Motor parameter self-adaptive adjusting method and system based on intelligent mechanical arm

Abstract

The application discloses a motor parameter self-adaptive adjusting method and system based on an intelligent mechanical arm and belongs to the technical field of mechanical arms. The application obtains joint structure data of a mechanical arm and a production task for controlling operation of the mechanical arm, generates a virtual operation model of the mechanical arm according to the joint structure data, and the joint structure data comprises joint information of the mechanical arm and motor information of a joint motor for controlling the joint. Motor virtual operation data of a virtual motor in the virtual operation model is determined according to the production task and the virtual operation model, and motor operation characteristics of the motor virtual operation data are extracted. Performance control data corresponding to the joint motor is determined according to the motor operation characteristics, and the beneficial effect of reducing power resource consumption of the mechanical arm in a task execution process is realized.

Description

Technical Field

[0001] This invention relates to the field of robotic arm technology, and in particular to a method and system for adaptive adjustment of motor parameters based on an intelligent robotic arm. Background Technology

[0002] With the increasing prevalence of robotic arms, they are undertaking more and more tasks in the production process. Common tasks include assembly, welding, painting, and quality inspection. Currently, for general-purpose robotic arms, only the end effector needs to be replaced, enabling various types of work. However, for multi-joint robotic arms, the number of joints required for different types of tasks varies. In practical applications, compared to robotic arms that are specifically designed to handle multiple tasks, there are often joints that are not used during operation. Therefore, the operating efficiency of general-purpose robotic arms is relatively low.

[0003] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this invention is to provide a method and system for adaptive adjustment of motor parameters based on an intelligent robotic arm, which aims to reduce the power consumption of the robotic arm during task execution.

[0005] To achieve the above objectives, the present invention provides a method for adaptive adjustment of motor parameters based on an intelligent robotic arm, the method comprising the following steps: The production task involves acquiring joint structure data of a robotic arm and controlling its operation, and generating a virtual operating model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors that control the joints. Based on the production task and the virtual operation model, determine the virtual motor operation data of the virtual motor in the virtual operation model, and extract the motor operation characteristics of the virtual motor operation data; The performance control data corresponding to the joint motor is determined based on the motor's operating characteristics.

[0006] Optionally, the production task includes: task type, task quantity, and utilization rate, and the step of determining the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model includes: The virtual operation model is controlled to simulate the execution of the first virtual action group corresponding to the task type, thereby obtaining virtual operation data; The motor performance change curve of the virtual motor is generated based on the virtual operation data, the utilization rate, the workload, and the motor parameters. The motor operation control data of the virtual motor is determined based on the virtual operation data, the task volume, and the motor performance change curve; The virtual operating data of the motor is determined based on the motor performance change curve and the motor operation control data.

[0007] Optionally, before the step of controlling the virtual operation model to simulate the execution of the first virtual action group corresponding to the task type to obtain virtual operation data, the method further includes: Based on the task type and a preset action database, at least one target action is determined to match, wherein the preset action database includes multiple operating actions of the robotic arm; The first virtual action group is generated based on at least one of the target actions.

[0008] Optionally, the step of determining the motor operation control data of the virtual motor based on the virtual operation data, the task volume, and the motor performance change curve includes: Determine whether the motor performance change curve meets production requirements; When the motor performance change curve meets the production requirements, the motor operation control data corresponding to the motor performance change curve is used as the motor operation control data. When the motor performance change curve does not meet production requirements, the steps are as follows: generate an adjustment plan based on the virtual operation data and the workload, update the motor performance change curve according to the adjustment plan, and determine whether the motor performance change curve meets production requirements.

[0009] Optionally, the performance control data includes current control data, and the motor operating characteristics include a first duration of longest continuous operation and a second duration of longest continuous standby. The step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the first duration is greater than or equal to the first preset duration, the first current during the operation of the joint motor is adjusted according to the continuous running time, and the first current is negatively correlated with the continuous running time. When the first duration is less than the first preset duration, the first current during the operation of the joint motor is determined to be the rated current; When the second duration is greater than or equal to the second preset duration, the working mode of the joint motor during the stationary process is determined to be half-working mode; When the second duration is less than the second preset duration, the working mode of the joint motor during the stationary process is determined to be the full-work mode.

[0010] The current in the half-working mode is less than the current in the full-working mode.

[0011] Optionally, the performance control data includes: subdivision number; the motor operating characteristics include: maximum angular displacement and average operating speed; and the step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the maximum angular displacement is less than the preset angular displacement, and the average running speed is less than the preset running speed, the subdivision number is determined to be the first subdivision number; When the maximum angular displacement is less than the preset angular displacement, and the average running speed is greater than or equal to the preset running speed, the subdivision number is determined to be the second subdivision number. When the maximum angular displacement is greater than or equal to the preset angular displacement, the subdivision number is determined based on the average running speed; Wherein, the first subdivision number is greater than the second subdivision number.

[0012] Optionally, the step of generating a virtual operating model of the robotic arm based on the joint structure data includes: Extract the geometric structure data and corresponding physical attributes of the joint information. The physical attributes include: the mass, center of mass position, inertia tensor, joint type, and range of motion of each link. A virtual operating model is generated based on the geometric structure data, corresponding physical properties, and motor information.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a motor parameter adaptive adjustment system based on an intelligent robotic arm, the motor parameter adaptive adjustment system based on an intelligent robotic arm comprising: The acquisition module is used to acquire the joint structure data of the robotic arm and the production task of controlling the operation of the robotic arm, and generate a virtual operation model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors that control the joints. The simulation module is used to determine the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model, and to extract the motor operation characteristics of the virtual motor operation data. The adjustment module is used to determine the performance control data corresponding to the joint motor based on the motor operating characteristics.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a robotic arm, the robotic arm comprising: a memory, a processor, and a motor parameter adaptive adjustment program based on the intelligent robotic arm stored in the memory and executable on the processor, the motor parameter adaptive adjustment program based on the intelligent robotic arm being configured to implement the steps of the motor parameter adaptive adjustment method based on the intelligent robotic arm as described above.

[0015] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a motor parameter adaptive adjustment program based on an intelligent robotic arm, wherein when the motor parameter adaptive adjustment program based on an intelligent robotic arm is executed by a processor, the program implements the steps of the motor parameter adaptive adjustment method based on an intelligent robotic arm as described above.

[0016] This invention proposes an adaptive adjustment method for motor parameters based on an intelligent robotic arm. This method acquires joint structure data of the robotic arm and the production task controlling its operation. A virtual operation model of the robotic arm is generated based on the joint structure data. Virtual operation data of the virtual motors in the virtual operation model is determined based on the production task and the virtual operation model. This simulates the moving and stationary joints of the robotic arm when performing different tasks, thereby distinguishing different motor operation characteristics before the robotic arm starts operating. The method also extracts the motor operation characteristics from the virtual operation data and determines the performance control data corresponding to the joint motors based on these characteristics. This allows for the adjustment of parameters affecting motor operation, such as the current subdivision, according to the motor operation characteristics, achieving adaptive parameter adjustment. This reduces the power consumption of a general-purpose robotic arm when performing simple tasks and improves its stability when performing complex tasks. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the robotic arm in the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the adaptive adjustment method for motor parameters based on an intelligent robotic arm according to the present invention. Figure 3 This is a flowchart illustrating the second embodiment of the adaptive adjustment method for motor parameters based on an intelligent robotic arm according to the present invention. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] Reference Figure 1 , Figure 1 This is a schematic diagram of the robotic arm structure in the hardware operating environment of the embodiment of the present invention.

[0020] like Figure 1 As shown, the robotic arm may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, an interaction device 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The interaction device 1003 may include a display screen or an input unit such as a keyboard. Optionally, the interaction device 1003 may also be connected to the communication bus via standard wired or wireless interfaces. The network interface 1004 may optionally include standard wired or wireless interfaces (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a stable non-volatile memory (NVM), such as a disk drive. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0021] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the robotic arm and may include more or fewer parts than shown, or combine certain parts, or have different arrangements of parts.

[0022] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a motor parameter adaptive adjustment program based on an intelligent robotic arm.

[0023] exist Figure 1 In the robotic arm shown, the network interface 1004 is mainly used for data communication with other devices; the interactive device 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the robotic arm of the present invention can be set in the robotic arm. The robotic arm calls the motor parameter adaptive adjustment program based on the intelligent robotic arm stored in the memory 1005 through the processor 1001, and executes the motor parameter adaptive adjustment method based on the intelligent robotic arm provided in the embodiment of the present invention.

[0024] This invention provides a method for adaptive adjustment of motor parameters based on an intelligent robotic arm, referring to... Figure 2 , Figure 2This is a flowchart illustrating the first embodiment of a method for adaptive adjustment of motor parameters based on an intelligent robotic arm according to the present invention.

[0025] In this embodiment, the adaptive adjustment method for motor parameters based on the intelligent robotic arm includes: Step S1: Obtain the joint structure data of the robotic arm and the production task for controlling the operation of the robotic arm, and generate a virtual operation model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors controlling the joints. In this embodiment, the robotic arm can be used for grasping, spraying, disassembling, etc. It receives production scheduling information from a server, which determines the operation of the production line. This scheduling information is generally automatically generated based on orders or determined by scheduling personnel. After determining the production scheduling information, the production task can be determined. In this embodiment, the production task can determine the steps the robotic arm needs to perform on the produced items. Generally, the products are uniformly transported to the corresponding workstation of the robotic arm through a production line control. The robotic arm can be equipped with an image acquisition device and other types of recognition devices to identify and process the corresponding products. In this embodiment, a virtual operating model is constructed using the known joint and motor information of the robotic arm. Preferably, to improve the completeness of the virtual operating model, in addition to the joint information, it can also include the structural information of the production line and the corresponding operating information of the production line. Commonly, SiemensPlant Simulation can be used to construct the virtual operating model, supporting the import and conversion of various CAD files, and performing simulation and display functions, as well as deep data access for simulation.

[0026] Step S2: Determine the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model, and extract the motor operation characteristics of the virtual motor operation data; In this embodiment, the production task is simulated by a robotic arm in the virtual operation model. The virtual operation data of the motors is determined by recording the operation of the virtual motors within the virtual robotic arm. It should be noted that this virtual motor operation data refers to the data of each running virtual motor on the virtual robotic arm. Generally, the operation of the motors during the completion of the production task can be simulated using at least one production method. For a specific production task, some motors on the robotic arm are running at all times, while others are stationary, but still need to maintain a certain torque so that their joints remain relatively stationary while other parts of the robotic arm are running. The motor operation characteristics here may include: the total running time of each motor in completing the production task, the running frequency, the longest continuous running time, and the longest continuous stationary time.

[0027] Step S3: Determine the performance control data corresponding to the joint motor based on the motor operating characteristics.

[0028] In this embodiment, the joint motor is generally a stepper motor or a servo motor. Specifically, this embodiment uses a stepper motor as an example. Stepper motors typically control rotation and stillness by switching between high and low voltage levels, i.e., high and low voltage levels control the motor respectively. However, for a robotic arm that includes multiple motors, by identifying the motor's operating characteristics and determining its state during production, reducing the current of some motors can save power and reduce heat generation. Furthermore, in specific robotic arm operations, briefly increasing the actual current above the rated current allows the motor to output higher torque than rated, thereby improving the handling of heavy-load products. In addition, adjusting the microstepping, i.e., the micro-stepping mode, can effectively reduce vibration during motor operation, thus maintaining stability and improving the robotic arm's accuracy. The above data does not change the data controlling the robotic arm's operation, but it can improve the quality of the robotic arm's production tasks.

[0029] In this embodiment, by acquiring the joint structure data of the robotic arm and the production task controlling the operation of the robotic arm, and generating a virtual operation model of the robotic arm based on the joint structure data, the virtual operation data of the virtual motor in the virtual operation model is determined based on the production task and the virtual operation model. This simulates the joints that the robotic arm needs to move and the joints that are stationary when performing different tasks, thereby distinguishing different motor operation characteristics before the robotic arm starts operating. The motor operation characteristics of the virtual motor operation data are extracted, and the performance control data corresponding to the joint motor is determined based on the motor operation characteristics. This allows for the adjustment of parameters affecting the motor operation effect, such as the current subdivision of the motor, based on the motor operation characteristics, achieving adaptive parameter adjustment. This reduces the power consumption of the general-purpose robotic arm when performing simple tasks and improves the stability of the general-purpose robotic arm when performing complex tasks.

[0030] Furthermore, based on the first embodiment, a second embodiment of the present invention for the adaptive adjustment method of motor parameters of an intelligent robotic arm is proposed. In this embodiment, referring to... Figure 3 The production task includes: task type, task quantity, and utilization rate. The step of determining the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model includes: Step S21: Control the virtual operation model to simulate the execution of the first virtual action group corresponding to the task type to obtain virtual operation data; In this embodiment, when a task type is identified, the first virtual action group to be executed by the virtual robotic arm in the virtual operation model is determined according to the task type. This virtual action group can include combinations of multiple different virtual sub-actions. Preferably, the virtual operation data here is the operation data of a single product completing its processing task. That is, for the virtual operation of the same product, it is simulated only once.

[0031] Step S22: Generate the motor performance change curve of the virtual motor based on the virtual operation data, the utilization rate, the task volume, and the motor parameters; In this embodiment, the production task typically includes a corresponding limited production time. Therefore, the utilization rate can be obtained through the production time and the task volume. The utilization rate reflects the pressure on efficiency during the operation of the robotic arm.

[0032] Preferably, the operation status of a single operation of multiple virtual motors is determined based on the virtual operation data, and the operation time and operation interval of multiple operations of multiple virtual motors are determined based on the utilization rate and workload to obtain the first motor operation control data. Then, the temperature change curve of the virtual motor is generated based on the first motor operation control data and the motor parameters, and the motor performance change curve of the virtual motor is determined based on the temperature change curve.

[0033] The simulation is typically performed in the default mode. In the default mode, the simulated motor continuously executes the virtual running data based on the workload, and no pause time is set for the robotic arm even if the utilization rate is not 100%. In the second simulation mode, one or more pause durations can be set during the continuous execution of the virtual running data by the simulated motor, depending on the utilization rate. The pause duration can be statically set to a fixed value or dynamically set to different values. The motor performance curves corresponding to the simulations in different modes will differ. Furthermore, the motor performance here refers to torque and maximum speed, i.e., maximum operating speed.

[0034] Step S23: Determine the motor operation control data of the virtual motor based on the virtual operation data, the task volume, and the motor performance change curve; In this embodiment, the motor operation control data is the voltage level change data. The motor performance change curve is actually determined based on the first motor operation control data corresponding to the default mode. Therefore, it is necessary to determine whether the current motor performance change curve meets preset performance values ​​at each moment, such as torque and maximum speed.

[0035] When the motor performance change curve meets the preset performance value at each moment, the first motor operation control data is determined as the motor operation control data; When the motor performance change curve fails to meet the preset performance value at any given moment, the first virtual action group is updated, and / or the default mode is adjusted to the second mode, the number of returns is recorded, and the process returns to step S21. In this embodiment, the second mode is used for simulation to obtain the corresponding second motor operation control data.

[0036] When the number of returns exceeds a preset value, the second motor operation control data corresponding to the motor performance change curve is used as the motor operation control data, and the running time corresponding to the failure to meet the preset performance value is marked.

[0037] Preferably, there is more than one second mode. When the second mode has already been simulated, the step of adjusting the default mode to the second mode is modified to: updating the second mode; different second modes have different pause durations and / or the number of pause durations.

[0038] Step S24: Determine the virtual operation data of the motor based on the motor performance change curve and the motor operation control data.

[0039] In this step, the motor performance change curve and the motor operation control data are combined to form the motor virtual operation data.

[0040] In this embodiment, virtual operation data is obtained by controlling the virtual operation model to simulate the execution of the first virtual action group corresponding to the task type. Based on the virtual operation data, the utilization rate, the task volume, and the motor parameters, the motor performance change curve of the virtual motor and the motor operation control data of the virtual motor are generated. The motor virtual operation data is determined based on the motor performance change curve and the motor operation control data, thereby improving the accuracy of the motor virtual operation data.

[0041] Furthermore, based on the first or second embodiment, a third embodiment of the present invention for the adaptive adjustment method of motor parameters of an intelligent robotic arm is proposed. Before the step of controlling the virtual operation model to simulate the execution of the first virtual action group corresponding to the task type to obtain virtual operation data, the method further includes: Based on the task type and a preset action database, at least one target action is determined to match, wherein the preset action database includes multiple operating actions of the robotic arm; The first virtual action group is generated based on at least one of the target actions.

[0042] In this embodiment, multiple virtual action groups can be set and saved, wherein the operation actions of the robotic arm are obtained by saving the simulation process of Siemens Plant Simulation, thereby realizing the recording and reuse of the operation actions.

[0043] Furthermore, based on any of the above embodiments, a fourth embodiment of the adaptive adjustment method for motor parameters based on an intelligent robotic arm is proposed, wherein the step of determining the motor operation control data of the virtual motor according to the virtual operation data, the workload, and the motor performance change curve includes: Determine whether the motor performance change curve meets production requirements; When the motor performance change curve meets the production requirements, the motor operation control data corresponding to the motor performance change curve is used as the motor operation control data. When the motor performance change curve does not meet production requirements, the steps are as follows: generate an adjustment plan based on the virtual operation data and the workload, update the motor performance change curve according to the adjustment plan, and determine whether the motor performance change curve meets production requirements.

[0044] In this embodiment, the adjustment scheme is the second mode in the second embodiment. For example, the moment when the performance value of the motor performance change curve is lower than the preset performance value is marked as the target adjustment moment. A pause running time is set at the target adjustment moment. The pause running time is negatively correlated with the utilization rate, that is, the higher the utilization rate, the smaller the set pause running time.

[0045] In other embodiments, due to other limitations in production conditions, there may be only one specific production plan, in which the motor operation control data corresponding to the motor performance change curve is directly used as the motor operation control data.

[0046] In this embodiment, it is determined whether the motor performance change curve meets the production requirements, so that the production plan can be effectively adjusted based on the motor performance change curve, thereby ensuring that the robotic arm can be in relatively good operating conditions and thus improving the stability of operation.

[0047] Furthermore, based on any of the above embodiments, a fifth embodiment of the adaptive adjustment method for motor parameters of an intelligent robotic arm based on the present invention is proposed. The performance control data includes current control data, and the motor operating characteristics include a first duration of longest continuous operation and a second duration of longest continuous stillness. The step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the first duration is greater than or equal to the first preset duration, the first current during the operation of the joint motor is adjusted according to the continuous running time, and the first current is negatively correlated with the continuous running time. When the first duration is less than the first preset duration, the first current during the operation of the joint motor is determined to be the rated current; When the second duration is greater than or equal to the second preset duration, the working mode of the joint motor during the stationary process is determined to be half-working mode; When the second duration is less than the second preset duration, the working mode of the joint motor during the stationary process is determined to be the full-work mode.

[0048] The current in the half-working mode is less than the current in the full-working mode.

[0049] In this embodiment, it should be noted that when the first duration is greater than or equal to the first preset duration, it reflects continuous load operation. In this case, it is necessary to reduce the current during periods of lower load to lower the temperature of the joint motor or slow down the temperature rise. When the first duration is less than the first preset duration, it reflects that the joint motor is not operating under continuous load. Therefore, it can operate at its normal rated current. Furthermore, when a brief, planned peak load demand is identified, if the predicted temperature of the stepper motor is within a safe range, the motor can be temporarily allowed to operate at a peak current. This peak current is greater than the rated current of the stepper motor, thereby improving the instantaneous performance of the robotic arm for a period of time without damaging the motor.

[0050] Optionally, the half-working mode can be half the current of the full-working mode. Preferably, multiple levels are set in a gradient according to different second durations. For example, if the second preset duration is 10 minutes, and the second duration is greater than 20 minutes, a very low current is maintained or only the current is maintained when the robot arm is stationary. The very low current can be one-tenth of the full-working current, and only the encoder is kept running. When the second duration is greater than or equal to 10 minutes but less than 20 minutes, there can be almost no delay in responding to tasks. Furthermore, in the second embodiment, since when the number of returns is greater than a preset value, the second motor operation control data corresponding to the motor performance change curve is used as the motor operation control data, and the running time corresponding to the preset performance value is marked, the current can also be adjusted during the marked running time corresponding to the preset performance value.

[0051] In this embodiment, the first current during the operation of the joint motor is adjusted by using data from the first duration and the second duration, thereby effectively saving power resources while ensuring operational stability during the operation of the robotic arm.

[0052] Furthermore, the performance control data includes: subdivision count; the motor operating characteristics include: maximum angular displacement and average operating speed; and the step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the maximum angular displacement is less than the preset angular displacement, and the average running speed is less than the preset running speed, the subdivision number is determined to be the first subdivision number; When the maximum angular displacement is less than the preset angular displacement, and the average running speed is greater than or equal to the preset running speed, the subdivision number is determined to be the second subdivision number. When the maximum angular displacement is greater than or equal to the preset angular displacement, the subdivision number is determined based on the average running speed; Wherein, the first subdivision number is greater than the second subdivision number.

[0053] In this embodiment, preferably, a multi-level subdivision strategy library is constructed to adjust the subdivision number. When the maximum angular displacement is less than a preset angular displacement and the average running speed is less than a preset running speed, the highest subdivision number is used, such as 32 subdivisions or 64 subdivisions, to achieve smooth and ultra-fine micro-motion control. When the maximum angular displacement is less than the preset angular displacement and the average running speed is greater than or equal to the preset running speed, a medium subdivision number is used, such as 8 subdivisions or 16 subdivisions, to provide a sufficiently high pulse frequency to support fast operation while ensuring a certain level of accuracy. When the maximum angular displacement is greater than or equal to the preset angular displacement, representing the high-speed range, a low subdivision number is used, or no subdivision is performed.

[0054] Furthermore, based on any of the above embodiments, a sixth embodiment of the present invention for the adaptive adjustment method of motor parameters of an intelligent robotic arm is proposed, wherein the step of generating a virtual operating model of the robotic arm based on the joint structure data includes: Extract the geometric structure data and corresponding physical attributes of the joint information. The physical attributes include: the mass, center of mass position, inertia tensor, joint type, and range of motion of each link. A virtual operating model is generated based on the geometric structure data, corresponding physical properties, and motor information.

[0055] In this embodiment, based on geometric structure data and joint connection relationships, and by adding the mass, center of mass position, inertia tensor, joint type, and range of motion of each link, a complete kinematic chain from the base to the end effector of the robotic arm is established, thereby enabling the acquisition of a virtual running model.

[0056] Furthermore, this invention also proposes a motor parameter adaptive adjustment system based on an intelligent robotic arm, the motor parameter adaptive adjustment system based on an intelligent robotic arm comprising: The acquisition module is used to acquire the joint structure data of the robotic arm and the production task of controlling the operation of the robotic arm, and generate a virtual operation model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors that control the joints. The simulation module is used to determine the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model, and to extract the motor operation characteristics of the virtual motor operation data. The adjustment module is used to determine the performance control data corresponding to the joint motor based on the motor operating characteristics.

[0057] Furthermore, this invention also proposes a robotic arm, which includes: a memory, a processor, and a motor parameter adaptive adjustment program based on the intelligent robotic arm stored in the memory and executable on the processor. The motor parameter adaptive adjustment program based on the intelligent robotic arm is configured to implement the steps of the motor parameter adaptive adjustment method based on the intelligent robotic arm described in any of the above claims.

[0058] Furthermore, this embodiment of the invention also proposes a storage medium storing a motor parameter adaptive adjustment program based on an intelligent robotic arm. When the motor parameter adaptive adjustment program based on an intelligent robotic arm is executed by a processor, it implements the steps of the motor parameter adaptive adjustment method based on an intelligent robotic arm as described above.

[0059] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0060] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0061] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0062] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method for adaptive adjustment of motor parameters based on an intelligent robotic arm, characterized in that, The adaptive adjustment method for motor parameters based on the intelligent robotic arm includes the following steps: The production task involves acquiring joint structure data of a robotic arm and controlling its operation, and generating a virtual operating model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors that control the joints. Based on the production task and the virtual operation model, determine the virtual motor operation data of the virtual motor in the virtual operation model, and extract the motor operation characteristics of the virtual motor operation data; The performance control data corresponding to the joint motor are determined based on the motor operating characteristics. The production task includes: task type, task quantity, and utilization rate. The step of determining the virtual motor operation data in the virtual operation model based on the production task and the virtual operation model includes: The virtual operation model is controlled to simulate the execution of the first virtual action group corresponding to the task type, thereby obtaining virtual operation data; The motor performance change curve of the virtual motor is generated based on the virtual operation data, the utilization rate, the workload, and the motor parameters. The motor operation control data of the virtual motor is determined based on the virtual operation data, the task volume, and the motor performance change curve; The virtual operating data of the motor is determined based on the motor performance change curve and the motor operation control data; The performance control data includes current control data. The motor operating characteristics include a first duration of longest continuous operation and a second duration of longest continuous stationary operation. The step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the first duration is greater than or equal to the first preset duration, the first current during the operation of the joint motor is adjusted according to the continuous running time, and the first current is negatively correlated with the continuous running time. When the first duration is less than the first preset duration, the first current during the operation of the joint motor is determined to be the rated current; When the second duration is greater than or equal to the second preset duration, the working mode of the joint motor during the stationary process is determined to be half-working mode; When the second duration is less than the second preset duration, the working mode of the joint motor during the stationary process is determined to be the full-work mode; The current in the half-working mode is less than the current in the full-working mode.

2. The adaptive adjustment method for motor parameters based on an intelligent robotic arm as described in claim 1, characterized in that, Before the step of controlling the virtual operation model to simulate the execution of the first virtual action group corresponding to the task type and obtaining virtual operation data, the method further includes: Based on the task type and a preset action database, at least one target action is determined to match, wherein the preset action database includes multiple operating actions of the robotic arm; The first virtual action group is generated based on at least one of the target actions.

3. The adaptive adjustment method for motor parameters based on an intelligent robotic arm as described in claim 1, characterized in that, The step of determining the motor operation control data of the virtual motor based on the virtual operation data, the workload, and the motor performance change curve includes: Determine whether the motor performance change curve meets production requirements; When the motor performance change curve meets the production requirements, the motor operation control data corresponding to the motor performance change curve shall be used as the motor operation control data. When the motor performance change curve does not meet production requirements, the steps are as follows: generate an adjustment plan based on the virtual operation data and the workload, update the motor performance change curve according to the adjustment plan, and determine whether the motor performance change curve meets production requirements.

4. The adaptive adjustment method for motor parameters based on an intelligent robotic arm as described in claim 1, characterized in that, The performance control data includes: subdivision count; the motor operating characteristics include: maximum angular displacement and average operating speed; the step of determining the performance control data corresponding to the joint motor based on the motor operating characteristics includes: When the maximum angular displacement is less than the preset angular displacement, and the average running speed is less than the preset running speed, the subdivision number is determined to be the first subdivision number; When the maximum angular displacement is less than the preset angular displacement, and the average running speed is greater than or equal to the preset running speed, the subdivision number is determined to be the second subdivision number; When the maximum angular displacement is greater than or equal to the preset angular displacement, the subdivision number is determined based on the average running speed; Wherein, the first subdivision number is greater than the second subdivision number.

5. The adaptive adjustment method for motor parameters based on an intelligent robotic arm as described in any one of claims 1 to 4, characterized in that, The step of generating a virtual operating model of the robotic arm based on the joint structure data includes: Extract the geometric structure data and corresponding physical attributes of the joint information. The physical attributes include: the mass, center of mass position, inertia tensor, joint type, and range of motion of each link. A virtual operating model is generated based on the geometric structure data, corresponding physical properties, and motor information.

6. A motor parameter adaptive adjustment system based on an intelligent robotic arm, characterized in that, To implement the adaptive adjustment method for motor parameters based on an intelligent robotic arm as described in claim 1, the adaptive adjustment system for motor parameters based on an intelligent robotic arm comprises: The acquisition module is used to acquire the joint structure data of the robotic arm and the production task of controlling the operation of the robotic arm, and generate a virtual operation model of the robotic arm based on the joint structure data. The joint structure data includes: joint information of the robotic arm and motor information of the joint motors that control the joints. The simulation module is used to determine the virtual motor operation data of the virtual motor in the virtual operation model based on the production task and the virtual operation model, and to extract the motor operation characteristics of the virtual motor operation data. The adjustment module is used to determine the performance control data corresponding to the joint motor based on the motor operating characteristics.

7. A robotic arm, characterized in that, The robotic arm includes: a memory, a processor, and a motor parameter adaptive adjustment program based on the intelligent robotic arm stored in the memory and executable on the processor, wherein the motor parameter adaptive adjustment program based on the intelligent robotic arm is configured to implement the steps of the motor parameter adaptive adjustment method based on the intelligent robotic arm as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a motor parameter adaptive adjustment program based on an intelligent robotic arm. When the processor executes the motor parameter adaptive adjustment program based on the intelligent robotic arm, it implements the steps of the motor parameter adaptive adjustment method based on an intelligent robotic arm as described in any one of claims 1 to 5.

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