CN116451470A - Robot energy consumption modeling method and device based on multiple linear regression and dynamics - Google Patents
Robot energy consumption modeling method and device based on multiple linear regression and dynamics Download PDFInfo
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Abstract
The invention discloses a robot energy consumption modeling method and device based on multiple linear regression and dynamics, and provides a dynamic and static dual-power model construction method in the robot energy consumption modeling process, wherein the traditional method only calculates dynamic energy consumption and is difficult to ensure precision. On the premise that the joint angle curve in the motion process is known, the speed and acceleration curves of all joints are obtained through numerical differentiation; calculating a robot joint torque curve based on a Newton-Euler method, so as to obtain dynamic power; then collecting static power values of key points of the robot, and constructing a multiple linear regression model based on a least square method, namely the static power model; and combining the static power model and the dynamic power model, so that comprehensive energy consumption is calculated, and the evaluation accuracy effect is improved. The invention provides a reference-available method for realizing high-precision energy consumption evaluation of robots, and can be applied to the fields of digital twin, motion optimization and the like.
Description
Technical Field
The invention belongs to the technical field of robot kinematics, robot dynamics and performance simulation, and particularly relates to a method and a device for modeling robot energy consumption based on multiple linear regression and dynamics.
Background
In the simulation software of the robot, high-precision energy consumption performance simulation is a technical difficulty, and the energy consumption level of the robot can be directly analyzed in a simulation environment by constructing an energy consumption model, so that the efficiency of the analysis and optimization of the working performance of the robot is improved. The mainstream robot energy consumption assessment is realized based on robot dynamics, firstly, joint motion state parameters (angle, angular velocity and angular acceleration) are acquired, and then joint torque is calculated through a robot dynamics model, so that joint power and energy consumption are calculated. Currently, the key of energy consumption assessment research is the calculation efficiency and the assessment precision, and the 'quick and accurate' is an ideal target of energy consumption assessment.
In terms of computational efficiency, the computational efficiency of the kinetic model is an important influencing factor, and the Lagrange method is relatively intuitive, but compared with the Lagrange method, the Newton Euler method can be used for more efficient kinetic modeling.
In terms of evaluation accuracy, the existing method accurately analyzes the mechanism of dynamic energy consumption generation, but in actual situations, the robot energy consumption mainly comprises static energy consumption and dynamic energy consumption, wherein the static energy consumption is energy consumption generated when the robot is kept static after being electrified, the energy consumption has a large value, for example, an industrial robot with a load of 20kg, and the static energy consumption after being electrified can reach hundreds of watts, so that the method cannot be ignored. In the dynamic energy consumption calculation, if the joint is stationary at a certain moment, the power calculation result at the moment is also 0, which is different from the actual situation, and a larger evaluation error is brought.
Disclosure of Invention
In order to solve the defects in the prior art and realize efficient and high-precision static energy consumption evaluation, thereby realizing the purpose of high-precision energy consumption evaluation of the robot, the invention adopts the following technical scheme:
a robot energy consumption modeling method based on multiple linear regression and dynamics comprises the following steps:
step 1: acquiring a speed and acceleration curve of each joint based on numerical differentiation;
step 2: acquiring a robot joint torque curve based on a Newton-Euler method through the speed, the acceleration and the known joint angular speed of each joint;
step 3: constructing a dynamic power model; acquiring a dynamic power curve based on the joint speed and torque data;
step 4: based on the robot key points, collecting static power data;
step 5: static power model construction; based on the static power data, constructing a multiple linear regression model of the static energy consumption of the robot;
step 6: based on the static power model and the dynamic power model, the comprehensive energy consumption of the robot is obtained.
Further, the step 1 includes the following steps:
step 1.1: acquiring basic motion information, namely a joint angle-time curve;
step 1.2: based on the joint angle curve, calculating a joint speed and joint acceleration curve by a numerical differentiation method.
Further, the step 2 includes the following steps:
step 2.1: firstly, identifying dynamic parameters to obtain dynamic parameters of the robot, wherein the dynamic parameters comprise inertia tensor, centroid coordinates, mass, coulomb and viscous friction coefficient of each joint;
step 2.2: the dynamic parameters are carried into Newton-Euler method to construct dynamic model, newton-Euler method firstly recursively calculates speed and angular speed outwards, and then iteratively calculates moment tau of each joint inwards d In addition, it is also necessary to calculate the friction torque τ f ：
Wherein the method comprises the steps ofRepresenting a sequence of joint angular velocities, f c Represents the coulomb friction coefficient, f v Representing the coefficient of viscous friction.
Further, the dynamic power formula of the step 3 is as follows:
P i ＝|τ d,i ω i +τ f,i ω i |
wherein i represents the joint number, P i Represents the i-th joint dynamic power, τ d,i Represents the i-th joint driving torque, τ f,i Represents the ith joint friction torque omega i Indicating the i-th joint angular velocity.
Further, the step 4 includes the following steps:
step 4.1: collecting static energy consumption data required by modeling, firstly collecting key point position data comprising zero positions of joints, upper limit values and lower limit values of the joints and the like;
step 4.2: after the key point position is acquired, sufficient data acquisition is continued, so that the matrix is full-rank in least square regression, and the data acquisition is generally carried out on the basis of the principle of uniform sampling of each joint.
Further, in the step 5, a multiple linear regression model of the static energy consumption of the industrial robot is constructed based on the least square method, and the regression model is constructed in the form of m-ary n-degree polynomials according to the m joints of the industrial robot in consideration of the number of parameters and the fitting effect:
wherein P is on Represents static total power theta of robot after power-on 1 To theta m Each joint angle is represented by i, the joint number is represented by k 1 To k m Represents a non-negative integer, k 1 To k m The sum is a segment [0, n ]]Integer of a, a i Represent the firstCoefficients of the i term.
Further, in the step 6, the static power model is combined with the dynamic power model, and the comprehensive energy consumption of the robot is calculated based on numerical integration:
wherein i represents a joint number, f E Represents the total energy consumption in the motion process, θ represents the joint angle sequence, and since the angular velocity and the angular acceleration can be calculated according to angle differentiation, t is not taken as an input 0 Indicating the start time of movement, t 1 Represents the movement ending time, n represents the number of joints, P i Representing dynamic power, P on Representing static power.
The robot energy consumption modeling device based on the multiple linear regression and dynamics comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the robot energy consumption modeling method based on the multiple linear regression and dynamics when executing the executable codes.
The invention has the advantages that:
the invention relates to a robot energy consumption modeling method and a device based on multiple linear regression and dynamics,
the dynamic and static dual-power model construction method solves the problem that the existing energy consumption assessment model is difficult to achieve efficient and high-precision static energy consumption assessment, improves the precision of energy consumption assessment, achieves high-precision energy consumption assessment of a robot, and can be applied to the fields of digital twinning, motion optimization and the like.
Drawings
FIG. 1 is a flow chart of a method of modeling energy consumption in an embodiment of the invention.
FIG. 2 is a schematic diagram of a hardware structure for performing performance measurement on an energy consumption modeling method according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an energy consumption modeling apparatus in an embodiment of the present invention.
Detailed Description
The following describes specific embodiments of the present invention in detail with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
The robot energy consumption modeling method based on multiple linear regression and dynamics can be used for a conventional general six-axis industrial mechanical arm, and the model of a robot used for simulation test in the invention is a Kensified ABD series six-axis mechanical arm. As shown in fig. 1, the method specifically comprises the following steps:
step 1: based on the numerical differentiation, the velocity and acceleration curves of the joints are obtained.
On the premise that joint angle data and time sequence information are known, speed and acceleration data of each joint of the robot are calculated by two-point estimation, five-point estimation and other methods, and in the test example, numerical differential calculation can be performed by directly adopting a gradient function in Matlab, and the method comprises the following steps:
step 1.1: acquiring basic motion information, namely a joint angle-time curve;
step 1.2: based on the joint angle curve, calculating a joint speed and joint acceleration curve by a numerical differentiation method.
Step 2: the method for acquiring the robot joint torque curve based on the Newton-Euler method through the speed, the acceleration and the known joint angular velocity of each joint comprises the following steps:
step 2.1: firstly, identifying dynamic parameters to obtain dynamic parameters of the robot, wherein the dynamic parameters comprise inertia tensor, centroid coordinates, mass, coulomb and viscous friction coefficient of each joint;
step 2.2: the dynamic parameters are carried into Newton-Euler method to construct dynamic model, newton-Euler method firstly recursively calculates speed and angular speed outwards, and then iteratively calculates moment tau of each joint inwards d In addition, it is also necessary to calculate the friction torque τ f ：
Wherein the method comprises the steps ofFor the angular velocity sequence of the joint, f c Is the coulomb friction coefficient, f v Is the viscous friction coefficient.
Step 3: constructing a dynamic power model; based on the joint speed and torque data, a dynamic power curve is obtained:
wherein i is joint number, P i For the ith joint dynamic power τ d,i For the ith joint driving torque τ f,i Omega for the ith joint friction torque i The i-th joint angular velocity.
Step 4: based on the robot key points, collecting static power data; considering only the dynamic energy consumption model of step 3, the evaluation accuracy cannot be guaranteed, because there is a moment when some joint speed is 0 in the motion process, according to step 3, the power of the joint is 0, but in reality, the joint still consumes energy, i.e. static energy consumption, so that static energy consumption modeling is required, and the method specifically includes the following steps:
step 4.1: collecting static energy consumption data required by modeling, wherein key point position data including zero position of each joint, upper limit value and lower limit value of each joint and the like are required to be collected firstly;
step 4.2: after the key point position acquisition is completed, the data acquisition is continued in a sufficient quantity to ensure that the matrix is full-rank when the least square regression is carried out, and the data acquisition is generally carried out on the basis of the principle of uniformly sampling each joint.
Step 5: static power model construction; based on the static power data, a multiple linear regression model of the static energy consumption of the robot is constructed.
Based on the least square method, a multiple linear regression model of the static energy consumption of the industrial robot is built, and because the industrial robot is mostly a six-joint serial robot, the regression model is built in a six-element three-order polynomial form in consideration of the number of parameters and fitting effect:
wherein P is on For static total power theta of robot after power-on 1 To theta 6 For each joint angle, i is the joint number, k 1 To k 6 Non-negative integer, k 1 To k 6 The sum is a segment [0,3 ]]Integer of a, a i Is the coefficient of the i-th term.
Step 6: based on the static power model and the dynamic power model, the comprehensive energy consumption of the robot is obtained.
Combining the static power model with the dynamic power model, thereby calculating the comprehensive energy consumption, and calculating the comprehensive energy consumption of the robot based on numerical integration:
wherein i is the joint number, f E For the total energy consumption in the motion process, θ is the joint angle sequence, and since the angular velocity and the angular acceleration can be calculated according to angle differentiation, t is not taken as an input 0 For the start time of movement, t 1 For the end time of the movement, n is the number of joints, P i For dynamic power, P on Is static power.
The objects and effects of the present invention will become more apparent from the following description of the embodiments, which is made in accordance with the present invention, for a total of three test cases, each having three test paths.
The performance of the invention is measured: the actual motion energy consumption of the robot is measured by power meter measurement and numerical integration calculation, and the experimental configuration is shown in fig. 2.
Example 1:
| path 1 | Path 2 | Path 3 | |
| Actual energy consumption/J | 1737.3 | 1624.7 | 1526.1 |
| Assessment of energy consumption/J | 1482.0 | 1408.6 | 1309.0 |
| Precision of | 85.3％ | 86.7％ | 85.8％ |
Example 2:
| path 1 | Path 2 | Path 3 | |
| Actual energy consumption/J | 2771.3 | 2321.0 | 2162.6 |
| Assessment of energy consumption/J | 2601.6 | 2175.1 | 1986.1 |
| Precision of | 93.9％ | 93.7％ | 91.8％ |
Example 3:
| path 1 | Path 2 | Path 3 | |
| Actual energy consumption/J | 1477.7 | 1491.9 | 1492.1 |
| Assessment of energy consumption/J | 1371.4 | 1367.7 | 1354.4 |
| Precision of | 92.8％ | 91.7％ | 90.8％ |
The invention further provides an embodiment of the robot energy consumption modeling device based on the multiple linear regression and dynamics, corresponding to the embodiment of the robot energy consumption modeling method based on the multiple linear regression and dynamics.
Referring to fig. 3, the device for modeling the energy consumption of the robot based on the multiple linear regression and the dynamics provided by the embodiment of the invention comprises a memory and one or more processors, wherein executable codes are stored in the memory, and the one or more processors are used for realizing the method for modeling the energy consumption of the robot based on the multiple linear regression and the dynamics in the embodiment when executing the executable codes.
The embodiment of the robot energy consumption modeling device based on the multiple linear regression and dynamics can be applied to any device with data processing capability, such as a computer or the like. The apparatus embodiments may be implemented by software, or may be implemented by hardware or a combination of hardware and software. Taking software implementation as an example, the device in a logic sense is formed by reading corresponding computer program instructions in a nonvolatile memory into a memory by a processor of any device with data processing capability. In terms of hardware, as shown in fig. 3, a hardware structure diagram of an apparatus with data processing capability according to the present invention where the robot energy consumption modeling apparatus based on multiple linear regression and dynamics is located is shown in fig. 3, except for a processor, a memory, a network interface, and a nonvolatile memory shown in fig. 3, where the apparatus with data processing capability according to the present invention in the embodiment generally includes other hardware according to the actual function of the apparatus with data processing capability, which is not described herein again.
The implementation process of the functions and roles of each unit in the above device is specifically shown in the implementation process of the corresponding steps in the above method, and will not be described herein again.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The embodiment of the invention also provides a computer readable storage medium, wherein a program is stored on the computer readable storage medium, and when the program is executed by a processor, the robot energy consumption modeling method based on multiple linear regression and dynamics in the embodiment is realized.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of any of the data processing enabled devices described in any of the previous embodiments. The computer readable storage medium may be any external storage device that has data processing capability, such as a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), or the like, which are provided on the device. Further, the computer readable storage medium may include both internal storage units and external storage devices of any data processing device. The computer readable storage medium is used for storing the computer program and other programs and data required by the arbitrary data processing apparatus, and may also be used for temporarily storing data that has been output or is to be output.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the technical solutions according to the embodiments of the present invention.
Claims (8)
1. The robot energy consumption modeling method based on multiple linear regression and dynamics is characterized by comprising the following steps of:
step 1: acquiring the speed and the acceleration of each joint;
step 2: acquiring the joint torque of the robot based on the Newton-Euler method through the speed, the acceleration and the known joint angular speed of each joint;
step 3: constructing a dynamic power model; acquiring dynamic power based on the joint speed and torque data;
step 4: based on the robot key points, collecting static power data;
step 5: static power model construction; based on the static power data, constructing a multiple linear regression model of the static energy consumption of the robot;
step 6: based on the static power model and the dynamic power model, the comprehensive energy consumption of the robot is obtained.
2. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: the step 1 comprises the following steps:
step 1.1: acquiring basic motion information, namely a joint angle-time curve;
step 1.2: based on the joint angle curve, calculating a joint speed and joint acceleration curve by a numerical differentiation method.
3. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: the step 2 comprises the following steps:
step 2.1: firstly, identifying dynamic parameters to obtain dynamic parameters of a robot;
step 2.2: the dynamic parameters are carried into Newton-Euler method to construct dynamic model, newton-Euler method firstly recursively calculates speed and angular speed outwards, and then iteratively calculates moment tau of each joint inwards d In addition, it is also necessary to calculate the friction torque τ f ：
Wherein the method comprises the steps ofRepresenting a sequence of joint angular velocities, f c Represents the coulomb friction coefficient, f v Representing the coefficient of viscous friction.
4. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: the dynamic power formula of the step 3 is as follows:
P i ＝|τ d,i ω i +τ f,i ω i |
wherein i represents the joint number, P i Represents the i-th joint dynamic power, τ d,i Represents the i-th joint driving torque, τ f,i Represents the ith joint friction torque omega i Indicating the i-th joint angular velocity.
5. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: the step 4 comprises the following steps:
step 4.1: collecting static energy consumption data required by modeling, and firstly collecting key point position data;
step 4.2: and after the key point position is acquired, continuing to acquire enough data so as to enable the matrix to be full in rank during least square regression.
6. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: in the step 5, a multiple linear regression model of the static energy consumption of the industrial robot is constructed based on a least square method, and the regression model is constructed in an m-ary n-degree polynomial form according to an m-joint of the industrial robot:
wherein P is on Represents static total power theta of robot after power-on 1 To theta m Each joint angle is represented by i, the joint number is represented by k 1 To k m Represents a non-negative integer, k 1 To k m The sum is a segment [0, n ]]Integer of a, a i The coefficient representing the i-th term.
7. The method for modeling robot energy consumption based on multiple linear regression and dynamics according to claim 1, characterized in that: in the step 6, the static power model is combined with the dynamic power model, and the comprehensive energy consumption of the robot is calculated based on numerical integration:
wherein i represents a joint number, f E Represents total energy consumption in the motion process, theta represents a joint angle sequence, t 0 Indicating the start time of movement, t 1 Represents the movement ending time, n represents the number of joints, P i Representing dynamic power, P on Representing static power.
8. A robot energy consumption modeling apparatus based on multiple linear regression and dynamics, comprising a memory and one or more processors, the memory having executable code stored therein, the one or more processors, when executing the executable code, configured to implement the robot energy consumption modeling method based on multiple linear regression and dynamics of any one of claims 1-7.
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| CN116861596B (en) * | 2023-09-04 | 2024-01-02 | 常熟理工学院 | Dynamics modeling method and system for 6-degree-of-freedom parallel robot |
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