CN117086870A - Model processing method, mechanical arm control method, device, equipment and storage medium - Google Patents

Model processing method, mechanical arm control method, device, equipment and storage medium Download PDF

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Publication number
CN117086870A
CN117086870A CN202311090821.4A CN202311090821A CN117086870A CN 117086870 A CN117086870 A CN 117086870A CN 202311090821 A CN202311090821 A CN 202311090821A CN 117086870 A CN117086870 A CN 117086870A
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China
Prior art keywords
mechanical arm
error
model
actual
contact force
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CN202311090821.4A
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Chinese (zh)
Inventor
邝明
马昌训
喻畅
侯力玮
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Hunan Zoomlion Intelligent Aerial Work Machinery Co Ltd
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Hunan Zoomlion Intelligent Aerial Work Machinery Co Ltd
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Priority to CN202311090821.4A priority Critical patent/CN117086870A/en
Publication of CN117086870A publication Critical patent/CN117086870A/en
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    • 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
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • 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
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
    • 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
    • B25J9/1628—Programme controls characterised by the control loop
    • B25J9/1651—Programme controls characterised by the control loop acceleration, rate control
    • 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
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation

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  • Engineering & Computer Science (AREA)
  • Robotics (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Position Or Direction (AREA)

Abstract

The invention relates to the field of data processing, and discloses a model processing method, a mechanical arm control method, a device, equipment and a storage medium. The model processing method comprises the following steps: constructing a target dynamics model of the mechanical arm and the contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms; acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms; determining a position error of each group of actual positions and expected positions of the mechanical arm, a speed error of each group of actual speeds and expected speeds, an acceleration error of each group of actual accelerations and expected accelerations, and a contact force error of each group of actual contact forces and expected contact forces; based on the position error, the speed error, the acceleration error and the contact force error, constructing an impedance model of the mechanical arm; and updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm.

Description

Model processing method, mechanical arm control method, device, equipment and storage medium
Technical Field
The present invention relates to the field of data processing, and in particular, to a model processing method, a mechanical arm control method, a device, equipment, and a storage medium.
Background
An overhead working apparatus is a mechanical apparatus that transports an operator or other equipment to a desired height position, and is widely used in various overhead working scenarios. In general, the hydraulic equipment or the electric equipment drives the hydraulic oil cylinder, and the hydraulic oil cylinder drives the mechanical arm to move, so as to control the lifting or the descending of the working platform connected with the tail end of the mechanical arm, and enable operators or other equipment on the working platform to move to the desired height position. Under the condition that the working platform of the aerial working device is not contacted with a contact object such as a wall and generates contact force, the aerial working device can accurately control the position change of the tail end of the mechanical arm according to parameters such as an angle detected by the sensor, and the working platform connected with the tail end of the mechanical arm can move to a desired height position along a desired track.
In an operation scene that an operation platform is loaded with polishing equipment to polish an object or a cleaning equipment to clean a boss, a mechanical arm drives the operation platform to ascend or descend, the aerial operation equipment is contacted with a contact object such as a wall surface, and the tail end of the mechanical arm receives a contact force from the contact object. However, since the aerial working device cannot directly control the magnitude of the contact force of the tail end of the mechanical arm, the position of the tail end of the mechanical arm in the working scene is required to be dynamically adjusted according to the magnitude of the contact force. When the arm end receives a contact force from a contact object, the position of the arm end cannot be changed correspondingly according to the contact force, and an error exists between the actual position of the arm end and an expected position corresponding to the expected contact force, so that the actual track of the arm during movement is influenced. The actual track and the expected track have larger errors when the mechanical arm moves, so that the mechanical arm cannot move along the expected track, and the high-altitude operation is influenced.
Disclosure of Invention
The invention aims to provide equipment for solving the problem that an actual track and an expected track have large errors when a mechanical arm moves.
In order to achieve the above object, the present invention provides a model processing method, comprising:
constructing a target dynamics model of the mechanical arm and the contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms, wherein the target dynamics model comprises a first inertia matrix of the mechanical arm;
acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms;
determining a position error of each group of actual positions and expected positions of the mechanical arm, a speed error of each group of actual speeds and expected speeds, an acceleration error of each group of actual accelerations and expected accelerations, and a contact force error of each group of actual contact forces and expected contact forces;
constructing an impedance model of the mechanical arm based on the position error, the speed error, the acceleration error and the contact force error, wherein the impedance model comprises a second inertia matrix;
and updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm.
With reference to the first aspect, in a first possible implementation manner, constructing a target dynamics model of the mechanical arm and the contact object in cartesian space according to an angular displacement, an angular velocity, an angular acceleration, and a driving moment of a joint of at least one group of mechanical arms includes:
constructing a first dynamics model of the mechanical arm based on a Lagrangian method by utilizing the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of the mechanical arm;
obtaining a second dynamic model of the mechanical arm and the contact object in the joint space based on the first dynamic model, the jacobian matrix of the mechanical arm and the contact force between at least one group of mechanical arms and the contact object;
and converting the second dynamic model into a target dynamic model of the mechanical arm and the contact object in Cartesian space based on the angle conversion relation between the pose of the mechanical arm and the joints.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the model processing method further includes:
and updating the first dynamics model according to the friction model of the mechanical arm and the interference model of the mechanical arm.
With reference to the first aspect, in a third possible implementation manner, acquiring an actual position, an actual speed, an actual acceleration, and an actual contact force of at least one set of mechanical arms includes:
Under the condition that the mechanical arm moves along a preset track with a preset initial pretightening force, acquiring the motion parameters of at least one group of mechanical arms;
based on each group of motion parameters, the actual position, the actual speed, the actual acceleration and the actual contact force of each group of mechanical arms are respectively determined.
In a second aspect, the present application provides a method for controlling a mechanical arm, the method comprising:
determining a real-time contact force error of the real-time contact force of the mechanical arm and the expected contact force;
converting the real-time contact force error into a target offset of the position of the mechanical arm using a target impedance model, wherein the target impedance model is obtained according to the model processing method as in the first aspect;
and controlling the mechanical arm to move to a preset position based on the target offset.
With reference to the second aspect, in a first possible implementation manner, converting, using a target impedance model, a contact force error into a target offset of a position of the mechanical arm includes:
determining a real-time position error of a real-time position and an expected position of the mechanical arm, a real-time speed error of a real-time speed and an expected speed and a real-time acceleration error of a real-time acceleration and an expected acceleration;
inputting the real-time position error, the real-time speed error, the real-time acceleration error and the real-time contact force error of the mechanical arm into a target impedance model to obtain damping matrix parameters and rigidity matrix parameters of the target impedance model;
The damping matrix parameters, the rigidity matrix parameters and the contact force errors are input into a target impedance model, and the contact force errors are converted into target offset of the position of the mechanical arm by using the target impedance model.
In a third aspect, the present application provides a model processing apparatus comprising:
the dynamic model construction module is used for constructing a target dynamic model of the mechanical arm and the contact object in a Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms, wherein the target dynamic model comprises a first inertial matrix of the mechanical arm;
the parameter acquisition module is used for acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms;
the error determining module is used for determining the position error of each group of actual positions and expected positions of the mechanical arm, the speed error of each group of actual speeds and expected speeds, the acceleration error of each group of actual accelerations and expected accelerations and the contact force error of each group of actual contact forces and expected contact forces;
the impedance model building module is used for building an impedance model of the mechanical arm based on the position error, the speed error, the acceleration error and the contact force error, wherein the impedance model comprises a second inertia matrix;
The target impedance model obtaining module is used for updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm.
In a fourth aspect, the present application provides a robot arm control device, including:
the contact force error acquisition module is used for determining the real-time contact force error between the real-time contact force of the mechanical arm and the expected contact force;
the contact force error conversion module is used for converting the real-time contact force error into a target offset of the position of the mechanical arm by using a target impedance model, wherein the target impedance model is obtained according to the model processing method as the first aspect;
and the mechanical arm control module is used for controlling the mechanical arm to move to a preset position based on the target offset.
In a fifth aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the model processing method as in the first aspect, or implements the robot arm control method as in the second aspect.
In a sixth aspect, the present application provides a machine-readable storage medium, wherein a computer program is stored on the machine-readable storage medium, and when the computer program is executed by a processor, the model processing method as in the first aspect is implemented, or the robot arm control method as in the second aspect is implemented.
The application provides a model processing method, which comprises the following steps: constructing a target dynamics model of the mechanical arm and the contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms; acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms; determining a position error of each group of actual positions and expected positions of the mechanical arm, a speed error of each group of actual speeds and expected speeds, an acceleration error of each group of actual accelerations and expected accelerations, and a contact force error of each group of actual contact forces and expected contact forces; based on the position error, the speed error, the acceleration error and the contact force error, constructing an impedance model of the mechanical arm; and updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm. The target impedance model can be changed according to the pose change of the mechanical arm, and the obtained target impedance model has high accuracy and good adaptability. The target impedance model can track the force at the tail end of the mechanical arm, and then the movement track of the mechanical arm can be corrected by using the target impedance model, so that the mechanical arm can move along the expected track. Meanwhile, only damping matrix parameters and stiffness matrix parameters are needed to be identified, so that matrix parameter identification difficulty of the target impedance model is reduced.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain, without limitation, the embodiments of the application. In the drawings:
FIG. 1 shows a flow chart of a model processing method provided by an embodiment of the present application;
fig. 2 shows a schematic structural diagram of a mechanical arm according to an embodiment of the present application;
FIG. 3 shows a flowchart of a method for controlling a mechanical arm according to an embodiment of the present application
Fig. 4 is a schematic structural diagram of a model processing device according to an embodiment of the present application;
fig. 5 shows a schematic structural diagram of a mechanical arm control device according to an embodiment of the present application.
Detailed Description
The following describes the detailed implementation of the embodiments of the present application with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the application, are not intended to limit the application.
The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.
The terms "comprises," "comprising," "including," or any other variation thereof, are intended to cover a specific feature, number, step, operation, element, component, or combination of the foregoing, which may be used in various embodiments of the present invention, and are not intended to first exclude the presence of or increase the likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the invention belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein in connection with the various embodiments of the invention.
Example 1
Referring to fig. 1, fig. 1 shows a flowchart of a model processing method according to an embodiment of the present application. The model processing method in fig. 1 includes:
s110, constructing a target dynamics model of the mechanical arm and the contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms, wherein the target dynamics model comprises a first inertia matrix of the mechanical arm.
A robot arm is a device that moves to a certain point in space to perform a job in response to a command. Taking the mechanical arm of the aerial working vehicle as an example, when the work such as object polishing and concave-convex cleaning is performed, the mechanical arm moves to a certain point in a space so as to move a working platform connected with the tail end of the mechanical arm to a preset height position. And determining dynamics and dynamic characteristics of the mechanical arms according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joints of at least one group of mechanical arms, and further constructing a target dynamics model of the contact force between the mechanical arms and the contact object in Cartesian space. It is to be understood that the more the number of angular displacements, angular velocities, angular accelerations, and driving moments of the joints of the mechanical arm are obtained, the more accurate the target kinetic model is constructed.
In Cartesian space, the pose of the robotic arm may be determined by a coordinate system, wherein the pose of the robotic arm includes the position and pose of the robotic arm. The contact force between the mechanical arm and the contact object can be the force applied to the mechanical arm by the contact object in the interaction scene of the mechanical arm, and can be the force from the contact object received by the mechanical arm. It should be understood that a force sensor can be used to detect the force from the contact object received by the mechanical arm, and further, a dynamic model is used to convert the contact force between the mechanical arm and the contact object into other parameters to control the movement of the mechanical arm.
In an embodiment of the present application, a target dynamics model of a mechanical arm and a contact object in a cartesian space is constructed according to an angular displacement, an angular velocity, an angular acceleration, and a driving torque of a joint of at least one group of mechanical arms, including:
constructing a first dynamics model of the mechanical arm based on a Lagrangian method by utilizing the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of the mechanical arm;
obtaining a second dynamic model of the mechanical arm and the contact object in the joint space based on the first dynamic model, the jacobian matrix of the mechanical arm and the contact force between at least one group of mechanical arms and the contact object;
And converting the second dynamic model into a target dynamic model of the mechanical arm and the contact object in Cartesian space based on the angle conversion relation between the pose of the mechanical arm and the joints.
The Lagrangian method is to obtain the motion law of the mechanical arm in space according to the change law of parameters such as angular displacement, angular velocity and angular acceleration of the tail end of the mechanical arm when the tail end of the mechanical arm moves from one point to another point. Utilizing angular displacement, angular speed, angular acceleration and driving moment of joints of the mechanical arm, and constructing a first dynamics model of the mechanical arm based on a Lagrangian method:
wherein θ is the angular displacement of each joint of the mechanical arm,for the angular velocity of each joint of the arm, < >>Angular acceleration for each joint of a robotic armA degree; m (θ) is the inertial matrix of the mechanical arm, < ->The gravity matrix is the centripetal force and the Golgi force matrix of the mechanical arm, G (theta) is the gravity matrix of the mechanical arm, and tau is the driving moment of the mechanical arm.
Referring to fig. 2, fig. 2 shows a schematic structural diagram of a mechanical arm according to an embodiment of the present application.
For ease of understanding, embodiments of the present application provide a robotic arm that is disposed at two joints of an overhead working vehicle, i.e., the robotic arm includes a first joint and a second joint. As shown in the figure, θ 1 For angular displacement of the first joint, θ 2 The angular velocities and angular accelerations of the first and second joints are not shown in the figures for the angular displacement of the second joint.
And constructing a first dynamics model of the mechanical arm based on the Lagrangian method by utilizing the angular displacement, the angular velocity, the angular acceleration and the driving moment of the first joint and the second joint. And obtaining a second dynamic model of the mechanical arm and the contact object under the joint space based on the first dynamic model, the jacobian matrix of the mechanical arm and the contact force between the mechanical arm and the contact object. And converting the second dynamic model into a target dynamic model of the mechanical arm and the contact object in Cartesian space based on the angle conversion relation between the pose of the mechanical arm and the joints.
In an embodiment of the present application, the model processing method further includes:
and updating the first dynamics model according to the friction model of the mechanical arm and the interference model of the mechanical arm.
In order to improve the accuracy of the first dynamics model, the first dynamics model is updated according to the friction model of the mechanical arm and the interference model of the mechanical arm. The updated first dynamics model is:
wherein θ is the angular displacement of each joint of the mechanical arm,for the angular velocity of each joint of the arm, < > >Angular acceleration for each joint of the robotic arm; m (θ) is the inertial matrix of the mechanical arm, < ->The gravity matrix is the centripetal force and the Golgi force matrix of the mechanical arm, G (theta) is the gravity matrix of the mechanical arm, and tau is the driving moment of the mechanical arm; />Is a friction model of a mechanical arm, tau d The interference model of the mechanical arm is used for representing that the mechanical arm is interfered by external load and external environment.
Under the condition that matrix parameters of the first dynamics model are known or the accuracy of the first dynamics model is high, the first dynamics model can be directly utilized to obtain a second dynamics model of the mechanical arm and the contact object in joint space. In case the matrix parameters of the first kinetic model are unknown or the accuracy of the first kinetic model is low, the first kinetic model needs to be updated. For easy understanding, in the embodiment of the present application, the updated first dynamics model, the jacobian matrix of the mechanical arm, and the contact force between the mechanical arm and the contact object are used to obtain a second dynamics model of the mechanical arm and the contact object in the joint space:
wherein θ is the angular displacement of each joint of the mechanical arm in joint space,for the angular velocity of each joint of the manipulator in joint space +. >Angular acceleration for each joint of the mechanical arm in joint space; m (θ) is the inertial matrix of the second kinetic model,>g (theta) is a gravity matrix of the second dynamics model, and tau is a driving moment of the mechanical arm; />For the friction model of the mechanical arm in joint space, τ d Is an interference model of the mechanical arm in joint space; j (theta) T To the jacobian matrix of the mechanical arm in joint space, F e Is the contact force between the mechanical arm and the contact object. It is to be understood that F e The moment between the mechanical arm and the contact object can also be the moment, and the moment is not repeated here.
Based on the angle conversion relation between the pose of the mechanical arm and the joints, converting the second dynamics model into a target dynamics model of the mechanical arm and the contact object in a Cartesian space:
wherein x is the actual position of the end of the mechanical arm in Cartesian space,for the actual speed of the robot arm tip in Cartesian space,/and/or>The actual acceleration of the tail end of the mechanical arm in Cartesian space; m is M x For parameters of the inertial matrix of the target kinetic model, i.e. parameters of the first inertial matrix, C x G is a parameter of a centripetal force and a Golgi force matrix of a target dynamics model x Is the object ofParameters of a gravity matrix of the kinetic model; />F for the joint friction model of the mechanical arm in Cartesian space τd Is an interference model of the mechanical arm in Cartesian space; f (F) x For driving the mechanical arm in Cartesian space to drive the matrix, F e Is the contact force or moment between the mechanical arm and the contact object in the Cartesian space.
S120, acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms.
When the tail end of the mechanical arm contacts with the contact object through the working platform or contacts with the contact object through equipment carried on the working platform, the working platform connected with the tail end of the mechanical arm ascends or descends along the contact object when the mechanical arm moves along the track. The method comprises the steps of obtaining the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arm tail ends.
In an embodiment of the present application, obtaining an actual position, an actual speed, an actual acceleration, and an actual contact force of at least one group of mechanical arms includes:
under the condition that the mechanical arm moves along a preset track with a preset initial pretightening force, acquiring the motion parameters of at least one group of mechanical arms;
based on each group of motion parameters, the actual position, the actual speed, the actual acceleration and the actual contact force of each group of mechanical arms are respectively determined.
The pre-tightening force is a force applied in advance to prevent gaps or relative sliding in connection, and the value of the pre-tightening force is set according to actual requirements, and is not limited herein. In the implementation of the application, the preset initial pretightening force is the contact force between the tail end and the contact object in the initial state of the mechanical arm. The mechanical arm is controlled to move along the same preset track with preset initial pretightening forces with different values. Setting a motion parameter sampling time, and acquiring motion parameters of at least one group of mechanical arms under the condition that the mechanical arms move along a preset track with a preset initial pretightening force. It is to be understood that when the mechanical arm is controlled to move along the same preset track, the motion parameters can be acquired by different values of preset initial pretightening force. The more motion parameters are acquired, the higher the accuracy of the constructed model is.
The type of the motion parameter is set according to actual requirements, and is not limited herein. For ease of understanding, the motion parameters in the embodiments of the present application include an angular velocity, an angular displacement, and a force of the end of the mechanical arm, where the force of the end of the mechanical arm may be obtained by a force sensor disposed at the end of the mechanical arm, which is not described herein. Based on each set of motion parameters, the actual position, the actual speed and the actual acceleration of each set of mechanical arms are determined. The forward kinematics model of the mechanical arm can be established by using a rotation method, and the motion parameters are input into the forward kinematics model to solve the actual position, the actual speed, the actual acceleration and the actual contact force of the mechanical arm, which are not described herein.
S130, determining the position error of each group of actual positions and expected positions of the mechanical arm, the speed error of each group of actual speeds and expected speeds, the acceleration error of each group of actual accelerations and expected accelerations and the contact force error of each group of actual contact forces and expected contact forces.
In the process of controlling the movement of the mechanical arm, the mechanical arm cannot move along the expected track due to parameter errors. And acquiring errors of the actual positions and the expected positions of the tail ends of the mechanical arms of each group, and obtaining the position errors when the tail ends of the mechanical arms move along the track. And obtaining errors of the actual speed and the expected speed of the tail end of each group of mechanical arms, and obtaining the speed errors when the tail ends of the mechanical arms move along the track. And acquiring errors of actual acceleration and expected acceleration of the tail end of each group of mechanical arms, and obtaining an acceleration error when the tail end of the mechanical arm moves along the track. And acquiring errors of actual contact force and expected contact force of the tail ends of each group of mechanical arms, and obtaining the contact force errors when the tail ends of the mechanical arms move along the track.
And S140, constructing an impedance model of the mechanical arm based on the position error, the speed error, the acceleration error and the contact force error, wherein the impedance model comprises a second inertia matrix.
And determining the impedance characteristic of the mechanical arm through the position error, the speed error, the acceleration error and the contact force error. The impedance characteristics of the mechanical arm are usually used for constructing an impedance model of the mechanical arm. Specifically, the general expression of the impedance model of the mechanical arm is:
wherein e is the position error of the tail end of the mechanical arm,for the speed error of the robot arm end, +.>The acceleration error at the tail end of the mechanical arm is delta F, and the contact force error of the mechanical arm is delta F; m is an inertia matrix of the impedance model, namely a second inertia matrix of the impedance model in the embodiment; b is a damping matrix of the impedance model, and K is a rigidity matrix of the impedance model. In the general expression of the impedance model, the inertia matrix parameter, the damping matrix parameter and the stiffness matrix parameter of the impedance model are unknown, so that the matrix parameter identification difficulty of the impedance model is high.
And S150, updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm.
Regarding a target dynamics model of the mechanical arm and the contact object in a Cartesian space, a first inertia matrix of the target dynamics model can represent equivalent mass properties of the mechanical arm under the action of external force, and further a second inertia matrix of the impedance model can be updated into the first inertia matrix to obtain a target impedance model of the mechanical arm:
Wherein e is the position error of the tail end of the mechanical arm,for the speed error of the robot arm end, +.>The acceleration error at the tail end of the mechanical arm is delta F, and the contact force error of the mechanical arm is delta F; m is M x The inertia matrix of the target dynamics model is the first inertia matrix of the target dynamics model in the embodiment; b is a damping matrix of the impedance model, and K is a rigidity matrix of the impedance model. The target impedance model has a higher accuracy than an empirically simplified impedance model.
The inertia matrix of the target impedance model is a first inertia matrix of the target dynamics model comprising a mechanical arm, the target dynamics model is a dynamics model expressed by joint angle parameters of the mechanical arm, and the pose of the mechanical arm has a conversion relation with the angle of the joint. The target impedance model can be changed according to the pose change of the mechanical arm, and the obtained target impedance model has high accuracy and good adaptability. The target impedance model can track the force at the tail end of the mechanical arm, and then the movement track of the mechanical arm can be corrected by using the target impedance model, so that the mechanical arm can move along the expected track.
Meanwhile, when matrix parameter identification is carried out on the target impedance model, only damping matrix parameters and stiffness matrix parameters are needed to be identified. As the number of matrix parameters to be identified is reduced, the matrix parameter identification difficulty of the target impedance model is reduced, and the accuracy of the target impedance model is improved.
The application provides a model processing method, which comprises the following steps: constructing a target dynamics model of the mechanical arm and the contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms; acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms; determining a position error of each group of actual positions and expected positions of the mechanical arm, a speed error of each group of actual speeds and expected speeds, an acceleration error of each group of actual accelerations and expected accelerations, and a contact force error of each group of actual contact forces and expected contact forces; based on the position error, the speed error, the acceleration error and the contact force error, constructing an impedance model of the mechanical arm; and updating the second inertia matrix of the impedance model into the first inertia matrix to obtain a target impedance model of the mechanical arm. The target impedance model can be changed according to the pose change of the mechanical arm, and the obtained target impedance model has high accuracy and good adaptability. The target impedance model can track the force at the tail end of the mechanical arm, and then the movement track of the mechanical arm can be corrected by using the target impedance model, so that the mechanical arm can move along the expected track. Meanwhile, only damping matrix parameters and stiffness matrix parameters are needed to be identified, so that matrix parameter identification difficulty of the target impedance model is reduced.
Example 2
Referring to fig. 3, fig. 3 shows a flowchart of a method for controlling a mechanical arm according to an embodiment of the application. The mechanical arm control method in fig. 2 includes:
s210, determining a real-time contact force error of the real-time contact force of the mechanical arm and the expected contact force.
The real-time contact force of the mechanical arm is obtained, wherein the real-time contact force of the mechanical arm can be obtained by using a force sensor arranged at the tail end of the mechanical arm, and the detailed description is omitted. And determining a real-time contact force error of the real-time contact force of the mechanical arm and the expected contact force.
S220, converting the real-time contact force error into a target offset of the position of the mechanical arm by using a target impedance model, wherein the target impedance model is obtained according to the model processing method as in the embodiment 1.
When the obtained actual contact force and the expected contact force are in error, the tail end of the mechanical arm cannot move along the expected track. The contact force error is input to a target impedance model, and the contact force error is converted into a target offset of the position of the mechanical arm by using the target impedance model.
In an embodiment of the present application, converting a contact force error into a target offset of a position of a robot arm using a target impedance model includes:
Determining a real-time position error of a real-time position and a desired position of the mechanical arm, a real-time speed error of a real-time speed and a desired speed, and a real-time acceleration error of a real-time acceleration and a desired acceleration
Inputting the real-time position error, the real-time speed error, the real-time acceleration error and the real-time contact force error of the mechanical arm into a target impedance model to obtain damping matrix parameters and rigidity matrix parameters of the target impedance model;
the damping matrix parameters, the rigidity matrix parameters and the contact force errors are input into a target impedance model, and the contact force errors are converted into target offset of the position of the mechanical arm by using the target impedance model.
When matrix parameters of the target impedance model are determined, the real-time position, the real-time speed and the real-time acceleration of the mechanical arm are required to be obtained, and then the real-time position error, the real-time speed error and the real-time acceleration error of the real-time position and the expected position of the mechanical arm and the real-time acceleration error of the real-time acceleration and the expected acceleration of the mechanical arm are determined.
And inputting the real-time position error, the real-time speed error, the real-time acceleration error and the real-time contact force error of the mechanical arm into the target impedance model to obtain damping matrix parameters and rigidity matrix parameters of the target impedance model. It is to be understood that the more the data amount of the position error, the speed error, the acceleration error and the contact force error input to the target impedance model, the more accurate the damping matrix parameters and the stiffness matrix parameters are identified. The damping matrix parameters and the stiffness matrix parameters of the target impedance model can be obtained through identification by using a genetic algorithm or other identification algorithms, and the damping matrix parameters and the stiffness matrix parameters of the target impedance model can be obtained by using a neural network, which are not described herein.
The inertia matrix of the target impedance model is the first inertia matrix of the target dynamic model of the mechanical arm and the contact object in Cartesian space, and only damping matrix parameters and rigidity matrix parameters of the target impedance model are needed to be identified in the embodiment of the application, so that the matrix parameter identification difficulty of the target impedance model is reduced. The damping matrix parameters, the rigidity matrix parameters and the contact force errors are input into a target impedance model, and the contact force errors are converted into target offset of the position of the mechanical arm by using the target impedance model.
And S230, controlling the mechanical arm to move to a preset position based on the target offset.
Since the target impedance model has high accuracy and good adaptability, the target offset obtained by using the target impedance model also has high accuracy. Based on the target offset, the desired trajectory of the robotic arm is updated. And controlling the position of the tail end of the mechanical arm along the updated expected track so that the mechanical arm moves to a preset position. The mechanical arm is controlled to move to the preset position based on the target offset, so that the position of the tail end of the mechanical arm is dynamically adjusted according to the size of the expected contact force, and the actual contact force of the tail end of the mechanical arm can be controlled by tracking the expected contact force.
Example 3
Referring to fig. 4, fig. 4 is a schematic structural diagram of a model processing device according to an embodiment of the application. The model processing apparatus 300 in fig. 4 includes:
the dynamics model construction module 310 is configured to construct a target dynamics model of the mechanical arm and the contact object in cartesian space according to the angular displacement, the angular velocity and the angular acceleration of the joint of at least one group of mechanical arms, where the target dynamics model includes a first inertial matrix of the mechanical arm;
the parameter obtaining module 320 is configured to obtain an actual position, an actual speed, and an actual acceleration of at least one group of mechanical arms;
an error determination module 330, configured to determine a position error of each set of actual positions and desired positions of the mechanical arm, a speed error of each set of actual speeds and desired speeds, and an acceleration error of each set of actual accelerations and desired accelerations;
the target impedance model obtaining module 340 is configured to construct an impedance model of the mechanical arm based on the position error, the velocity error, and the acceleration error, where the impedance model includes a second inertia matrix;
the target impedance model obtaining module 350 is configured to update the second inertia matrix of the impedance model to the first inertia matrix, and obtain a target impedance model of the mechanical arm.
In an embodiment of the present application, the dynamics model construction module 310 includes:
the first dynamics model construction submodule is used for constructing a first dynamics model of the mechanical arm based on a Lagrangian method by utilizing the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of the mechanical arm;
the second dynamics model construction submodule is used for obtaining a second dynamics model of the mechanical arm and the contact object under the joint space based on the first dynamics model, the jacobian matrix of the mechanical arm and the contact force between at least one group of mechanical arms and the contact object;
the target dynamics model conversion sub-module is used for converting the second dynamics model into a target dynamics model of the mechanical arm and the contact object in a Cartesian space based on the angle conversion relation between the pose of the mechanical arm and the joint.
In an embodiment of the present application, the model processing apparatus 300 further includes:
and the first dynamics model updating module is used for updating the first dynamics model according to the friction force model of the mechanical arm and the interference model of the mechanical arm.
In an embodiment of the present application, the parameter obtaining module 320 includes:
the motion parameter acquisition sub-module is used for acquiring motion parameters of at least one group of mechanical arms under the condition that the mechanical arms move along a preset track with a preset initial pretightening force;
And the actual parameter determination submodule is used for respectively determining the actual position, the actual speed, the actual acceleration and the actual contact force of each group of mechanical arms based on each group of motion parameters.
The model processing device 300 is configured to perform the corresponding steps in the above model processing method, and specific implementation of each function is not described herein. Furthermore, the alternative example in embodiment 1 is also applicable to the model processing apparatus 300 of embodiment 2.
Example 4
Referring to fig. 5, fig. 5 shows a schematic structural diagram of a mechanical arm control device according to an embodiment of the application. The robot arm control device 400 in fig. 5 includes:
a contact force error obtaining module 410, configured to obtain an actual contact force of at least one group of mechanical arms, and obtain a contact force error between the actual contact force and an expected contact force of each group of mechanical arms;
a contact force error conversion module 420 for converting the contact force error into a target offset of the position of the mechanical arm using a target impedance model, wherein the target impedance model is obtained according to the model processing method as in embodiment 1;
the mechanical arm control module 430 is configured to control the mechanical arm to move to a preset position based on the target offset.
In an embodiment of the present application, the contact force error conversion module 420 includes:
The real-time error determination submodule is used for determining a real-time position error of a real-time position and an expected position of the mechanical arm, a real-time speed error of a real-time speed and an expected speed and a real-time acceleration error of a real-time acceleration and an expected acceleration;
the matrix parameter obtaining submodule is used for inputting the real-time position error, the real-time speed error, the real-time acceleration error and the real-time contact force error of the mechanical arm into the target impedance model to obtain damping matrix parameters and rigidity matrix parameters of the target impedance model;
the target offset obtaining submodule is used for inputting the damping matrix parameters, the rigidity matrix parameters and the contact force errors into a target impedance model, and converting the contact force errors into target offsets of the positions of the mechanical arms by using the target impedance model.
The robot control device 400 is configured to perform the corresponding steps in the above-mentioned robot control method, and specific implementation of each function is not described herein. In addition, the alternative example in embodiment 1 is also applicable to the robot arm control device 400 of embodiment 2.
The embodiment of the application also provides a computer device, which comprises a memory and a processor, wherein the memory stores a computer program, and the computer program realizes the model processing method as in the embodiment 1 or realizes the mechanical arm control method as in the embodiment 2 when being executed by the processor.
The dynamics model construction module 310, the position velocity acceleration acquisition module 320, the error acquisition module 330, the impedance model construction module 340, the impedance model construction module 350, the contact force acquisition module 360, the contact force error conversion module 370, the mechanical arm control module 380, and the like in the present embodiment are all stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, and the problem that the actual track and the expected track have larger errors when the mechanical arm moves is solved by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
The embodiment of the application also provides a machine-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the model processing method as in embodiment 1 or implements the mechanical arm control method as in embodiment 2.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Machine-readable storage media, including both non-transitory and removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims (10)

1. A model processing method, characterized in that the model processing method comprises:
constructing a target dynamics model of the mechanical arm and a contact object in Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of a joint of at least one group of mechanical arms, wherein the target dynamics model comprises a first inertia matrix of the mechanical arm;
acquiring at least one group of actual position, actual speed, actual acceleration and actual contact force of the mechanical arm;
determining a position error of each group of the actual position and the expected position of the mechanical arm, a speed error of each group of the actual speed and the expected speed, an acceleration error of each group of the actual acceleration and the expected acceleration, and a contact force error of each group of the actual contact force and the expected contact force;
constructing an impedance model of the mechanical arm based on the position error, the speed error, the acceleration error and the contact force error, wherein the impedance model comprises a second inertia matrix;
And updating the second inertia matrix of the impedance model into the first inertia matrix to obtain the target impedance model of the mechanical arm.
2. The model processing method according to claim 1, wherein the constructing a target dynamics model of the mechanical arm and the contact object in a cartesian space according to the angular displacement, the angular velocity, the angular acceleration, and the driving moment of the joint of the at least one group of mechanical arms comprises:
constructing a first dynamics model of the mechanical arm based on a Lagrangian method by utilizing the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of the mechanical arm;
obtaining a second dynamic model of the mechanical arm and the contact object under joint space based on the first dynamic model, the jacobian matrix of the mechanical arm and at least one group of contact forces between the mechanical arm and the contact object;
and converting the second dynamic model into a target dynamic model of the mechanical arm and the contact object in a Cartesian space based on the angle conversion relation between the pose of the mechanical arm and the joint.
3. The model processing method according to claim 2, characterized in that the model processing method further comprises:
And updating the first dynamics model according to the friction model of the mechanical arm and the interference model of the mechanical arm.
4. The method of claim 1, wherein the obtaining at least one set of the actual position, the actual velocity, the actual acceleration, and the actual contact force of the mechanical arm comprises:
under the condition that the mechanical arm moves along a preset track with a preset initial pretightening force, acquiring at least one group of motion parameters of the mechanical arm;
and respectively determining the actual position, the actual speed, the actual acceleration and the actual contact force of each group of mechanical arms based on each group of motion parameters.
5. The mechanical arm control method is characterized by comprising the following steps of:
determining a real-time contact force error of the real-time contact force of the mechanical arm and the expected contact force;
converting the real-time contact force error into a target offset of the position of the robotic arm using a target impedance model, wherein the target impedance model is derived according to the model processing method of any one of claims 1-4;
and controlling the mechanical arm to move to a preset position based on the target offset.
6. The method according to claim 5, wherein the converting the contact force error into the target offset of the position of the robot arm using a target impedance model includes:
Determining a real-time position error of a real-time position and an expected position of the mechanical arm, a real-time speed error of a real-time speed and an expected speed and a real-time acceleration error of a real-time acceleration and an expected acceleration;
inputting the real-time position error, the real-time speed error, the real-time acceleration error and the real-time contact force error of the mechanical arm into the target impedance model to obtain damping matrix parameters and rigidity matrix parameters of the target impedance model;
and inputting the damping matrix parameters, the rigidity matrix parameters and the contact force errors into the target impedance model, and converting the contact force errors into target offset of the position of the mechanical arm by using the target impedance model.
7. A model processing apparatus, characterized in that the model processing apparatus comprises:
the dynamic model construction module is used for constructing a target dynamic model of the mechanical arm and the contact object in a Cartesian space according to the angular displacement, the angular speed, the angular acceleration and the driving moment of the joint of at least one group of mechanical arms, wherein the target dynamic model comprises a first inertial matrix of the mechanical arm;
the parameter acquisition module is used for acquiring the actual position, the actual speed, the actual acceleration and the actual contact force of at least one group of mechanical arms;
The error determining module is used for determining the position error of each group of the actual position and the expected position of the mechanical arm, the speed error of each group of the actual speed and the expected speed, the acceleration error of each group of the actual acceleration and the expected acceleration and the contact force error of each group of the actual contact force and the expected contact force;
an impedance model building module, configured to build an impedance model of the mechanical arm based on the position error, the velocity error, the acceleration error, and the contact force error, where the impedance model includes a second inertia matrix;
and the target impedance model obtaining module is used for updating the second inertia matrix of the impedance model into the first inertia matrix to obtain the target impedance model of the mechanical arm.
8. A robot arm control device, characterized in that the robot arm control device comprises:
the contact force error acquisition module is used for determining the real-time contact force error between the real-time contact force of the mechanical arm and the expected contact force;
a contact force error conversion module for converting the real-time contact force error into a target offset of the position of the mechanical arm using a target impedance model, wherein the target impedance model is obtained according to the model processing method according to any one of claims 1 to 4;
And the mechanical arm control module is used for controlling the mechanical arm to move to a preset position based on the target offset.
9. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the model processing method according to any one of claims 1 to 4 or implements the robot arm control method according to any one of claims 5 to 6.
10. A machine-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the model processing method according to any one of claims 1 to 4 or implements the robot arm control method according to any one of claims 5 to 6.
CN202311090821.4A 2023-08-28 2023-08-28 Model processing method, mechanical arm control method, device, equipment and storage medium Pending CN117086870A (en)

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