A control method and system for a three-degree-of-freedom parallel robot

Abstract

The present invention relates to the field of parallel robot control technology, and in particular to a control method and system for a three-degree-of-freedom parallel robot. The method comprises the following steps: extracting the manipulator joint rotation range from the manipulator design parameters, and performing force limit load analysis between different joint rotation angles to obtain joint angle force limit load data; performing mobile inertial momentum simulation on the joint angle force limit load data, and then performing load jitter variance calculation to obtain inertial load jitter variance; performing feedback collaborative control design based on inertial load jitter variance to obtain a manipulator feedback collaborative control strategy; performing manipulator collaborative control firmware design based on the manipulator feedback collaborative control strategy to obtain a manipulator collaborative control firmware. The present invention makes the parallel robot control technology more perfect by optimizing the parallel robot control technology.

Description

Technical Field

[0001] The present invention relates to the technical field of parallel robot control, and in particular to a control method and system for a three-degree-of-freedom parallel robot. Background Art

[0002] The three-degree-of-freedom parallel robot is a high-precision robot widely used in precision manufacturing, assembly, medical treatment, inspection and testing and other fields. The parallel robot is different from the traditional serial robot. It is composed of multiple serially connected robotic arms and has higher structural rigidity and stronger load capacity. The three-degree-of-freedom parallel robot, as the name implies, has three degrees of freedom, which usually refers to the ability to perform translational motion in three-dimensional space, including linear motion in the X, Y, and Z axis directions, as well as rotational motion. Compared with the serial robot, the kinematic characteristics of the three-degree-of-freedom parallel robot make it more advantageous in terms of spatial force and precision control, and can provide higher rigidity and less deformation, thereby improving its precision and stability. However, the control method of the traditional three-degree-of-freedom parallel robot has the problems of low accuracy in identifying the vibration caused by the inertia during the transportation of the grasped object, and the inability to accurately control the motion state. Summary of the invention

[0003] Based on this, it is necessary to provide a control method and system for a three-degree-of-freedom parallel robot to solve at least one of the above technical problems.

[0004] To achieve the above object, a control method for a three-degree-of-freedom parallel robot is provided, the method comprising the following steps:

[0005] Step S1: obtaining manipulator design parameters and robot task execution data of a three-degree-of-freedom parallel robot; extracting the manipulator joint rotation range based on the manipulator design parameters to obtain manipulator joint rotation range data; performing force limit load-bearing analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain joint angle force limit load-bearing data;

[0006] Step S2: obtaining the manipulator grasping operation path data; performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the manipulator grasping operation path data to obtain the joint load inertial momentum data; performing a load jitter variance calculation based on the joint load inertial momentum data to obtain the inertial load jitter variance;

[0007] Step S3: Based on the inertial load jitter variance, the manipulator motion parameter control constraint is performed to obtain motion parameter control constraint data; feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy;

[0008] Step S4: Design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy to obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

[0009] Preferably, step S1 comprises the following steps:

[0010] Step S11: obtaining manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot;

[0011] Step S12: extracting the manipulator joint rotation range from the manipulator design parameters to obtain the manipulator joint rotation range data;

[0012] Step S13: performing grabbing load limit analysis on the manipulator design parameters to obtain grabbing load limit data of the manipulator;

[0013] Step S14: performing force limit load-bearing analysis on the manipulator joint rotation range data between different joint rotation angles according to the manipulator grabbing load limit data, and obtaining joint angle force limit load-bearing data.

[0014] Preferably, step S2 comprises the following steps:

[0015] Step S21: Acquire the robot grasping operation path data;

[0016] Step S22: extracting the required time limit from the robot task execution data to obtain task execution time limit data; extracting the grasped object quality from the robot task execution data to obtain grasped object quality data;

[0017] Step S23: performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the grasped object mass data, the task execution time limit data, and the manipulator grasping operation path data to obtain the joint load inertial momentum data;

[0018] Step S24: performing inertial jitter load correlation analysis on the joint angle force limit bearing data based on the joint load inertial momentum data to obtain inertial jitter load correlation data;

[0019] Step S25: Calculate the load jitter variance of the inertial jitter load correlation data to obtain the inertial load jitter variance.

[0020] Preferably, step S23 includes the following steps:

[0021] Step S231: performing robot path operation rate matching based on the task execution time limit data and the robot grabbing operation path data to obtain the robot path operation rate matching data;

[0022] Step S232: Decomposing the gravity action azimuth component of the joint angle force limit bearing data according to the grasped object mass data and the robot path operation speed matching data to obtain gravity action azimuth component decomposition data;

[0023] Step S233: performing gravity trajectory segment difference evaluation on the manipulator grasping operation path data according to the gravity action azimuth component decomposition data to obtain gravity trajectory segment difference data;

[0024] Step S234: performing a movement inertia momentum simulation according to the gravity trajectory segment difference data to obtain the joint load inertia momentum data.

[0025] Preferably, step S232 includes the following steps:

[0026] Calculate the change of the manipulator's rotation radius based on the manipulator's path running speed matching data to obtain the manipulator's rotation radius change data;

[0027] According to the grasped object mass data and the robot path running speed matching data, the robot rotation radius change data is analyzed for the radius change joint force orientation change to obtain the joint force orientation change data;

[0028] Constructing a spatial coordinate system for the force orientation change data of the joint to obtain a force orientation change spatial coordinate system;

[0029] Decomposing the mass data of the grasped object by gravity vector according to the space coordinate system of force orientation change, and obtaining the decomposition data of the object's gravity vector;

[0030] Based on the decomposition data of the gravity action vector of the object, the instantaneous inclined plane gravity load average difference is calculated for the space coordinate system with the force orientation change, and the instantaneous inclined plane gravity load average difference is obtained;

[0031] According to the decomposition of the gravity action vector of the object, the joint axial moment distribution data is analyzed for the joint angle force limit load data to obtain the joint axial moment distribution data;

[0032] The gravity action azimuth component decomposition data is obtained by decomposing the gravity action vector of the object, the instantaneous inclined plane gravity load difference and the joint axial moment distribution data.

[0033] Preferably, step S24 comprises the following steps:

[0034] Step S241: calculating the limit angle load tolerance of the joint angle force limit load data to obtain the limit angle load tolerance;

[0035] Step S242: evaluating the effective inertia and coupling inertia of the joint load inertia momentum data to obtain the joint load effective inertia and the joint load coupling inertia respectively;

[0036] Step S243: performing an inertia imbalance divergence evaluation between different limit angles on the joint load effective inertia and the joint load coupling inertia according to the limit angle load tolerance, and obtaining limit angle inertia imbalance divergence data;

[0037] Step S244: performing inertial jitter load correlation analysis on the joint angle force limit bearing data according to the local angle inertial imbalance divergence data, the joint load effective inertia and the joint load coupling inertia to obtain inertial jitter load correlation data.

[0038] Preferably, step S243 includes the following steps:

[0039] According to the limit angle load tolerance, the effective inertia of the joint load is fitted with the effective inertia partition difference to obtain the effective inertia partition difference data;

[0040] Based on the effective inertia partition difference data and the joint load coupling inertia, the joint inertia imbalance ratio is analyzed to obtain the joint inertia imbalance ratio;

[0041] Based on the effective inertia partition difference data and the joint inertia imbalance ratio, the inertia coupling angle vector offset is evaluated to obtain the inertia coupling angle offset vector;

[0042] According to the joint inertia imbalance ratio and the inertia coupling angle offset vector, the inertia imbalance nonlinear gradient analysis is performed to obtain the inertia imbalance nonlinear gradient data;

[0043] According to the inertial imbalance nonlinear gradient data, the joint inertial imbalance ratio and the inertial coupling angle offset vector, the inertial imbalance divergence between different extreme angles is evaluated to obtain the extreme angle inertial imbalance divergence data.

[0044] Preferably, step S3 comprises the following steps:

[0045] Step S31: performing motion parameter control constraints on the manipulator based on the inertial load jitter variance to obtain motion parameter control constraint data;

[0046] Step S32: normalizing the motion parameter control constraint data to obtain motion parameter control normalized data;

[0047] Step S33: performing feedback collaborative control design according to the motion parameter control normalization data and the joint load inertia momentum data to obtain the manipulator feedback collaborative control strategy.

[0048] Preferably, step S31 includes the following steps:

[0049] Step S311: dividing the feasible boundary of the manipulator output force based on the inertial load jitter variance to obtain the feasible boundary data of the output force;

[0050] Step S312: performing joint damping optimization processing on the output force feasible boundary data to obtain joint damping optimization data;

[0051] Step S313: optimizing the inertial load distribution according to the output force feasible boundary data and the joint damping optimization data to obtain the inertial load distribution optimization data;

[0052] Step S314: Based on the output force feasible boundary data, the joint damping optimization data and the inertial load distribution optimization data, the manipulator motion parameter control constraint is performed to obtain the motion parameter control constraint data.

[0053] Preferably, the present invention further provides a control system of a three-degree-of-freedom parallel robot, which is used to execute the control method of the three-degree-of-freedom parallel robot as described above, and the control system of the three-degree-of-freedom parallel robot comprises:

[0054] The limit load analysis module is used to obtain the manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot; extract the manipulator joint rotation range based on the manipulator design parameters to obtain the manipulator joint rotation range data; perform force limit load analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain the joint angle force limit load data;

[0055] The inertial jitter analysis module is used to obtain the robot grasping operation path data; according to the robot grasping operation path data, the mobile inertial momentum simulation is performed on the joint angle force limit bearing data to obtain the joint load inertial momentum data; based on the joint load inertial momentum data, the load jitter variance is calculated to obtain the inertial load jitter variance;

[0056] The feedback collaborative design module is used to constrain the motion parameter control of the manipulator based on the inertial load jitter variance to obtain the motion parameter control constraint data; the feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy;

[0057] The control firmware design module is used to design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy, obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

[0058] The beneficial effect of the present invention is that by obtaining the robot design parameters and task execution data, the joint rotation range is extracted, and the mechanical limits of each joint during the task execution process can be accurately analyzed. This provides a scientific basis for the load-bearing capacity of the robot under different working conditions, helps to identify potential force bottlenecks and limits, and thus provides data support for subsequent optimization. The joint angle force limit load analysis can ensure that the robot will not cause mechanical damage or unstable operation due to overload operation during operation, thereby improving the durability and safety of the robot. The robot grasping path data is used to simulate the inertial momentum of the joint in motion, and then calculate the load change of each joint. By calculating the load jitter variance, the jitter effect caused by the inertial change when the robot performs the task can be evaluated, and the factors affecting the task accuracy and stability can be identified. The calculation of the load jitter variance helps to optimize the dynamic response of the robot, improve the control accuracy, and thus reduce the error during the task execution process, which is particularly important in high-precision grasping and operation. The control parameters of the robot motion process are effectively constrained by the inertial load jitter variance to ensure the stable operation of the robot in high-precision tasks. Through precise control strategies, the robot's motion state and posture can be adjusted in real time, thereby reducing errors caused by inertia or external interference, ensuring that the robot can complete tasks efficiently and accurately. The feedback collaborative control strategy further optimizes the collaborative motion in the multi-degree-of-freedom system, allowing each joint to work in a coordinated manner to ensure the maximization of overall performance. By designing and deploying the manipulator collaborative control firmware, the previous control strategy is converted into actual operation instructions and loaded into the three-degree-of-freedom parallel robot for actual control. This process ensures the robot's ability to work collaboratively in complex tasks. The firmware design closely combines the control strategy and physical execution to ensure that the robot can adjust its actions in real time based on feedback information to avoid task failures caused by control lag or deviation. By implementing this collaborative control firmware, the robot can perform tasks more efficiently and accurately in actual tasks, enhancing the robot's adaptability and reliability. Therefore, the present invention optimizes the control method of a traditional three-degree-of-freedom parallel robot, solves the problems of low accuracy in identifying vibrations caused by inertia during the transportation of grasping objects and inability to accurately control the motion state of the traditional three-degree-of-freedom parallel robot, improves the accuracy of identifying vibrations caused by inertia and improves the ability to control the motion state of the manipulator. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of a control method for a three-degree-of-freedom parallel robot;

[0060] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0061] Figure 3 for Figure 1 Detailed implementation steps of step S3 in FIG.

[0062] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0063] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0064] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0065] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0066] To achieve this, please refer to Figures 1 to 3 , a control method for a three-degree-of-freedom parallel robot, the method comprising the following steps:

[0067] Step S1: obtaining manipulator design parameters and robot task execution data of a three-degree-of-freedom parallel robot; extracting the manipulator joint rotation range based on the manipulator design parameters to obtain manipulator joint rotation range data; performing force limit load-bearing analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain joint angle force limit load-bearing data;

[0068] Step S2: obtaining the manipulator grasping operation path data; performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the manipulator grasping operation path data to obtain the joint load inertial momentum data; performing a load jitter variance calculation based on the joint load inertial momentum data to obtain the inertial load jitter variance;

[0069] Step S3: Based on the inertial load jitter variance, the manipulator motion parameter control constraint is performed to obtain motion parameter control constraint data; feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy;

[0070] Step S4: Design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy to obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

[0071] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a schematic flow chart of a control method for a three-degree-of-freedom parallel robot according to the present invention. In this example, the control method for the three-degree-of-freedom parallel robot includes the following steps:

[0072] Step S1: obtaining manipulator design parameters and robot task execution data of a three-degree-of-freedom parallel robot; extracting the manipulator joint rotation range based on the manipulator design parameters to obtain manipulator joint rotation range data; performing force limit load-bearing analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain joint angle force limit load-bearing data;

[0073] In an embodiment of the present invention, the design parameters of the manipulator used by the three-degree-of-freedom parallel robot are first obtained from the manufacturer. These design parameters include data such as the joint types, rotation range, maximum load, and structural configuration of the manipulator, and need to be combined with the task execution data of the robot. The task execution data includes the specific grasping task type, the weight and size of the grasped object, the material properties of the object, etc. These data can be obtained by manual input or from the sensor of the manipulator control system. Next, the rotation range of the manipulator joint is extracted according to the manipulator design parameters. This process analyzes the structure of each joint of the manipulator to identify the maximum and minimum boundaries of its rotation angle. By measuring the angle limit of the manipulator joint, the rotation range data of each joint can be obtained. These data include the rotation angle range of each joint, for example, the shoulder joint rotation range is -30° to +30°, the elbow joint is -60° to +60°, etc. After obtaining the rotation range data of the manipulator joint, the force limit load analysis is further performed. The force limit load analysis is based on the design parameters, material properties and mechanical structure of each joint of the robot, and the maximum force bearing capacity of each joint at different rotation angles is calculated by statics and dynamics analysis. To this end, static equilibrium equations and finite element analysis methods can be used to derive the load-bearing capacity of the joint at each rotation angle. The calculated result is the joint angle force limit load data, which includes the maximum load bearing capacity of each joint and the force distribution at different angles. This data is helpful for the optimization and constraint design of the robot movement in the subsequent steps.

[0074] Step S2: obtaining the manipulator grasping operation path data; performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the manipulator grasping operation path data to obtain the joint load inertial momentum data; performing a load jitter variance calculation based on the joint load inertial momentum data to obtain the inertial load jitter variance;

[0075] In an embodiment of the present invention, the manipulator grasping operation path data is first obtained. The design of the manipulator grasping path needs to be combined with specific task requirements and is usually generated by a path planning algorithm. The path planning algorithm determines the motion trajectory of the manipulator based on the manipulator's workspace, the starting position required by the task, the target position and the path constraints (such as obstacle avoidance, precise positioning, etc.). The path data includes the sequence of spatial coordinate points, the time series and the speed and acceleration data of each point that the manipulator passes from the starting point to the target point. Then, based on the manipulator grasping operation path data, the mobile inertial momentum simulation of the joint angle force limit bearing data is performed. Specifically, the mobile inertial momentum simulation needs to consider the mass of the object grasped by the manipulator and the dynamic performance of the manipulator at each point on the path. At this time, a dynamic model (such as a Lagrangian dynamic model, a Newton-Euler dynamic model, and the following steps are the same) is used to simulate the torque and inertial effect of each joint of the manipulator in the process of grasping the object. The inertial momentum calculation involves factors such as the mass of the object, the path speed of the manipulator, the angle change of each joint, and the effect of gravity on the object. By calculating the dynamic inertia of each joint at different path points, the joint load inertia momentum data is obtained. After obtaining the joint load inertia momentum data, the inertia load jitter variance calculation is continued. This step analyzes the inertia momentum data through statistical methods to calculate the variance of the inertia load change between different path points. The load jitter variance reflects the load fluctuation caused by factors such as object mass, path motion and external disturbance when the manipulator grasps an object on the path. Through this variance data, the stability of the manipulator in the process of grasping objects can be evaluated and the control strategy can be further optimized.

[0076] Wherein, the calculation formula of the inertial momentum is:

[0077] ;

[0078] In the formula, Indicates The moment of inertia of each joint, represents the mass of the grasped object, represents the average value of the robot path speed, represents the gravity influence coefficient, Indicates The angle change of each joint, Represents the centrifugal force of joint movement, Represents the overall crawling process time coefficient;

[0079] According to the inertial moment value of the joint obtained above, the inertial load jitter variance is calculated, and the calculation formula is as follows:

[0080] First, we need to calculate the average value of the joint load inertia momentum on different paths , The calculation method of is as follows:

[0081] ;

[0082] in, Indicates the total number of inertial load data collected at different waypoints, which means if the inertial loads of multiple waypoints are recorded during the grasping process, Indicates The moment of inertia of each joint;

[0083] Then, according to the average value of the moment of inertia of the joint load , calculate the inertial load jitter variance, the calculation method is as follows:

[0084] ;

[0085] in, represents the inertial load jitter variance, represents the total number of inertial load data collected at different path points, Indicates The moment of inertia of each joint, Represents the average value of the joint load inertia moment on different paths.

[0086] Step S3: Based on the inertial load jitter variance, the manipulator motion parameter control constraint is performed to obtain motion parameter control constraint data; feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy;

[0087] In the embodiment of the present invention, based on the inertial load jitter variance data, the control constraints of the manipulator motion parameters are first performed. By analyzing the inertial load jitter variance, the constraints of the motion of each joint of the manipulator are determined. These constraints include the maximum motion speed, maximum acceleration, maximum force, etc. of the joint. The motion parameter control constraint data is based on the analysis results of the inertial load jitter, and by limiting the motion range, speed and acceleration of different joints, the stability of the manipulator when grasping objects is ensured, and overload or severe vibration is avoided. Next, feedback collaborative control design is performed according to the motion parameter control constraint data. In this process, a feedback collaborative control strategy is designed based on the joint angle, load data and constraints. The core of this strategy is to adjust the motion state of each joint of the manipulator through real-time feedback to ensure that the manipulator can adaptively adjust the motion parameters when performing tasks to avoid jitter and instability caused by inertial effects. The specific feedback collaborative control method can be implemented by methods such as proportional-integral-differential (PID) control, fuzzy control or robust control. By monitoring the state of the manipulator in real time during the control process and comparing it with the set target state, the control instructions are adjusted to ensure that the manipulator completes the grasping task; specifically, during the feedback control process, the system will continuously read the current position (joint angle) of each joint and then compare it with the target position. The control system adjusts the control signal of the joint driver through the error (the difference between the target angle and the actual angle). The proportional-integral-differential (PID) control algorithm is usually used to adjust the joint angle in real time to achieve precise movement. When PID control is used, the controller generates a corresponding adjustment signal based on the angle error by adjusting the proportional (P), integral (I) and differential (D) parameters. For example, the proportional part of the PID algorithm generates an immediate feedback based on the current position error, the integral part is used to accumulate the error, and the differential part is predictively adjusted according to the angular velocity, thereby improving the response speed and stability of the manipulator. The adjustment of the joint angle is closely related to the load data and constraints. For example, when the load increases, the joint movement speed needs to be slowed down to prevent the manipulator from becoming unstable due to excessive force. In this case, the rate of change of the joint angle will be limited by the motion constraints; the current grasping state is judged in real time based on the load data. If the load exceeds the set safety threshold (defined by the constraints), the system will automatically adjust the joint's motion strategy, slow down the movement speed, adjust the joint angle or change the grasping posture to avoid damaging the manipulator or the object. The real-time change of the load directly affects the setting of the constraints in the control strategy. For example, under high load conditions, the system will dynamically adjust the maximum acceleration and maximum movement speed limits to ensure the stability and safety of the manipulator during the movement; the constraints are physical limits set based on the results of the inertial load jitter variance analysis, mainly including the maximum movement speed, maximum acceleration, and maximum load-bearing capacity of the joint.The role of constraints is to ensure that the manipulator can operate within a safe range when performing tasks, especially when operating at high speed or high load, to avoid overload or severe vibration. According to the inertia and load of the joint, the system will set the maximum movement speed limit of the joint to avoid jitter or instability caused by too fast movement speed. The control system will set the maximum acceleration value according to the inertia of the manipulator and the weight of the grasped object to ensure that the acceleration process does not cause excessive inertial reaction force and avoid vibration. The system will set the maximum force range according to the mass of the grasped object and the bearing capacity of the manipulator joint to prevent joint overload. The feedback collaborative control strategy combines PID control, fuzzy control and robust control methods to dynamically adjust the manipulator based on joint angle, load data and constraints. The control system conducts a comprehensive analysis of the three parameters through real-time feedback and adjusts the joint motion parameters (such as speed, acceleration, torque, etc.) according to the set constraints. For example, when a large error in the joint angle is detected, the control system will adjust the speed and acceleration to reduce the error; if the load exceeds the set safety range, the control system will reduce the movement speed or change the movement direction of the joint to avoid instability of the manipulator. By collaboratively optimizing the angles, loads, and constraints of each joint, the control system can ensure that the manipulator's motion not only meets the task requirements but is also always in a safe and stable state in multi-task and complex environments. The collaborative control strategy continuously updates the control instructions based on real-time data, allowing the manipulator to adapt to different work scenarios (such as grasping, handling, assembly, etc.).

[0088] Step S4: Design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy to obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

[0089] In an embodiment of the present invention, based on the manipulator feedback collaborative control strategy, the manipulator collaborative control firmware is first designed. The firmware includes a control program that combines hardware and software, which is responsible for converting the feedback control strategy into control instructions and transmitting them to the execution unit of the manipulator. The firmware design includes the configuration of the input and output interfaces of the manipulator control system, the coding implementation of the control algorithm, and the interface debugging with the robot hardware system. During the design process, special consideration needs to be given to the real-time performance, robustness and compatibility of the control algorithm with the robot hardware system. The development of the control firmware can be achieved through an embedded development platform, using C language or embedded real-time operating system (RTOS) for program writing. After the firmware design is completed, the collaborative control firmware is sent to the three-degree-of-freedom parallel robot. This process usually uploads the firmware to the controller of the manipulator through the communication interface of the robot control system. After the firmware is uploaded, the controller starts to execute the control strategy, monitors the state of each joint of the manipulator in real time, and adjusts the motion command according to the feedback control strategy, and finally realizes the precise movement and stable grasping of the manipulator.

[0090] Step S1 includes the following steps:

[0091] Step S11: obtaining manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot;

[0092] Step S12: extracting the manipulator joint rotation range from the manipulator design parameters to obtain the manipulator joint rotation range data;

[0093] Step S13: performing grabbing load limit analysis on the manipulator design parameters to obtain grabbing load limit data of the manipulator;

[0094] Step S14: performing force limit load-bearing analysis on the manipulator joint rotation range data between different joint rotation angles according to the manipulator grabbing load limit data, and obtaining joint angle force limit load-bearing data.

[0095] In an embodiment of the present invention, the manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot are first obtained. The manipulator design parameters include the structure type of the manipulator, the geometric dimensions of each joint, the maximum rotation angle of the joint, the load capacity of the joint and other mechanical characteristics. These parameters are usually provided by the robot manufacturer and can be obtained through the internal data acquisition interface of the robot control system. The manipulator task execution data includes the specific task type, the weight of the grasped object, the size of the object, the grasping method and the material properties of the object. The task execution data is usually obtained through user input or from a task description file. In practical applications, task data can be dynamically acquired according to different task types. For example, for a handling task on an automated production line, the task execution data includes the target position, the moving trajectory, the mass and size of the target object, etc. The acquisition of task execution data needs to be accurate to ensure that an accurate basis is provided for subsequent path planning and control. Based on the design parameters of the manipulator, the rotation range of the manipulator joints is extracted. This process requires analysis of the structure and rotation limit of each joint of the manipulator, which is usually determined by mechanical design and dynamic analysis. By parsing the design parameters of the joints, the maximum and minimum rotation angles of each joint are obtained, and then the joint rotation range data of the manipulator is extracted. Taking a three-degree-of-freedom parallel robot as an example, assume that each joint of the manipulator is a rotational joint, and the rotation range of the joint is limited by the geometric structure of the mechanism to which it is connected. For example, the rotation range of a joint is physically limited by the supporting structure to which it is connected, with a maximum rotation angle of ±45°, while the maximum rotation angle of another joint is ±90°. By analyzing the manipulator design drawings and combining the linkage relationship between the joints, the rotation range of each joint can be accurately calculated. After obtaining the rotation range data, it is also necessary to perform data verification on these data to ensure that they conform to the actual physical characteristics of the manipulator. Perform a grasping load limit analysis on the manipulator design parameters. The purpose of grasping load limit analysis is to evaluate the maximum load that the manipulator can withstand when performing a grasping task and optimize the manipulator design based on the load size. The grasping load limit is usually affected by multiple factors, including the structural strength of the manipulator joint, the dynamic performance of the manipulator, the friction coefficient of the joint, and the accuracy of the control system. In specific implementation, the maximum load capacity of each joint is first determined based on the design parameters of the manipulator. This can be done by analyzing the dynamic model of the manipulator, especially considering the torque that each joint withstands when grasping the load. For example, when grasping heavy objects, the shoulder joint, elbow joint, and wrist joint of the manipulator are subjected to different torques and loads. In order to ensure that the manipulator does not have structural failures during the grasping process, static and dynamic models are used, combined with the strength limit of the material to calculate and obtain the load limit of each joint. During the analysis process, it is also necessary to set specific values ​​for the grasping load in combination with actual task data. For example, for certain heavier objects, the manipulator must be designed to withstand higher grasping loads.In the process of grasping load limit analysis, the results obtained by static calculation include the maximum load capacity, load distribution and torque of each joint, which provide a basis for the subsequent control algorithm. Based on the grasping load limit data of the manipulator, the force limit load analysis between different joint rotation angles is performed on the manipulator joint rotation range data. The goal of this process is to accurately calculate the force distribution of the manipulator under different joint angle combinations and determine which angle combinations will cause the manipulator joint to be overloaded. First, the grasping load limit data is combined with the joint rotation range data to form a relationship model between the torque and load capacity of the joint at different angles. This model considers the force and torque of each joint at different rotation angles through mechanical analysis. Assuming that when a joint of the manipulator is at different rotation angles, the torque of the external grasping load will also change, thereby affecting the force condition of the joint. In specific implementation, the finite element analysis (FEA) method can be used to calculate the force limit of each joint. In the analysis process, by setting different rotation angles, the working state of the manipulator under various loads is simulated. The rotation range of each joint is simulated and calculated through the mechanical model to obtain the force limit load data at each angle. The calculated load-bearing limit data include the maximum load capacity under different angle combinations, the torque borne by the joints, etc. These data provide an important basis for subsequent motion control and path planning, ensuring that the manipulator can operate within a safe working range when performing grasping tasks, and avoiding manipulator failure or failure due to excessive angles or heavy loads.

[0096] Step S2 includes the following steps:

[0097] Step S21: Acquire the robot grasping operation path data;

[0098] Step S22: extracting the required time limit from the robot task execution data to obtain task execution time limit data; extracting the grasped object quality from the robot task execution data to obtain grasped object quality data;

[0099] Step S23: performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the grasped object mass data, the task execution time limit data, and the manipulator grasping operation path data to obtain the joint load inertial momentum data;

[0100] Step S24: performing inertial jitter load correlation analysis on the joint angle force limit bearing data based on the joint load inertial momentum data to obtain inertial jitter load correlation data;

[0101] Step S25: Calculate the load jitter variance of the inertial jitter load correlation data to obtain the inertial load jitter variance.

[0102] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0103] Step S21: Acquire the robot grasping operation path data;

[0104] In the embodiment of the present invention, the grabbing operation path data of the manipulator is first obtained. The data contains the path information of the manipulator from the starting position to the target position, and is usually acquired in real time through a task description file or through a robot control system. The grabbing operation path data includes the geometric shape of the path, the starting and ending positions of the path, the key points in the path, and the motion trajectory required by the path.

[0105] Step S22: extracting the required time limit from the robot task execution data to obtain task execution time limit data; extracting the grasped object quality from the robot task execution data to obtain grasped object quality data;

[0106] In an embodiment of the present invention, the required time limit and the mass data of the grasped object are extracted from the robot task execution data. The required time limit data includes the total execution time of the task and the time limit required for each task step. Usually, these data are given in the task description file or acquired by the robot control system through real-time perception. The task execution time limit includes the maximum movement time of each section on the path, the earliest completion time of the task, etc. The grasped object quality data is the quality information of the object extracted from the task execution data. The quality of the grasped object is usually monitored in real time by a sensor (such as a force sensor or a weighing sensor) or set in the task description. In specific implementation, the target object information of the task, including the mass of the object, is first extracted from the task execution file. If the mass data is obtained by a sensor, the robot control system will read the sensor data in real time, and process the acquired quality data through a data processing algorithm (such as a filtering algorithm) to obtain stable and accurate object quality data.

[0107] Step S23: performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the grasped object mass data, the task execution time limit data, and the manipulator grasping operation path data to obtain the joint load inertial momentum data;

[0108] In the embodiment of the present invention, the mass data of the grasped object, the task execution time limit data and the manipulator grasping operation path data are used to simulate the moving inertial momentum of the joint angle force limit bearing data. This process mainly calculates the relationship between the load change and the inertial moment of each joint during the grasping and movement of the manipulator. First, the acceleration of the object during the movement of the manipulator is obtained by combining the grasping path of the manipulator, the mass of the object and the task execution time limit. According to the path planning data, the acceleration and speed of the object during the movement of the manipulator are calculated, and then the inertial momentum of each joint at different path points is obtained. The inertial momentum is the product of the mass of the object and the movement speed, which reflects the inertial effect of the object during the movement. By calculating with mathematical formulas, the torque change of the object at different joint angles can be obtained. Then, combined with the joint structure and force limit when the manipulator is designed, the dynamic equation is used for calculation to determine the inertial load borne by each joint. Finally, the joint load inertial momentum data is obtained, which includes the load condition of each joint during the movement, the magnitude of the inertial moment and its relationship with the mass and path of the object. This data will provide important input for subsequent inertial jitter load correlation analysis.

[0109] Step S24: performing inertial jitter load correlation analysis on the joint angle force limit bearing data based on the joint load inertial momentum data to obtain inertial jitter load correlation data;

[0110] In an embodiment of the present invention, an inertial jitter load correlation analysis is performed on the joint angle force limit bearing data based on the joint load inertial momentum data. The goal of this analysis is to study the jitter effect of the inertial force of the joint at different angles on the movement of the manipulator, and to analyze the load correlation relationship between different angles. The specific operation process is: first, the joint load inertial momentum data and the joint angle force limit bearing data are compared and analyzed to determine the relationship between inertial momentum and load at different joint angles. According to the principle of dynamics, the inertial force of the joint during movement causes the manipulator to have different degrees of jitter, especially when grabbing heavy objects, the inertial effect causes a large load change. In order to quantify this effect, it is necessary to calculate the load jitter variance at different angles, which can be calculated by simulation experiments or mathematical models. In this analysis, the jitter variance analysis method is used to calculate the inertial load fluctuation at each joint rotation angle, and the result is compared with the joint force limit data. The inertial jitter load correlation data is obtained by calculation, which reflects the correlation between the force and inertial effect of each joint under different grasping tasks, as well as the torque fluctuation generated when performing the task. Ultimately, the inertial jitter load correlation data provides a key basis for subsequent robot control algorithms and task scheduling, helping to optimize the robot's motion control under different load and path conditions.

[0111] Step S25: Calculate the load jitter variance of the inertial jitter load correlation data to obtain the inertial load jitter variance.

[0112] In an embodiment of the present invention, the load jitter variance of the inertial jitter load correlation data is calculated to obtain the inertial load jitter variance. This calculation is used to quantify the inertial load fluctuation of the manipulator under different task loads, and analyze the impact of the jitter amplitude on the movement of the manipulator. First, the inertial jitter load correlation data is used, which reflects the inertial fluctuation of the joint load and contains the load change information under different joint angles. The specific operation is: first, the load fluctuation value of each joint at different path points is extracted from the inertial jitter load correlation data. For each joint, the time series of the joint load change is calculated according to the inertial load fluctuation during the movement. Each data point in the time series corresponds to a load value at a specific moment, which records the fluctuation caused by the inertial force on the joint. Then, the variance calculation formula is used to process these data.

[0113] Preferably, step S23 includes the following steps:

[0114] Step S231: performing robot path operation rate matching based on the task execution time limit data and the robot grabbing operation path data to obtain the robot path operation rate matching data;

[0115] Step S232: Decomposing the gravity action azimuth component of the joint angle force limit bearing data according to the grasped object mass data and the robot path operation speed matching data to obtain gravity action azimuth component decomposition data;

[0116] Step S233: performing gravity trajectory segment difference evaluation on the manipulator grasping operation path data according to the gravity action azimuth component decomposition data to obtain gravity trajectory segment difference data;

[0117] Step S234: performing a movement inertia momentum simulation according to the gravity trajectory segment difference data to obtain the joint load inertia momentum data.

[0118] In the embodiment of the present invention, the task execution time limit data and the manipulator grabbing operation path data are first obtained to determine the path operation rate of the manipulator when executing the task. The task execution time limit data records the time interval from the beginning to the end of the task, and the manipulator grabbing operation path data includes the position, speed and acceleration of each trajectory point required by the manipulator along a specific path during the task execution process. Next, by analyzing the path data, the total operation time of the manipulator path and the time of each segment of the path are allocated according to the task time limit, so as to adjust the speed of each path segment to ensure that all actions of the path are completed within the task time limit. The specific method includes: first, based on the coordinate points and motion curves of the manipulator path, the distance of each segment on the path is calculated. Then, according to the task execution time limit, the movement rate of the path is set according to the distance and required time of each path segment. This rate matching ensures that the manipulator will not have an inappropriate movement speed due to an excessively long path or insufficient task time. Finally, the obtained manipulator path operation rate matching data provides a basis for subsequent path optimization and motion control. Decomposition of the gravity azimuth component is performed. First, the mass data of the grasped object is obtained. This data reflects the mass characteristics of the object, which in turn affects the gravity of the object at different positions. In addition, the obtained robot path operation rate matching data is used to guide the movement of the robot at each point on the path. Based on this information, the gravity of the object at each path point can be calculated, taking into account the direction and angle of gravity. The specific calculation method is: First, use the gravitational acceleration constant , combined with the mass of the object , can be obtained through the formula Calculate the gravity on the object. Then, by decomposing the gravity force vector at different azimuth angles, the components of gravity in various directions are obtained. At each key point on the manipulator path, according to the current posture and path direction of the manipulator, the decomposition results of gravity in different directions are calculated. These decomposition results reflect the load changes of gravity in various directions during the manipulator grasping process, providing data support for the force analysis of the joints. Based on the decomposition data of the gravity azimuth component, the gravity trajectory segment difference of the manipulator grasping operation path data is evaluated. By comparing the gravity component with each position on the path, the difference in gravity influence between different path segments is evaluated. The specific implementation method is as follows: First, according to the manipulator grasping path data, identify each section of the path (for example, a straight section, a curved section, etc.), and then compare the gravity azimuth component of each path segment with other path segments. By calculating the difference in gravity between path segments, the sections with large gravity changes, that is, the path segments with large force changes, can be identified. For each path segment, the weighted average method is used to combine the gravity components in each path segment to calculate the gravity trajectory characteristics of the path segment. According to the magnitude and speed of gravity change, the difference between the path segments is evaluated, and finally the gravity trajectory segment difference data is obtained. The focus of this step is to clarify the places where the gravity changes greatly in each path segment by subdividing the gravity trajectory, so as to provide more accurate path data for the subsequent inertial momentum simulation. Use the gravity trajectory segment difference data to simulate the mobile inertial momentum, so as to obtain the joint load inertial momentum data. This step simulates the load change caused by inertia during the movement of the manipulator, especially the inertial momentum change caused by the gravity of the object and the path change when grasping the object. First, using the obtained gravity trajectory segment difference data, according to the gravity change of each path segment and the motion state of the manipulator, the inertial force borne by each joint when moving on the path is calculated. These inertial forces are caused by the mass of the object, the curvature of the path, and the change of speed in the path segment. Through the numerical integration method, combined with the manipulator dynamics model, the inertial momentum change of each joint during the movement is calculated. Finally, the joint load inertial momentum data is obtained, which reflects the joint load fluctuation caused by inertia and gravity changes during the execution of the robot path.

[0119] Preferably, step S232 includes the following steps:

[0120] Calculate the change of the manipulator's rotation radius based on the manipulator's path running speed matching data to obtain the manipulator's rotation radius change data;

[0121] According to the grasped object mass data and the robot path running speed matching data, the robot rotation radius change data is analyzed for the radius change joint force orientation change to obtain the joint force orientation change data;

[0122] Constructing a spatial coordinate system for the force orientation change data of the joint to obtain a force orientation change spatial coordinate system;

[0123] Decomposing the mass data of the grasped object by gravity vector according to the space coordinate system of force orientation change, and obtaining the decomposition data of the object's gravity vector;

[0124] Based on the decomposition data of the gravity action vector of the object, the instantaneous inclined plane gravity load average difference is calculated for the space coordinate system with the force orientation change, and the instantaneous inclined plane gravity load average difference is obtained;

[0125] According to the decomposition of the gravity action vector of the object, the joint axial moment distribution data is analyzed for the joint angle force limit load data to obtain the joint axial moment distribution data;

[0126] The gravity action azimuth component decomposition data is obtained by decomposing the gravity action vector of the object, the instantaneous inclined plane gravity load difference and the joint axial moment distribution data.

[0127] In an embodiment of the present invention, first, the path operation rate matching data of the manipulator is obtained, including the speed and path information of the manipulator at each moment during the movement process. The kinematic model of the manipulator is used to calculate the radius change of the manipulator rotating at different path points. Specifically, the path operation rate matching data contains the movement speed and movement curvature of each joint of the manipulator. By inputting the path data into the geometric calculation formula, the rotation radius of each movement stage can be calculated, and then the data of the rotation radius changing over time can be obtained. After obtaining the rotation radius change data, it is necessary to analyze the changes in the torque and direction of each joint of the manipulator during the rotation process. First, combined with the mass data of the grasped object, the direction and magnitude of the force applied by the object to the manipulator joint are calculated. The mass of the object affects the load of the manipulator, and then affects the force of each joint. On this basis, combined with the manipulator path operation rate matching data, the change trend of the force and torque borne by each joint of the manipulator during the change of the rotation radius is analyzed. In this analysis process, it is necessary to calculate the relationship between the joint angle change and the joint force caused by the rotation radius according to each point on the path, and obtain the change data of the joint force orientation. This process requires the use of dynamic models, especially the analysis of the relationship between joint torque and joint angle. After the joint force orientation change data is obtained, it is necessary to construct a spatial coordinate system for these data. In order to more accurately analyze the direction and magnitude of the force, it is necessary to define a coordinate system in three-dimensional space, which includes the position of the end effector of the manipulator, the mechanical parameters of each joint, and the external load applied by the object. First, it is necessary to define the origin, axial direction and related scales of the coordinate system based on the joint force orientation change data. Through the rotation matrix and the transformation matrix, the original two-dimensional force data can be mapped into the three-dimensional space to construct a three-dimensional coordinate system representing the direction and magnitude of the force. At this point, the data constructed by the spatial coordinate system can be expressed as the force change trend of each joint relative to the external load. Next, based on the constructed spatial coordinate system, the mass data of the grasped object is converted into a gravity action vector. According to Newton's second law, the relationship between the mass and acceleration of an object in a gravitational field gives the gravity action vector. The direction of the gravity action vector is consistent with the direction of gravity of the object, and its magnitude is equal to the mass of the object multiplied by the gravitational acceleration ( ). In order to accurately analyze the influence of gravity on an object in three-dimensional space, it is necessary to decompose the gravity action vector and project it along the axes of the spatial coordinate system. At this time, the decomposition result of the gravity action vector is the force magnitude in each direction, generating the gravity action vector decomposition data. After obtaining the gravity action vector decomposition data of the object, it is necessary to further analyze the distribution of gravity under different force orientations. Specifically, an inclined plane is selected as a reference in the spatial coordinate system, and the load of the object's gravity on the inclined plane is calculated. By calculating the dot product of the gravity action vector and the normal vector of the inclined plane, the gravity load of the object on the inclined plane can be obtained. The purpose of this calculation step is to evaluate how the gravity of the object is distributed on the inclined plane at different positions and angles, and to calculate the instantaneous gravity load difference. The gravity action vector decomposition data can not only help analyze the effect of the object's gravity, but also provide a basis for the distribution of the axial torque of the joint. By correlating the gravity action vector with the axial direction of each joint of the manipulator, the distribution of the axial torque of each joint can be obtained. This process requires combining each component of gravity with the angle data of the joint to derive the axial torque generated on each joint and analyze the relationship between the torque and the change of the joint angle. Based on this data, the joint axial torque distribution data can be obtained, so as to understand the specific torque of each joint during the grasping process. Finally, the force direction of the manipulator is further analyzed by combining the object gravity vector decomposition data, the instantaneous inclined plane gravity load difference and the joint axial torque distribution data. The purpose of this analysis is to fully understand how the object's gravity affects the force of each joint of the manipulator. By combining the above data and using the gravity component decomposition method, the changing trend of the force direction of each joint can be obtained more accurately, and then the final gravity azimuth component decomposition data can be obtained. This process involves matrix operations. The direction of force and torque can be derived by linearly transforming each data to derive the force and torque distribution in different directions.

[0128] Preferably, step S24 comprises the following steps:

[0129] Step S241: calculating the limit angle load tolerance of the joint angle force limit load data to obtain the limit angle load tolerance;

[0130] Step S242: evaluating the effective inertia and coupling inertia of the joint load inertia momentum data to obtain the joint load effective inertia and the joint load coupling inertia respectively;

[0131] Step S243: performing an inertia imbalance divergence evaluation between different limit angles on the joint load effective inertia and the joint load coupling inertia according to the limit angle load tolerance, and obtaining limit angle inertia imbalance divergence data;

[0132] Step S244: performing inertial jitter load correlation analysis on the joint angle force limit bearing data according to the local angle inertial imbalance divergence data, the joint load effective inertia and the joint load coupling inertia to obtain inertial jitter load correlation data.

[0133] In the embodiment of the present invention, it is first necessary to process the load-bearing data of the joint angle force limit of the manipulator. Specifically, when each joint of the manipulator performs a grasping task, its load-bearing torque and angle range are physically limited. In order to ensure that the manipulator can operate stably, tolerance analysis must be performed on the angle load limit of each joint. The limit angle tolerance of the joint is calculated by combining the actual force conditions of the manipulator under different working conditions through the angle load limit calculation formula based on the mechanical model (including force analysis based on the lever principle and the torque balance model, and based on the static equilibrium condition). This process gradually calculates the maximum tolerable angle variation range of each joint by considering the nonlinear relationship between the joint angle and the force distribution. The obtained limit angle load tolerance data provides constraints for subsequent force analysis, ensuring that the adjustment of the joint angle will not exceed its physical load range. First, the joint load inertia momentum data is obtained according to the motion state of each joint of the manipulator during the motion process. Then, based on the principle of dynamic mechanics, the two important parameters of effective inertia and coupling inertia are obtained by calculating the load inertia data of each joint. Effective inertia refers to the inertia related to factors such as the mass of the grasped object and the path speed during the joint movement. The coupled inertia takes into account the inertial coupling effect generated when multiple joints interact and move with each other. The calculation process involves the derivation of the motion model of each joint of the manipulator, combining the physical connection and mechanical feedback relationship between the joints, decomposing the inertia through the inertia matrix, and finally obtaining the data of effective inertia and coupled inertia. This data provides the necessary physical basis for the subsequent inertial imbalance and torque distribution analysis. By constraining the angle tolerance of the joint, the inertial imbalance of the manipulator joints at different angles is evaluated. First, the limit angle load tolerance obtained in the previous step is combined with the effective inertia and coupled inertia of the joint. This process analyzes the motion trajectory of each joint of the manipulator at different angles and combines different inertial parameters to calculate the inertial imbalance divergence of the manipulator during the grasping task. The specific operation is based on the multi-body dynamics model to evaluate how the inertial force and torque affect the stability of the joint when the joint angle changes. If the inertial force is large at certain angles, it will lead to instability of the joint movement, which will in turn cause inertial imbalance divergence. In this way, it is possible to identify unstable areas where the manipulator joints are prone to appear when performing tasks, thereby providing data support for the adjustment of the control strategy. Combining the inertial imbalance divergence data of the local angle, the effective inertia of the joint load and the coupling inertia, the relationship between the joint angle force limit and the inertial jitter is further explored through the inertial jitter load correlation analysis. Inertial jitter load refers to the jitter effect caused by the imbalance of the inertial force of each joint during the grasping process of the manipulator. This step accurately correlates the jitter load by analyzing the inertial fluctuations caused by the force and angle changes of each joint when the manipulator performs tasks.This process involves an in-depth analysis of the inertial data, combined with the actual motion trajectory, inertial parameters, angle changes, and joint force data of the joint, to derive the correlation between the inertial jitter load and the joint angle force limit. Ultimately, this analysis can provide guidance for the motion control of the manipulator to avoid operational instability or task failure caused by inertial jitter.

[0134] Preferably, step S243 includes the following steps:

[0135] According to the limit angle load tolerance, the effective inertia of the joint load is fitted with the effective inertia partition difference to obtain the effective inertia partition difference data;

[0136] Based on the effective inertia partition difference data and the joint load coupling inertia, the joint inertia imbalance ratio is analyzed to obtain the joint inertia imbalance ratio;

[0137] Based on the effective inertia partition difference data and the joint inertia imbalance ratio, the inertia coupling angle vector offset is evaluated to obtain the inertia coupling angle offset vector;

[0138] According to the joint inertia imbalance ratio and the inertia coupling angle offset vector, the inertia imbalance nonlinear gradient analysis is performed to obtain the inertia imbalance nonlinear gradient data;

[0139] According to the inertial imbalance nonlinear gradient data, the joint inertial imbalance ratio and the inertial coupling angle offset vector, the inertial imbalance divergence between different extreme angles is evaluated to obtain the extreme angle inertial imbalance divergence data.

[0140] In an embodiment of the present invention, first, according to the joint angle bearing tolerance data of the manipulator, by analyzing the effective inertia of the load of each joint at different angles, partition difference fitting is performed. The specific operation includes dividing the joint angle into several intervals, and fitting according to the angle bearing tolerance and effective inertia data in each interval. The effective inertia data in each partition is regressed and fitted using the least squares method to more accurately reveal the inertia difference of the joint at different angles. Through this method, the effective inertia partition difference data of each joint at different angles can be obtained, that is, the inertia change of the joint at different angles. These data can provide support for subsequent joint inertia analysis. Based on the obtained effective inertia partition difference data and the load coupling inertia data of the manipulator joint, the inertia imbalance ratio analysis of each joint is performed. The inertia imbalance ratio refers to the ratio of the effective inertia of the joint to the coupling inertia. First, the ratio of the effective inertia partition difference data of each joint at different angles to the coupling inertia of the joint is calculated. Specifically, for each joint, the inertia imbalance ratio of each joint at a specific angle is obtained by calculating the ratio of the effective inertia partition difference data to the coupling inertia of the joint. Through this process, the inertial imbalance of the joints at different angles can be revealed, providing key data for the subsequent inertial coupling analysis. Based on the obtained joint inertial imbalance ratio and effective inertia partition difference data, the inertial coupling angle vector offset evaluation is performed. This evaluation process analyzes the influence of the inertial imbalance ratio of each joint on the angle offset to obtain a vector offset that describes the relationship between the joint angle and the inertial imbalance. The specific operation is to combine the inertial imbalance ratio of each joint with the effective inertial partition difference data through vector analysis to evaluate the coupling relationship between the rotation angle and inertial imbalance of each joint. Through the coordinated analysis of the angle and inertial imbalance ratio of each joint, an offset vector is obtained to quantify the relationship between the angle change and the inertial imbalance. The nonlinear gradient analysis of inertial imbalance is performed using the joint inertial imbalance ratio and the inertial coupling angle offset vector data. The nonlinear gradient analysis of inertial imbalance analyzes the gradient relationship between joint angle and inertial imbalance by establishing a nonlinear function model. The specific implementation method is to use the finite difference method or the gradient descent method to calculate the rate of change of inertial imbalance at different angles to obtain the nonlinear gradient of inertial imbalance under angle change. The nonlinear gradient analysis results can reveal the complex relationship between joint angle change and inertial imbalance in actual operation. The final output is the nonlinear gradient data of inertial imbalance, which provides a detailed numerical basis for evaluating the force and inertial imbalance of joints at different angles. Finally, based on the obtained inertial imbalance nonlinear gradient data, joint inertial imbalance ratio and inertial coupling angle offset vector, the inertial imbalance divergence between different extreme angles is evaluated. This step evaluates the imbalance risk caused by analyzing the inertial imbalance divergence of the joint at different extreme angles.During the calculation process, it is necessary to match and compare the inertial imbalance ratio and the offset vector at different angles to calculate the divergence trend of the inertial imbalance under a specific angle change. The evaluation results provide the inertial imbalance divergence data of each joint at different angles, which is of great significance for avoiding unstable behavior or failure caused by inertial imbalance in the actual operation of the manipulator joint.

[0141] Preferably, step S3 comprises the following steps:

[0142] Step S31: performing motion parameter control constraints on the manipulator based on the inertial load jitter variance to obtain motion parameter control constraint data;

[0143] Step S32: normalizing the motion parameter control constraint data to obtain motion parameter control normalized data;

[0144] Step S33: performing feedback collaborative control design according to the motion parameter control normalization data and the joint load inertia momentum data to obtain the manipulator feedback collaborative control strategy.

[0145] As an example of the present invention, refer to Figure 3 As shown, in this example, step S3 includes:

[0146] Step S31: performing motion parameter control constraints on the manipulator based on the inertial load jitter variance to obtain motion parameter control constraint data;

[0147] In an embodiment of the present invention, firstly, the control constraints of the motion parameters of the manipulator are set based on the jitter variance of the inertial load of the manipulator. The inertial load jitter variance is statistically calculated by analyzing the force changes of each joint of the robot during the movement, especially the jitter fluctuations caused by the inertial load during the movement. The specific steps are to measure and record the inertial load jitter of each joint at different stages of the robot's movement, calculate the variance value of the jitter, and characterize the fluctuation amplitude of the inertial load. The variance value can reflect the impact of the inertial fluctuation of the manipulator on the control performance at different angles and speeds. According to the variance data, the control constraints of the motion parameters are set to ensure that the control system can adapt to such fluctuations and avoid control errors or stability problems caused by excessive inertial fluctuations. The contents of the constraints include but are not limited to the maximum speed, maximum acceleration and maximum torque of the manipulator, so as to avoid exceeding the load-bearing capacity or causing joint damage during movement.

[0148] Step S32: normalizing the motion parameter control constraint data to obtain motion parameter control normalized data;

[0149] In the embodiment of the present invention, on the basis of step S31, the obtained motion parameter control constraint data generally includes information such as speed, acceleration and load of different joints. The dimensions and ranges of these data are quite different. In order to make each data be compared and optimized under the same standard, the minimum-maximum normalization process is adopted. After normalization, the range of each data will be limited to between 0 and 1, eliminating the influence of different units and dimensions on subsequent control design. This process makes all motion parameters have the same scale, which helps the optimization process when designing the control strategy in the future. The normalized data can not only simplify the calculation, but also improve the control accuracy and the response speed of the controller.

[0150] Step S33: performing feedback collaborative control design according to the motion parameter control normalization data and the joint load inertia momentum data to obtain the manipulator feedback collaborative control strategy.

[0151] In an embodiment of the present invention, after obtaining the motion parameter control normalization data in step S32, it is combined with the joint load inertia momentum data to perform feedback collaborative control design. The joint load inertia momentum data reflects the inertial effect of each joint in motion, and the processing of the data helps to evaluate the force conditions of different joints and their impact on the overall motion. Combining the control normalization data and the inertia momentum data, a feedback control system can be designed to ensure that the manipulator has the best motion control performance when performing tasks. It includes: establishing a multivariable control model through the joint load inertia momentum data and the motion parameter control normalization data. The model can describe the relationship between the motion state of each joint of the manipulator and the external load. According to the control objectives, such as minimizing the position error, maximizing the energy efficiency, etc., an optimization objective function is designed; according to the established control model, the control gain is adjusted to ensure that the feedback loop can effectively respond to the force changes of each joint of the manipulator. The selection of feedback gain is the key to the design of the control strategy, which affects the stability and response speed of the system. The determination of the gain value can be adjusted according to the system performance requirements by numerical optimization methods, such as least squares method or particle swarm optimization (PSO). In a multi-degree-of-freedom parallel manipulator system, the movements of each joint are often interrelated, so a collaborative control strategy needs to be designed. The core of this strategy is to ensure the coordinated work of each joint by transmitting position information, speed information, and force information between the joints. Collaborative control methods include model-based coordinated control and data-based learning control. The former uses mathematical modeling to predict the coupling relationship between joints, while the latter adjusts the control strategy based on real-time data. Based on the above steps, a feedback control algorithm is implemented, specifically including: using a proportional-integral-derivative (PID) controller or other more advanced control methods (such as robust control, predictive control, etc.) to adjust the joint motion state in real time to ensure that the manipulator maintains a smooth inertial motion during operation and minimize the impact of inertial jitter.

[0152] Preferably, step S31 includes the following steps:

[0153] Step S311: dividing the feasible boundary of the manipulator output force based on the inertial load jitter variance to obtain the feasible boundary data of the output force;

[0154] Step S312: performing joint damping optimization processing on the output force feasible boundary data to obtain joint damping optimization data;

[0155] Step S313: optimizing the inertial load distribution according to the output force feasible boundary data and the joint damping optimization data to obtain the inertial load distribution optimization data;

[0156] Step S314: Based on the output force feasible boundary data, the joint damping optimization data and the inertial load distribution optimization data, the manipulator motion parameter control constraint is performed to obtain the motion parameter control constraint data.

[0157] In an embodiment of the present invention, firstly, the feasible boundary of the manipulator output force is defined based on the inertial load jitter variance data of the manipulator. The inertial load jitter variance is used to quantify the jitter change caused by inertial action during the movement of the manipulator. The larger the jitter variance, the higher the instability of the inertial load of the manipulator, which leads to the inaccuracy of the manipulator output force. Therefore, for different motion states (such as acceleration, deceleration, uniform speed, etc.), it is necessary to determine the boundary range of the output force. In the specific implementation process, by monitoring the inertial load of the manipulator at different speeds and accelerations, combined with the variance data of the inertial jitter, the maximum output force and the minimum output force of the manipulator in the motion state are determined by statistical analysis methods (such as maximum value, standard deviation, etc.). Combined with the structure and motion characteristics of the manipulator, the feasible boundary of the output force in the motion state is obtained. These boundary data will be used as constraints in subsequent motion control to ensure that the manipulator output force is within a controllable range and avoid overload or excessive inertial fluctuations. After obtaining the feasible boundary data of the output force in step S311, the joint damping optimization process is performed next. Joint damping is the inhibitory effect on the joint motion state when controlling the movement of the manipulator, which can effectively reduce the jitter caused by the inertial load. The selection of joint damping directly affects the motion stability of the manipulator. In this step, the damping coefficient of the joint is determined by analyzing the feasible boundary data of the output force. The specific implementation method is to first estimate the vibration intensity of each joint under different output forces based on the mechanical characteristics in the feasible boundary data of the output force. Then, according to the motion characteristics of the joint, mathematical tools such as Lagrange method or variational method are used to derive an optimization objective function. The main purpose of the objective function is to minimize the motion jitter of the joint while maintaining the stability of the system. By adjusting the joint damping, the damping of the joint can minimize the jitter within the given output force boundary and maximize the efficiency of energy consumption. During the optimization process, numerical methods (such as gradient descent method) can be used to adjust the damping coefficient and obtain the final joint damping optimization data. Based on the feasible boundary data of the output force obtained in step S311 and the joint damping optimization data obtained in step S312, inertial load distribution optimization is performed. The purpose of inertial load distribution optimization is to make each joint of the manipulator bear a reasonable load while satisfying the motion constraints, so as to avoid the performance degradation of a joint due to overload. The core issue of inertial load distribution is how to reasonably distribute the load among the joints so that the manipulator can complete the task stably and efficiently. The specific implementation method is to use a load distribution algorithm (such as a dynamic programming algorithm or optimal control theory) to calculate the inertial load that each joint should bear under different motion modes, combining the damping characteristics, mechanical model and motion state of the joint. In this process, the output force feasible boundary data is used as a boundary condition constraint to ensure that the load of each joint does not exceed its maximum load-bearing capacity. Finally, the calculated inertial load distribution optimization data will be used in subsequent motion control decisions to optimize the motion efficiency and stability of the manipulator.After the inertial load distribution optimization is completed in step S313, the output force feasible boundary data, joint damping optimization data and inertial load distribution optimization data are finally combined to set the manipulator motion parameter control constraint. The core of this step is to combine these data to reasonably constrain the motion of the manipulator, ensuring that the manipulator does not exceed the physical limit during the execution of the task, and can maintain good motion performance at the same time. In the specific operation, all the motion parameters of the manipulator are first defined, including the speed, acceleration, output force, etc. of each joint. Then, according to the output force feasible boundary data, the maximum and minimum force values ​​that each joint can withstand during the movement are determined. Then, according to the joint damping optimization data, the damping coefficient of each joint is adjusted to ensure that it can effectively absorb the jitter caused by the inertial load. Finally, through the inertial load distribution optimization data, the load of each joint is reasonably distributed to ensure that the load of each joint is uniform and avoid the occurrence of overload. Through the comprehensive application of these data, the final motion parameter control constraint data is obtained, which will be used as core parameters in the motion control of the manipulator to guide the manipulator to perform precise movements.

[0158] Preferably, the present invention further provides a control system of a three-degree-of-freedom parallel robot, which is used to execute the control method of the three-degree-of-freedom parallel robot as described above, and the control system of the three-degree-of-freedom parallel robot comprises:

[0159] The limit load analysis module is used to obtain the manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot; extract the manipulator joint rotation range based on the manipulator design parameters to obtain the manipulator joint rotation range data; perform force limit load analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain the joint angle force limit load data;

[0160] The inertial jitter analysis module is used to obtain the robot grasping operation path data; according to the robot grasping operation path data, the mobile inertial momentum simulation is performed on the joint angle force limit bearing data to obtain the joint load inertial momentum data; based on the joint load inertial momentum data, the load jitter variance is calculated to obtain the inertial load jitter variance;

[0161] The feedback collaborative design module is used to constrain the motion parameter control of the manipulator based on the inertial load jitter variance to obtain the motion parameter control constraint data; the feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy;

[0162] The control firmware design module is used to design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy, obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

[0163] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0164] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A control method for a three-degree-of-freedom parallel robot, characterized in that: The following steps are involved: Step S1: Obtaining manipulator design parameters and robot task execution data of a three-degree-of-freedom parallel robot; Extract the rotation range of the manipulator joints based on the manipulator design parameters to obtain the manipulator joint rotation range data; According to the design parameters of the manipulator, the force limit load-bearing analysis between different joint rotation angles is performed on the manipulator joint rotation range data to obtain the joint angle force limit load-bearing data; Step S2: obtaining the manipulator grasping operation path data; performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the manipulator grasping operation path data to obtain the joint load inertial momentum data; performing a load jitter variance calculation based on the joint load inertial momentum data to obtain the inertial load jitter variance; Step S3: Based on the inertial load jitter variance, the manipulator motion parameter control constraint is performed to obtain motion parameter control constraint data; feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy; Step S4: Design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy to obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

2. The control method of the three-degree-of-freedom parallel robot according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot; Step S12: extracting the manipulator joint rotation range from the manipulator design parameters to obtain the manipulator joint rotation range data; Step S13: performing grabbing load limit analysis on the manipulator design parameters to obtain grabbing load limit data of the manipulator; Step S14: performing force limit load-bearing analysis on the manipulator joint rotation range data between different joint rotation angles according to the manipulator grabbing load limit data, and obtaining joint angle force limit load-bearing data.

3. The control method of the three-degree-of-freedom parallel robot according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: Acquire the robot grasping operation path data; Step S22: extracting the required time limit from the robot task execution data to obtain task execution time limit data; extracting the grasped object quality from the robot task execution data to obtain grasped object quality data; Step S23: performing a mobile inertial momentum simulation on the joint angle force limit bearing data according to the grasped object mass data, the task execution time limit data, and the manipulator grasping operation path data to obtain the joint load inertial momentum data; Step S24: performing inertial jitter load correlation analysis on the joint angle force limit bearing data based on the joint load inertial momentum data to obtain inertial jitter load correlation data; Step S25: Calculate the load jitter variance of the inertial jitter load correlation data to obtain the inertial load jitter variance.

4. The control method of the three-degree-of-freedom parallel robot according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: performing robot path operation rate matching based on the task execution time limit data and the robot grabbing operation path data to obtain the robot path operation rate matching data; Step S232: Decomposing the gravity action azimuth component of the joint angle force limit bearing data according to the grasped object mass data and the robot path operation speed matching data to obtain gravity action azimuth component decomposition data; Step S233: performing gravity trajectory segment difference evaluation on the manipulator grasping operation path data according to the gravity action azimuth component decomposition data to obtain gravity trajectory segment difference data; Step S234: performing a movement inertia momentum simulation according to the gravity trajectory segment difference data to obtain the joint load inertia momentum data.

5. The control method of the three-degree-of-freedom parallel robot according to claim 4, characterized in that: Step S232 includes the following steps: Calculate the change of the manipulator's rotation radius based on the manipulator's path running speed matching data to obtain the manipulator's rotation radius change data; According to the grasped object mass data and the robot path running speed matching data, the robot rotation radius change data is analyzed for the radius change joint force orientation change to obtain the joint force orientation change data; Constructing a spatial coordinate system for the force orientation change data of the joint to obtain a force orientation change spatial coordinate system; Decomposing the mass data of the grasped object by gravity vector according to the space coordinate system of force orientation change, and obtaining the decomposition data of the object's gravity vector; Based on the decomposition data of the gravity action vector of the object, the instantaneous inclined plane gravity load average difference is calculated for the space coordinate system with the force orientation change, and the instantaneous inclined plane gravity load average difference is obtained; According to the decomposition of the gravity action vector of the object, the joint axial moment distribution data is analyzed for the joint angle force limit load data to obtain the joint axial moment distribution data; The gravity action azimuth component decomposition data is obtained by decomposing the gravity action vector of the object, the instantaneous inclined plane gravity load difference and the joint axial moment distribution data.

6. The control method of the three-degree-of-freedom parallel robot according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: calculating the limit angle load tolerance of the joint angle force limit load data to obtain the limit angle load tolerance; Step S242: evaluating the effective inertia and coupling inertia of the joint load inertia momentum data to obtain the joint load effective inertia and the joint load coupling inertia respectively; Step S243: performing an inertia imbalance divergence evaluation between different limit angles on the joint load effective inertia and the joint load coupling inertia according to the limit angle load tolerance, and obtaining limit angle inertia imbalance divergence data; Step S244: performing inertial jitter load correlation analysis on the joint angle force limit bearing data according to the local angle inertial imbalance divergence data, the joint load effective inertia and the joint load coupling inertia to obtain inertial jitter load correlation data.

7. The control method of the three-degree-of-freedom parallel robot according to claim 6, characterized in that: Step S243 includes the following steps: According to the limit angle load tolerance, the effective inertia of the joint load is fitted with the effective inertia partition difference to obtain the effective inertia partition difference data; Based on the effective inertia partition difference data and the joint load coupling inertia, the joint inertia imbalance ratio is analyzed to obtain the joint inertia imbalance ratio; Based on the effective inertia partition difference data and the joint inertia imbalance ratio, the inertia coupling angle vector offset is evaluated to obtain the inertia coupling angle offset vector; According to the joint inertia imbalance ratio and the inertia coupling angle offset vector, the inertia imbalance nonlinear gradient analysis is performed to obtain the inertia imbalance nonlinear gradient data; According to the inertial imbalance nonlinear gradient data, the joint inertial imbalance ratio and the inertial coupling angle offset vector, the inertial imbalance divergence between different extreme angles is evaluated to obtain the extreme angle inertial imbalance divergence data.

8. The control method of the three-degree-of-freedom parallel robot according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: performing motion parameter control constraints on the manipulator based on the inertial load jitter variance to obtain motion parameter control constraint data; Step S32: normalizing the motion parameter control constraint data to obtain motion parameter control normalized data; Step S33: performing feedback collaborative control design according to the motion parameter control normalization data and the joint load inertia momentum data to obtain the manipulator feedback collaborative control strategy.

9. The control method of the three-degree-of-freedom parallel robot according to claim 8, characterized in that: Step S31 The following steps are involved: Step S311: dividing the feasible boundary of the manipulator output force based on the inertial load jitter variance to obtain the feasible boundary data of the output force; Step S312: performing joint damping optimization processing on the output force feasible boundary data to obtain joint damping optimization data; Step S313: optimizing the inertial load distribution according to the output force feasible boundary data and the joint damping optimization data to obtain the inertial load distribution optimization data; Step S314: Based on the output force feasible boundary data, the joint damping optimization data and the inertial load distribution optimization data, the manipulator motion parameter control constraint is performed to obtain the motion parameter control constraint data.

10. A control system for a three-degree-of-freedom parallel robot, characterized in that: The control method for executing the three-degree-of-freedom parallel robot according to claim 1, wherein the control system of the three-degree-of-freedom parallel robot comprises: The limit load analysis module is used to obtain the manipulator design parameters and robot task execution data of the three-degree-of-freedom parallel robot; extract the manipulator joint rotation range based on the manipulator design parameters to obtain the manipulator joint rotation range data; perform force limit load analysis between different joint rotation angles on the manipulator joint rotation range data based on the manipulator design parameters to obtain the joint angle force limit load data; The inertial jitter analysis module is used to obtain the robot grasping operation path data; according to the robot grasping operation path data, the mobile inertial momentum simulation is performed on the joint angle force limit bearing data to obtain the joint load inertial momentum data; based on the joint load inertial momentum data, the load jitter variance is calculated to obtain the inertial load jitter variance; The feedback collaborative design module is used to constrain the motion parameter control of the manipulator based on the inertial load jitter variance to obtain the motion parameter control constraint data; the feedback collaborative control design is performed according to the motion parameter control constraint data to obtain the manipulator feedback collaborative control strategy; The control firmware design module is used to design the manipulator collaborative control firmware based on the manipulator feedback collaborative control strategy, obtain the manipulator collaborative control firmware, and send the manipulator collaborative control firmware to the three-degree-of-freedom parallel robot to execute the control method of the three-degree-of-freedom parallel robot.

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