynamic response of the tire is captured in real time, and the tire is sensed to lose weight based on the clamping feedback data. If the weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range. Scan the status of the target placement area, predict the stability of the tire after placement through the behavior prediction model, and adjust the tire angle during the tire transplantation process based on the stability prediction results; After the transplanting task is completed, record the tire data and transplanting data of this transplanting.

3. The working method of the wheel robot according to claim 2, characterized in that: The scanning of the working area, building a three-dimensional map of the area, identifying the area where the tire is located and the target area, identifying obstacles in the map, and planning a navigation path for the wheel robot specifically includes: Collect data on the work area, capture the contour information of the work area, identify the tire stacking position and target placement position, obtain complete spatial data of the work area, and generate a depth map; Based on the generated depth map, the environmental data is converted into point cloud data, and the multi-view data is integrated through point cloud stitching technology. The pose calibration and overlapping area alignment technology are used to establish a three-dimensional work area map containing static objects and dynamic objects. Mark the tire stacking area and target placement area in the three-dimensional work area map, extract the geometric features of the tires, including size, position and relative stacking relationship, and record the boundary information of the target placement area; Analyze the obstacles in the 3D work area map, and generate the shortest obstacle avoidance path based on the tire position and target placement area combined with the marked obstacle range.

4. The working method of the wheel robot according to claim 3, characterized in that: The method of scanning the surface pattern of the target tire, generating a pattern feature map, analyzing the pattern type, detecting the tire surface information, and recording dynamic parameters, and generating a tire force field distribution map based on the tire shape, material and stacking method, specifically includes: Scan the target tire to capture the tire's surface pattern, material properties, geometric features, and texture features, and obtain the tire's image information and depth information; Preprocess the captured tire images, extract the tire pattern features from the preprocessed images, generate a pattern feature map, analyze the pattern type, depth and distribution, and identify the tire wear status based on the tire type; Based on the geometric features, material properties and pattern characteristics of the tire, a force field distribution model of the tire is constructed, including the tire force area, friction force distribution and center of gravity position; Combining all tire characteristics and the force field distribution model, a complete tire force field distribution map is generated.

5. The working method of the wheel robot according to claim 4, characterized in that: Based on the tire force field distribution map, a behavior prediction model is established to predict the dynamic performance of the tire under different gripping angles and gripping forces, and to generate a dynamic adjustment range of the gripping strategy, specifically including: Based on the collected tire features, the generated tire force field distribution map is integrated to build a behavior prediction model. The tire's geometric features and force field distribution data are input to predict the dynamic performance of the tire under changes in gripping points, angles, and forces, including slip angles, tilt trends, and stability after gripping. Based on the behavior prediction model, a gripping strategy for the current tire is generated, including gripping point position, gripping force range, and gripping angle range.

6. The working method of the wheel robot according to claim 5, characterized in that: The predicted dynamic performance of the tire under changes in gripping point, angle and force, including slip angle, tilt trend and stability after gripping, is specifically: Calculate the slip angle: ； in, represents the slip angle, which is the angle at which the tire slides after grabbing. is the tangential force, i.e. the force parallel to the contact surface, is the normal force, i.e. the force perpendicular to the contact surface; Calculate the slope trend: ； in, is the change in tilt angle, that is, the degree of tilt of the tire during the gripping process. is the tire weight, is the center of gravity offset distance, calculated from the force field distribution diagram, is the moment of inertia of the tire; Calculate the stability after grabbing: ； in, To obtain the stability index, When , it means the grasping is in a stable state. , indicating that the crawling failed. is the friction coefficient, which is calculated from the tire material properties and texture characteristics.

7. The working method of the wheel robot according to claim 6, characterized in that: The generation of the grabbing strategy for the current tire includes grabbing points, grabbing force range, and grabbing angle range, specifically: Calculate the comprehensive score of each candidate grab point: ； in, To grab the point The overall rating of is the weight factor; Determine the grip strength scope: The friction force generated by the clamping force is greater than the tangential force plus a safety margin ,Right now: ； The gripping force does not exceed the maximum load allowed by the tire ,Right now: ； Get the final grip strength scope: ； Determine the grip angle scope: ； in: ； ； and Indicates the minimum and maximum values ​​of the grab angle, Indicates the allowed tilt trend threshold, Indicates the grab angle is Grasping stability.

8. The working method of the wheel robot according to claim 5, characterized in that: The dynamic response of the tire is captured in real time during the tire transplanting process, and the tire is sensed to lose weight based on the clamping feedback data. If a weightlessness signal is triggered, the gripping force is recalculated within the dynamic adjustment range; Real-time monitoring of the dynamic state of the tire during the transplanting process, including tire displacement, posture change, tilt angle and slip trend, and comparing the monitoring data with the prediction results of the behavioral model; The force sensor and tactile sensor installed on the gripper collect gripping force information in real time to ensure that the gripping force is within the predetermined range, and calculate the instant changes in normal force and tangential force based on the feedback data; Combine real-time monitoring data and clamping feedback data to determine whether to trigger a weightlessness signal, including: ； If the weightlessness signal is triggered, the range is dynamically adjusted according to the pre-generated grasping strategy, the grasping force is recalculated, and the normal force applied by the gripper is maximized based on the force field distribution map. ; ； ； in, is the current tangential force, For additional safety margin; The dynamically adjusted grasping strategy is executed in real time, and the grasping action is closed-loop controlled by combining monitoring data and feedback data.

9. The working method of the wheel robot according to claim 8, characterized in that: The scanning target placement area state, predicting the stability of the tire after placement through a behavior prediction model, and adjusting the tire angle during the tire transplantation process based on the stability prediction result, specifically includes: Extract the target placement area from the 3D work area map, and partition the surface of the target placement area into suitable placement areas and high-risk areas. At the same time, mark the area priority score by calculating the surface flatness, tilt angle and obstacle interference degree; Based on the geometric characteristics of the target placement area and the force field distribution map of the tire, combined with the center of gravity position, the stability index of the tire after placement is calculated ; ； Set the index threshold. If the stability index is lower than the index threshold, recalculate the placement angle and position of the tire and adjust the direction of the tire during the transplanting process. During tire transplantation, the tire gripper angle and posture are adjusted in real time to make the tire conform to the updated placement strategy and ensure that the final placement position and angle meet the stability requirements; After the tire is successfully placed, the force sensor and vision system confirm whether the tire is in a stable state, and record the status data of the placement area and the final placement parameters of the tire.

10. The working method of the wheel robot according to claim 9, characterized in that: If the stability index is lower than the index threshold, the placement angle and position of the tire are recalculated, specifically: ； ； in, For the new placement angle, is the placement angle, Indicates the stability index Maximum placement angle , Represents the new placement coordinates, represents the regional priority score for target placement, Indicates the region priority score for target placement The placement position at which the maximum value is reached .

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