Method, device and equipment for optimizing robot simulation model and storage medium

Abstract

The application discloses an optimization method and device of a robot simulation model, equipment and a storage medium, relates to the technical field of robot simulation, and comprises the following steps: obtaining a model file of a three-dimensional model of a robot and a smooth trajectory generation function; combining the model file and a physical behavior model to construct a first robot simulation model; performing simulation optimization on the first robot simulation model based on the smooth trajectory generation function to obtain a second robot simulation model; obtaining real-time running data of a physical prototype; and performing simulation optimization on the second robot simulation model according to the real-time running data and the smooth trajectory generation function to obtain a target robot simulation model. The simulation optimization of the robot simulation model through the smooth trajectory generation function and the real-time running data of the physical prototype can effectively reduce the joint vibration amplitude, thereby improving the smoothness of the robot in executing a target gesture action.

Description

Technical Field

[0001] This application relates to the field of robotic arm simulation technology, and in particular to methods, apparatus, equipment and storage media for optimizing robotic arm simulation models. Background Technology

[0002] With the deep integration of artificial intelligence and advanced manufacturing technology, robotic arm simulation technology has become a core component of robot system development. Traditional robotic arm simulation optimization relies on repeated trial and error with physical prototypes, resulting in long cycles, high costs, and high deviations.

[0003] Existing robotic arm simulation technology uses open-loop output with step angles during simulation, without considering the influence of internal physical disturbances and external environmental disturbances during robotic arm movement. This results in large joint vibration amplitudes, leading to poor smoothness of robotic arm movement. Summary of the Invention

[0004] The main purpose of this application is to provide an optimization method, device, equipment and storage medium for a robot simulation model, which aims to solve the technical problem that current robot simulation models do not take into account the poor smoothness of the movement process due to the susceptibility of the robot to disturbances during movement.

[0005] To achieve the above objectives, this application proposes an optimization method for a robotic arm simulation model, the optimization method comprising: Obtain the model file of the robot's 3D model and the function to generate a smooth trajectory; The model file and the physical behavior model are combined to construct the first robotic arm simulation model; The simulation model of the first robotic arm is optimized based on the smooth trajectory generation function to obtain the simulation model of the second robotic arm. The real-time operating data of the physical prototype is obtained, and the second manipulator simulation model is optimized based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

[0006] In one embodiment, the step of optimizing the first robotic arm simulation model based on the smooth trajectory generation function to obtain the second robotic arm simulation model includes: Configure simulation parameters for the first robotic arm simulation model to obtain the configured first robotic arm simulation model; Obtain the global simulation time; The global simulation time is input into the configured first robotic arm simulation model to obtain local parameters; Based on the local parameters and the smooth trajectory generation function, the configured first robot simulation model is optimized to obtain the second robot simulation model.

[0007] In one embodiment, the step of optimizing the configured first robotic arm simulation model based on the local parameters and the smooth trajectory generation function to obtain the second robotic arm simulation model includes: The local parameters are input into the smooth trajectory generation function to obtain the servo target angle array; The target angle array of the servo motor is filtered to obtain the actual drive angle; The first driving command is determined based on the actual driving angle. Based on the first driving command, the configured first robot simulation model is driven under different working conditions to perform simulation optimization, thereby obtaining the second robot simulation model.

[0008] In one embodiment, the local parameters include at least the real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joint, and the target angle of the robot arm joint. The step of inputting the local parameters into the smooth trajectory generation function to obtain the servo motor target angle array includes: The real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joint, and the target angle of the robot arm joint are input into the smooth trajectory generation function to obtain a smooth trajectory. Generate a target angle array for the servo motor based on the smooth trajectory.

[0009] In one embodiment, the step of driving the configured first robot simulation model under different working conditions based on the first driving command to perform simulation optimization to obtain a second robot simulation model includes: Based on the first driving command, the configured first manipulator simulation model is driven to perform simulated motion under different working conditions, and the timing curves of each joint angle during the simulated motion are obtained. The joint angle timing curve and the servo motor target angle array are compared to determine the first simulation deviation value; Based on the first simulation deviation value, the configured first robot simulation model is iteratively optimized to obtain the second robot simulation model.

[0010] In one embodiment, the steps of acquiring real-time operating data of the physical prototype, and optimizing the second robotic arm simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target robotic arm simulation model include: The real-time operating data of the physical prototype is obtained, and the joint posture data in the real-time operating data is input into the smooth trajectory generation function to obtain the second driving command; The second manipulator simulation model is driven by the second driving command to perform simulated motion, and the simulation output during the simulated motion process is collected. The second robotic arm simulation model is optimized based on the real-time operating data and the simulation output to obtain the target robotic arm simulation model.

[0011] In one embodiment, the step of optimizing the second robotic arm simulation model based on the real-time operating data and the simulation output to obtain the target robotic arm simulation model includes: The real-time running data and the simulation output are compared and calculated to obtain the second simulation deviation value; Based on the second simulation deviation value, the hardware offset matrix in the second robot simulation model is corrected and optimized to obtain the target robot simulation model.

[0012] Furthermore, to achieve the above objectives, this application also proposes an optimization device for a robotic arm simulation model, the optimization device comprising: The acquisition module is used to acquire the model file of the robot's 3D model and the smooth trajectory generation function; The processing module is used to combine the model file and the physical behavior model to construct the first robot simulation model; The optimization module is used to perform simulation optimization on the first manipulator simulation model based on the smooth trajectory generation function to obtain the second manipulator simulation model; The optimization module is also used to acquire real-time operating data of the physical prototype, and to perform simulation optimization on the second manipulator simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

[0013] In addition, to achieve the above objectives, this application also proposes an optimization device for a robotic arm simulation model, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the optimization method for the robotic arm simulation model as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the optimization method for the robot simulation model as described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the optimization method for the robotic arm simulation model as described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: 1) By combining the pre-built smooth trajectory generation function with the simulation process, the joint vibration amplitude can be effectively reduced, thereby improving the smoothness of the robot hand performing the target gesture action.

[0017] 2) Optimizing the simulation model by using a smooth trajectory generation function can make the simulated motion trajectory smoother, thereby improving the fidelity and reliability of the robot simulation model.

[0018] 3) By combining the simulation model of the robot arm with the real-time operating data of the physical prototype for motion simulation, the accuracy of virtual-real motion mapping can be improved and residual tracking deviations can be eliminated. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating an embodiment of the optimization method for the robot simulation model of this application. Figure 2 This is an assembly diagram of a three-dimensional model of a robotic arm provided in an embodiment of the optimization method for the robotic arm simulation model of this application; Figure 3 A schematic diagram comparing the robot simulation model and the physical prototype provided in an embodiment of the optimization method for the robot simulation model of this application; Figure 4 This is a flowchart illustrating the second embodiment of the optimization method for the robot simulation model of this application. Figure 5 This is a flowchart illustrating the third embodiment of the optimization method for the robot simulation model of this application. Figure 6 This is a schematic diagram of the module structure of the optimization device for the robot simulation model in an embodiment of this application; Figure 7 This is a schematic diagram of the hardware operating environment involved in the optimization method of the robot simulation model in the embodiments of this application.

[0022] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0024] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0025] The main solution of this application embodiment is as follows: obtain the model file of the three-dimensional model of the robot and the smooth trajectory generation function; combine the model file and the physical behavior model to construct a first robot simulation model; perform simulation optimization on the first robot simulation model based on the smooth trajectory generation function to obtain a second robot simulation model; obtain the real-time running data of the physical prototype, and perform simulation optimization on the second robot simulation model according to the real-time running data and the smooth trajectory generation function to obtain the target robot simulation model.

[0026] Because existing methods for constructing robotic arms prioritize manufacturing physical prototypes before debugging the control program, lacking a pre-simulation verification stage, structural interference and control logic defects can only be addressed through trial and error with multiple prototype manufacturing steps. This results in long development cycles, high material debugging costs, and significant motion deviations. Alternatively, the simulation process may employ open-loop output with step angles, failing to consider the susceptibility of the robotic arm to internal physical disturbances and external environmental disturbances during movement. This leads to large joint vibration amplitudes, resulting in poor smoothness of the robotic arm's motion.

[0027] This application provides a solution that utilizes a pre-defined smooth trajectory generation function to effectively reduce joint vibration amplitude, thereby improving the smoothness of the robot arm's execution of target hand gestures. Furthermore, the robot arm simulation model is optimized based on the smooth trajectory generation function, achieving high fidelity and high reliability. The simulation model is then further optimized using real-time operating data from a physical prototype to obtain the target robot arm simulation model, which improves the accuracy of virtual-real motion mapping and eliminates residual tracking deviations.

[0028] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of realizing the above functions, such as an optimization system for a robotic arm simulation model. The following description uses an optimization system for a robotic arm simulation model as an example to illustrate this embodiment and the subsequent embodiments. All actions involving the acquisition of signals, information, or data in this application are performed in accordance with the relevant data protection regulations of the country where the application is located and with authorization from the owner of the corresponding device.

[0029] Based on this, embodiments of this application provide an optimization method for a robot simulation model, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the optimization method for the robot simulation model of this application.

[0030] In this embodiment, the optimization method for the robotic arm simulation model includes steps S10 to S40: Step S10: Obtain the model file of the robot's 3D model and the smooth trajectory generation function.

[0031] It should be noted that a 1:1 parametric model of the bionic hand can be pre-built using 3D computer-aided design and modeling software (such as SOLIDWORKS), thus constructing a 3D model of the robotic hand. (See reference...) Figure 2 , Figure 2 This is an assembly diagram of the 3D model of the robotic arm. The overall structure of the 3D model of the robotic arm includes at least a hand base, four bionic fingers, eight SCS009 serial bus servos, and a multi-stage linkage universal transmission mechanism.

[0032] The individual finger employs a multi-stage linkage and universal joint coupling transmission structure, where a single servo motor input torque can simultaneously drive both the proximal and distal finger joints. Without external tactile sensors, it relies on mechanical coupling via linkages to achieve adaptive envelope of irregularly shaped objects. Upon contact with an object, the mechanism passively deforms to evenly distribute the clamping force. Each bionic finger is equipped with two SCS009 servos as power input units. Through a multi-stage linkage universal joint transmission mechanism, the rotational motion output by the servos is converted into the reciprocating oscillation of the finger joints, driving four movable finger joints with two-way power coupling, forming a single-finger underactuated multi-joint linkage mechanism. The entire device consists of eight SCS009 serial bus servos forming eight independent power units, collaboratively driving a total of 16 finger joints across four fingers, thus achieving the required degrees of freedom for dexterous multi-finger operation with a compact drive layout.

[0033] Subsequently, using geometric assembly relationships such as coaxial mating, surface coincidence, width alignment, and parallel constraints, a fully constrained virtual assembly is performed on the 3D model of the robot to accurately reproduce the actual kinematic connections between each component. Then, static interference checks and full-stroke motion interference analysis are performed to identify and eliminate potential dead points, self-collisions, or insufficient clearance areas during the mechanism's movement. Based on the actual manufacturing process and the density and mechanical properties of the printing material, physical parameters such as mass, center of gravity position, and moment of inertia tensor are defined for each component. After completing the above processing, the 3D model of the robot is exported as a STEP (Standard for the Exchange of Product Model Data) model file or an IGES (Initial Graphics Exchange Specification) model file as input for subsequent robot simulation model construction.

[0034] In addition, the smooth interpolation function is specifically the S-shaped smooth interpolation function. This function is the core motion trajectory generation algorithm in the simulation optimization process of the robot in this application. It can solve the problem of "how to make the robot move smoothly from point A to point B and ensure the smooth trajectory" and ensure that the robot will not shake violently during the entire movement process.

[0035] Step S20: Combine the model file and the physical behavior model to construct the first robotic arm simulation model.

[0036] Understandably, the aforementioned model files can be imported into simulation software (the simulation software used in this solution is usually the domestic Tongyuan System Modeling and Simulation Software 2026a, abbreviated as MWORKS.Sysplorer2026a). When importing the model files, the part mass, inertia tensor, and center of gravity parameters can be completely preserved, matching the physical properties of the 3D printed PLA material. Motion limits can be set for all rotating joints, limiting the maximum bending angle of the joint to ±110° and the lateral swing to ±60°, avoiding overtravel jamming of the mechanism. In the simulation software, the model files are combined with physical behavior models covering multiple domains of mechanics, electrical, and control to construct an integrated digital twin simulation model of the three domains of mechanics, electricity, and control (i.e., the first manipulator simulation model). Specifically, in the mechanical domain, based on the actual motion relationships of the manipulator, kinematic and dynamic constraints such as revolute joints, universal joints, and fixed joints are configured for each component, including finger joints, links, universal joints, servos, and base, forming a multibody dynamics model of the whole machine. In the electrical domain, a drive circuit model of the SCS009 serial bus servo and an ESP32 communication signal model are built to simulate the transmission and reception of real electrical signals, power drive, and communication protocol timing. In the control domain, a standard C language code calling interface is reserved through the C-Caller module for directly embedding user-defined control algorithms such as smooth trajectory generation functions, five-action timing scheduling logic, and single-finger hardware offset calibration matrices, to achieve real-time data interaction and closed-loop simulation between the controller model and the mechanical and electrical physical models.

[0037] In one possible implementation, three user-defined control algorithms—the smooth trajectory generation function, the five-action timing scheduling logic, and the single-finger hardware offset calibration matrix—can be embedded into the CCaller external code call interface of the first robot simulation model for updating, forming a first robot simulation model with embedded control algorithms, and then updating the first robot simulation model to the first robot simulation model with embedded control algorithms.

[0038] Step S30: Based on the smooth trajectory generation function, perform simulation optimization on the first manipulator simulation model to obtain the second manipulator simulation model.

[0039] It should be emphasized that the goal of simulation optimization of the first robotic arm simulation model is to optimize the timing parameters of the control algorithm embedded in the first robotic arm simulation model (including at least hardware offset compensation parameters, duration of various gesture actions, and maximum amplitude of finger lateral / bending swing).

[0040] Step S40: Obtain real-time operating data of the physical prototype, and perform simulation optimization on the second manipulator simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

[0041] It should be noted that a physical prototype usually refers to a physical prototype of a robotic arm that can operate independently. (See reference...) Figure 3 , Figure 3 This is a diagram comparing the robot arm simulation model and the physical prototype. The left side of the diagram shows the robot arm simulation model, and the right side shows the physical prototype corresponding to the robot arm simulation model.

[0042] Specifically, after simulation verification, all hand structural components can be 3D printed using PLA (polylactic acid) material based on SolidWorks design drawings. These components include custom parts such as finger joints, linkages, and a universal joint. The printed hand structural components are then assembled with eight SCS009 serial bus servos to form a complete robotic arm. Regarding the control system, the ESP32 development board can be used as the main control unit. Based on the Arduino development environment, the mature control algorithms (including smooth trajectory generation functions, five-action timing scheduling logic, and single-finger hardware offset calibration matrix) verified through the C-Caller interface during the simulation phase can be ported to embedded C++ code. Simultaneously, an SCS009 servo serial communication driver program is written and burned onto the ESP32 control board. This allows for the synchronous position command issuance and status feedback acquisition of the eight servos via the serial bus protocol, thus completing the hardware integration and control deployment of the physical prototype.

[0043] Understandably, by obtaining real-time operating data from the physical prototype, the simulation model of the second robotic arm can be optimized again. The goal of this simulation optimization is to optimize the hardware offset matrix embedded in the simulation model of the second robotic arm.

[0044] To facilitate understanding, this embodiment provides a specific implementation of an optimization method for a robotic arm simulation model: First, the distal phalanx, proximal phalanx, transmission linkage, universal joint, servo base, and hand shell can be drawn in modules; sub-assemblies (single finger assembly → four-finger array → complete shell assembly) are completed using coaxiality, coincidence, width, and parallel constraints; the software motion simulation module detects no structural dead points throughout the entire stroke; the PLA material density is defined, and the mass and inertia tensor of each part are automatically calculated, thereby exporting the STEP model file. After importing the STEP model file into the MWORKS.Sysplorer2026a simulation software, the geometric dimensions are automatically parsed, and a revolute joint and universal joint are added to each hinge position; fixed joints are added to the base and servo base to lock spatial displacement; a servo drive electrical model is built, and a C-Caller external code input / output interface is reserved, thereby constructing the first robotic arm simulation model. A standard C modular program was written, with a smooth interpolation function at its core. A hardware offset calibration matrix was used to adapt to the assembly tolerances of the four fingers. The program automatically loops through five preset gesture actions in a 30.7s cycle, resetting to zero 0.3s after each action. A local compiler was specified in the MWORDS simulation interface, and after verifying compilation permissions, the .c file was bound to the C-Caller module. The global simulation time was used as the algorithm input, and eight target angle arrays for the servos were output. The first drive command was determined based on these eight target angle arrays. A signal splitting link was built to distribute the first drive command to the eight servo position drivers. First-order inertial elements were used to simulate servo mechanical hysteresis. A complete 30.7s loop simulation was run, outputting the timing curves of each joint angle. The duration of the S-shaped trajectory was repeatedly adjusted to address the wave motion phase difference, grasping closing speed, and index finger swing amplitude, eliminating angle overshoot and motion stuttering. A simulation of grasping a cylindrical object was performed to verify the adaptive envelope effect of the underactuated link, resulting in a second simulated robot model optimized by simulation. A physical prototype of the robotic arm was constructed based on the second robotic arm simulation model. Both the simulation model and the physical prototype were simultaneously activated, and visual and temporal comparisons were performed on wave dance, synchronized grasping, scissor hand gesture, OK gesture, and index finger waving. During testing, the system combined machine vision feedback with actual motion timing data to rigorously compare and analyze the pose states of the simulation model and the physical prototype during operation, thereby accurately identifying the posture deviation between the virtual and physical environments. Based on the measured virtual-physical deviation, the pose offset compensation matrix in the underlying code of the control system was promptly corrected to eliminate spatial position errors and ensure the consistency of the mapping from virtual commands to physical execution, thus obtaining the target robotic arm simulation model. The physical prototype was then modified and optimized based on the target robotic arm simulation model. A grip strength test was performed on the modified and optimized physical prototype: holding a 500ml water bottle in static suspension for 10 seconds with slight horizontal shaking, without slipping or bottle deformation, verifying the grasping stability.In addition, the simulation and physical object run synchronously for three complete 30.7s cycles, with an average error of 0.24° for the angle of each finger joint and a maximum error of ≤0.3°; the finger movements are smooth and trembling-free under S-shaped trajectory control; it can stably hold a fully loaded 518g cylindrical object, meeting the requirements of light-load operation; the entire development process saves 42% of development time compared to the traditional prototype-first approach, and reduces the number of 3D printing reworks by 2 / 3.

[0045] This embodiment is not only applicable to the optimization of robotic arm simulation models, but can also be extended to application scenarios such as intelligent logistics handling robots, laboratory automation equipment, special inspection robots, and university robot teaching and training platforms.

[0046] This embodiment provides a method for optimizing a robotic arm simulation model. It involves acquiring a 3D model file of the robotic arm and a smooth trajectory generation function; combining the model file with a physical behavior model to construct a first robotic arm simulation model; optimizing the first robotic arm simulation model based on the smooth trajectory generation function to obtain a second robotic arm simulation model; acquiring real-time operating data of a physical prototype; and optimizing the second robotic arm simulation model based on the real-time operating data and the smooth trajectory generation function to obtain a target robotic arm simulation model. This method effectively reduces joint vibration amplitude, thereby improving the smoothness of the robotic arm performing the target gesture.

[0047] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 Step S30 includes steps S301 to S304: Step S301: Configure simulation parameters for the first robotic arm simulation model to obtain the configured first robotic arm simulation model.

[0048] Understandably, simulation parameters need to be configured for the first robotic arm simulation model before simulation optimization. Simulation parameters typically refer to a series of values ​​and settings required to build, run, and drive the simulation model. These parameters directly determine the model's physical behavior, control accuracy, and the efficiency and stability of the simulation calculation. The simulation parameters include at least the solver type (the solver used in this application is typically the Dassl solver), the total simulation cycle (e.g., set to 30.7s), preset gestures (this application primarily pre-sets five sets of gestures: wave dance, synchronized grasping, scissor gesture, OK gesture, and index finger waving), and the reset interval between gestures (e.g., set to 0.3s).

[0049] The solver refers to the internal physics / mathematical calculation engine of the simulation software. The total simulation cycle usually refers to the total time of the entire simulation test process from start to stop (e.g., 30.7 seconds to complete five sets of gesture actions). The reset interval between actions usually refers to the waiting interval that is forcibly inserted between completing the previous set of actions (e.g., the "wave dance" action) and starting the next set of actions (e.g., the "synchronous grab" action).

[0050] Step S302: Obtain the global simulation time.

[0051] It should be noted that the global simulation time usually refers to the monotonically increasing absolute timestamp (timeline) in the simulation model. It ensures that the mechanical, electrical, and control domains can work together in the same timeframe, which is the fundamental guarantee that the simulation model can "operate like a real system".

[0052] Step S303: Input the global simulation time into the configured first robot simulation model to obtain local parameters.

[0053] Understandably, by inputting the global simulation time into the configured first robotic arm simulation model, the local parameters used for calculation can be determined within the configured first robotic arm simulation model based on the global simulation time and the current gesture action (such as currently performing a "V-sign" gesture) through a series of processes.

[0054] Step S304: Based on the local parameters and the smooth trajectory generation function, perform simulation optimization on the configured first manipulator simulation model to obtain the second manipulator simulation model.

[0055] It should be emphasized that the local parameters include at least the real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joints, and the target angle of the robot arm joints.

[0056] In one feasible implementation, step S304 may include steps A11 to A14: Step A11: Input the local parameters into the smooth trajectory generation function to obtain the servo target angle array; It should be noted that the servo target angle array usually refers to a one-dimensional array (or list) whose length is equal to the number of joints (servos) (for example, if the simulation model contains 8 SCS009 serial bus servos, it will output 8 sets of servo target angle arrays). Each value in the array represents the specific angle that the corresponding joint servo should rotate to.

[0057] In one embodiment, the step of inputting the local parameters into the smooth trajectory generation function to obtain the servo motor target angle array includes: inputting the real-time simulation time, the start time of the manipulator's action, the duration of the preset gesture action, the starting angle of the manipulator joint, and the target angle of the manipulator joint into the smooth trajectory generation function to obtain a smooth trajectory; and generating the servo motor target angle array based on the smooth trajectory.

[0058] Specifically, by inputting local parameters into the smooth trajectory generation function for relevant calculations, a smooth trajectory can be obtained. The expression for the smooth trajectory generation function is as follows:

[0059] In the above formula, This represents the starting angle of the robotic arm joint. The target angle for the robotic arm joint. For real-time simulation moments, This represents the starting moment when the robotic arm performs its actions. The preset duration of the action, The trajectory is smoothed. After obtaining the smoothed trajectory, a series of processes can be performed on it to obtain the target angle array of the servo motor.

[0060] Step A12: Filter the target angle array of the servo motor to obtain the actual drive angle; Understandably, filtering typically refers to using a low-pass filter's mathematical model to approximate the mechanical response delay of a servo motor (i.e., the gradual, non-abrupt "inertial lag" process between "receiving a command" and "actually reaching the correct position"). Furthermore, the filtering process is as follows:

[0061] In the above formula, For the (current) actual driving angle, The filtering smoothing coefficient is... For the (current) servo target angle array, This represents the actual driving angle at the previous moment.

[0062] Step A13: Determine the first drive command based on the actual drive angle; Understandably, drive commands typically refer to real-time electrical signals or digital commands that are processed from the actual drive angle and sent directly to the actuator (or controller).

[0063] Step A14: Based on the first driving instruction, drive the configured first robot simulation model under different working conditions to perform simulation optimization and obtain the second robot simulation model.

[0064] It should be noted that this simulation optimization mainly focuses on optimizing the timing parameters of the control algorithm in the first robotic arm simulation model after configuration.

[0065] By using a smooth trajectory generation function to determine the smooth motion trajectory of the robot arm and using a low-pass filtering algorithm to simulate the mechanical response delay of the servo motor, the first drive command can be determined, making the simulation model more realistic and the movements smoother.

[0066] In one embodiment, the step of driving the configured first manipulator simulation model under different working conditions based on the first driving command to perform simulation optimization to obtain a second manipulator simulation model includes: driving the configured first manipulator simulation model to perform simulated motion under different working conditions based on the first driving command, and obtaining the timing curves of each joint angle during the simulated motion; comparing the timing curves of the joint angles with the target angle array of the servo motor to determine a first simulation deviation value; and iteratively optimizing the configured first manipulator simulation model based on the first simulation deviation value to obtain a second manipulator simulation model.

[0067] In this context, "different working conditions" typically refer to the ability of the underactuated structure used in the robot simulation model to adapt to the grasping of objects of various shapes (e.g., grasping a 500g cylindrical object under no-load conditions). Therefore, the timing curves of each joint angle during the simulated motion under different working conditions can be obtained. These timing curves represent the actual responses calculated by the simulation solver, signifying "the actual sequence of joint positions after considering physical factors such as mechanical inertia, electrical delay, friction, and gravity." The servo motor target angle array, on the other hand, typically represents the desired commands generated by the control algorithm, signifying "the ideal sequence of positions the servo motor should reach." Therefore, the servo motor target angle array can be compared with the timing curves of each joint angle, and the differences between them can be quantified to obtain the first simulation deviation value. Based on this first simulation deviation value, the configured first robot simulation model can be iteratively optimized, primarily optimizing the timing parameters of the control algorithm in the configured first robot simulation model (e.g., hardware offset compensation parameters, duration of various hand gestures, and maximum amplitude of lateral / flexion swing of the fingers). The iteratively optimized simulation model is then used as the second robot simulation model.

[0068] The simulation model of the first manipulator, after being configured under different working conditions by driving the first driving command determined by the algorithm, is optimized. The timing curves of each joint angle obtained during the simulation are compared with the target angle array of the servo motor. Based on the comparison, the simulation model is optimized, thereby improving the adaptability of the manipulator simulation model under different working conditions.

[0069] This embodiment configures simulation parameters for the first robotic arm simulation model to obtain the configured first robotic arm simulation model; obtains the global simulation time; inputs the global simulation time into the configured first robotic arm simulation model to obtain local parameters; and optimizes the configured first robotic arm simulation model based on the local parameters and the smooth trajectory generation function to obtain the second robotic arm simulation model. Optimizing the simulation model using local parameters and the smooth trajectory generation function can improve the fidelity and reliability of the robotic arm simulation model.

[0070] Based on the first embodiment of this application, in the third embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 Step S40 includes steps S401 to S403: Step S401: Obtain the real-time running data of the physical prototype, and input the joint posture data in the real-time running data into the smooth trajectory generation function to obtain the second driving command.

[0071] It should be noted that the real-time operating data of the physical prototype usually refers to the data collected and transmitted in real time by various sensors installed on a real robotic arm in the physical world during actual operation, reflecting its real motion state and physical interaction. The second drive command is usually a direct mapping of the actual operating intention, and can be used to drive the simulation model for a second simulation optimization.

[0072] It should be understood that, based on the design drawings, all hand structure components can be 3D printed using PLA material to complete the assembly of the linkages, universal joints, and servo motors, thereby constructing a physical prototype of the robotic arm. Specifically, using the ESP32 as the main controller, embedded software development is carried out based on the Arduino development environment: First, the C control code verified by the second robotic arm simulation model is ported to the ESP32. The control logic layer does not need to be rewritten, only the servo motor serial communication driver layer needs to be converted to adapt to the ESP32 hardware platform; at the same time, the SCS009 servo motor serial driver program is written and burned to the ESP32 control board, realizing the synchronous position command of 8 servos through the serial bus protocol. To achieve strict consistency between the virtual and real motion scales, the angle conversion coefficient Step=0.293 can be uniformly set so that the simulation angle unit corresponds one-to-one with the servo motor register position value, ensuring accurate synchronization of the motion mapping between the digital twin model and the physical prototype.

[0073] A two-way data communication link is then established between the simulation platform and the physical prototype. This link can utilize a serial port, Ethernet, or CAN (Controller Area Network) bus to achieve real-time data interaction, allowing the acquisition of real-time operational data from the physical prototype. The joint attitude data from this real-time operational data is then input into a smooth trajectory generation function. After a series of calculations and processing, the second drive command is obtained.

[0074] Step S402: Drive the second robotic arm simulation model to perform simulated motion based on the second driving command, and collect the simulation output during the simulated motion process.

[0075] Understandably, the purpose of step S402 is to enable the second robotic arm simulation model to perform the same motion task as the physical prototype, and to record the simulation output during the simulation motion (the simulation output includes at least the actual joint angles, angular velocities, joint torques, and other data).

[0076] Step S403: Optimize the second robotic arm simulation model based on the real-time running data and the simulation output to obtain the target robotic arm simulation model.

[0077] It should be noted that after obtaining the real-time running data and simulation output, the real-time running data and simulation output can be compared to determine the difference between the simulation output and the real-time running data. Then, the simulation model of the second robot can be optimized to obtain the simulation model of the target robot.

[0078] In one embodiment, the step of optimizing the second robotic arm simulation model based on the real-time running data and the simulation output to obtain the target robotic arm simulation model includes: comparing and calculating the real-time running data and the simulation output to obtain a second simulation deviation value; and correcting and optimizing the hardware offset matrix in the second robotic arm simulation model based on the second simulation deviation value to obtain the target robotic arm simulation model.

[0079] Specifically, the hardware offset matrix typically refers to a set of fixed parameters obtained after offline calibration of a physical prototype of the robotic arm. These parameters are used to compensate for manufacturing tolerances and assembly deviations of components. The hardware offset matrix is ​​an affine transformation parameter model used to compensate for manufacturing and assembly errors, and it is key to achieving "consistency between virtual and real motion scales." By comparing the simulation output with real-time running data, a second simulation deviation value can be obtained. Then, based on the second simulation deviation value, the parameters in the hardware offset matrix embedded in the second robotic arm simulation model are corrected and optimized to obtain the target robotic arm simulation model.

[0080] In one implementation, the corrected matrix parameters from step S403 can be synchronously sent to the physical prototype as a calibration update signal to complete the bidirectional calibration between the robot simulation model and the physical prototype. After bidirectional calibration, gripping load tests can be conducted using the physical prototype to verify the adaptive grasping performance of the underactuated linkage mechanism.

[0081] By comparing and calculating the real-time running data and simulation output, the running deviation value is obtained. Then, based on the running deviation value, the hardware offset matrix in the simulation model of the second robot arm is corrected and optimized, which can improve the accuracy of virtual and real motion mapping and eliminate residual tracking deviation.

[0082] This embodiment acquires real-time operating data of the physical prototype, inputs the joint posture data from the real-time operating data into the smooth trajectory generation function to obtain a second driving command; drives the second manipulator simulation model to perform simulated motion based on the second driving command, and collects the simulation output during the simulation motion process; optimizes the second manipulator simulation model based on the real-time operating data and the simulation output to obtain a target manipulator simulation model. By combining the second manipulator simulation model with the real-time operating data of the physical prototype for simulated motion, the accuracy of virtual-real motion mapping can be improved.

[0083] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the optimization method of the robot simulation model of this application. Any simple transformations based on this technical concept are within the protection scope of this application.

[0084] This application also provides an optimization device for a robot simulation model; please refer to [reference needed]. Figure 6The optimization device for the robotic arm simulation model includes: Module 10 is used to acquire the model file of the robot's 3D model and the smooth trajectory generation function.

[0085] The processing module 20 is used to combine the model file and the physical behavior model to construct the first robot simulation model.

[0086] The optimization module 30 is used to perform simulation optimization on the first manipulator simulation model based on the smooth trajectory generation function to obtain the second manipulator simulation model.

[0087] The optimization module 30 is also used to acquire real-time operating data of the physical prototype, and to perform simulation optimization on the second manipulator simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

[0088] The robot simulation model optimization device provided in this application adopts the robot simulation model optimization method in the above embodiments, which can solve the technical problem that current robot simulation models do not consider the poor smoothness of the movement process due to the susceptibility of the robot to disturbances during movement. Compared with the prior art, the beneficial effects of the robot simulation model optimization device provided in this application are the same as those of the robot simulation model optimization method provided in the above embodiments, and other technical features in the robot simulation model optimization device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0089] In one embodiment, the optimization module 30 is further configured to configure simulation parameters for the first robotic arm simulation model to obtain a configured first robotic arm simulation model; obtain the global simulation time; input the global simulation time into the configured first robotic arm simulation model to obtain local parameters; and perform simulation optimization on the configured first robotic arm simulation model based on the local parameters and the smooth trajectory generation function to obtain a second robotic arm simulation model.

[0090] In one embodiment, the optimization module 30 is further configured to input the local parameters into the smooth trajectory generation function to obtain a servo motor target angle array; filter the servo motor target angle array to obtain the actual driving angle; determine a first driving command based on the actual driving angle; and perform simulation optimization on the configured first manipulator simulation model under different working conditions based on the first driving command to obtain a second manipulator simulation model.

[0091] In one embodiment, the optimization module 30 is further configured to input the real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joint, and the target angle of the robot arm joint into the smooth trajectory generation function to obtain a smooth trajectory; and generate a servo motor target angle array based on the smooth trajectory.

[0092] In one embodiment, the optimization module 30 is further configured to drive the configured first manipulator simulation model to perform simulated motion under different working conditions based on the first driving command, and obtain the timing curves of each joint angle during the simulated motion; compare the timing curves of the joint angles with the target angle array of the servo motor to determine the first simulation deviation value; and perform iterative optimization of the configured first manipulator simulation model based on the first simulation deviation value to obtain the second manipulator simulation model.

[0093] In one embodiment, the optimization module 30 is further configured to acquire real-time operating data of the physical prototype, input the joint posture data in the real-time operating data into the smooth trajectory generation function to obtain a second driving instruction; drive the second manipulator simulation model to perform simulated motion based on the second driving instruction, and collect the simulation output during the simulation motion process; and perform simulation optimization on the second manipulator simulation model according to the real-time operating data and the simulation output to obtain a target manipulator simulation model.

[0094] In one embodiment, the optimization module 30 is further configured to compare and calculate the real-time running data and the simulation output to obtain a second simulation deviation value; and to correct and optimize the hardware offset matrix in the second manipulator simulation model based on the second simulation deviation value to obtain the target manipulator simulation model.

[0095] This application provides an optimization device for a robotic arm simulation model. The optimization device for the robotic arm simulation model includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the optimization method for the robotic arm simulation model in the first embodiment described above.

[0096] The following is for reference. Figure 7The diagram illustrates a structural schematic of an optimization device suitable for implementing the robotic arm simulation model of the embodiments of this application. The optimization device for the robotic arm simulation model in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), tablets, PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The optimized device of the robotic arm simulation model shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0097] like Figure 7 As shown, the optimization device for the robot simulation model may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the optimization device for the robot simulation model. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the optimization device of the robot simulation model to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows an optimization device for a robot simulation model with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.

[0098] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0099] The robot simulation model optimization device provided in this application adopts the robot simulation model optimization method in the above embodiments, which can solve the technical problem that current robot simulation models do not consider the poor smoothness of the movement process due to the susceptibility of the robot to disturbances during movement. Compared with the prior art, the beneficial effects of the robot simulation model optimization device provided in this application are the same as the beneficial effects of the robot simulation model optimization method provided in the above embodiments, and other technical features in the robot simulation model optimization device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0100] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the optimization method of the robotic arm simulation model in the above embodiments.

[0103] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0104] The aforementioned computer-readable storage medium may be included in the optimization device of the robot simulation model; or it may exist independently and not be assembled into the optimization device of the robot simulation model.

[0105] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the optimization device of the robotic arm simulation model, the optimization device of the robotic arm simulation model causes the following: it acquires the model file of the three-dimensional model of the robotic arm and a smooth trajectory generation function; it combines the model file with a physical behavior model to construct a first robotic arm simulation model; it performs simulation optimization on the first robotic arm simulation model based on the smooth trajectory generation function to obtain a second robotic arm simulation model; it acquires real-time operating data of a physical prototype, and performs simulation optimization on the second robotic arm simulation model based on the real-time operating data and the smooth trajectory generation function to obtain a target robotic arm simulation model.

[0106] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0108] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0109] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the optimization method of the above-described robotic arm simulation model. This solves the technical problem that current robotic arm simulation models do not consider the susceptibility of the robotic arm to disturbances during movement, resulting in poor smoothness of the movement process. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the optimization method of the robotic arm simulation model provided in the above embodiments, and will not be elaborated upon here.

[0110] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the optimization method for the robotic arm simulation model as described above.

[0111] The computer program product provided in this application can solve the technical problem that current robot simulation models do not consider the susceptibility of the robot to disturbances during movement, resulting in poor smoothness of the movement process. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the optimization method for the robot simulation model provided in the above embodiments, and will not be repeated here.

[0112] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. An optimization method for a robotic arm simulation model, characterized in that, The optimization method for the robotic arm simulation model includes: Obtain the model file of the robot's 3D model and the function to generate a smooth trajectory; The model file and the physical behavior model are combined to construct the first robotic arm simulation model; The simulation model of the first robotic arm is optimized based on the smooth trajectory generation function to obtain the simulation model of the second robotic arm. The real-time operating data of the physical prototype is obtained, and the second manipulator simulation model is optimized based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

2. The method as described in claim 1, characterized in that, The step of optimizing the first robotic arm simulation model based on the smooth trajectory generation function to obtain the second robotic arm simulation model includes: Configure simulation parameters for the first robotic arm simulation model to obtain the configured first robotic arm simulation model; Obtain the global simulation time; The global simulation time is input into the configured first robotic arm simulation model to obtain local parameters; Based on the local parameters and the smooth trajectory generation function, the configured first robot simulation model is optimized to obtain the second robot simulation model.

3. The method as described in claim 2, characterized in that, The step of optimizing the configured first robotic arm simulation model based on the local parameters and the smooth trajectory generation function to obtain the second robotic arm simulation model includes: The local parameters are input into the smooth trajectory generation function to obtain the servo target angle array; The target angle array of the servo motor is filtered to obtain the actual drive angle; The first driving command is determined based on the actual driving angle. Based on the first driving command, the configured first robot simulation model is driven under different working conditions to perform simulation optimization, thereby obtaining the second robot simulation model.

4. The method as described in claim 3, characterized in that, The local parameters include at least the real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joint, and the target angle of the robot arm joint. The step of inputting the local parameters into the smooth trajectory generation function to obtain the servo motor target angle array includes: The real-time simulation time, the start time of the robot arm's action, the duration of the preset gesture action, the starting angle of the robot arm joint, and the target angle of the robot arm joint are input into the smooth trajectory generation function to obtain a smooth trajectory. Generate a target angle array for the servo motor based on the smooth trajectory.

5. The method as described in claim 3, characterized in that, The step of optimizing the configured first robotic arm simulation model based on the first driving command under different working conditions to obtain the second robotic arm simulation model includes: Based on the first driving command, the configured first manipulator simulation model is driven to perform simulated motion under different working conditions, and the timing curves of each joint angle during the simulated motion are obtained. The joint angle timing curve and the servo motor target angle array are compared to determine the first simulation deviation value; Based on the first simulation deviation value, the configured first robot simulation model is iteratively optimized to obtain the second robot simulation model.

6. The method as described in claim 1, characterized in that, The steps of acquiring real-time operating data of the physical prototype, and optimizing the second robotic arm simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target robotic arm simulation model include: The real-time operating data of the physical prototype is obtained, and the joint posture data in the real-time operating data is input into the smooth trajectory generation function to obtain the second driving command; The second manipulator simulation model is driven by the second driving command to perform simulated motion, and the simulation output during the simulated motion process is collected. The second robotic arm simulation model is optimized based on the real-time operating data and the simulation output to obtain the target robotic arm simulation model.

7. The method as described in claim 6, characterized in that, The step of optimizing the second robotic arm simulation model based on the real-time operating data and the simulation output to obtain the target robotic arm simulation model includes: The real-time running data and the simulation output are compared and calculated to obtain the second simulation deviation value; Based on the second simulation deviation value, the hardware offset matrix in the second robot simulation model is corrected and optimized to obtain the target robot simulation model.

8. An optimization device for a robotic arm simulation model, characterized in that, The optimization device for the robotic arm simulation model includes: The acquisition module is used to acquire the model file of the robot's 3D model and the smooth trajectory generation function; The processing module is used to combine the model file and the physical behavior model to construct the first robot simulation model; The optimization module is used to perform simulation optimization on the first manipulator simulation model based on the smooth trajectory generation function to obtain the second manipulator simulation model; The optimization module is also used to acquire real-time operating data of the physical prototype, and to perform simulation optimization on the second manipulator simulation model based on the real-time operating data and the smooth trajectory generation function to obtain the target manipulator simulation model.

9. An optimization device for a robotic arm simulation model, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the optimization method for the robot simulation model as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the optimization method for the robot simulation model as described in any one of claims 1 to 7.

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