Operation analysis method and system for fusion of mobile robot and industrial mechanical arm

Abstract

The invention discloses an operation analysis method and system for a mobile robot fused with an industrial mechanical arm, and the method comprises the steps: receiving a work order, then splitting a task, determining sub-task parameters, and optimizing the predicted time based on historical data; mapping and positioning are synchronized in the moving process, and the positioning precision is guaranteed through an SLAM model and prediction-correction logic; planning a global path and a residence position, and coping with a dynamic obstacle through a weighted scoring method and an MPC model; the operation position is precisely aligned to compensate the ground unevenness error; recognizing a workpiece and selecting an optimal grabbing point; the contact force and rigidity are dynamically adjusted when operation is executed, and automatic retreating and fine adjustment are performed when abnormity occurs; the grabbing quality and the advancing safety are confirmed; and data is updated after ending, and low-power automatic charging is realized. According to the method, the problems that a traditional technology path and operation planning are separated, sensing is single, robustness is poor, an operation strategy is difficult to adapt, and a standardized quick change and calibration mechanism is lacked are solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of mobile robot and industrial robot arm fusion analysis, and particularly relates to a mobile robot fusion industrial robot arm operation analysis method and system. BACKGROUND

[0002] In the field of industrial automation and intelligent manufacturing, the fusion application of mobile robots and industrial robot arms has become a key direction to improve operation flexibility and efficiency. The core requirement is to realize mobile-operation integration and collaboration to adapt to dynamic and semi-structured industrial scenes.

[0003] At present, the traditional technology relies on a single sensor for environment and workpiece perception, or uses a 3D laser sensor to realize global positioning, or relies on a monocular / dual-camera to complete workpiece recognition and pose estimation, lacking multi-source sensor redundancy design. When a single sensor fails due to obstruction, light changes, dust interference, etc., the system is prone to lose environmental perception ability, resulting in positioning interruption or operation deviation. In addition, the time synchronization accuracy between multiple sensors is insufficient, and the external parameter calibration is easily affected by temperature and vibration, resulting in errors. These problems will gradually accumulate to form positioning drift, directly leading to position deviation when the robot arm grabs the workpiece, or unable to accurately align during assembly, seriously affecting operation precision and consistency.

[0004] The operation strategy of the existing composite robot mostly adopts a hard coding method, i.e., presetting fixed parameters for specific workpieces and specific scenes. When the operation target changes or the operation scene adjusts, the parameters need to be manually reset, which is not only cumbersome to operate, but also causes extended equipment downtime, making it difficult to adapt to flexible production requirements of multiple varieties and small batches. At the same time, the existing technology lacks online learning and reasoning ability for the operation process, and cannot optimize the strategy based on historical operation data, further limiting the adaptability of the system to complex scenes. When the operation tool needs to be switched, the old tool needs to be manually disassembled, the new tool needs to be installed, and the tool coordinate system and the robot base coordinate system need to be manually recalibrated, which is time-consuming and prone to calibration errors due to manual operation. SUMMARY

[0005] Therefore, the application provides a mobile robot fusion industrial robot arm operation analysis method and system, which solves the problem of the fragmentation of traditional technical paths and operation planning, poor single robustness of perception, difficulty in self-adaptation of operation strategy, and lack of standardized quick change and calibration mechanism.

[0006] To achieve the above purpose, the application provides the following technical scheme: a mobile robot fusion industrial robot arm operation analysis method, comprising the following steps:

[0007] S1: Task receiving and decomposition: After receiving the work order task, the fixed flow table is formed according to the logical sequence of the execution of the work order task, the dependency relationship between the subtasks and the parallel execution links are determined, the target position, target posture, required tools and estimated time of each subtask are determined, the estimated time is determined based on the initial value of the historical execution data, and the historical data is updated according to the actual time consumption after the completion of the subtask to optimize the subsequent estimated time;

[0008] S2: Mapping and positioning: The environment map is constructed synchronously during the movement of the robot, and the positioning process adopts a two-step logic of prediction-correction, first calculates the position prediction value based on the real-time data of the wheeled odometer and the inertial measurement unit, and then corrects the position prediction value combined with the observation data of the laser sensor or the visual camera;

[0009] S3: Global path and residence position planning: Avoiding the forbidden area and crowded section in the environment when planning the global path, comprehensively evaluating the occupation risk, detour distance and turning number of the path to select the optimal path; after the robot reaches the work station, the optimal parking posture is determined as the residence posture according to the distance from the work point, the effective field of view range of the sensor and the reachability of the mechanical arm;

[0010] S4: Work site fine alignment: When the robot enters the preset close distance range from the work site, the positioning label at the work site is recognized by the visual camera, the relative position and relative posture of the robot and the workpiece are calculated based on the label position information, and the robot is slightly adjusted in posture according to the relative position and posture to compensate for the positioning deviation caused by uneven ground;

[0011] S5: Target recognition and grasp point selection: The workpiece image is collected by the visual camera, and target detection and pose estimation are completed to determine the clippable area of the workpiece, the contact stability, friction condition and edge risk of the potential grasp points in the clippable area are comprehensively evaluated, and the optimal grasp point is selected; the path of the mechanical arm approaching and exiting the workpiece is planned to reduce the invalid motion;

[0012] S6: Operation execution: When the mechanical arm approaches the workpiece according to the planned path, the motion speed and end stiffness are reduced; after contacting the workpiece, the contact force is adjusted based on the real-time data of the force sensor to adapt to the work requirements, different stiffness control strategies are adopted in the contact stage and the exit stage; if the force or torque exceeds the safety upper limit, the mechanical arm is controlled to retreat along the contact normal direction and perform lateral posture fine adjustment until the force and torque return to the normal range.

[0013] As a preferred solution for the work analysis method of mobile robots integrating industrial mechanical arms, in step S2, the SLAM mathematical model is used to construct the environment map, and the map construction and positioning fusion are realized through multi-source data collected by the laser sensor and the visual camera, and the formula is:

[0014]

[0015] wherein, is the pose of robot at time k, is the pose of robot at time k-1, is the control input at time k, is the control input noise, is the sensor observation at time k, is the observation model function, is the environment map, is the observation noise.

[0016] As a preferred solution of the mobile robot combined with industrial robot operation analysis method, in step S3, the global path planning adopts a weighted scoring method to select the optimal path, and the formula is:

[0017]

[0018] wherein, is the path comprehensive score, is the path occupancy risk, is the path detour distance, is the path turning number, , , are the weight coefficients corresponding to the occupancy risk, detour distance and turning number respectively, and the path with the minimum comprehensive score is selected as the global path.

[0019] As a preferred solution of the mobile robot combined with industrial robot operation analysis method, in step S3, the dynamic obstacle processing of global path planning adopts a model predictive control mathematical model, and dynamic obstacle avoidance is realized through rolling optimization, and the formula is:

[0020]

[0021]

[0022]

[0023] wherein, is the prediction time domain, is the predicted robot pose at time t+j at time t, is the optimized control input at time t+j at time t, is the objective function, minimizing the path time and collision risk, is the robot motion model, is the pose constraint, is the control input constraint.

[0024] ​As the preferred solution of the operation analysis method of the mobile robot combined with the industrial robot arm, in step S4, the robot is slightly corrected in pose according to the relative position and pose to compensate for the positioning deviation caused by uneven ground, and the formula is:

[0025]

[0026] In the formula, is the transformation matrix of the robot base coordinate system relative to the workpiece coordinate system, is the transformation matrix of the robot base coordinate system relative to the camera coordinate system, is the transformation matrix of the camera coordinate system relative to the positioning tag coordinate system, is the transformation matrix of the positioning tag coordinate system relative to the workpiece coordinate system; is the ideal alignment pose parameter of the robot, is the current actual pose parameter calculated based on the transformation matrix, is the pose correction amount.

[0027] As the preferred solution of the operation analysis method of the mobile robot combined with the industrial robot arm, in step S5, the simplified scoring method is used to select the optimal grasping point, and the formula is:

[0028]

[0029] In the formula, is the comprehensive score of the grasping point, is the contact area of the grasping point, is the friction coefficient of the grasping surface, is the edge risk coefficient of the grasping point, , , are the weight coefficients corresponding to the contact area, the friction coefficient and the edge risk coefficient respectively, and the comprehensive score is selected as the optimal grasping point;

[0030] In step S6, the formula for adjusting the contact force based on the real-time data of the force sensor to adapt to the operation requirements is:

[0031]

[0032] In the formula, is the contact force at the end of the robot arm, is the end stiffness coefficient, is the end displacement amount; the stiffness coefficient is reduced in the contact stage, and the stiffness coefficient is restored in the exit stage;

[0033] In step S6, the positioning error and contact disturbance are also absorbed through impedance control during operation execution, and the formula of impedance control is:

[0034]

[0035] wherein, is a damping coefficient, is the end motion velocity, by increasing the damping coefficient shock and slip in the operation process are suppressed.

[0036] As a preferred solution of the operation analysis method of the mobile robot integrated with the industrial robot arm, after step S6, the method further comprises:

[0037] S7: quality and safety confirmation: after grabbing the workpiece, the weight change of the workpiece is detected to verify the effectiveness of grabbing, and the position and posture of the workpiece are reviewed through a vision camera to confirm the quality of grabbing; during the movement of the robot, the movement trend of the front target is predicted in real time, and if it is judged that the front target may enter the safety distance within the preset time, the robot is controlled to slow down or pause, or the path is re-planned to realize obstacle avoidance;

[0038] S8: end and reset: after the operation is completed, the robot evacuates the operation area according to the preset return path, and synchronously updates the inventory data and the task state; when it is detected that the power of the robot is lower than the preset threshold, the robot is controlled to automatically dock with the charging device to complete the charging operation.

[0039] The application also provides a mobile robot integrated with an industrial robot arm operation analysis system, comprising:

[0040] The task receiving and decomposing module is used for splitting the fixed flow table according to the execution logic of the work order task after receiving the work order task, clearly defining the dependency relationship between the subtasks and the parallel executable links, and synchronously determining the target position, target posture, required tool and estimated time corresponding to each subtask, wherein the estimated time is determined based on the initial value of the historical execution data, and the historical data is updated according to the actual time consumption after the completion of the subtask to optimize the subsequent estimated time;

[0041] The mapping and positioning module is used for synchronously constructing the environment map during the movement of the robot, and the positioning process adopts the two-step logic of prediction-correction, first calculating the position prediction value based on the real-time data of the wheeled odometer and the inertial measurement unit, and then correcting the position prediction value in combination with the observation data of the laser sensor or the vision camera;

[0042] The global path and residence position planning module is used for avoiding the forbidden area and crowded section in the environment when planning the global path, and comprehensively evaluating the occupation risk, detour distance and turning number of the path to select the optimal path; after the robot reaches the operation station, the optimal parking posture is determined as the residence posture in combination with the distance from the operation point, the effective field of view range of the sensor and the accessibility of the robot arm;

[0043] The workstation alignment module is used to identify the positioning tag at the workstation through a vision camera when the robot enters a preset close range to the workstation. Based on the tag position information, it calculates the relative position and relative posture of the robot and the workpiece, and makes a small posture correction to the robot according to the relative position and posture to compensate for the positioning deviation caused by uneven ground.

[0044] The target recognition and gripping point selection module is used to acquire workpiece images through a vision camera and complete target detection and pose estimation, determine the gripping area of ​​the workpiece, comprehensively evaluate the contact stability, friction conditions and edge risks of potential gripping points within the gripping area, select the optimal gripping point, and plan the path for the robotic arm to approach and exit the workpiece to reduce ineffective movement.

[0045] The operation execution module is used to reduce the movement speed and end effector stiffness when the robotic arm approaches the workpiece according to the planned path; after contacting the workpiece, it adjusts the contact force based on real-time data from the force sensor to adapt to the operation requirements, and adopts different stiffness control strategies for the contact stage and the exit stage; if the force or torque is detected to exceed the safety limit, it controls the robotic arm to retreat along the contact normal and performs lateral posture fine adjustment until the force and torque are restored to the normal range.

[0046] As a preferred solution for a mobile robot integrated with an industrial robotic arm operation analysis system, the mapping and localization module uses a Simultaneous Localization and Map Building (SLAM) mathematical model to construct the environmental map. Map building and localization fusion are achieved through multi-source data collected by laser sensors and vision cameras. The formula is as follows:

[0047]

[0048] In the formula, Let K be the robot's pose at time k. Let K be the robot's pose at time k-1. For the control input at time k, To control input noise, Let k be the sensor observation value at time k. For the observation model function, For environmental maps, To observe noise.

[0049] As a preferred solution for the operation analysis system integrating mobile robots and industrial robotic arms, the global path and dwell position planning module uses a weighted scoring method to select the optimal path for global path planning. The formula is as follows:

[0050]

[0051] In the formula, For the overall score of the path, To mitigate the risk of path occupancy, This represents the detour distance of the route. The number of turns on the path. , , The weighting coefficients for occupancy risk, detour distance, and number of turns are respectively used to select the comprehensive score. The shortest path is used as the global path;

[0052] In the global path and dwell position planning module, the dynamic obstacle handling of global path planning adopts a model predictive control mathematical model, and achieves dynamic obstacle avoidance through rolling optimization. The formula is as follows:

[0053]

[0054]

[0055]

[0056] In the formula, To predict the time domain, This is the robot pose predicted at time t+j from time t. The control input at time t+j is optimized for time t. The objective function is to minimize path time and collision risk. For robot motion models, For pose constraints, To control input constraints.

[0057] As a preferred solution for the operation analysis system integrating mobile robots and industrial robotic arms, the operation position fine alignment module uses the following formula to make minor posture corrections to the robot based on its relative position and posture to compensate for positioning deviations caused by uneven ground:

[0058]

[0059] In the formula, Let be the transformation matrix between the robot's base coordinate system and the workpiece coordinate system. Let be the transformation matrix between the robot's base coordinate system and the camera coordinate system. This is the transformation matrix between the camera coordinate system and the positioning label coordinate system. This is the transformation matrix between the coordinate system of the positioning label and the coordinate system of the workpiece; To achieve the ideal alignment pose parameters for the robot, These are the current actual pose parameters calculated based on the transformation matrix. This is the attitude correction amount.

[0060] As a preferred solution for the operation analysis system integrating mobile robots and industrial robotic arms, the target recognition and gripping point selection module uses a simplified scoring method to select the optimal gripping point, with the following formula:

[0061]

[0062] In the formula, To obtain a comprehensive score for the captured points, To capture the contact area, To determine the coefficient of friction of the gripping surface, To capture the risk coefficient at the edge of the point, , , The weighting coefficients for contact area, friction coefficient, and edge risk coefficient are respectively selected for the comprehensive score. The highest potential crawl point is taken as the optimal crawl point;

[0063] As a preferred solution for a work analysis system integrating a mobile robot and an industrial robotic arm, the formula for adjusting the contact force based on real-time data from a force sensor to adapt to work requirements in the operation execution module is as follows:

[0064]

[0065] In the formula, For the contact force at the end of the robotic arm, This is the end stiffness coefficient. This refers to the end displacement; the stiffness coefficient is reduced during the contact phase. The stiffness coefficient is restored during the exit phase;

[0066] The operation execution module also absorbs positioning errors and contact disturbances through impedance control. The formula for impedance control is:

[0067]

[0068] in, The damping coefficient is... To determine the final velocity, increase the damping coefficient. Suppress vibrations and slippage during operation.

[0069] As a preferred solution for the operation analysis system of mobile robots integrated with industrial robotic arms, it also includes:

[0070] The quality and safety verification module is used to detect changes in the weight of the workpiece after it is grasped to verify the effectiveness of the grasping, and to verify the position and posture of the workpiece through a vision camera to confirm the grasping quality. During the robot's movement, the module predicts the movement trend of the target ahead in real time. If it is determined that the target ahead may enter the safe distance within a preset time, the module will first control the robot to slow down or pause, or replan the path to achieve obstacle avoidance.

[0071] The finishing and reset module is used to allow the robot to leave the work area according to a preset return path after the operation is completed, and to update the inventory data and task status simultaneously; when the robot's battery level is detected to be lower than a preset threshold, the robot is controlled to automatically dock with the charging device to complete the charging operation.

[0072] The beneficial effects of this invention are as follows:

[0073] First, the present invention adopts a joint framework of task motion planning and model predictive control to realize the trajectory collaborative planning of the mobile chassis and the six-axis robotic arm. While satisfying constraints such as collision, joint limit, and accessibility, it simultaneously optimizes the movement path and the robotic arm's motion trajectory, reduces idle travel and waiting time, and significantly shortens the single task completion time and the overall system throughput cycle.

[0074] Secondly, through the multi-source fusion design of laser, vision camera, inertial measurement unit and force sensor, and by adopting spatiotemporal alignment and graph optimization fusion algorithm, multi-sensor redundancy backup is achieved to avoid perception interruption caused by the failure of a single sensor; at the same time, through predictive-correction positioning logic, the cumulative error of odometer and inertial measurement is effectively corrected, maintaining centimeter-level positioning accuracy and robotic arm end-effector positioning accuracy in dynamic and semi-structured scenarios, solving the problems of single perception and positioning drift affecting the operation accuracy of existing technologies.

[0075] Third, the present invention can automatically switch gripper / suction device and force control parameters based on workpiece material, shape and accessibility; it can complete target detection and pose estimation in real time, and can adapt to different operation targets and scenarios without manual parameter tuning, significantly improving the success rate and robustness of the first operation, and solving the problems of hard coding of operation strategies and poor scenario universality in the existing technology.

[0076] Fourth, by embedding information such as workstations and restricted areas into semantic maps, the system can dynamically adjust its driving conditions and working posture according to the semantic context, thereby achieving proactive obstacle avoidance and safe yielding. This meets the functional safety and personnel safety requirements of industrial scenarios and solves the problems of complex safety compliance implementation and high risks in collaborative scenarios of existing technologies. Attached Figure Description

[0077] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0078] Figure 1 A schematic diagram of the technical route for the operation analysis method of mobile robot integrating industrial robotic arm provided in an embodiment of the present invention;

[0079] Figure 2 A schematic diagram of the operation analysis process of a mobile robot integrated with an industrial robotic arm provided in an embodiment of the present invention;

[0080] Figure 3 This is a diagram illustrating the architecture of a mobile robot integrated with an industrial robotic arm, provided in an embodiment of the present invention. Detailed Implementation

[0081] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0082] Example 1

[0083] See Figure 1 and Figure 2 This invention discloses a method for analyzing the operation of a mobile robot integrated with an industrial robotic arm, comprising the following steps:

[0084] S1: Task Reception and Decomposition: After receiving the work order task, it is logically decomposed into a fixed process table according to the execution sequence of the work order task, clarifying the dependencies between sub-tasks and the parallel execution links, and synchronously determining the target position, target posture, required tools and estimated time for each sub-task. The estimated time is determined based on the initial value of historical execution data. After the sub-task is completed, the historical data is updated according to the actual time consumption to optimize the subsequent estimated time.

[0085] Specifically, step S1 addresses the problems of chaotic task scheduling, resource mismatch, and large time prediction deviations in traditional operations. A topological sorting algorithm is used to organize subtask relationships, first identifying sequentially dependent subtasks, then filtering out parallel subtasks without dependency conflicts, forming an acyclic task flowchart to avoid subtask execution conflicts. The target position and orientation are locked according to the industrial coordinate system preset in the work order, ensuring uniformity of subtask operation coordinates; required tools are matched based on task type and workpiece characteristics, and corresponding equipment is called from the tool library; the estimated time uses a dynamic weighted update strategy, with the initial value determined based on the statistical average of historical subtasks of the same type. After a subtask is completed, historical data is updated, with the dynamic weights adaptively adjusted according to the number of executions, gradually improving the accuracy of subsequent time predictions and providing precise time basis for task scheduling.

[0086] S2: Mapping and Localization: An environmental map is built synchronously during the robot's movement. The localization process adopts a two-step logic of prediction and correction. First, the predicted position value is calculated based on the real-time data of the wheel odometry and inertial measurement unit. Then, the predicted position value is corrected by combining the observation data of the laser sensor or vision camera.

[0087] Specifically, step S2 enables the robot to perceive its environment and perform self-localization, solving the problems of large accumulated errors and map-environment disconnect in traditional localization. An incremental map building technique is employed. As the robot moves, a laser sensor collects 3D contour point cloud data of the environment, while a vision camera captures scene texture and semantic information, such as identifying "restricted areas" and "workstations." These two elements are fused through spatiotemporal alignment to generate a dense semantic map, which is updated in real-time as the robot moves, ensuring synchronization with environmental changes. Based on the Kalman filter core framework, a wheeled odometer calculates the distance traveled using wheel speed and diameter, while an inertial measurement unit (IMU) senses attitude changes using accelerometers and gyroscopes. These two elements are fused to output a predicted position value. When the laser sensor detects a fixed landmark or the vision camera identifies a positioning marker, the observed actual position is compared with the predicted value, the deviation is calculated, and the predicted value is corrected. The correction frequency increases with the increase of the positioning error, ensuring stable positioning accuracy.

[0088] S3: Global Path and Dwelling Position Planning: When planning the global path, avoid restricted areas and congested sections in the environment, and comprehensively evaluate the path occupancy risk, detour distance and number of turns to select the optimal path; after the robot arrives at the work station, combine the distance from the work point, the effective field of view of the sensors and the accessibility of the robotic arm to determine the optimal parking posture as the dwelling position posture.

[0089] Specifically, step S3 achieves global optimization of the path and parking posture, addressing the problem of traditional planning focusing only on a single indicator and neglecting operational convenience. This involves using a grid map method to discretize the environment into uniform grids, marking the risk weights of restricted areas and congested road sections. For each candidate path, a quantitative evaluation is conducted from three dimensions: safety, efficiency, and stability. Occupation risk reflects the probability of the path being blocked by obstacles; detour distance reflects the deviation between the actual path length and the straight-line distance; and the number of turns affects the robot's motion stability. The optimal path is selected through comprehensive evaluation, balancing safety, efficiency, and stability. With the feasibility of the robotic arm operation as the goal, the quantitative indicator of distance to the work point ensures that the robotic arm can operate without significant extension; the quantitative indicator of sensor field of view ensures that the vision camera and laser sensor are unobstructed and can collect data normally; and the quantitative indicator of robotic arm accessibility ensures that the workpiece is within the robotic arm's workspace and that the joint angles do not exceed safe limits.

[0090] S4: Precise alignment of the work station: When the robot enters the preset close range to the work station, the positioning tag at the work station is identified by the vision camera. Based on the tag position information, the relative position and relative posture of the robot and the workpiece are calculated. The robot is slightly corrected according to the relative position and posture to compensate for the positioning deviation caused by uneven ground.

[0091] Specifically, step S4 achieves millimeter-level precision alignment through visual recognition and coordinate conversion, resolving operational misalignment issues caused by uneven ground and chassis parking errors. This involves employing visual marker recognition technology, where positioning tags contain unique coded information. The visual camera can accurately capture the corner coordinates of the tags within a close-range range, calculating the tag's position and attitude parameters in the camera coordinate system. Based on a preset multi-coordinate system transformation relationship, the tag's coordinates in the camera coordinate system are sequentially transformed through the camera coordinate system, robot base coordinate system, and workpiece coordinate system to obtain the relative position and attitude deviation between the robot and the workpiece. The attitude correction amount is calculated based on the deviation, and the chassis drive wheels are controlled to make small adjustments, such as using the difference in speed between the left and right wheels for rotational correction, and using synchronous acceleration / deceleration of the two wheels for translational correction, compensating for positioning deviations caused by uneven ground and ensuring precise matching between the robot and the workstation.

[0092] S5: Target Recognition and Grip Point Selection: The system acquires workpiece images through a vision camera and performs target detection and pose estimation to determine the workpiece's gripping area. It comprehensively evaluates the contact stability, friction conditions, and edge risks of potential gripping points within the gripping area and selects the optimal gripping point. The system also plans the path for the robotic arm to approach and exit the workpiece to reduce ineffective movements.

[0093] Specifically, step S5 achieves precise grasping and path optimization through computer vision and mechanical stability assessment, solving the problems of easy slippage, workpiece damage, and excessive ineffective movement in traditional grasping. Specifically, a deep learning object detection algorithm is used to identify the workpiece contour from the image, eliminating ungripable areas such as protrusions and fragile parts. Combined with a pose estimation algorithm, the 6D pose of the workpiece in the camera coordinate system is calculated by matching the features of the workpiece surface point cloud with a preset 3D model. The 6D pose includes three translation parameters and three rotation parameters, providing precise positional basis for grasping. Based on static equilibrium theory, potential grasping points within the gripping area are comprehensively evaluated from three dimensions: contact stability, friction conditions, and edge risk, selecting the optimal grasping point. Larger contact areas are more stable, higher friction coefficients reduce slippage, and closer to the workpiece edge increases the likelihood of slippage or deformation. Simultaneously, the approach and exit paths of the robotic arm are planned, employing a straight-in, transition, and straight-out trajectory design to reduce idle travel and ineffective movements of the robotic arm, improving operational efficiency.

[0094] S6: Operation execution: When the robotic arm approaches the workpiece along the planned path, it reduces the movement speed and end effector stiffness; after contacting the workpiece, it adjusts the contact force based on real-time data from the force sensor to adapt to the operation requirements, and adopts different stiffness control strategies for the contact and exit phases; if the force or torque is detected to exceed the safety limit, it controls the robotic arm to retreat along the contact normal and performs lateral posture fine-tuning until the force and torque return to the normal range.

[0095] Specifically, step S6 utilizes force-controlled compliance to achieve safe and stable operation, addressing the issue of workpiece and equipment damage inherent in traditional rigid operations. During the approach phase, the robotic arm's speed and end effector stiffness are reduced to prevent rapid collisions that could cause workpiece displacement or surface damage. After contact with the workpiece, the end effector stiffness coefficient is dynamically adjusted based on real-time contact force data from the force sensor to match different operational requirements. For example, precision assembly requires low stiffness to ensure compliance, while heavy-duty gripping requires higher stiffness to ensure stability. When the force sensor detects that the force or torque exceeds the safety limit, the direction of the workpiece's reaction force on the robotic arm is determined through contact normal vector calculation. The robotic arm is then slightly retracted along the normal direction, while visual feedback is used for lateral posture fine-tuning until the force and torque return to normal range, preventing workpiece deformation or damage to robotic arm components caused by hard collisions. During the exit phase, the end effector stiffness is restored to ensure the robotic arm stably detaches from the workpiece.

[0096] In this embodiment, step S2, the construction of the environmental map adopts the Simultaneous Localization and Map Building SLAM mathematical model. Map building and localization fusion are achieved through multi-source data collected by laser sensors and visual cameras. The formula is as follows:

[0097]

[0098] In the formula, Let K be the robot's pose at time k. Let K be the robot's pose at time k-1. For the control input at time k, To control input noise, Let k be the sensor observation value at time k. For the observation model function, For environmental maps, To observe noise.

[0099] Specifically, this model is the core equation of Extended Kalman Filter SLAM (EKF-SLAM), achieving simultaneous optimization of localization and mapping, and solving the error accumulation problem caused by traditional separate localization and mapping. State prediction equation. Describe the dynamic evolution of the robot's pose. Given the pose from the previous time step, For current control inputs, such as the incremental movement of the wheeled odometer or the attitude change of the IMU, To control input noise, such as errors caused by wheel slippage or IMU drift (following a Gaussian distribution), this equation predicts the approximate range of the current pose. Observation Update Equation Establish the correlation between observations and pose / map. To observe the model function, the robot pose is... The feature points in the environmental map m are converted into theoretical observations from the sensors. These are the actual observations from the sensor. To reduce observation noise, the robot pose is corrected by comparing the deviations between theoretical and actual observations. Together with the environmental map m, it optimizes localization and mapping.

[0100] In this embodiment, in step S3, the global path planning uses a weighted scoring method to select the optimal path, and the formula is:

[0101]

[0102] In the formula, For the overall score of the path, To mitigate the risk of path occupancy, This represents the detour distance of the route. The number of turns on the path. , , The weighting coefficients for occupancy risk, detour distance, and number of turns are respectively used to select the comprehensive score. The shortest path is used as the global path.

[0103] Specifically, based on multi-attribute decision theory, the qualitative requirements of path planning are transformed into quantitative calculations, enabling the objective selection of the optimal path. The occupancy risk (R) is the probability of obstacle occupancy in the area traversed by the path, updated in real-time by sensor data; the higher the obstacle density and the more dynamic obstacles, the larger the R value. The detour distance (D) is the difference between the actual path length and the straight-line distance from the start to the end point; the larger the difference, the larger the D value, indicating lower path efficiency. The number of turns (T) is the number of turns ≥90° in the path; the more turns, the larger the T value, indicating poorer robot motion stability. Weighting coefficients are also used. , , The settings are based on the requirements of the work scenario, such as increasing the safety priority in scenarios. Efficiency-first scenarios are increasing. The path comprehensive score S is obtained through weighted calculation. The smaller the S, the safer, more efficient and stable the path is, which is used as the basis for global path selection.

[0104] In this embodiment, in step S3, the dynamic obstacle handling of global path planning adopts a model predictive control mathematical model, and dynamic obstacle avoidance is achieved through rolling optimization. The formula is:

[0105]

[0106]

[0107]

[0108] In the formula, To predict the time domain, This is the robot pose predicted at time t+j from time t. The control input at time t+j is optimized for time t. The objective function is to minimize path time and collision risk. For robot motion models, For pose constraints, To control input constraints.

[0109] Specifically, by optimizing the rolling time domain to handle dynamic obstacles, real-time, adaptive obstacle avoidance is achieved, solving the problem that traditional static obstacle avoidance cannot handle moving obstacles. The prediction time domain N sets the prediction range for multiple future moments, covering the possible movement trajectories of dynamic obstacles to ensure the predictive power; the objective function... The optimization comprehensively balances path time and collision risk by weighting and prioritizing both. Pose constraints are also considered. , Limiting the robot's movement boundaries in the environment and controlling input constraints. , Limit the robot's motion parameters such as speed and acceleration to ensure they meet hardware performance requirements; motion model. Describe the mapping relationship between robot pose and control input to ensure the rationality of the predicted pose; at each time t, only the first optimized control input is executed. At the next moment t+1, the obstacle status is re-collected, the prediction range is updated, and the system is optimized again to achieve continuous obstacle avoidance in dynamic environments.

[0110] In this embodiment, in step S4, the robot undergoes a small attitude correction based on its relative position and posture to compensate for the positioning deviation caused by uneven ground. The formula is as follows:

[0111]

[0112] In the formula, Let be the transformation matrix between the robot's base coordinate system and the workpiece coordinate system. Let be the transformation matrix between the robot's base coordinate system and the camera coordinate system. This is the transformation matrix between the camera coordinate system and the positioning label coordinate system. This is the transformation matrix between the coordinate system of the positioning label and the coordinate system of the workpiece; To achieve the ideal alignment pose parameters for the robot, These are the current actual pose parameters calculated based on the transformation matrix. This is the attitude correction amount.

[0113] Specifically, based on homogeneous coordinate transformation theory, pose transfer and deviation calculation between multiple coordinate systems are realized, providing a mathematical basis for accurate attitude correction. Each transformation matrix is ​​a 4×4 homogeneous matrix containing translation and rotation information. Preset via camera calibration; The real-time positioning of the tag is calculated by the visual camera based on the real-time identification of the tag, reflecting the real-time pose of the tag in the camera coordinate system; To establish a fixed conversion relationship between the positioning tag and the workpiece; through matrix multiplication. This transforms the coordinates in the workpiece coordinate system into coordinates in the robot base coordinate system, achieving pose association between the two. The transformation matrix... Extracting the robot's current actual pose parameters , , The ideal alignment pose parameters preset in the work order , , By comparison, the attitude correction amount is obtained. (X-axis translation correction) (Y-axis translation correction) (Rotation correction); The robot adjusts the chassis posture according to the correction amount to compensate for the positioning deviation caused by uneven ground and ensure the alignment accuracy of the work position.

[0114] In this embodiment, a simplified scoring method is used in step S5 to select the optimal grab point. The formula is as follows:

[0115]

[0116] In the formula, To obtain a comprehensive score for the captured points, To capture the contact area, To determine the coefficient of friction of the gripping surface, To capture the risk coefficient at the edge of the point, , , The weighting coefficients for contact area, friction coefficient, and edge risk coefficient are respectively selected for the comprehensive score. The highest potential crawl point is selected as the optimal crawl point.

[0117] Specifically, based on static equilibrium conditions, the stability of the gripping point is quantitatively evaluated to ensure that the workpiece does not slip or get damaged after gripping. The contact area A between the gripping point and the jaws is considered; the larger the area, the more uniform the gripping force distribution, the greater the friction, and the more stable the gripping. The friction coefficient F is determined by both the workpiece material and the jaw material; the larger F is, the greater the static friction under the same clamping force, and the less likely the workpiece is to slip. The distance between the gripping point and the edge of the workpiece is also considered; the closer the distance, the easier it is for the workpiece to slip off the edge or deform due to uneven force, resulting in a larger E value. The weighting coefficients a, b, and c are set according to the gripping stability requirements; the higher the comprehensive score G, the stronger the gripping point stability and the lower the risk, thus determining the optimal gripping point.

[0118] In this embodiment, in step S6, the formula for adjusting the contact force based on real-time data from the force sensor to adapt to the operational requirements is as follows:

[0119]

[0120] In the formula, For the contact force at the end of the robotic arm, This is the end stiffness coefficient. This refers to the end displacement; the stiffness coefficient is reduced during the contact phase. The stiffness coefficient is restored during the exit phase.

[0121] Specifically, based on Hooke's Law, a linear relationship between contact force and end-effector displacement is established to achieve precise control of the contact force, avoiding damage to the workpiece due to excessive contact force or failure of gripping due to insufficient contact force. K represents the force required for each unit displacement of the end-effector. The larger the K value, the more rigid the end-effector, and the more sensitive the contact force is to changes in displacement. The K value is reduced during the contact phase to make the contact force increase slowly with displacement, avoiding damage to the workpiece (such as flexible or brittle workpieces) due to instantaneous impact force. The K value is restored during the exit phase to ensure that the robotic arm end-effector quickly and stably detaches from the workpiece, reducing operation time.

[0122] In this embodiment, during step S6, impedance control is used to absorb positioning errors and contact disturbances during the operation. The formula for impedance control is:

[0123]

[0124] in, The damping coefficient is... To determine the final velocity, increase the damping coefficient. Suppress vibrations and slippage during operation.

[0125] Specifically, B represents the degree of influence of the end-effector velocity on the contact force. The larger the value of B, the stronger the system damping and the smoother the change in end-effector velocity, which can effectively suppress oscillations and slippage during operation. When there is a positioning error or contact disturbance, the end-effector contact force F can be kept stable by adjusting the values ​​of K and B.

[0126] In one possible embodiment, the method further includes step S7, quality and safety confirmation: after grasping the workpiece, the change in the weight of the workpiece is detected to verify the effectiveness of the grasp, and the position and posture of the workpiece are checked by a vision camera to confirm the grasp quality; during the robot's movement, the movement trend of the target ahead is predicted in real time. If it is determined that the target ahead may enter the safe distance within a preset time, the robot is first controlled to decelerate or pause, or the path is replanned to achieve obstacle avoidance.

[0127] Specifically, dual verification and dynamic prediction are employed to ensure both operational quality and travel safety. For quality verification, force sensors detect weight changes before and after grasping to verify successful workpiece grasping; a vision camera is used to check the workpiece's position and posture to confirm grasping accuracy meets requirements, avoiding "empty grasps," "incorrect grasps," or grasping deviations. For travel safety, motion parameters of targets ahead are collected in real time to predict their trajectory. If it is determined that the target may enter a safe distance within a preset time, graded response measures such as deceleration, pausing, or replanning the path are implemented to avoid collision risks.

[0128] In one possible embodiment, the process further includes step S8, completion and reset: After the task is completed, the robot leaves the work area along a preset return path, and the inventory data and task status are updated synchronously; when the robot's battery level is detected to be lower than a preset threshold, the robot is controlled to automatically dock with a charging device to complete the charging operation.

[0129] Specifically, the evacuation path is generated in reverse based on the global path planned by S3 to ensure the safety of the evacuation process; after the operation is completed, the inventory changes and task status are synchronized to the workshop management system through the industrial communication protocol; in terms of power management, the power is monitored in real time through the battery management module. When the power is lower than the preset threshold, the automatic charging process is triggered. The robot docks with the charging device through navigation to complete the automatic charging and ensure the endurance of subsequent operations.

[0130] The application scenarios of this invention are as follows:

[0131] Scenario 1: Assembly scenario on a smart manufacturing production line:

[0132] In production lines for automotive parts, electronic devices, and other products, there are assembly requirements involving multiple workstations and various product types. This invention utilizes the S1 task decomposition function to break down workpiece gripping, workstation transfer, and precise assembly into collaborative sub-tasks. Combined with the S2 mapping and positioning function, a production line environment map is constructed to ensure that the mobile robot accurately stops at each assembly workstation. The S4 workstation alignment function compensates for uneven ground conditions, and the S6 operation execution stage uses a force control model to perform precise assembly actions such as bearing press-fitting and screw fastening. Simultaneously, impedance control absorbs contact disturbances during the assembly process, preventing damage to parts. For example, in an automotive dashboard assembly line, it can transfer different models of dashboards from the material area to the assembly workstation and perform flexible operations such as wire harness insertion / removal and clip installation, adapting to the needs of mixed-product production lines.

[0133] Scenario 2: Intelligent picking and handling in warehousing and logistics:

[0134] In e-commerce warehousing, industrial raw material warehouses, and other scenarios, it is necessary to achieve full automation of the process from location positioning and workpiece grabbing to shelving storage. This invention avoids congested sections such as shelving aisles and personnel movement within the warehouse through S3 global path planning, and combines a dynamic obstacle handling model to deal with dynamic interference from forklifts and personnel; the S5 target recognition and grab point selection function can identify cartons and boxes of different sizes through a vision camera, calculate the optimal grab point and plan the robotic arm path to avoid grabbing slippage; the S7 quality confirmation step uses weight detection and visual verification to ensure that the grabbing is correct, and then accurately moves the goods to the target location. After the operation is completed, the S8 finishing function updates the inventory data, realizing efficient flow of warehouse logistics.

[0135] Scenario 3: Industrial Equipment Maintenance and Repair

[0136] In the maintenance of large equipment (such as storage tanks, reactors, and motor units) in fields such as chemical engineering and energy, robots need to move to the side of the equipment to complete tasks such as inspection and parts replacement. This invention can construct a map of the equipment's surrounding environment through the SLAM model in S2, achieving precise positioning in confined spaces; the S4 fine alignment function corrects the robot's posture through positioning tags, ensuring that the robotic arm can reach the equipment's maintenance points; in the S6 operation execution phase, the stiffness of the robotic arm's end effector is reduced, and in conjunction with force sensors, actions such as filter replacement and bolt removal are completed, avoiding damage to the equipment's precision components; if force / torque exceeds limits during maintenance (such as bolt jamming), the robot can automatically retreat and fine-tune its posture to ensure the safety of maintenance operations.

[0137] Example 2

[0138] See Figure 3 Embodiment 2 of the present invention also provides a work analysis system for a mobile robot integrated with an industrial robotic arm, comprising:

[0139] The task receiving and decomposition module 100 is used to receive work order tasks, decompose them into a fixed process table according to the execution sequence of the work order tasks, clarify the dependencies between sub-tasks and the parallel execution links, and simultaneously determine the target position, target posture, required tools and estimated time for each sub-task. The estimated time is determined based on historical execution data to determine the initial value. After the sub-task is completed, the historical data is updated according to the actual time consumption to optimize the subsequent estimated time.

[0140] The mapping and localization module 200 is used to synchronously build an environmental map during robot movement. The localization process adopts a two-step logic of prediction and correction. First, the predicted position value is calculated based on the real-time data of the wheel odometry and inertial measurement unit. Then, the predicted position value is corrected by combining the observation data of the laser sensor or vision camera.

[0141] The global path and dwell position planning module 300 is used to avoid restricted areas and congested sections in the environment when planning the global path. It comprehensively evaluates the path occupancy risk, detour distance and number of turns to select the optimal path. After the robot arrives at the work station, it determines the optimal parking posture as the dwell position posture by combining the distance from the work point, the effective field of view of the sensor and the accessibility of the robotic arm.

[0142] The workstation alignment module 400 is used to identify the positioning tag at the workstation through a vision camera when the robot enters a preset close range to the workstation. Based on the tag position information, it calculates the relative position and relative attitude of the robot and the workpiece, and makes a small attitude correction to the robot according to the relative position and attitude to compensate for the positioning deviation caused by uneven ground.

[0143] The target recognition and gripping point selection module 500 is used to acquire workpiece images through a vision camera and complete target detection and pose estimation, determine the gripping area of ​​the workpiece, comprehensively evaluate the contact stability, friction conditions and edge risks of potential gripping points within the gripping area, select the optimal gripping point, and plan the path for the robotic arm to approach and exit the workpiece to reduce ineffective movement.

[0144] The operation execution module 600 is used to reduce the movement speed and end effector stiffness when the robotic arm approaches the workpiece according to the planned path; after contacting the workpiece, it adjusts the contact force based on real-time data from the force sensor to adapt to the operation requirements, and adopts different stiffness control strategies for the contact stage and the exit stage; if the force or torque is detected to exceed the safety limit, it controls the robotic arm to retreat along the contact normal and performs lateral posture fine adjustment until the force and torque are restored to the normal range.

[0145] In this embodiment, the mapping and positioning module 200 constructs an environmental map using a Simultaneous Localization and Map Building (SLAM) mathematical model. Map building and positioning fusion are achieved through multi-source data collected by a laser sensor and a visual camera. The formula is as follows:

[0146]

[0147] In the formula, Let K be the robot's pose at time k. Let K be the robot's pose at time k-1. For the control input at time k, To control input noise, Let k be the sensor observation value at time k. For the observation model function, For environmental maps, To observe noise.

[0148] In this embodiment, the global path and dwell position planning module 300 uses a weighted scoring method to select the optimal path for global path planning. The formula is as follows:

[0149]

[0150] In the formula, For the overall score of the path, To mitigate the risk of path occupancy, This represents the detour distance of the route. The number of turns on the path. , , The weighting coefficients for occupancy risk, detour distance, and number of turns are respectively used to select the comprehensive score. The shortest path is used as the global path;

[0151] In the global path and dwell position planning module 300, the dynamic obstacle handling of global path planning adopts a model predictive control mathematical model, and achieves dynamic obstacle avoidance through rolling optimization. The formula is as follows:

[0152]

[0153]

[0154]

[0155] In the formula, To predict the time domain, This is the robot pose predicted at time t+j from time t. The control input at time t+j is optimized for time t. The objective function is to minimize path time and collision risk. For robot motion models, For pose constraints, To control input constraints.

[0156] In this embodiment, the working position fine alignment module 400 performs a small posture correction on the robot based on the relative position and posture to compensate for the positioning deviation caused by uneven ground. The formula is as follows:

[0157]

[0158] In the formula, Let be the transformation matrix between the robot's base coordinate system and the workpiece coordinate system. Let be the transformation matrix between the robot's base coordinate system and the camera coordinate system. This is the transformation matrix between the camera coordinate system and the positioning label coordinate system. This is the transformation matrix between the coordinate system of the positioning label and the coordinate system of the workpiece; To achieve the ideal alignment pose parameters for the robot, These are the current actual pose parameters calculated based on the transformation matrix. This is the attitude correction amount.

[0159] In this embodiment, the target recognition and grasping point selection module 500 uses a simplified scoring method to select the optimal grasping point, and the formula is as follows:

[0160]

[0161] In the formula, To obtain a comprehensive score for the captured points, To capture the contact area, To determine the coefficient of friction of the gripping surface, To capture the risk coefficient at the edge of the point, , , The weighting coefficients for contact area, friction coefficient, and edge risk coefficient are respectively selected for the comprehensive score. The highest potential crawl point is taken as the optimal crawl point;

[0162] In this embodiment, the formula for adjusting the contact force based on real-time data from the force sensor to adapt to the operational requirements in the operation execution module 600 is as follows:

[0163]

[0164] In the formula, For the contact force at the end of the robotic arm, This is the end stiffness coefficient. This refers to the end displacement; the stiffness coefficient is reduced during the contact phase. The stiffness coefficient is restored during the exit phase;

[0165] The operation execution module 600 also absorbs positioning errors and contact disturbances through impedance control. The formula for impedance control is:

[0166]

[0167] in, The damping coefficient is... To determine the final velocity, increase the damping coefficient. Suppress vibrations and slippage during operation.

[0168] This embodiment also includes:

[0169] The quality and safety verification module 700 is used to detect changes in the weight of the workpiece after it is grasped to verify the effectiveness of the grasping, and to verify the position and posture of the workpiece through a vision camera to confirm the grasping quality. During the robot's movement, the robot predicts the movement trend of the target ahead in real time. If it is determined that the target ahead may enter the safe distance within a preset time, the robot is controlled to slow down or pause, or the path is replanned to achieve obstacle avoidance.

[0170] The finishing and reset module 800 is used to ensure that after the operation is completed, the robot leaves the work area according to the preset return path and updates the inventory data and task status simultaneously; when the robot's battery level is detected to be lower than the preset threshold, the module controls the robot to automatically dock with the charging device to complete the charging operation.

[0171] It should be noted that the information interaction and execution process between the modules of the above system are based on the same concept as the method embodiment in Embodiment 1 of this application, and the resulting technical effects are the same as those in the method embodiment of this application. For details, please refer to the description in the method embodiment shown above in this application, and it will not be repeated here.

[0172] Example 3

[0173] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium storing program code for a job analysis method of a mobile robot integrated with an industrial robotic arm. The program code includes instructions for executing the job analysis method of the mobile robot integrated with an industrial robotic arm as described in Embodiment 1 or any possible implementation thereof.

[0174] Computer-readable storage media can be any available medium that a computer can access, or a data storage device such as a server or data center that integrates one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0175] Example 4

[0176] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0177] The processor and the memory communicate with each other via a bus; the memory stores program instructions that can be executed by the processor, and the processor can execute the operation analysis method of mobile robot integrated with industrial robotic arm according to Embodiment 1 or any possible implementation thereof by calling the program instructions.

[0178] Specifically, a processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory. This memory can be integrated into the processor or located outside the processor and exist independently.

[0179] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0180] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0181] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for analyzing the operation of a mobile robot integrated with an industrial robotic arm, characterized in that, Includes the following steps: S1: Task Reception and Decomposition: After receiving the work order task, it is decomposed into a fixed process table according to the execution sequence of the work order task, clarifying the dependencies between sub-tasks and the parallel execution links, and simultaneously determining the target position, target posture, required tools and estimated time for each sub-task. The estimated time is determined based on the initial value of historical execution data. After the sub-task is completed, the historical data is updated according to the actual time consumption to optimize the subsequent estimated time. S2: Mapping and Localization: An environmental map is built synchronously during the robot's movement. The localization process adopts a two-step logic of prediction and correction. First, the predicted position value is calculated based on the real-time data of the wheel odometry and inertial measurement unit. Then, the predicted position value is corrected by combining the observation data of the laser sensor or vision camera. S3: Global Path and Dwelling Position Planning: When planning the global path, avoid restricted areas and congested sections in the environment. Comprehensively evaluate the path occupancy risk, detour distance, and number of turns to select the optimal path. After the robot arrives at the work station, combine the distance from the work point, the effective field of view of the sensors, and the accessibility of the robotic arm to determine the optimal parking posture as the dwelling position posture. S4: Precise alignment of the work station: When the robot enters the preset close range to the work station, the positioning tag at the work station is identified by the vision camera. Based on the tag position information, the relative position and relative attitude of the robot and the workpiece are calculated. The robot is slightly corrected in attitude according to the relative position and attitude to compensate for the positioning deviation caused by uneven ground. S5: Target recognition and gripping point selection: The workpiece image is acquired by a vision camera and the target detection and pose estimation are completed to determine the gripping area of ​​the workpiece. The contact stability, friction conditions and edge risks of potential gripping points in the gripping area are comprehensively evaluated to select the optimal gripping point. Plan the path for the robotic arm to approach and exit the workpiece to reduce unnecessary movements; S6: Operation execution: When the robotic arm approaches the workpiece along the planned path, reduce the movement speed and end effector stiffness; After contacting the workpiece, the contact force is adjusted based on real-time data from the force sensor to adapt to the operational requirements. Different stiffness control strategies are adopted for the contact phase and the exit phase. If the force or torque exceeds the safety limit, the robotic arm is controlled to retract along the contact normal and make minor lateral posture adjustments until the force and torque return to the normal range.

2. The operation analysis method for mobile robots integrated with industrial robotic arms according to claim 1, characterized in that, In step S2, the environmental map is constructed using a Simultaneous Localization and Map Building (SLAM) mathematical model. Map building and localization fusion are achieved through multi-source data collected by laser sensors and visual cameras. The formula is as follows: ； In the formula, Let K be the robot's pose at time k. Let K be the robot's pose at time k-1. For the control input at time k, To control input noise, Let k be the sensor observation value at time k. For the observation model function, For environmental maps, To observe noise.

3. The operation analysis method for mobile robots integrated with industrial robotic arms according to claim 1, characterized in that, In step S3, the global path planning uses a weighted scoring method to select the optimal path, with the following formula: ； In the formula, For the overall score of the path, To mitigate the risk of path occupancy, This represents the detour distance of the route. The number of turns on the path. , , The weighting coefficients for occupancy risk, detour distance, and number of turns are respectively used to select the comprehensive score. The shortest path is used as the global path; In step S3, the dynamic obstacle handling of global path planning adopts a model predictive control mathematical model, and dynamic obstacle avoidance is achieved through rolling optimization. The formula is: ； ； ； In the formula, To predict the time domain, This is the robot pose predicted at time t+j from time t. The control input at time t+j is optimized for time t. The objective function is to minimize path time and collision risk. For robot motion models, For pose constraints, To control input constraints.

4. The operation analysis method for mobile robots integrated with industrial robotic arms according to claim 1, characterized in that, In step S4, the robot undergoes a small attitude correction based on its relative position and orientation to compensate for the positioning deviation caused by uneven ground. The formula is as follows: ； In the formula, Let be the transformation matrix between the robot's base coordinate system and the workpiece coordinate system. Let be the transformation matrix between the robot's base coordinate system and the camera coordinate system. This is the transformation matrix between the camera coordinate system and the positioning label coordinate system. This is the transformation matrix between the coordinate system of the positioning label and the coordinate system of the workpiece; To achieve the ideal alignment pose parameters for the robot, These are the current actual pose parameters calculated based on the transformation matrix. This is the attitude correction amount.

5. The operation analysis method for mobile robots integrated with industrial robotic arms according to claim 1, characterized in that, In step S5, a simplified scoring method is used to select the optimal grab point. The formula is as follows: ； In the formula, To capture a comprehensive score, To capture the contact area, To determine the coefficient of friction of the gripping surface, To capture the risk coefficient at the edge of the point, , , The weighting coefficients for contact area, friction coefficient, and edge risk coefficient are respectively selected for the comprehensive score. The highest potential crawl point is taken as the optimal crawl point; In step S6, the formula for adjusting the contact force based on real-time data from the force sensor to adapt to operational requirements is as follows: ； In the formula, For the contact force at the end of the robotic arm, This is the end stiffness coefficient. This refers to the end displacement. Reduce stiffness coefficient during contact phase The stiffness coefficient is restored during the exit phase; In step S6, during the operation, impedance control is used to absorb positioning errors and contact disturbances. The formula for impedance control is: ； in, The damping coefficient is... To determine the final velocity, increase the damping coefficient. Suppress vibrations and slippage during operation.

6. The operation analysis method for mobile robots integrated with industrial robotic arms according to claim 1, characterized in that, After step S6, the following also includes: S7: Quality and Safety Confirmation: After grasping the workpiece, detect the change in workpiece weight to verify the effectiveness of the grasp, and verify the position and posture of the workpiece through a vision camera to confirm the grasp quality; during the robot's movement, predict the movement trend of the target ahead in real time. If it is determined that the target ahead may enter the safe distance within a preset time, control the robot to slow down or pause, or replan the path to achieve obstacle avoidance. S8: Finishing and Reset: After the operation is completed, the robot leaves the work area according to the preset return path and updates the inventory data and task status simultaneously; when the robot's battery level is detected to be lower than the preset threshold, the robot is controlled to automatically dock with the charging device to complete the charging operation.

7. A job analysis system integrating a mobile robot and an industrial robotic arm, characterized in that, include: The task receiving and decomposition module is used to receive work order tasks, decompose them into a fixed process table according to the execution sequence of the work order tasks, clarify the dependencies between sub-tasks and the parallel execution links, and simultaneously determine the target position, target posture, required tools and estimated time for each sub-task. The estimated time is determined based on historical execution data to determine the initial value. After the sub-task is completed, the historical data is updated according to the actual time consumption to optimize the subsequent estimated time. The mapping and localization module is used to synchronously build an environmental map during robot movement. The localization process adopts a two-step logic of prediction and correction. First, the predicted position value is calculated based on the real-time data of the wheel odometry and inertial measurement unit. Then, the predicted position value is corrected by combining the observation data of the laser sensor or vision camera. The global path and dwell position planning module is used to avoid restricted areas and congested sections in the environment when planning the global path. It comprehensively evaluates the path occupancy risk, detour distance and number of turns to select the optimal path. After the robot arrives at the work station, it determines the optimal parking posture as the dwell position posture by combining the distance from the work point, the effective field of view of the sensors and the accessibility of the robotic arm. The workstation alignment module is used to identify the positioning tag at the workstation through a vision camera when the robot enters a preset close range to the workstation. Based on the tag position information, it calculates the relative position and relative posture of the robot and the workpiece, and makes a small posture correction to the robot according to the relative position and posture to compensate for the positioning deviation caused by uneven ground. The target recognition and gripping point selection module is used to acquire workpiece images through a vision camera and complete target detection and pose estimation, determine the gripping area of ​​the workpiece, comprehensively evaluate the contact stability, friction conditions and edge risks of potential gripping points within the gripping area, and select the optimal gripping point. Plan the path for the robotic arm to approach and exit the workpiece to reduce unnecessary movements; The operation execution module is used to reduce the movement speed and end effector stiffness when the robotic arm approaches the workpiece according to the planned path. After contacting the workpiece, the contact force is adjusted based on real-time data from the force sensor to adapt to the operational requirements. Different stiffness control strategies are adopted for the contact phase and the exit phase. If the force or torque exceeds the safety limit, the robotic arm is controlled to retract along the contact normal and make minor lateral posture adjustments until the force and torque return to the normal range.

8. The operation analysis system for mobile robots integrated with industrial robotic arms according to claim 7, characterized in that, In the mapping and localization module, the environmental map is constructed using a Simultaneous Localization and Map Building (SLAM) mathematical model. Map building and localization fusion are achieved through multi-source data collected by laser sensors and visual cameras. The formula is as follows: ； In the formula, Let K be the robot's pose at time k. Let K be the robot's pose at time k-1. For the control input at time k, To control input noise, Let k be the sensor observation value at time k. For the observation model function, For environmental maps, To observe noise; In the global path and dwell position planning module, the global path planning uses a weighted scoring method to select the optimal path, and the formula is: ； In the formula, For the overall score of the path, To mitigate the risk of path occupancy, This represents the detour distance of the route. The number of turns on the path. , , The weighting coefficients for occupancy risk, detour distance, and number of turns are respectively used to select the comprehensive score. The shortest path is used as the global path; In the global path and dwell position planning module, the dynamic obstacle handling of global path planning adopts a model predictive control mathematical model, and achieves dynamic obstacle avoidance through rolling optimization. The formula is as follows: ； ； ； In the formula, To predict the time domain, This is the robot pose predicted at time t+j from time t. The control input at time t+j is optimized for time t. The objective function is to minimize path time and collision risk. For robot motion models, For pose constraints, To control input constraints.

9. The operation analysis system for mobile robots integrated with industrial robotic arms according to claim 7, characterized in that, In the workstation alignment module, the formula for making minor posture corrections to the robot based on the relative position and attitude to compensate for positioning deviations caused by uneven ground is as follows: ； In the formula, Let be the transformation matrix between the robot's base coordinate system and the workpiece coordinate system. Let be the transformation matrix between the robot's base coordinate system and the camera coordinate system. This is the transformation matrix between the camera coordinate system and the positioning label coordinate system. This is the transformation matrix between the coordinate system of the positioning label and the coordinate system of the workpiece; To achieve the ideal alignment pose parameters for the robot, These are the current actual pose parameters calculated based on the transformation matrix. This is the attitude correction amount; In the target recognition and grasping point selection module, a simplified scoring method is used to select the optimal grasping point. The formula is as follows: ； In the formula, To capture a comprehensive score, To capture the contact area, To determine the coefficient of friction of the gripping surface, To capture the risk coefficient at the edge of the point, , , The weighting coefficients for contact area, friction coefficient, and edge risk coefficient are respectively selected for the comprehensive score. The highest potential crawl point is taken as the optimal crawl point; In the operation execution module, the formula for adjusting the contact force based on real-time data from the force sensor to adapt to the operational requirements is as follows: ； In the formula, For the contact force at the end of the robotic arm, This is the end stiffness coefficient. This refers to the end displacement. Reduce stiffness coefficient during contact phase The stiffness coefficient is restored during the exit phase; The operation execution module also absorbs positioning errors and contact disturbances through impedance control. The formula for impedance control is: ； in, The damping coefficient is... To determine the final velocity, increase the damping coefficient. Suppress vibrations and slippage during operation.

10. The operation analysis system for mobile robots integrated with industrial robotic arms according to claim 7, characterized in that, Also includes: The quality and safety verification module is used to detect changes in the weight of the workpiece after it is grasped to verify the effectiveness of the grasping, and to verify the position and posture of the workpiece through a vision camera to confirm the grasping quality. During the robot's movement, the module predicts the movement trend of the target ahead in real time. If it is determined that the target ahead may enter the safe distance within a preset time, the module will first control the robot to slow down or pause, or replan the path to achieve obstacle avoidance. The finishing and reset module is used to allow the robot to leave the work area according to a preset return path after the operation is completed, and to update the inventory data and task status simultaneously; when the robot's battery level is detected to be lower than a preset threshold, the robot is controlled to automatically dock with the charging device to complete the charging operation.

Images