Method and system for measuring and calculating energy consumption of multi-degree-of-freedom mechanical arm
By establishing a motion model and energy consumption model of a multi-degree of freedom robot arm, combining electrical loss power consumption and friction power consumption, the problem of ignoring electrical loss and friction power consumption in the existing models is solved, and more accurate and comprehensive energy consumption calculation is achieved, supporting robotic arm energy consumption optimization.
Patent Information
- Application Number
- CN202311779350.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-22
- Publication Date
- 2025-06-24
AI Technical Summary
Existing robotic arm energy consumption models ignore electrical loss power consumption and friction power consumption, resulting in high calculation errors in some cases.
A multi-degree of freedom robotic arm energy consumption calculation method is proposed. By obtaining the structure, degree of freedom and energy consumption data of the robotic arm, a motion model and energy consumption model are established, and energy consumption calculation is optimized on this basis. The specific steps include establishing a link coordinate system, motion equation, dynamic equation and energy consumption formula, combining experimental data and simulation verification.
This method can more accurately estimate the energy consumption of the robotic arm in different working states, comprehensively consider the power consumption, power loss and friction power consumption, and provide more comprehensive energy consumption information to help users optimize energy consumption.
Smart Images
Figure CN120197463A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of data processing, and particularly relates to a method and system for measuring the energy consumption of a multi-degree-of-freedom robotic arm. Background Art
[0002] A robotic arm is an automated device widely used in modern manufacturing, with high flexibility and precision. However, with the continuous development and application of robotic arms, their energy consumption issues have increasingly attracted attention. The energy consumption of a robotic arm not only directly affects production costs but may also have a negative impact on the environment. Therefore, accurately measuring and optimizing the energy consumption of a robotic arm is of great significance for improving production efficiency, reducing energy consumption, protecting the environment, etc.
[0003] Currently, the main methods for measuring the energy consumption of a robotic arm include the following:
[0004] Energy consumption measurement based on workload: By measuring the load size of the robotic arm during operation and combining the proportional relationship between the load and energy consumption, the energy consumption of the robotic arm is calculated. This method is simple and easy to implement, but the accuracy is relatively low because the actual energy consumption is affected by various factors, such as the structure, materials, and manufacturing process of the robotic arm.
[0005] Energy consumption measurement based on experimental data: By obtaining the energy consumption data of the robotic arm under different working conditions through experiments, and then establishing an energy consumption model based on these data. This method has high accuracy, but requires a large amount of experimental data and a complex modeling process.
[0006] Energy consumption measurement based on machine learning: Using machine learning algorithms to learn and train a large amount of data to establish an energy consumption prediction model. This method has high prediction accuracy and flexibility, but requires a large amount of computing resources and time.
[0007] However, the above methods all have certain limitations in the process of measuring energy consumption. For example, the measurement method based on workload ignores the influence of other factors; the measurement method based on experimental data requires a large amount of experimental data and a modeling process; the measurement method based on machine learning requires a large amount of computing resources and time. Therefore, how to accurately and efficiently measure the energy consumption of a robotic arm remains an urgent problem to be solved.
[0008] To solve the above problems, an energy consumption model of a robotic arm has been proposed in the existing technology. However, it usually only considers the work power consumption of the robotic arm, that is, the energy consumed by the robotic arm when performing tasks. In a conventional robotic arm energy consumption model, researchers only take the sum of the squares of the torques of each joint of the robotic arm, that is, the energy consumed to overcome its own gravity, as well as the Coriolis force and centrifugal force, and the energy consumed to move the object carried by the end structure of the robotic arm as the method for calculating the energy consumption of the robotic arm. This model may have a high error in some cases because it ignores the electrical loss power consumption and frictional power consumption of the robotic arm. The robotic arm still consumes energy to maintain its posture due to gravity when it is not performing tasks. In addition, the movement of the robotic arm also faces frictional resistance, which will also lead to an increase in energy consumption.
[0009] To overcome these defects, this application proposes a method and system for calculating the energy consumption of a robotic arm with multiple degrees of freedom, further improving the accuracy and robustness of the calculation. Summary of the Invention
[0010] The purpose of this application is to provide a method and system for calculating the energy consumption of a robotic arm with multiple degrees of freedom, aiming to solve the problem that the existing robotic arm energy consumption model ignores the electrical loss power consumption and frictional power consumption.
[0011] To achieve the above purpose, this application provides the following technical solutions:
[0012] This application provides a method for calculating the energy consumption of a robotic arm with multiple degrees of freedom, including:
[0013] Obtain the structure, degrees of freedom, and energy consumption data of the robotic arm;
[0014] Establish a motion model of the robotic arm according to the structure and degrees of freedom of the robotic arm;
[0015] Establish an energy consumption model of the robotic arm based on the energy consumption data;
[0016] After optimizing the energy consumption model, calculate the energy consumption of the robotic arm.
[0017] Furthermore, in the step of establishing a motion model of the robotic arm according to the structure and degrees of freedom of the robotic arm, it specifically includes the following steps:
[0018] Establish a link coordinate system: Establish a link coordinate system on each joint of the robotic arm to obtain the positions and postures of the joints of the robotic arm; the origin of the link coordinate system usually coincides with the rotation center of the joint, and the direction of the coordinate axis is the same as the rotation direction of the joint;
[0019] Establish a motion equation: According to the link coordinate system and the structure of the robotic arm, establish a motion equation between each joint; the motion equation is represented by a rotation matrix or a homogeneous transformation matrix;
[0020] Solve the motion equation: By solving the motion equation, obtain the position and orientation of the end effector of the robotic arm in the global coordinate system;
[0021] Verify the motion model: Verify the accuracy and reliability of the motion model through experiments or simulations, and evaluate and improve it based on experimental data or simulation results.
[0022] Furthermore, in the step of establishing the energy consumption model of the robotic arm based on the energy consumption data, the following specific steps are included:
[0023] Obtain the energy consumption data of the robotic arm;
[0024] Establish the dynamic equation of the joint friction force of the robotic arm;
[0025] Calculate the friction joint torque vector through the Stribeck model;
[0026] Obtain the energy consumption formula according to the torque and electrical parameters of the joint servo motor.
[0027] Furthermore, in the step of establishing the dynamic equation of the joint friction force of the robotic arm, the following specific steps are included:
[0028] Establish the dynamic equation including the joint friction force of the robotic arm, and the calculation formula is:
[0029]
[0030] where τ is the position vector of the robot joint, τ is the joint torque vector, M(q) is the inertia matrix of the robot arm, is the velocity term matrix related to centrifugal force and Coriolis force, and G(q) is the gravity term.
[0031] Furthermore, in the step of calculating the friction joint torque vector through the Stribeck model, the following specific steps are included:
[0032] The friction joint torque vector τ f The calculation formula is:
[0033]
[0034] where is the joint angular velocity, f c is the Coulomb friction coefficient, f s is the maximum static friction coefficient, v s is the velocity coefficient of the Stribeck model, and ζ is a constant.
[0035] Furthermore, in the step of obtaining the energy consumption formula according to the torque and electrical parameters of the joint servo motor, the following specific steps are included:
[0036] Obtain the torque and electrical parameters of the servo motors of each joint of the robotic arm, and the reduction gear parameters of the robotic arm joints, so as to obtain the final energy consumption formula:
[0037]
[0038] Among them, P W 、P f 、P H are the work power, friction work power, and power consumption of the robotic arm respectively; j is each joint of the corresponding robotic arm; i is the transmission ratio of the reduction gear; η is the transmission efficiency; I q is the cross-axis stator current of the servo motor; R s is the stator resistance; K t is the motor torque constant.
[0039] This application provides a multi-degree-of-freedom robotic arm energy consumption measurement system, including:
[0040] An acquisition module: acquire the structure, degrees of freedom, and energy consumption data of the robotic arm;
[0041] A model establishment module: establish a motion model of the robotic arm according to the structure and degrees of freedom of the robotic arm; establish an energy consumption model of the robotic arm based on the energy consumption data;
[0042] A measurement module: after optimizing the energy consumption model, measure the energy consumption of the robotic arm.
[0043] This application provides a device, which includes a processor and a memory coupled to the processor. Among them, the memory stores program instructions for implementing a multi-degree-of-freedom robotic arm energy consumption measurement method; the processor is used to execute the program instructions stored in the memory to implement multi-degree-of-freedom robotic arm energy consumption measurement.
[0044] This application provides a storage medium, which stores program instructions that can be run by a processor, and the program instructions are used to execute a multi-degree-of-freedom robotic arm energy consumption measurement method.
[0045] This application provides a multi-degree-of-freedom robotic arm energy consumption measurement method and system, which has the following beneficial effects:
[0046] (1) Compared with the existing method that only considers work power consumption, the technical solution of this application comprehensively considers three main power consumption items: work power consumption, power loss power consumption, and friction power consumption. Therefore, it can more accurately estimate the energy consumption of the robotic arm under different working conditions, provide more accurate energy consumption data for users and system operators, and help them better evaluate the energy consumption of the robotic arm.
[0047] (2) Since the new energy consumption calculation method comprehensively considers more energy consumption items, including power loss and friction power consumption, it provides more comprehensive energy consumption information, enabling users to understand the energy consumption characteristics of the robotic arm more comprehensively and providing a more solid basis for energy consumption optimization and equipment usage decisions.
[0048] (3) By accurately measuring the energy consumption of the robotic arm under different tasks, the technical solution of this application can provide valuable guidance for energy-saving optimization. System operators can reasonably plan the work tasks of the robotic arm based on the energy consumption data and optimize the control strategy, thereby reducing the energy consumption of the robotic arm.
[0049] (4) The technical solution of this application provides a more accurate and comprehensive method for measuring the energy consumption of the robotic arm, which helps to improve energy utilization efficiency, reduce energy waste, and contribute to sustainable development.
[0050] (5) The comprehensiveness and accuracy of the new energy consumption calculation method proposed in this application endow it with great potential for application expansion. In addition to robotic arms, it can also be applied to other automated equipment and industrial robots, providing technical support for energy consumption measurement and optimization in a wider range of fields. BRIEF DESCRIPTION OF THE DRAWINGS
[0051] Figure 1 It is a schematic flowchart of a method for measuring the energy consumption of a multi-degree-of-freedom robotic arm according to Embodiment 1 of this application;
[0052] Figure 2 It is a schematic structural diagram of a system for measuring the energy consumption of a multi-degree-of-freedom robotic arm according to Embodiment 2 of this application;
[0053] Figure 3 It is a schematic structural diagram of the equipment according to Embodiment 3 of this application;
[0054] Figure 4 It is a schematic structural diagram of the storage medium according to Embodiment 4 of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0055] It should be understood that the specific embodiments described herein are only used to explain this application and are not used to limit this application.
[0056] Next, the technical solutions in the embodiments of this application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application.
[0057] Embodiment 1
[0058] Please refer to Figure 1, is a schematic flow chart of a method for calculating the energy consumption of a multi-degree-of-freedom robotic arm according to Embodiment 1 of the present application; the steps include:
[0059] S1: Obtain the structure, degrees of freedom, and energy consumption data of the robotic arm.
[0060] In this embodiment, the structure of the robotic arm generally includes parts such as a base, linkages, joints, and an end effector. The base is the foundation of the robotic arm, the linkages connect the joints, and the joints achieve the movement of the robotic arm through rotation or translation. The end effector is the gripper or tool of the robotic arm, used to perform various tasks.
[0061] The degrees of freedom of the robotic arm refer to the number of its independent movements. A typical robotic arm usually has three degrees of freedom, including the movement and rotation of the base in a plane, and the rotation between linkages. These degrees of freedom allow the robotic arm to perform various complex movements and operations.
[0062] S2: Establish a motion model of the robotic arm according to the structure and degrees of freedom of the robotic arm.
[0063] In this embodiment, establish a link coordinate system: establish a link coordinate system on each joint of the robotic arm to obtain the positions and postures of the joints of the robotic arm; the origin of the link coordinate system usually coincides with the rotation center of the joint, and the direction of the coordinate axis is consistent with the rotation direction of the joint;
[0064] Establish a motion equation: according to the link coordinate system and the structure of the robotic arm, establish the motion equations between the joints; the motion equations are represented by a rotation matrix or a homogeneous transformation matrix;
[0065] Solve the motion equation: by solving the motion equation, obtain the position and posture of the end effector of the robotic arm in the global coordinate system;
[0066] Verify the motion model: verify the accuracy and reliability of the motion model through experiments or simulations, and evaluate and improve it through experimental data or simulation results.
[0067] S3: Establish an energy consumption model of the robotic arm based on the energy consumption data.
[0068] In this embodiment, in the step of establishing an energy consumption model of the robotic arm based on the energy consumption data, it specifically includes steps S31 to S34. The implementation method of this step is described in detail below.
[0069] S31: Obtain the energy consumption data of the robotic arm.
[0070] Preprocess the energy consumption data:
[0071] Data acquisition: Use appropriate measurement devices and methods, such as ammeters, voltmeters, pressure sensors, etc., to collect the energy consumption data during the operation of the robotic arm.
[0072] Data cleaning: Clean the collected data to remove outliers, missing values, and duplicate values. This can be done by writing cleaning programs or using statistical methods.
[0073] Data standardization: Standardize the data obtained from different measurement devices or methods to ensure that they are compared and analyzed under the same dimension or unit.
[0074] Data transformation: According to the needs of analysis, transform the data, such as logarithmic transformation, smoothing, etc., to reduce the noise and fluctuations in the data, and improve the stability and analyzability of the data.
[0075] By preprocessing the energy consumption data, more accurate and reliable energy consumption data can be obtained, providing basic data for subsequent energy consumption analysis and calculation.
[0076] S32: Establish the dynamic equation of the robotic arm joint friction force.
[0077] Establish the dynamic equation including the robotic arm joint friction force, and the calculation formula is:
[0078]
[0079] where τ is the position vector of the robot joint, τ is the joint torque vector, M(q) is the inertia matrix of the robot arm, is the velocity term matrix related to the centrifugal force and Coriolis force, and G(q) is the gravity term.
[0080] Precise control: By establishing the dynamic equation, the change of the friction force of the robotic arm joint during the movement can be accurately described. This makes the control of the robotic arm more precise, reduces errors, and improves the motion accuracy.
[0081] The dynamic equation is used to optimize the motion trajectory of the robotic arm. Considering the influence of the joint friction force on the motion, a more energy-efficient and efficient trajectory planning method is selected. It helps to reduce energy consumption and improve the working efficiency of the robotic arm. At the same time, it can evaluate the friction force performance of the robotic arm joint under different conditions, which helps to understand the performance of the joint and provides a basis for the maintenance, replacement, or improvement of the joint. By compensating and controlling the joint friction force, the stability and anti-interference ability of the robotic arm can be improved.
[0082] S33: Calculate the friction joint torque vector through the Stribeck model.
[0083] The friction joint torque vector τ f The calculation formula is:
[0084]
[0085] where is the joint angular velocity, and f c is the Coulomb friction coefficient, and f s is the maximum static friction coefficient, and v s is the velocity coefficient of the Stribeck model, and ζ is a constant.
[0086] The frictional joint torque vector is an important parameter for describing the frictional torque received by a joint during movement. By calculating the frictional joint torque vector, the magnitude and direction of the frictional torque of the joint under different motion states can be understood. At the same time, it can also provide a reference for the control of the joint. By adjusting the motion parameters of the joint or applying appropriate control forces, the influence of the frictional torque can be reduced, and the stability and efficiency of the joint can be improved.
[0087] S34: Obtain the energy consumption formula based on the torque and electrical parameters of the joint servo motor.
[0088] Obtain the torque and electrical parameters of the servo motor of each joint of the robotic arm and the reduction gear parameters of the robotic arm joints, and the final energy consumption formula can be obtained:
[0089]
[0090] where P W 、P f 、P H are the work power of the robotic arm, the frictional work power, and the power consumption of the electrical energy respectively; j is each joint of the corresponding robotic arm; i is the transmission ratio of the reduction gear; η is the transmission efficiency; I q is the quadrature axis stator current of the servo motor; R s is the stator resistance; K t is the motor torque constant.
[0091] By establishing an energy consumption model, the energy consumption situation of the servo motor can be accurately evaluated, providing accurate data support for energy management and optimization. Based on the energy consumption formula, the energy-saving design of the servo motor can be guided. On the premise of ensuring the normal operation of the equipment, reducing energy consumption means less maintenance cost and higher production efficiency.
[0092] S4: After optimizing the energy consumption model, measure the energy consumption of the robotic arm.
[0093] In this embodiment, the prediction performance of the model is optimized by adjusting the model parameters. In the actual environment, the optimized energy consumption model is verified and tested, and the prediction results of the model are compared with the actual energy consumption data to ensure the accuracy and reliability of the model. During the application process of the energy consumption model, actual operation data is collected and compared with the prediction results of the model. According to the feedback results, the model is adjusted and optimized to further improve the prediction accuracy and applicability of the model.
[0094] At the same time, considering environmental factors, the energy consumption of the device is affected by various environmental factors, such as temperature, humidity, load, etc. When optimizing the energy consumption model, the influence of these factors on energy consumption should be considered and incorporated into the model.
[0095] According to the working state of the robotic arm, parameters such as joint angles, speeds, and loads are substituted into the energy consumption model to calculate the corresponding work power consumption, electrical loss power consumption, and friction power consumption, and then the total energy consumption is obtained. Provide more accurate energy consumption data for users and system operators to help them better evaluate the energy consumption of the robotic arm.
[0096] In summary, Embodiment 1 of this application is based on three main power consumption items: work power consumption, electrical loss power consumption, and friction power consumption, and integrates them into an energy consumption calculation method. By considering the energy consumption in these three aspects simultaneously, it can more comprehensively reflect the energy consumption of the robotic arm in different working states; establish a comprehensive energy consumption model by correlating the energy consumption calculation of the robotic arm with parameters, more accurately predict the energy consumption of the robotic arm under different tasks and working conditions, and comprehensively consider the work power consumption, electrical loss power consumption, and friction power consumption.
[0097] Embodiment 2
[0098] Please refer to Figure 2 , which is a schematic structural diagram of a multi-degree-of-freedom robotic arm energy consumption measurement system according to Embodiment 2 of this application; the specific content includes:
[0099] Acquisition module: Acquire the structure, degrees of freedom, and energy consumption data of the robotic arm;
[0100] Model establishment module: Establish a motion model of the robotic arm according to the structure and degrees of freedom of the robotic arm; establish an energy consumption model of the robotic arm based on the energy consumption data;
[0101] Measurement module: After optimizing the energy consumption model, measure the energy consumption of the robotic arm.
[0102] Embodiment 3
[0103] Please refer to Figure 3 , which is a schematic structural diagram of the device according to Embodiment 3 of this application. The device 50 includes a processor 51 and a memory 52 coupled to the processor 51.
[0104] The memory 52 stores program instructions for implementing the above-mentioned method for calculating the energy consumption of a multi-degree-of-freedom robotic arm.
[0105] The processor 51 is configured to execute the program instructions stored in the memory 52 to implement the calculation of the energy consumption of the multi-degree-of-freedom robotic arm.
[0106] Among them, the processor 51 can also be referred to as a CPU (Central Processing Unit).
[0107] The processor 51 may be an integrated circuit chip with signal processing capabilities. The processor 51 can also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.
[0108] Embodiment 4
[0109] Please refer to Figure 4 , which is a schematic structural diagram of the storage medium according to Embodiment 4 of the present application. The storage medium of the embodiment of the present application stores a program file 61 capable of implementing all the above methods. Among them, the program file 61 can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods according to various embodiments of the present invention. The foregoing storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc that can store program codes, or devices such as a computer, a server, a mobile phone, or a tablet.
[0110] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article or method including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, apparatus, article or method. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, apparatus, article or method including the element.
[0111] The above are only the preferred embodiments of the present application, and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present application, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present application.
[0112] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present application. The scope of the present application is defined by the appended claims and their equivalents.
[0113] Certainly, the present invention may also have other various implementation manners. Based on this implementation manner, other implementation manners obtained by those of ordinary skill in the art without any creative work belong to the scope protected by the present invention.
Claims
1. A method for measuring the energy consumption of a multi-degree-of-freedom robotic arm, characterized in that, including: Obtain the structure, degrees of freedom, and energy consumption data of the robotic arm; Establish a motion model of the robotic arm based on the structure and degrees of freedom of the robotic arm; Establish an energy consumption model of the robotic arm based on the energy consumption data; After optimizing the energy consumption model, measure the energy consumption of the robotic arm.
2. The energy consumption measurement method of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that In the step of establishing a motion model of the robotic arm based on the structure and degrees of freedom of the robotic arm, the following steps are specifically included: Establish a link coordinate system: Establish a link coordinate system on each joint of the robotic arm to obtain the positions and postures of the joints of the robotic arm; The origin of the link coordinate system usually coincides with the rotation center of the joint, and the direction of the coordinate axis is consistent with the rotation direction of the joint; Establish a motion equation: Based on the link coordinate system and the structure of the robotic arm, establish a motion equation between the joints; The motion equation is represented by a rotation matrix or a homogeneous transformation matrix; Solve the motion equation: By solving the motion equation, obtain the position and posture of the end effector of the robotic arm in the global coordinate system; Verify the motion model: Verify the accuracy and reliability of the motion model through experiments or simulations, and evaluate and improve it through experimental data or simulation results.
3. A method for calculating the energy consumption of a multi-degree-of-freedom robotic arm according to claim 1, characterized in that, In the step of establishing an energy consumption model of the robotic arm based on the energy consumption data, the following steps are specifically included: Obtain the energy consumption data of the robotic arm; Establish a dynamic equation of the joint friction force of the robotic arm; Calculate the friction joint torque vector through the Stribeck model; Obtain an energy consumption formula based on the torque and electrical parameters of the joint servo motor.
4. A method for measuring the energy consumption of a multi-degree-of-freedom robotic arm according to claim 3, characterized in that, In the step of establishing a dynamic equation of the joint friction force of the robotic arm, the following steps are specifically included: Establish a dynamic equation including the joint friction force of the robotic arm, and the calculation formula is: where τ is the position vector of the robot joint, τ is the joint torque vector, M(q) is the inertia matrix of the robot arm, is the velocity term matrix related to the centrifugal force and the Coriolis force, and G(q) is the gravity term.
5. A method for measuring the energy consumption of a multi-degree-of-freedom robotic arm according to claim 3, characterized in that, In the step of calculating the friction joint torque vector through the Stribeck model, the following steps are specifically included: The frictional joint torque vector τ f is calculated by the following formula: where is the joint angular velocity, and f c is the Coulomb friction coefficient, and f s is the maximum static friction coefficient, v s is the velocity coefficient of the Stribeck model, and ζ is a constant.
6. A method for measuring the energy consumption of a multi-degree-of-freedom robotic arm according to claim 3, characterized in that, In the step of obtaining an energy consumption formula based on the torque and electrical parameters of the joint servo motor, the following steps are specifically included: Obtain the torque and electrical parameters of the servo motor of each joint of the robotic arm and the reduction gear parameters of the robotic arm joints, and obtain the final energy consumption formula: Among them, P W , P f , P H are respectively the working power of the robotic arm, the power consumed by friction, and the power consumption of the electricity consumption; j is each joint of the corresponding robotic arm; i is the transmission ratio of the reducer; η is the transmission efficiency; I q is the quadrature-axis stator current of the servo motor; R s is the stator resistance; K t is the motor torque constant.
7. A system for calculating the energy consumption of a multi-degree-of-freedom robotic arm according to the method described in claim 1, characterized in that, including: Acquisition module: Obtain the structure, degrees of freedom, and energy consumption data of the robotic arm; Model establishment module: Establish a motion model of the robotic arm based on the structure and degrees of freedom of the robotic arm; Establish an energy consumption model of the robotic arm based on the energy consumption data; Measurement module: After optimizing the energy consumption model, measure the energy consumption of the robotic arm.
8. A device, characterized in that, The device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing a method for measuring the energy consumption of a robotic arm with multiple degrees of freedom according to any one of claims 1-6; the processor is configured to execute the program instructions stored in the memory to implement the measurement of the energy consumption of the robotic arm with multiple degrees of freedom.
9. A storage medium, characterized in that, Store program instructions that can be run by a processor, and the program instructions are used to execute a method for measuring the energy consumption of a robotic arm with multiple degrees of freedom according to any one of claims 1-6.