WO2020133270A1 - 机器人的动力学参数辨识方法、机器人和存储装置 - Google Patents
机器人的动力学参数辨识方法、机器人和存储装置 Download PDFInfo
- Publication number
- WO2020133270A1 WO2020133270A1 PCT/CN2018/125045 CN2018125045W WO2020133270A1 WO 2020133270 A1 WO2020133270 A1 WO 2020133270A1 CN 2018125045 W CN2018125045 W CN 2018125045W WO 2020133270 A1 WO2020133270 A1 WO 2020133270A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- speed
- friction
- drive motor
- robot
- rotation speed
- Prior art date
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/18—Stabilised platforms, e.g. by gyroscope
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Definitions
- the invention relates to the technical field of automatic control, in particular to a robot dynamic parameter identification method, a robot and a storage device.
- Robot dynamics studies the relationship between robot joint force, torque and joint motion.
- the torque that the driver of the joint should provide can be calculated according to the target position and speed of the joint, and the robot can be controlled according to the result.
- the ideal dynamic model only considers inertial force, Coriolis force (centrifugal force) and gravity, but in actual situations, the friction torque cannot be ignored, otherwise it will affect the control and operation of the robot. Therefore, it is necessary to first use the friction model to calculate the friction torque, and remove the influence of the friction torque from the total torque provided by the drive, so as to obtain the driving torque under the ideal dynamic model that can be used to calculate the inertia parameters.
- the inventor of the present invention found in the research process of the prior art that the stribeck model is often used in the process of identifying the robot dynamics parameters, especially the friction model parameters, and the stribeck model contains an exponential part
- the high degree of nonlinearity makes the optimization and solution process of the friction model and dynamic model very complicated. Therefore, it will cause a waste of the computing power of the robot control system.
- the invention provides a robot dynamic parameter identification method, a robot and a storage device, which are used to solve the problem that the dynamic parameter identification process in the prior art is too complicated.
- a technical solution provided by the present invention is to provide a robot dynamic parameter identification method.
- the method includes: at a plurality of different speeds, the driving motor is rotated at a uniform speed, and the corresponding armature current at each speed is collected; according to the corresponding armature current at each speed, the corresponding friction torque at each speed is calculated to Forming a plurality of data pairs of the rotation speed and the friction torque; and establishing the friction polynomial model according to the data pairs, wherein the friction polynomial model is used to describe the relationship between the rotation speed and the friction torque of the drive motor.
- a robot including a controller and a driving motor coupled to each other, wherein the controller can execute program instructions and perform the following methods: At a rotating speed, the driving motor is rotated at a uniform speed, and the corresponding armature current at each rotating speed is collected; according to the corresponding armature current at each rotating speed, the corresponding friction torque at each rotating speed is calculated to form a plurality of the rotating speeds A data pair with friction torque; and establishing the friction polynomial model based on the data pair, wherein the friction polynomial model is used to describe the relationship between the rotational speed of the drive motor and the friction torque.
- another technical solution provided by the present invention is to provide a storage device that stores program instructions, which can be loaded and execute the above-mentioned robot dynamic parameter identification method.
- the beneficial effect of the present invention is: by acquiring multiple data pairs of rotation speed and friction torque, a polynomial friction model can be established according to the data pairs.
- the polynomial friction model can not only be used as a linear model, but also has the ability to express non-linear characteristics, and has better adaptability. Therefore, the present invention can simplify the calculation in the process of robot dynamic parameter identification and save the computing power of the system.
- FIG. 1 is a schematic flowchart of an embodiment of a robot dynamic parameter identification method of the present invention.
- FIG. 2 is a schematic flowchart of another embodiment of the robot dynamic parameter identification method of the present invention.
- FIG. 3 is a schematic flowchart of another embodiment of the robot dynamic parameter identification method of the present invention.
- FIG. 4 is a schematic flowchart of an embodiment of a method for identifying inertial parameters of a robot of the present invention.
- FIG. 5 is a schematic structural view of an embodiment of the robot of the present invention.
- FIG. 1 is a schematic flowchart of an embodiment of a robot dynamic parameter identification method of the present invention. The method includes the following steps.
- the robot can have multiple joints, and each joint can be provided with one or more drive motors to drive the joint motion, so as to realize the motion of the robot.
- a driving motor is taken as an example for description.
- the driving motor of the robot may be a DC motor or an AC motor, which is not limited herein.
- the drive motor is moved at a constant speed at different rotation speeds.
- the drive motor can be moved at a lower speed and a constant speed for a period of time, and then the rotation speed is gradually increased, and a constant speed movement is maintained at each rotation speed for a period of time. And collect the corresponding armature current at each speed.
- multiple sets of armature current data can be collected at each speed, and then the average value can be obtained to obtain the corresponding armature current at that speed, or, when the armature current changes periodically, each group of The DC and AC components of the armature current data are extracted and averaged separately.
- the drive motor can be rotated at a constant speed at the initial test speed (for example, 1°/s), and the armature current corresponding to the initial test speed can be collected. Then, the rotation speed is increased by a variable step length each time (or, if the initial rotation speed is selected to be a higher rotation speed, the rotation speed may be gradually reduced in variable steps in the subsequent test) to obtain the subsequent test rotation speed.
- the drive motor is rotated at a uniform speed, and the armature current corresponding to the subsequent test speed is collected until the target speed is reached. Both the initial speed and the target speed can be specified by the operator based on experience.
- the initial speed and the target speed can be set to the lowest speed and the highest speed that the drive motor can reach, respectively.
- the higher the rotation speed the larger the value of the variable step length, which means that the rotation speed is denser in the low speed section, so that the result obtained can better describe the friction torque in Non-linear characteristics in the low speed section.
- the step size can be set to 1°/s, that is, the rotation speed is changed by 1°/s each time, while in the high speed section, the step length can be set to 2°/s or 3°/s, etc. It can be understood that, in other embodiments, the step of changing the rotation speed may also be a fixed value.
- S102 Calculate the corresponding friction torque at each rotation speed according to the corresponding armature current at each rotation speed to form a plurality of data pairs of rotation speed and friction torque.
- the corresponding friction torque at each speed can be calculated. Specifically, since the drive motors move at a constant speed at all speeds, the acceleration is zero, and there is no inertial force, it can be considered that the DC component of the torque output by the drive motor is all used to overcome the friction torque. Therefore, by extracting the DC component of the armature current at each speed, and according to the relationship between the output torque of the drive motor and the armature current, the corresponding torque magnitude for overcoming the friction torque, that is, the magnitude of the friction torque, can be calculated . In this way, in step S102, a plurality of data pairs of rotational speed and friction torque are obtained.
- S103 Establish a friction polynomial model based on these data pairs.
- the friction polynomial model is used to describe the relationship between the rotational speed of the drive motor and the friction torque.
- step S103 a polynomial model is used to fit the data obtained in step S102, thereby obtaining various parameters in the polynomial model.
- the obtained friction polynomial model can be used to describe the relationship between the speed of the drive motor and the friction torque, that is, given any speed of the drive motor, you can know the magnitude of the friction torque received by the joint during operation.
- the friction polynomial can be regarded as a linear model
- the least square method can be used for curve fitting, and the fitting method is simple and fast. It can be understood that other parameter optimization and fitting methods can also be used to fit the friction polynomial.
- a friction polynomial model By acquiring multiple data pairs of rotational speed and friction torque, a friction polynomial model can be established based on the data pairs.
- the friction polynomial model can not only be used as a linear model, but also has the ability to express non-linear characteristics, and has better adaptability. Therefore, the present invention can simplify the calculation in the process of robot dynamic parameter identification and save the computing power of the system.
- FIG. 2 is a schematic flowchart of another embodiment of the robot dynamic parameter identification method of the present invention. As shown in Figure 2, the method includes the following steps.
- S202 Calculate the corresponding friction torque at each rotation speed according to the corresponding armature current at each rotation speed to form a plurality of data pairs of rotation speed and friction torque.
- Steps S201 and S202 are similar to the aforementioned S101 and S102, and will not be repeated here.
- step S204 it is determined whether there is a rotation speed greater than and less than the reference rotation speed in the data pair. If it exists, steps S205 and S206 are executed, otherwise Go to step S207.
- the tested speed-friction torque data pair is divided into a high-speed section and a low-speed section using the reference speed V_border as a boundary, and the data in the high-speed section and the low-speed section are fitted using a polynomial model, respectively.
- the friction model of the low-speed section and the friction model of the high-speed section are obtained respectively, and the curve of the friction model of the low-speed section and the curve of the friction model of the high-speed section are intersected at points (V_border, tor_border), so that the complete speed range of the drive motor is obtained Within the continuous friction torque characteristic curve.
- the K-degree polynomial model can be used for data fitting in both the low-speed section and the high-speed section, where the K value can be selected based on empirical values, and the K value in the low-speed section and the high-speed section can be the same or different.
- the friction model in the low-speed section and the friction model in the high-speed section are obtained separately, so that the stribeck phenomenon in the low-speed section can be better described.
- the polynomial friction model in the high-speed section can also be used. Better characterize the linear and nonlinear characteristics of the friction torque in the high-speed section.
- the reference rotation speed V_border is the lowest rotation speed.
- a single K-degree polynomial model can be used to fit the data pair to obtain the joint Polynomial model of friction. It can be understood that this case can also be considered as a special case of the piecewise polynomial function.
- FIG. 3 is a schematic flowchart of another embodiment of the robot dynamic parameter identification method of the present invention. As shown in FIG. 3, the method includes the following steps.
- S301 Set the drive motor to position mode or speed mode at multiple different speeds.
- the servo system usually has control modes of position loop, speed loop and current loop, and the drive motor of the robot also belongs to a kind of servo system. Since we want the drive motor to move at a constant speed during the test, the position loop or speed loop can be used for control. That is, in step S301, the drive motor is set to position mode or speed mode for control.
- S302 Move the driving motor from the first position to the second position at a uniform speed, and return to the first position from the second position at a uniform speed, during which the armature current of the driving motor is collected.
- the drive motor is moved from the first position to the second position at a constant speed, and from the second position to the first position at a constant speed, so that the armature current and reverse electric Armature current.
- S303 Calculate the corresponding friction torque at each rotation speed according to the corresponding armature current at each rotation speed to form a plurality of data pairs of rotation speed and friction torque.
- the speed-friction torque data pair of the drive motor forward rotation can be obtained, and the armature current of the drive motor reverse rotation at each speed can be obtained.
- Speed-friction torque data pair According to the armature current of the drive motor rotating forward at each speed, the speed-friction torque data pair of the drive motor forward rotation can be obtained, and the armature current of the drive motor reverse rotation at each speed can be obtained. Speed-friction torque data pair.
- S304 Use the data of the rotation speed and friction torque during the uniform speed movement of the drive motor from the first position to the second position to establish a forward rotation friction model of the drive motor.
- step S304 and step S305 the rotational speed-friction torque data pairs of the forward rotation and reverse rotation of the drive motor obtained in the previous step are used, and the polynomial model is used to fit, respectively, to establish the forward rotation friction model of the drive motor and Reverse the friction model.
- the friction torque of the drive motor can be accurately calculated from the rotation speed of the drive motor.
- the friction torque when the drive motor is reversed can be accurately calculated from the rotation speed of the drive motor. In the actual situation, there may be some differences in the friction characteristics of the drive motor during forward and reverse rotations. The friction characteristics of the drive motor can be more accurately described by establishing the forward rotation friction model and the reverse rotation friction model respectively.
- the solution in the embodiment of FIG. 3 may be used in combination with the technical solution in FIG. 1 and/or FIG. 2.
- the method using the piecewise polynomial model described in the embodiment of FIG. 2 can be used.
- the robot's dynamic parameters also include the robot's inertial parameters, such as the mass, center of mass, and rotational inertia of each axis. Therefore, after acquiring the friction polynomial model of the robot according to any of the above embodiments, the present invention further provides a method for identifying the inertial parameters of the robot,
- the dynamic model of the robot considering friction factors can be expressed as:
- the inertial parameters of the robot such as mass, centroid and rotational inertia are included in H(q), And G(q).
- the method shown in FIG. 4 may be performed.
- the method includes the following steps.
- S401 Select the excitation trajectory identified by the inertial parameters, make the drive motor run according to the excitation trajectory, and collect the feedback position, feedback speed, and feedback current of the drive motor.
- the excitation trajectory refers to the path we expect the drive motor to move, that is, at what speed and position the drive motor moves. In an ideal situation, if there is no system error, you only need to make the drive motor move at a number of different speeds, accelerations, and positions (the specific number is related to the number of inertial parameters), and collect the corresponding feedback signals. The dynamic curve of the robot. However, in actual situations, due to the interference of system noise and measurement errors, the path of the driving motor cannot be arbitrarily selected, otherwise the calculation error will be too large, resulting in inaccurate results or the calculation cannot converge.
- step S401 the excitation trajectory identified by the inertial parameters is first selected, the drive motor is operated according to the excitation trajectory, and the feedback position, feedback speed, and feedback current of the drive motor are collected.
- the excitation trajectory can be selected according to experience or calculated by an optimization algorithm.
- the initial value of the excitation trajectory parameters can be arbitrarily selected, or the initial value can be selected based on empirical values, and then optimization parameters of the excitation trajectory are optimized using genetic algorithms and other optimization algorithms, with the goal of considering system errors and measurement errors Minimize the deviation of the fitting calculation.
- the specific method can refer to the description in the related art.
- S402 Calculate the corresponding friction torque according to the feedback speed and the friction polynomial model of the drive motor, and calculate the output torque according to the feedback current, thereby obtaining the driving torque under the ideal dynamic model.
- the corresponding friction torque can be calculated, and the output torque of the drive motor can be calculated according to the feedback current.
- the driving torque under the ideal dynamic model that is, the dynamic model after ignoring the friction torque or removing the influence of the friction torque
- the driving torque at various points on the excitation path can be obtained.
- S403 Fit the inertial parameters of the robot according to the driving torque, the feedback position and the feedback speed.
- the parameters related to the inertial parameters include the position vector, the speed vector and the acceleration vector in addition to the driving torque, where the position vector and the speed vector at a point on the excitation path are the collected feedback position and feedback speed, and the acceleration The vector can be obtained by numerically differentiating the feedback speed. Therefore, on this basis, the inertial parameters of the robot can be fitted by combining the linear dynamic inverse solution model. In the fitting process, optimization algorithms such as least squares can be used, which is not limited here.
- each inertial parameter of the robot can be further obtained, thereby obtaining all required robot dynamic parameters.
- the obtained dynamic parameters can be used for calculation and control.
- FIG. 5 is a schematic structural diagram of an embodiment of a robot 500 provided by the present invention.
- the robot 500 includes a communication bus 501, a controller 502, a memory 403, and a driving motor 504.
- the controller 502 and the memory 503 are coupled through the communication bus 501.
- the memory 503 stores program data, and the program data can be loaded by the controller 502 and execute the robot dynamic parameter identification method of any of the above embodiments. Understandably, in some other embodiments, the memory 503 may be set in the same physical device by different controllers 502, but the method of any of the above embodiments is performed by combining the robot 500 with the network.
- the robot 500 may be a robot such as an industrial robot or a home robot, such as a multi-joint robot arm or a humanoid robot. Those skilled in the art can understand that as long as the robot involved in joint motion can use the technical solution provided by the present invention.
- the functions described in the above embodiments are implemented in software and sold or used as independent products, they can be stored in a device with a storage function. That is, the present invention also provides a storage device that stores a program.
- the program data in the storage device can be executed to realize the robot dynamic parameter identification method in the foregoing embodiment, and the storage device includes but is not limited to a U disk, an optical disk, a server, or a hard disk.
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Control Of Electric Motors In General (AREA)
- Manipulator (AREA)
Abstract
Description
Claims (20)
- 一种机器人的动力学参数辨识方法,其特征在于,包括:在多个不同转速下,使机器人的驱动电机匀速转动,并采集各转速下对应的电枢电流;根据所述各转速下对应的电枢电流,计算各转速下对应的摩擦力矩,以形成多个所述转速与摩擦力矩的数据对;以及根据所述数据对建立所述摩擦多项式模型,其中所述摩擦多项式模型用以描述所述驱动电机的转速和摩擦力矩之间的关系。
- 如权利要求1所述的方法,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤包括:根据所述各转速下对应的摩擦力矩,找到其中的最小摩擦力矩及对应的参考转速;当所述数据对中同时存在大于和小于所述参考转速的转速时,将小于所述参考转速的转速范围作为低速段,对所述低速段内的转速和摩擦力矩的数据对进行拟合,得到低速摩擦模型,将大于所述参考转速的转速范围作为高速段,对所述高速段内的转速和摩擦力矩的数据对进行拟合,得到高速摩擦模型。
- 如权利要求2所述的方法,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤进一步包括:当所述数据对中不同时存在大于和小于所述参考转速的转速时,对所有转速和摩擦力矩的数据对进行拟合,得到所述摩擦多项式模型。
- 如权利要求1所述的方法,其特征在于,所述在多个不同转速下,使所述驱动电机匀速转动,并采集各转速下对应的电枢电流的步骤包括:在多个不同转速下,将驱动电机设置为位置模式或者速度模式,使所述驱动电机从第一位置匀速运动至第二位置,并从第二位置匀速返回第一位置,期间采集所述驱动电机的电枢电流。
- 如权利要求4所述的方法,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤包括:使用所述驱动电机从第一位置匀速运动至第二位置过程中的所述转速与摩擦力矩的数据对建立所述驱动电机的正转摩擦模型;以及使用所述驱动电机从第二位置匀速返回第一位置过程中的的所述转速与摩擦力矩的数据对建立所述驱动电机的反转摩擦模型。
- 如权利要求1所述的方法,其特征在于,在所述在多个不同转速下使所述驱动电机匀速转动并采集各转速下对应的电枢电流的步骤包括:在初始测试转速下使所述驱动电机匀速转动,并采集所述初始测试转速对应的电枢电流;将转速每次增加或减少可变步长从而得到后续测试转速,在所述后续测试转速下使所述驱动电机匀速转动,并采集所述后续测试转速对应的电枢电流,直到达到目标转速;其中,转速越高,可变步长的值越大。
- 如权利要求1所述的方法,其特征在于,所述摩擦多项式模型为用于表征所述转速与摩擦力矩的关系的1元K次多项式,其中K为大于或等于2的正整数。
- 如权利要求1所述的方法,其特征在于,所述摩擦多项式模型使用最小二乘法进行曲线拟合。
- 如权利要求1所述的方法,其特征在于,还包括:选择惯性参数辨识的激励轨迹,使所述驱动电机按所述激励轨迹运行,并采集所述驱动电机的反馈位置、反馈速度和反馈电流;根据所述反馈速度和所述驱动电机的所述摩擦多项式模型计算对应的摩擦力矩,并根据所述反馈电流计算输出扭矩,从而得到理想动力学模型下的驱动力矩;根据所述驱动力矩、所述反馈位置和所述反馈速度对所述机器人的惯性参 数进行拟合。
- 如权利要求9所述的方法,其特征在于,所述选择惯性参数辨识的激励轨迹的步骤包括:选取激励轨迹参数的初始值;以及采用遗传算法对所述激励轨迹参数进行优化。
- 一种机器人,其特征在于,包括互相耦合的控制器和驱动电机,其中,所述控制器可执行程序指令并执行以下方法:在多个不同转速下,使机器人的驱动电机匀速转动,并采集各转速下对应的电枢电流;根据所述各转速下对应的电枢电流,计算各转速下对应的摩擦力矩,以形成多个所述转速与摩擦力矩的数据对;以及根据所述数据对建立所述摩擦多项式模型,其中所述摩擦多项式模型用以描述所述驱动电机的转速和摩擦力矩之间的关系。
- 如权利要求11所述的机器人,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤包括:根据所述各转速下对应的摩擦力矩,找到其中的最小摩擦力矩及对应的参考转速;当所述数据对中同时存在大于和小于所述参考转速的转速时,将小于所述参考转速的转速范围作为低速段,对所述低速段内的转速和摩擦力矩的数据对进行拟合,得到低速摩擦模型,将大于所述参考转速的转速范围作为高速段,对所述高速段内的转速和摩擦力矩的数据对进行拟合,得到高速摩擦模型。
- 如权利要求12所述的机器人,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤进一步包括:当所述数据对中仅存在大于所述参考转速的转速时,对所有转速和摩擦力矩的数据对进行拟合,得到所述摩擦多项式模型。
- 如权利要求11所述的机器人,其特征在于,所述在多个不同转速下, 使所述驱动电机匀速转动,并采集各转速下对应的电枢电流的步骤包括:在多个不同转速下,将驱动电机设置为位置模式或者速度模式,使所述驱动电机从第一位置匀速运动至第二位置,并从第二位置匀速返回第一位置,期间采集所述驱动电机的电枢电流。
- 如权利要求14所述的机器人,其特征在于,所述根据所述数据对建立所述摩擦多项式模型的步骤包括:使用所述驱动电机从第一位置匀速运动至第二位置过程中的所述转速与摩擦力矩的数据对建立所述驱动电机的正转摩擦模型;以及使用所述驱动电机从第二位置匀速返回第一位置过程中的的所述转速与摩擦力矩的数据对建立所述驱动电机的反转摩擦模型。
- 如权利要求11所述的机器人,其特征在于,所述方法还包括:选择惯性参数辨识的激励轨迹,使所述驱动电机按所述激励轨迹运行,并采集所述驱动电机的反馈位置、反馈速度和反馈电流;根据所述反馈速度和所述驱动电机的所述摩擦多项式模型计算对应的摩擦力矩,并根据所述反馈电流计算输出扭矩,从而得到理想动力学模型下的驱动力矩;根据所述驱动力矩、所述反馈位置和所述反馈速度对所述机器人的惯性参数进行拟合。
- 如权利要求16所述的机器人,其特征在于,所述选择惯性参数辨识的激励轨迹的步骤包括:选取激励轨迹参数的初始值;以及采用遗传算法对所述激励轨迹参数进行优化。
- 如权利要求11所述的机器人,其特征在于,在所述在多个不同转速下使所述驱动电机匀速转动并采集各转速下对应的电枢电流的步骤包括:在初始测试转速下使所述驱动电机匀速转动,并采集所述初始测试转速对应的电枢电流;将转速每次增加或减少可变步长从而得到后续测试转速,在所述后续测试转速下使所述驱动电机匀速转动,并采集所述后续测试转速对应的电枢电流,直到达到目标转速;其中,转速越高,可变步长的值越大。
- 如权利要求11所述的机器人,其特征在于:所述摩擦多项式模型为用于表征所述转速与摩擦力矩的关系的1元K次多项式,其中K为大于或等于2的正整数;以及所述摩擦多项式模型使用最小二乘法进行曲线拟合。
- 一种具有存储功能的装置,其特征在于,存储有程序指令,所述程序指令可被加载并执行一种机器人的动力学参数辨识方法,所述方法包括:在多个不同转速下,使所述驱动电机匀速转动,并采集各转速下对应的电枢电流;根据所述各转速下对应的电枢电流,计算各转速下对应的摩擦力矩,以形成多个所述转速与摩擦力矩的数据对;以及根据所述数据对建立所述摩擦多项式模型,其中所述摩擦多项式模型用以描述所述驱动电机的转速和摩擦力矩之间的关系。
Priority Applications (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2018/125045 WO2020133270A1 (zh) | 2018-12-28 | 2018-12-28 | 机器人的动力学参数辨识方法、机器人和存储装置 |
CN201880087307.6A CN111788040B (zh) | 2018-12-28 | 2018-12-28 | 机器人的动力学参数辨识方法、机器人和存储装置 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
PCT/CN2018/125045 WO2020133270A1 (zh) | 2018-12-28 | 2018-12-28 | 机器人的动力学参数辨识方法、机器人和存储装置 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2020133270A1 true WO2020133270A1 (zh) | 2020-07-02 |
Family
ID=71126773
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2018/125045 WO2020133270A1 (zh) | 2018-12-28 | 2018-12-28 | 机器人的动力学参数辨识方法、机器人和存储装置 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN111788040B (zh) |
WO (1) | WO2020133270A1 (zh) |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112507480A (zh) * | 2020-11-25 | 2021-03-16 | 浙江同善人工智能技术有限公司 | 一种惯性参数辨识方法 |
CN112677156A (zh) * | 2020-12-30 | 2021-04-20 | 法奥(淄博)智能装备有限公司 | 一种机器人关节摩擦力补偿方法 |
CN112947069A (zh) * | 2021-01-28 | 2021-06-11 | 内蒙古大学 | 一种移动两轮机器人的控制方法 |
CN113001588A (zh) * | 2021-03-08 | 2021-06-22 | 陕西知感通和物联科技有限公司 | 一种足式机器人动力学参数辨识方法 |
CN113189865A (zh) * | 2021-01-27 | 2021-07-30 | 上海电气智能康复医疗科技有限公司 | 基于动力学参数辨识的康复机器人控制方法、***、设备及介质 |
CN114425770A (zh) * | 2020-10-29 | 2022-05-03 | 北京配天技术有限公司 | 一种工业机器人示教控制方法、电子设备和存储介质 |
CN114636571A (zh) * | 2020-12-16 | 2022-06-17 | 北京配天技术有限公司 | 白盒测试方法、装置、电子设备以及存储介质 |
CN114800519A (zh) * | 2022-05-20 | 2022-07-29 | 天津大学 | 一种考虑摩擦的六自由度工业机器人动力学参数辨识方法 |
CN114888803A (zh) * | 2022-05-19 | 2022-08-12 | 山东新一代信息产业技术研究院有限公司 | 一种基于迭代优化的机械臂动力学参数辨识方法 |
CN114896731A (zh) * | 2022-05-19 | 2022-08-12 | 法奥意威(苏州)机器人***有限公司 | 一种机械传动***的动力学参数辨识方法及相关装置 |
CN115674190A (zh) * | 2022-09-30 | 2023-02-03 | 深圳市越疆科技有限公司 | 协作机械臂及其运动控制方法、碰撞检测方法、控制*** |
CN116175567A (zh) * | 2023-02-14 | 2023-05-30 | 合肥合滨智能机器人有限公司 | 一种机械臂关节摩擦模型参数和动力学参数的辨识方法 |
CN118123846A (zh) * | 2024-05-06 | 2024-06-04 | 佛山华数机器人有限公司 | 一种机器人摩擦力矩和惯性关联力矩的快速分离方法 |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112528434B (zh) * | 2020-12-04 | 2023-01-06 | 上海新时达机器人有限公司 | 信息识别方法、装置、电子设备和存储介质 |
CN113051673B (zh) * | 2020-12-14 | 2023-09-26 | 华南理工大学 | 一种机器人改进Stribeck摩擦模型辨识方法 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH07270229A (ja) * | 1994-03-30 | 1995-10-20 | Toshiba Corp | 回転機器試験装置 |
CN103344243A (zh) * | 2013-07-02 | 2013-10-09 | 北京航空航天大学 | 一种航空遥感惯性稳定平台摩擦参数辨识方法 |
CN104678763A (zh) * | 2015-01-21 | 2015-06-03 | 浙江工业大学 | 基于最小二乘支持向量机的机电伺服***摩擦补偿和动态面控制方法 |
CN105404157A (zh) * | 2016-01-06 | 2016-03-16 | 北京理工大学 | 一种基于规定性能参数估计的自适应控制方法 |
CN106998161A (zh) * | 2017-03-31 | 2017-08-01 | 深圳市伟创电气有限公司 | 一种伺服***的转动惯量辨识方法及装置 |
CN107263467A (zh) * | 2017-05-11 | 2017-10-20 | 广州视源电子科技股份有限公司 | 控制机器人转动关节运动的方法和装置及机器人 |
CN108717492A (zh) * | 2018-05-18 | 2018-10-30 | 浙江工业大学 | 基于改进的人工蜂群算法的机械臂动力学模型辨识方法 |
Family Cites Families (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101850549B (zh) * | 2010-04-30 | 2011-12-28 | 苏州博实机器人技术有限公司 | 一种机器人专用关节特性检测与参数调节装置 |
CN103926875B (zh) * | 2014-04-18 | 2016-08-31 | 东南大学 | 一种滚珠丝杠进给***摩擦补偿方法 |
JP6104463B2 (ja) * | 2014-04-22 | 2017-03-29 | 三菱電機株式会社 | 電動機制御装置 |
CN104638999B (zh) * | 2014-08-27 | 2018-01-19 | 北京理工大学 | 基于分段神经网络摩擦模型的双电机伺服***控制方法 |
CN106695458A (zh) * | 2017-02-22 | 2017-05-24 | 华中科技大学 | 一种数控机床进给***的摩擦力建模方法 |
CN108582078A (zh) * | 2018-05-15 | 2018-09-28 | 清华大学深圳研究生院 | 一种面向直接示教的机械臂零力控制方法 |
CN108890650A (zh) * | 2018-08-30 | 2018-11-27 | 无锡信捷电气股份有限公司 | 基于动力学参数辨识的ptp加速度优化方法及装置 |
-
2018
- 2018-12-28 WO PCT/CN2018/125045 patent/WO2020133270A1/zh active Application Filing
- 2018-12-28 CN CN201880087307.6A patent/CN111788040B/zh active Active
Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPH07270229A (ja) * | 1994-03-30 | 1995-10-20 | Toshiba Corp | 回転機器試験装置 |
CN103344243A (zh) * | 2013-07-02 | 2013-10-09 | 北京航空航天大学 | 一种航空遥感惯性稳定平台摩擦参数辨识方法 |
CN104678763A (zh) * | 2015-01-21 | 2015-06-03 | 浙江工业大学 | 基于最小二乘支持向量机的机电伺服***摩擦补偿和动态面控制方法 |
CN105404157A (zh) * | 2016-01-06 | 2016-03-16 | 北京理工大学 | 一种基于规定性能参数估计的自适应控制方法 |
CN106998161A (zh) * | 2017-03-31 | 2017-08-01 | 深圳市伟创电气有限公司 | 一种伺服***的转动惯量辨识方法及装置 |
CN107263467A (zh) * | 2017-05-11 | 2017-10-20 | 广州视源电子科技股份有限公司 | 控制机器人转动关节运动的方法和装置及机器人 |
CN108717492A (zh) * | 2018-05-18 | 2018-10-30 | 浙江工业大学 | 基于改进的人工蜂群算法的机械臂动力学模型辨识方法 |
Cited By (19)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114425770A (zh) * | 2020-10-29 | 2022-05-03 | 北京配天技术有限公司 | 一种工业机器人示教控制方法、电子设备和存储介质 |
CN112507480B (zh) * | 2020-11-25 | 2024-06-07 | 浙江同善人工智能技术有限公司 | 一种惯性参数辨识方法 |
CN112507480A (zh) * | 2020-11-25 | 2021-03-16 | 浙江同善人工智能技术有限公司 | 一种惯性参数辨识方法 |
CN114636571A (zh) * | 2020-12-16 | 2022-06-17 | 北京配天技术有限公司 | 白盒测试方法、装置、电子设备以及存储介质 |
CN112677156A (zh) * | 2020-12-30 | 2021-04-20 | 法奥(淄博)智能装备有限公司 | 一种机器人关节摩擦力补偿方法 |
CN113189865B (zh) * | 2021-01-27 | 2024-03-26 | 上海际知医疗科技有限公司 | 基于动力学参数辨识的康复机器人控制*** |
CN113189865A (zh) * | 2021-01-27 | 2021-07-30 | 上海电气智能康复医疗科技有限公司 | 基于动力学参数辨识的康复机器人控制方法、***、设备及介质 |
CN112947069A (zh) * | 2021-01-28 | 2021-06-11 | 内蒙古大学 | 一种移动两轮机器人的控制方法 |
CN113001588A (zh) * | 2021-03-08 | 2021-06-22 | 陕西知感通和物联科技有限公司 | 一种足式机器人动力学参数辨识方法 |
CN114888803A (zh) * | 2022-05-19 | 2022-08-12 | 山东新一代信息产业技术研究院有限公司 | 一种基于迭代优化的机械臂动力学参数辨识方法 |
CN114888803B (zh) * | 2022-05-19 | 2024-01-30 | 山东新一代信息产业技术研究院有限公司 | 一种基于迭代优化的机械臂动力学参数辨识方法 |
CN114896731A (zh) * | 2022-05-19 | 2022-08-12 | 法奥意威(苏州)机器人***有限公司 | 一种机械传动***的动力学参数辨识方法及相关装置 |
CN114800519B (zh) * | 2022-05-20 | 2023-09-26 | 天津大学 | 一种考虑摩擦的六自由度工业机器人动力学参数辨识方法 |
CN114800519A (zh) * | 2022-05-20 | 2022-07-29 | 天津大学 | 一种考虑摩擦的六自由度工业机器人动力学参数辨识方法 |
CN115674190A (zh) * | 2022-09-30 | 2023-02-03 | 深圳市越疆科技有限公司 | 协作机械臂及其运动控制方法、碰撞检测方法、控制*** |
CN115674190B (zh) * | 2022-09-30 | 2024-05-07 | 深圳市越疆科技股份有限公司 | 协作机械臂及其运动控制方法、碰撞检测方法、控制*** |
CN116175567A (zh) * | 2023-02-14 | 2023-05-30 | 合肥合滨智能机器人有限公司 | 一种机械臂关节摩擦模型参数和动力学参数的辨识方法 |
CN116175567B (zh) * | 2023-02-14 | 2023-11-24 | 合肥合滨智能机器人有限公司 | 一种机械臂关节摩擦模型参数和动力学参数的辨识方法 |
CN118123846A (zh) * | 2024-05-06 | 2024-06-04 | 佛山华数机器人有限公司 | 一种机器人摩擦力矩和惯性关联力矩的快速分离方法 |
Also Published As
Publication number | Publication date |
---|---|
CN111788040A (zh) | 2020-10-16 |
CN111788040B (zh) | 2023-06-06 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2020133270A1 (zh) | 机器人的动力学参数辨识方法、机器人和存储装置 | |
CN106313044B (zh) | 一种工业机器人前馈力矩补偿方法 | |
JP5996127B2 (ja) | 摩擦同定方法および摩擦同定装置 | |
CN108319228B (zh) | 一种数控***轨迹规划中加减速控制方法 | |
CN109623810B (zh) | 一种机器人平滑的时间最优轨迹规划的方法 | |
CN101369133B (zh) | 基于灰色预估器的低速摩擦伺服***滑模变结构控制方法 | |
CN104638999B (zh) | 基于分段神经网络摩擦模型的双电机伺服***控制方法 | |
JPWO2014021433A1 (ja) | ロボット装置およびその制御方法 | |
CN106020124A (zh) | 伺服马达控制装置以及碰撞检测方法 | |
CN109940609B (zh) | 一种基于中心对称静摩擦模型的机器人动力学建模方法 | |
CN114043480A (zh) | 一种基于模糊控制的自适应阻抗控制算法 | |
CN111965976B (zh) | 基于神经网络观测器的机器人关节滑模控制方法及*** | |
WO2017172367A1 (en) | Automatic determination of maximum acceleration for motion profiles | |
WO2023115784A1 (zh) | 机器人运动信息规划方法及相关装置 | |
CN113051673A (zh) | 一种机器人改进Stribeck摩擦模型辨识方法 | |
CN107363832A (zh) | 一种工业机器人前馈力矩实时计算方法 | |
CN112528434A (zh) | 信息识别方法、装置、电子设备和存储介质 | |
CN110569767B (zh) | 运动参数识别方法、***、设备及计算机可读存储介质 | |
CN108608427A (zh) | 机器人力控牵引过程中的避奇异方法及装置 | |
CN114859708A (zh) | 一种单连杆柔性机械臂跟踪控制方法 | |
CN114179089A (zh) | 一种机械臂的鲁棒区域跟踪控制方法 | |
CN116423522A (zh) | 一种摩擦力自适应补偿的机器人拖动示教零力控制方法 | |
CN116945177A (zh) | 上甑机器人补料路径规划方法、装置、电子设备及介质 | |
JP2014117787A (ja) | 制御装置 | |
Hansen et al. | Optimal motion planning for energy efficient multi-axis applications |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 18944851 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 18944851 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 15/11/2021) |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 18944851 Country of ref document: EP Kind code of ref document: A1 |