CN115338869A - Master-slave control method and system for master-slave heterogeneous teleoperation system - Google Patents

Master-slave control method and system for master-slave heterogeneous teleoperation system Download PDF

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CN115338869A
CN115338869A CN202211111162.3A CN202211111162A CN115338869A CN 115338869 A CN115338869 A CN 115338869A CN 202211111162 A CN202211111162 A CN 202211111162A CN 115338869 A CN115338869 A CN 115338869A
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slave
master
robot
end robot
mapping
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杨克己
张亚南
金浩然
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Zhejiang University ZJU
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Zhejiang University ZJU
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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
    • B25J9/1615—Programme controls characterised by special kind of manipulator, e.g. planar, scara, gantry, cantilever, space, closed chain, passive/active joints and tendon driven manipulators
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J13/00—Controls for manipulators
    • 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

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  • Robotics (AREA)
  • Mechanical Engineering (AREA)
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  • General Health & Medical Sciences (AREA)
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Abstract

The invention discloses a master-slave control method and a system of a master-slave heterogeneous teleoperation system, which comprises the following steps: an operator inputs connecting rod parameters of the slave end robot to carry out forward kinematics and inverse kinematics analysis to obtain forward kinematics and inverse kinematics models; an operator modifies the master-slave mapping proportionality coefficient to realize the motion of the slave-end robot with different efficiencies and precisions; a variable proportion incremental master-slave mapping method is designed to realize the mapping from the position increment of the master-end robot to the position increment of the slave-end robot, a BP neural network is designed to predict and compensate master-slave following errors, and the master-slave following errors are further reduced. The invention can realize the large-range movement of the slave robot in the free space, and also ensures the movement positioning precision in the fine operation space, and has strong universality and convenient implementation.

Description

Master-slave control method and system for master-slave heterogeneous teleoperation system
Technical Field
The invention belongs to the field of teleoperation robot control, and particularly relates to a master-slave control method and system of a master-slave heterogeneous teleoperation system.
Background
With the rapid development of robot technology, robots have been widely used in various fields of industrial production and daily life. However, in a complex and variable application scenario, such as a nuclear plant, a chemical plant, an underwater operation environment and an aviation operation environment, a manual operation mode cannot be adopted due to a severe working environment or personnel safety hazard, and a fully autonomous operation mode of a robot cannot be adopted due to unstructured working field environment and limited level of robot intelligence. The teleoperation can combine human intelligence and the robot accurately, an operator generates a motion control signal by controlling the master robot and transmits the motion control signal to the slave robot through a communication network, the slave robot moves and works in a complex and dangerous environment according to a received instruction and simultaneously feeds back a working state, and the operator judges and makes a decision according to feedback information. The teleoperation not only protects the health and the safety of personnel, but also expands the operational capability of the personnel and improves the operation accuracy. Teleoperation techniques are currently widely used in the nuclear industry, medical surgery, aeronautics and oceanics.
The teleoperation system is divided into a master-slave isomorphism configuration and a master-slave isomorphism configuration according to whether the mechanical structures of the master-end robot and the slave-end robot are the same or not. The master-slave heterogeneous teleoperation system has various forms and strong universality and is widely applied because the master-end robot and the slave-end robot are independently designed. One key problem of the master-slave heterogeneous teleoperation system is that the working spaces of the master-side robot and the slave-side robot are not consistent, so that a proper master-slave mapping method needs to be designed to realize master-slave space matching.
Common master-slave mapping methods for master-slave heterogeneous teleoperation systems include absolute mapping, incremental mapping, constant-ratio mapping, variable-ratio mapping, and the like. The absolute mapping needs to accurately model the working spaces of the master-end robot and the slave-end robot to solve the mapping function, however, the shape and the size of the working space of the master-end robot and the slave-end robot are greatly different, the modeling of the mapping function of the working space is complex, and the mapping accuracy is difficult to guarantee. The incremental mapping method does not need to establish a working space mapping relation between the master end robot and the slave end robot, but establishes a mapping relation between the position increment of the master end and the position increment of the slave end, so that the mapping function can be suspended at any time to adjust the position of the master end robot, and the operation flexibility is strong. The constant-scale mapping is to map the primary-end increment to the secondary-end increment through a fixed scale coefficient, and is difficult to adapt to the motion efficiency and precision of different scenes. The variable-scale mapping is that an operator sets a mapping scale factor according to the slave-end feedback information so as to realize quick movement or fine movement, and the operation flexibility is strong.
When the slave-end robot carries out obstacle avoidance and operation, if the slave-end robot has large following error and low positioning accuracy, the slave-end robot can easily send violent collision with an obstacle or an operation target, and safety accidents are caused. Therefore, the master-slave following error needs to be reduced, and the high-precision end positioning of the slave-end robot is ensured. In addition, through a large amount of experimental data, the following error of the slave robot at the next moment is closely related to the terminal movement speed, the following error and the position increment at the current moment.
Disclosure of Invention
The invention aims to provide a master-slave control method of a master-slave heterogeneous teleoperation system, which realizes the mapping from a small-range working space of a master end to a large-range working space of a slave end, can ensure the flexibility of large-range movement in a free space of a slave end robot, can reduce master-slave following errors and ensures accurate positioning during fine operation.
The invention adopts the following technical scheme: a master-slave control method of a master-slave heterogeneous teleoperation system is applied to the master-slave heterogeneous teleoperation system, and the system comprises a master-end robot and a slave-end robot which have different mechanical structures, and a master-slave control system which is connected with the master-end robot and the slave-end robot; the master-slave control method comprises the following steps:
(1) Modeling from end-robot kinematics; performing forward kinematics and inverse kinematics analysis according to the connecting rod parameters of the slave end robot to obtain forward kinematics and inverse kinematics models;
(2) Controlling the master position and the slave position; acquiring position increment of the master-end robot, and calculating to obtain an expected position of the slave-end robot by a variable ratio incremental master-slave mapping method; inputting the tail end moving speed, the following error and the position increment of the slave robot into a BP neural network to output a predicted following error, and compensating the predicted error to an expected position; inputting the compensated expected position into the slave end robot inverse kinematics model to solve an expected joint angle, and finally realizing joint angle control through a PD model;
(3) Performing master-slave adjustment; in the process of controlling the master position and the slave position, the start and stop of the master position and the slave position are detected in real time, and the step (2) is returned when the function is started; when master-slave adjustment is needed, the master-slave position control function is needed to be suspended, the slave-end robot stops moving, and the master-slave adjustment comprises the steps of modifying master-slave mapping proportionality coefficients and/or adjusting the position of the master-end robot; the step of modifying the master-slave mapping proportionality coefficient is to realize the motion of the slave-end robot with different efficiencies and precisions by modifying the master-slave mapping proportionality coefficient by an operator.
Further, the step (2) specifically comprises:
acquiring the tail end position P of the main-end robot at the current moment t m (t) and the end position P of the last time (t-1) m (t-1) obtaining the end position increment delta P of the current time t m (t)：
ΔP m (t)＝P m (t)-P m (t-1)
The main end robot is enabled to be in delta P from time (t-1) to t m (t) is mapped to position increment delta P from the end robot at time t to (t + 1) through a variable ratio increment formula s (t+1)＝KΔP m (t), wherein K is a 3 × 3 master-slave mapping diagonal coefficient matrix; then the delta P s (t + 1) to the desired position of the slave robot at the current time
Figure BDA0003843211480000021
To obtain the desired position at the next moment
Figure BDA0003843211480000022
Figure BDA0003843211480000023
Inputting the joint angle of the slave end robot at the current moment into a positive kinematic model to solve the position of the slave end robot terminal:
P s (t)＝FK(θ s (t))
wherein P is s (t) and θ s (t) indicates the slave end robots are currently present, respectivelyThe terminal position and joint angle of the moment, FK (-) represents solving the function from the positive kinematics of the end robot; from P s (t) and
Figure BDA0003843211480000031
obtaining a master-slave following error e (t) of the slave robot in the sampling time:
Figure BDA0003843211480000032
compensating the expected position of the slave robot by the error and the predicted following error of the BP neural network
Figure BDA0003843211480000033
The calculation formula can be modified as follows:
Figure BDA0003843211480000034
wherein e pred For following errors of BP neural network prediction, will
Figure BDA0003843211480000035
Inputting the angle into a slave end robot inverse kinematics model to solve to obtain an expected joint angle:
Figure BDA0003843211480000036
wherein
Figure BDA0003843211480000037
Representing the expected joint angle from the end robot, IK (-) represents solving the function from the inverse kinematics of the end robot; and finally, realizing the control of the joint angle of the slave robot through a PD model.
Further, the BP neural network prediction following error specifically includes:
establishing a three-layer BP neural network model, which comprises an input layer, a hidden layer and an output layer; the input of the BP neural network is the movement speed, the following error and the position increment of the tail end of the robot along the X axis, the Y axis and the Z axis at the current moment, so that an input layer has 9 neurons; the predicted output of the network is the master-slave following error of the next moment, so the output layer has 3 neurons; the number of the neurons of the hidden layer is 20; the neuron activation function is a ReLu function, adam is adopted as the optimization function, and hyper-parameters are all default parameters of a sklern function library;
under the condition of error prediction and compensation without a BP neural network, operating personnel controls the master-end robot to move according to different master-slave mapping proportion coefficients and speeds, controls the slave-end robot to move by a variable proportion incremental master-slave mapping method, acquires training data, and inputs the training data into the BP neural network to train a fitting regression function;
in the teleoperation process, the movement speed, the following error and the position increment of the tail end of the end robot along the X, Y and Z axes at the current moment are input into a trained BP neural network to predict the following error e at the next moment pred And compensated into the desired position.
On the other hand, the invention also provides a master-slave control system of the master-slave heterogeneous teleoperation system, which comprises a slave-end robot kinematics modeling module, a master-slave mapping proportionality coefficient modification module, a master-slave adjustment module and a master-slave position control module;
the slave end robot kinematic modeling module performs forward kinematic analysis and inverse kinematic analysis according to the slave end robot connecting rod parameters to obtain forward kinematic models and inverse kinematic models, and can calculate the tail end position of the slave end robot according to the joint angle of the slave end robot or calculate the joint angle according to the tail end position;
the master-slave mapping proportionality coefficient modification module modifies master-slave mapping coefficients according to the motion state of the fed-back slave end robot by an operator, and scales the proportionality coefficients in a free space to realize large-range rapid motion; when the robot approaches to an obstacle or works, the scale factor is reduced, so that the fine movement in a smaller range is realized, and the positioning precision of the tail end is ensured;
the master-slave adjusting module is used for adjusting the position of the master-end robot and/or modifying the master-slave motion mapping proportionality coefficient through the master-slave mapping proportionality coefficient modifying module after stopping the master-slave position control function, then restarting the master-slave position control function, and continuing teleoperation on the motion of the slave-end robot;
the master-slave position control module firstly maps the position increment of the master-end robot into the position increment of the slave-end robot by using a variable-proportion incremental master-slave mapping method, and then calculates the expected position of the slave-end robot; and then predicting a following error by using a BP neural network and compensating to a desired position, obtaining a desired joint angle by using a slave-end robot kinematics modeling module, and finally realizing joint angle control by using a PD model.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the invention realizes the mapping from the small-range working space of the master-end robot to the large-range working space of the slave-end robot through incremental position control, realizes the manual adjustment of the movement efficiency and precision of the slave-end robot through variable ratio control, and improves the operation flexibility.
2. The method feeds back the master-slave following error of the sampling time to reduce the following error, further reduces the master-slave following error by an error prediction and compensation method based on the BP neural network, and ensures the positioning accuracy during fine operation.
3. The invention has convenient implementation and strong universality, ensures the control comfort of operators, and can simultaneously meet the requirements of quick movement of free space and accurate positioning of fine operation space.
Drawings
Fig. 1 is a schematic structural diagram of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a master robot of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a slave-end robot of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention;
fig. 4 is a flowchart of a master-slave control method of the master-slave heterogeneous teleoperation system according to an embodiment of the present invention.
Fig. 5 is an effect diagram of a master-slave control method of the master-slave heterogeneous teleoperation system according to an embodiment of the present invention.
Detailed Description
Further details of the feasibility and technical solution of the present invention are described in the following with reference to the accompanying drawings and the embodiments. It should be understood that the examples described herein are only for the purpose of illustrating the present invention and are not to be construed as limiting the present invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention. A typical teleoperation system comprises five parts, namely an operator, a master-end robot, a master-slave control system, a slave-end robot and an operation environment. In the master-slave heterogeneous teleoperation system, a master-slave robot adopts a Sigma.7 hand controller, a slave robot adopts a UR5 mechanical arm, a master-slave control system adopts a PC as a development platform, and a master-slave control software design is carried out based on an open-source ROS framework of a robot operation system by adopting a control mode of an upper computer and a lower computer. An upper computer of the master-slave control system is communicated with the Sigma.7 force feedback hand controller through a USB interface and a bus of a PC (personal computer), receives the motion state of the Sigma.7 force feedback hand controller, inputs the motion state into a master-slave control method for processing, outputs a motion control instruction of the UR5 mechanical arm and sends the motion control instruction through an Ethernet; after receiving the motion control instruction of the upper computer, the lower computer of the master-slave control system drives the UR5 mechanical arm to complete corresponding actions; and the lower computer sends the motion state of the UR5 mechanical arm to the upper computer in real time.
Referring to fig. 2, fig. 2 is a schematic diagram of a master robot of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention. The Sigma.7 hand controller structure is a composite structure consisting of a Delta parallel structure, an interactive series structure and a clamping structure, and has seven degrees of freedom, wherein the parallel structure is used for realizing position control. In other embodiments, the main-end device of the embodiment of the present invention may be replaced by a Phantom Omni device of SensAble Technology or an Omega series product of forced depth.
Referring to fig. 3, fig. 3 is a schematic diagram of a slave robot of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention. UR5 is a six-degree-of-freedom mechanical arm, a connecting rod coordinate system is modeled, connecting rod parameters shown in the table 1 are listed, and the connecting rod parameters are input into the master-slave control method to carry out forward kinematics and inverse kinematics analysis, so that forward kinematics and inverse kinematics models are obtained.
TABLE 1 Slave robot link parameters for Master-Slave heterogeneous teleoperation System of an embodiment of the present invention
Joint Length of connecting rod/mm Connecting rod torsion angle/rad Connecting rod wheelbase/mm Knuckle angle/rad
1 0 π/2 89.2 θ 1
2 -425 0 0 θ 2
3 -392 0 0 θ 3
4 0 π/2 109.3 θ 4
5 0 π/2 94.75 θ 5
6 0 0 82.5 θ 6
For this embodiment, master-slave motion mapping is essentially the control of the slave UR5 arm end by manipulating the master sigma.7 hand such that the UR5 arm end position follows the sigma.7 hand motion.
Referring to fig. 4, fig. 4 is a flowchart illustrating a master-slave control method of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention. The master-slave control method implemented by the invention comprises the following specific steps:
step 1: the link parameters of the slave end robot shown in table 1 were input to perform forward kinematics and inverse kinematics analysis, and a model of the slave end robot for forward kinematics and inverse kinematics was obtained.
Step 2: starting a master-slave position control function:
an operator controls the main-end robot to move to obtain the tail end position P of the main-end robot at the current moment t m (t) and the end position P of the last time (t-1) m (t-1) obtaining the end position increment delta P of the current time t m (t)：
ΔP m (t)＝P m (t)-P m (t-1)
The main end robot is enabled to be in delta P from time (t-1) to t m (t) is mapped to position increment delta P from the end robot at time t to (t + 1) through a variable ratio increment formula s (t+1)＝KΔP m (t), wherein K is a 3 × 3 master-slave mapping diagonal coefficient matrix. Then the delta P s (t + 1) to the desired position of the slave robot at the current time
Figure BDA0003843211480000051
To obtain the desired position at the next moment
Figure BDA0003843211480000052
Figure BDA0003843211480000061
Inputting the joint angle of the slave end robot at the current moment into a positive kinematic model to solve the position of the slave end robot terminal:
P s (t)＝FK(θ s (t))
wherein P is s (t) and θ s (t) represents the end position and the joint angle, respectively, from the current moment of the end robot, and FK (. Cndot.) represents solving the function from the positive kinematics of the end robot.
From P s (t) and
Figure BDA0003843211480000062
the master-slave following error e (t) of the slave robot in the sampling time can be obtained:
Figure BDA0003843211480000063
compensating the error from the desired position of the robot, then
Figure BDA0003843211480000064
The calculation formula can be modified as follows:
Figure BDA0003843211480000065
and establishing a three-layer BP neural network model, which comprises an input layer, a hidden layer and an output layer. The input of the BP neural network is the movement speed, the following error and the position increment of the tail end of the robot along the X axis, the Y axis and the Z axis at the current moment, so that an input layer has 9 neurons; the predicted output of the network is the master-slave following error of the next moment, so the output layer has 3 neurons; the number of neurons in the hidden layer was 20. The neuron activation function is a ReLu function, adam is adopted as the optimization function, and hyper-parameters are all default parameters of a skleann function library.
Under the condition of error prediction and compensation without a BP neural network, an operator controls the master-end robot to move according to different master-slave mapping proportionality coefficients and speeds, controls the slave-end robot to move through the variable-proportion incremental master-slave mapping method, obtains training data, and inputs the training data into the BP neural network to train a fitting regression function.
In the teleoperation process, the movement speed, the following error and the position increment of the tail end of the end robot along the X, Y and Z axes at the current moment are input into a trained BP neural network to predict the following error e at the next moment pred And compensate into the desired position:
Figure BDA0003843211480000066
will be provided with
Figure BDA0003843211480000069
Inputting the angle into a slave end robot inverse kinematics model to solve to obtain an expected joint angle:
Figure BDA0003843211480000067
wherein
Figure BDA0003843211480000068
Representing the desired joint angle from the end robot, IK (-) represents solving the function from the inverse kinematics of the end robot. And finally, realizing the control of the joint angle of the slave robot through a PD model.
And step 3: when the master robot reaches the limit of the working space, the master-slave position control function is suspended, the slave robot stops moving, and the position of the master robot is adjusted to facilitate control. The master-slave mapping scaling factor can also be modified to achieve different movement speeds, and the master-slave position control function is restarted after the operations are completed.
The invention provides a master-slave control method suitable for a master-slave heterogeneous teleoperation system, which realizes the mapping from a small-range working space of a master-end robot to a large-range working space of a slave-end robot through incremental position control, realizes the manual adjustment of the motion efficiency and precision of the slave-end robot through variable proportion control, improves the operation flexibility, reduces the master-slave following error through an error prediction and compensation method based on a BP neural network, and ensures the positioning precision during fine operation.
Referring to fig. 5, fig. 5 is a diagram illustrating an effect of a master-slave control method of a master-slave heterogeneous teleoperation system according to an embodiment of the present invention, wherein (a) in fig. 5 is a diagram illustrating a prediction error compensation of a non-BP neural network, and (b) in fig. 5 is a diagram illustrating a prediction error compensation of a BP neural network. The master end robot is controlled to remotely operate the slave end robot to move twice, the control speeds of the two times are approximately equal, the data sampling time is 0.01s, the BP neural network prediction error compensation is not used at one time, and the BP neural network prediction error compensation is used at the other time. Comparing the two graphs, the BP neural network prediction compensation reduces the master-slave following error, and improves the tail end movement and positioning accuracy of the slave end mechanical arm.
On the other hand, the invention also provides a master-slave control system of the master-slave heterogeneous teleoperation system, which comprises a slave-end robot kinematics modeling module, a master-slave mapping proportionality coefficient modification module, a master-slave adjustment module and a master-slave position control module;
the slave end robot kinematic modeling module performs forward kinematic analysis and inverse kinematic analysis according to the slave end robot connecting rod parameters to obtain forward kinematic models and inverse kinematic models, and can calculate the tail end position of the slave end robot according to the joint angle of the slave end robot or calculate the joint angle according to the tail end position; the module specifically refers to the implementation steps of a master-slave control method of a master-slave heterogeneous teleoperation system.
The master-slave mapping scale factor modification module modifies master-slave mapping scale factors according to the motion state of the fed-back slave-end robot, and adjusts the scale factors to be large in free space so as to realize large-range rapid motion; when the robot approaches to an obstacle or works, the scale factor is reduced, so that the fine movement in a smaller range is realized, and the positioning precision of the tail end is ensured; the module specifically refers to the implementation steps of a master-slave control method of a master-slave heterogeneous teleoperation system.
The master-slave adjusting module is used for adjusting the position of the master-end robot after stopping the master-slave position control function, modifying the master-slave motion mapping proportionality coefficient through the master-slave mapping proportionality coefficient modifying module, then restarting the master-slave position control function, and continuing teleoperation on the motion of the slave-end robot; the module specifically refers to the implementation steps of a master-slave control method of a master-slave heterogeneous teleoperation system.
The master-slave position control module firstly maps the position increment of the master-end robot into the position increment of the slave-end robot by using a variable-proportion incremental master-slave mapping method, and then calculates the expected position of the slave-end robot; and then predicting a following error by using a BP neural network and compensating the following error to a desired position, obtaining a desired joint angle by using a slave-end robot kinematics modeling module, and finally realizing joint angle control by using a PD model, wherein the module specifically realizes the realization steps of a master-slave control method of a master-slave heterogeneous teleoperation system in the process.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the appended claims.

Claims (4)

1. A master-slave control method of a master-slave heterogeneous teleoperation system is characterized in that the method is applied to the master-slave heterogeneous teleoperation system, and the system comprises a master-end robot and a slave-end robot which have different mechanical structures, and a master-slave control system which is connected with the master-end robot and the slave-end robot; the master-slave control method comprises the following steps:
(1) Modeling from end robot kinematics; performing forward kinematics and inverse kinematics analysis according to the connecting rod parameters of the slave end robot to obtain forward kinematics models and inverse kinematics models;
(2) Controlling the master position and the slave position; acquiring position increment of the master-end robot, and calculating to obtain an expected position of the slave-end robot by a variable-ratio incremental master-slave mapping method; inputting the tail end moving speed, the following error and the position increment of the slave end robot into a BP neural network to output a predicted following error, and compensating the predicted error to a desired position; inputting the compensated expected position into the slave end robot inverse kinematics model to solve an expected joint angle, and finally realizing joint angle control through a PD model;
(3) Master-slave adjustment; in the process of controlling the master-slave position, the start and stop of the master-slave position control function are detected in real time, and the function returns to the step (2) when the function is started; when master-slave adjustment is needed, the master-slave position control function is needed to be suspended, the slave-end robot stops moving, and the master-slave adjustment comprises the steps of modifying master-slave mapping proportionality coefficients and/or adjusting the position of the master-end robot; the modification of the master-slave mapping scaling factor is specifically that an operator modifies the master-slave mapping scaling factor to realize the motion of the slave-end robot with different efficiencies and accuracies.
2. The master-slave control method according to claim 1, wherein the step (2) specifically comprises:
acquiring the tail end position P of the master robot at the current moment t m (t) and the end position P of the last time (t-1) m (t-1) obtaining the end position increment delta P of the current time t m (t)：
ΔP m (t)＝P m (t)-P m (t-1)
The main terminalΔ P of the robot from time (t-1) to t m (t) is mapped to position increment delta P from the end robot at time t to (t + 1) through a variable ratio increment formula s (t+1)＝KΔP m (t), wherein K is a 3 × 3 master-slave mapping diagonal coefficient matrix; then the delta P s (t + 1) to the desired position of the slave robot at the current time
Figure FDA0003843211470000011
To obtain the desired position at the next moment
Figure FDA0003843211470000012
Figure FDA0003843211470000013
Inputting the joint angle of the slave end robot at the current moment into a positive kinematic model to solve the position of the slave end robot terminal:
P s (t)＝FK(θ s (t))
wherein P is s (t) and θ s (t) respectively representing the terminal position and the joint angle from the current moment of the end robot, and FK (-) represents solving a function from the positive kinematics of the end robot; from P s (t) and
Figure FDA0003843211470000014
obtaining a master-slave follow error e (t) of the slave end robot in sampling time:
Figure FDA0003843211470000015
compensating the expected position of the slave robot by the error and the predicted following error of the BP neural network
Figure FDA0003843211470000021
The calculation formula can be modified as follows:
Figure FDA0003843211470000022
wherein e pred For following errors of BP neural network prediction, will
Figure FDA0003843211470000023
Inputting the angle into a slave end robot inverse kinematics model to solve to obtain an expected joint angle:
Figure FDA0003843211470000024
wherein
Figure FDA0003843211470000025
Represents the expected joint angle from the end-robot, IK (-) represents the inverse kinematics solution function from the end-robot; and finally, realizing the control of the joint angle of the slave robot through a PD model.
3. The master-slave control method according to claim 2, wherein the BP neural network prediction following error specifically comprises:
establishing a three-layer BP neural network model, which comprises an input layer, a hidden layer and an output layer; the input of the BP neural network is the movement speed, the following error and the position increment of the tail end of the robot along the X axis, the Y axis and the Z axis at the current moment, so that an input layer has 9 neurons; the predicted output of the network is the master-slave following error of the next moment, so the output layer has 3 neurons; the number of the neurons of the hidden layer is 20; the neuron activation function is a ReLu function, adam is adopted as the optimization function, and hyper-parameters are all default parameters of a sklern function library;
under the condition of error prediction and compensation without a BP neural network, an operator controls the master-end robot to move according to different master-slave mapping proportion coefficients and speeds, controls the slave-end robot to move by a variable proportion incremental master-slave mapping method, acquires training data, and inputs the training data into the BP neural network to train a fitting regression function;
in the teleoperation process, the movement speed, the following error and the position increment of the tail end of the end robot along the X, Y and Z axes at the current moment are input into a trained BP neural network to predict the following error e at the next moment pred And compensated into the desired position.
4. A master-slave control system of a master-slave heterogeneous teleoperation system is characterized by comprising a slave-end robot kinematics modeling module, a master-slave mapping proportionality coefficient modification module, a master-slave adjustment module and a master-slave position control module;
the slave end robot kinematics modeling module carries out forward kinematics and inverse kinematics analysis according to the slave end robot connecting rod parameters to obtain forward kinematics and inverse kinematics models, and can calculate the tail end position of the slave end robot according to the joint angle of the slave end robot or calculate the joint angle according to the tail end position;
the master-slave mapping scale factor modification module modifies master-slave mapping scale factors according to the motion state of the fed-back slave-end robot, and adjusts the scale factors to be large in free space so as to realize large-range rapid motion; when the robot approaches to an obstacle or works, the scale factor is reduced, so that the fine movement in a smaller range is realized, and the positioning precision of the tail end is ensured;
the master-slave adjusting module is used for adjusting the position of the master-end robot and/or modifying the master-slave motion mapping proportionality coefficient through the master-slave mapping proportionality coefficient modifying module after stopping the master-slave position control function, then restarting the master-slave position control function, and continuing teleoperation on the motion of the slave-end robot;
the master-slave position control module firstly maps the position increment of the master-end robot into the position increment of the slave-end robot by using a variable-proportion incremental master-slave mapping method, and then calculates the expected position of the slave-end robot; and then predicting a following error by using a BP neural network and compensating to a desired position, obtaining a desired joint angle by using a slave robot kinematics modeling module, and finally realizing joint angle control by using a PD model.
CN202211111162.3A 2022-09-13 2022-09-13 Master-slave control method and system for master-slave heterogeneous teleoperation system Pending CN115338869A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117017507A (en) * 2023-10-09 2023-11-10 华中科技大学同济医学院附属协和医院 Precise master-slave control system and method for puncture operation robot
CN117021118A (en) * 2023-10-08 2023-11-10 中北大学 Dynamic compensation method for digital twin track error of parallel robot
CN117532616A (en) * 2023-12-18 2024-02-09 浙江大学 Master-slave heterogeneous similarity mapping control method and system for redundant hydraulic mechanical arm

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117021118A (en) * 2023-10-08 2023-11-10 中北大学 Dynamic compensation method for digital twin track error of parallel robot
CN117021118B (en) * 2023-10-08 2023-12-15 中北大学 Dynamic compensation method for digital twin track error of parallel robot
CN117017507A (en) * 2023-10-09 2023-11-10 华中科技大学同济医学院附属协和医院 Precise master-slave control system and method for puncture operation robot
CN117017507B (en) * 2023-10-09 2023-12-19 华中科技大学同济医学院附属协和医院 Precise master-slave control system of puncture operation robot
CN117532616A (en) * 2023-12-18 2024-02-09 浙江大学 Master-slave heterogeneous similarity mapping control method and system for redundant hydraulic mechanical arm

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