CN111515958A - Network delay estimation and compensation method of robot remote control system - Google Patents

Network delay estimation and compensation method of robot remote control system Download PDF

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CN111515958A
CN111515958A CN202010409030.3A CN202010409030A CN111515958A CN 111515958 A CN111515958 A CN 111515958A CN 202010409030 A CN202010409030 A CN 202010409030A CN 111515958 A CN111515958 A CN 111515958A
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robot
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torque
network delay
slave robot
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CN111515958B (en
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产林平
黄庆卿
王平
康真
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Chongqing University of Post and Telecommunications
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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
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
    • G05B19/408—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by data handling or data format, e.g. reading, buffering or conversion of data
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    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/34—Director, elements to supervisory
    • G05B2219/34406—Effect of computer, communication delay in real time control
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Abstract

The invention relates to a network delay estimation and compensation method of a robot remote control system, belonging to the technical field of robot remote control. Aiming at the problem of network delay of a robot remote control system, the method is characterized in that all negative influences caused by the network delay are summarized into a network interference torque, an extended active observer IEAOB is adopted at a main robot end to carry out online real-time estimation on the network interference torque and parameters of a slave robot dynamics model, the model parameters obtained through estimation are used for obtaining a prepared slave robot dynamics model, and meanwhile, the influences caused by the estimated network interference on the network delay are used for compensating. The method can be used for efficiently estimating and compensating the network delay under the condition that the robot dynamic model parameters are difficult to accurately obtain.

Description

Network delay estimation and compensation method of robot remote control system
Technical Field
The invention belongs to the technical field of robot remote control, and relates to a network delay estimation and compensation method of a robot remote control system.
Background
The robot remote control system is a remote operation control system which is used for remotely operating machines in environments which are difficult to access due to long distance or harm to people and completing relatively complex and accurate operation. Due to the characteristics, the robot remote control system has wide application prospect in many fields. The application of the robot remote control system in new operation fields such as deep sea exploration, deep stratum, outer space, strong radiation and the like brings hope for solving the problems. However, the problem of network delay is a very important problem faced in implementing a robot remote control system. Network delays can cause the teleoperated robot to be unstable and difficult to operate. Furthermore, IP network based delays are typically time varying, which makes control of the robot remote control system more cumbersome. Therefore, it is a hot spot of research on how to solve the problem of network delay of the robot remote control system. The network delay control methods proposed at present mainly include the following: the method comprises a control method based on passivity, a control method based on a virtual internal model, a control method based on a network communication interference observer (CDOB), a control method based on an H-infinity theory and a control method based on a Lyapunov-like function.
Among these methods, a control method based on a network Communication Disturbance Observer (CDOB) is advantageous in that it does not rely on a model of network delay and is receiving increasing attention. The core idea of the control method is to classify all negative influences caused by network delay into a network interference item (NB), estimate the network interference item on line by designing CDOB, and then compensate the influences caused by the network delay by the estimated network interference. Kenji et al propose a method for constructing CDOB using a conventional interference observer (DOB) for network delay estimation and compensation, however this method is based on perfect interference suppression from the robot side, where the interference includes: internal disturbances (uncertain from robot-end robot dynamics model parameters) and external disturbances (friction effects and environmental noise, etc.). Not only does this require the CDOB designed by this method to finally get the estimated network interference through a low-pass filter, but the estimated network interference of the CDOB can only be equal to the actual value if the cut-off frequency of the low-pass filter is chosen to be infinite, which obviously does not apply to the actual situation. Therefore, how to construct a CDOB is a problem to be solved, which can effectively suppress the influence caused by various interferences and can accurately estimate the network interference.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for estimating and compensating network delay of a robot remote control system, which resolves all negative effects caused by network delay into a network interference torque, performs online real-time estimation on the network interference torque and parameters of a robot dynamics model by using an extended active observer (IEAOB) at a host robot end, obtains a prepared robot dynamics model by using the estimated model parameters, and compensates for the effects caused by network delay by using the estimated network interference.
In order to achieve the purpose, the invention provides the following technical scheme:
a network delay estimation and compensation method of a robot remote control system specifically comprises the following steps:
s1: according to the concept of network interference, all negative effects caused by the time delay T are summarized to the interference moment TdPerforming the following steps;
s2: estimating parameters of a slave robot model and external environment torque T by adopting an extended active observer IEAOB at a slave robot end to obtain an accurate slave robot dynamic model;
s3: using control torque T at the main robot endmAnd fed back slave robot position signals with network delay
Figure BDA0002492430450000021
On-line estimation of network disturbance torque by IEAOB
Figure BDA0002492430450000022
And from the robot dynamics model parameters;
s4: acquiring an estimated slave robot dynamics model at the master robot end by using the slave robot model parameters obtained in the step S3;
s5: and obtaining a corresponding position signal by the estimated network interference torque through the estimated slave robot model, and then superposing the position signal and a fed back slave robot position signal with network delay to obtain a position signal without the influence of the network delay, thereby realizing the network delay compensation.
Further, in step S1, the time delay t is t ═ t1+t2Wherein t is1Network communication delay from a master robot end to a slave robot end, namely control channel delay; t is t2The method comprises the steps of delaying network communication from a robot end to a main robot end, namely delaying a feedback channel;
the disturbance torque Td＝Tc(1-e-ts)，TcFor input of the control torque, s is a laplace frequency domain transform symbol.
Further, in step S2, the IEAOB is used at the slave robot end to match the parameters of the robot kinetic model
Figure BDA0002492430450000023
And external environment moment TeAnd estimating, wherein the specific steps of estimating comprise:
s21: the slave robot dynamics model was determined to be:
Figure BDA0002492430450000024
wherein, thetasIs the inertial parameter of the robot and is,
Figure BDA0002492430450000025
qsacceleration, velocity and position signals, Ms(qs,qs) In order to be the inertia, the inertia is,
Figure BDA0002492430450000026
coriolis force and centripetal force, gs(qs,θs) In order to be a gravitational torque,
Figure BDA0002492430450000027
in order to be a friction force, the friction force,
Figure BDA0002492430450000028
Figure BDA0002492430450000029
Tsin order to control the moment from the robot,
Figure BDA00024924304500000210
is a function of the coulomb friction coefficient,
Figure BDA00024924304500000211
is a viscous friction coefficient;
s22: by expanding the system state vector
Figure BDA00024924304500000212
The slave robot dynamics model is extended to:
Figure BDA0002492430450000031
Figure BDA0002492430450000032
wherein, YsFor system output, GsIs an identity matrix, Hs＝[I 0 0 0 0 0]In order to observe the matrix for the states,
Figure BDA0002492430450000033
and
Figure BDA0002492430450000034
respectively process noise and measurement noise,
Figure BDA0002492430450000035
and
Figure BDA0002492430450000036
respectively representing the change rates of the external environment force, the friction coefficient and the model parameter;
s23: will be derived from the robot dynamics model parameters
Figure BDA0002492430450000037
And external environment moment TeThe variation process of (2) is simulated into a random walk process, namely a Gaussian-Markov chain, and the IEAOB is adopted to estimate the random walk process as follows:
Figure BDA0002492430450000038
wherein the content of the first and second substances,
Figure BDA0002492430450000039
Figure BDA00024924304500000310
Figure BDA00024924304500000311
Figure BDA00024924304500000312
wherein the content of the first and second substances,
Figure BDA00024924304500000313
a corresponding covariance matrix is represented,
Figure BDA00024924304500000314
representing the corresponding estimate;
Figure BDA00024924304500000315
wherein the content of the first and second substances,
Figure BDA0002492430450000041
Figure BDA0002492430450000042
Figure BDA0002492430450000043
Figure BDA0002492430450000044
s24: the external environment torque estimated in the step S23
Figure BDA0002492430450000045
And from robot dynamics model parameters
Figure BDA0002492430450000046
The feedback acts on the slave robot model to obtain an accurate slave robot dynamics model of
Figure BDA0002492430450000047
Figure BDA0002492430450000048
Wherein
Figure BDA0002492430450000049
Further, in step S3, an IEAOB is used to disturb the network torque T at the main robot enddAnd from robot dynamics model parameters
Figure BDA00024924304500000410
The estimation is performed, the specific steps are similar to steps S21, S22, S23 and S24, except that the estimation of the external environment torque in step S23 is changed to the estimation of the network disturbance torque, and finally the estimated network disturbance torque is obtained as
Figure BDA00024924304500000411
Further, in step S5, the network disturbance torque estimated in step S3 is processed to obtain a corresponding position signal from the robot dynamics model estimated in step S4
Figure BDA00024924304500000412
Slave robot position signal with network delay for combining it with feedback
Figure BDA00024924304500000413
Are superposed to obtain the final feedback position signal of
Figure BDA00024924304500000414
I.e. to compensate for the adverse effects of network delays.
The invention has the beneficial effects that: compared with the existing network delay control method, the method does not need to rely on a network delay model; meanwhile, the invention also considers the influence of model uncertainty and external environment noise in the actual robot remote control system, effectively inhibits the influence of the environment noise, and simultaneously carries out online estimation on the system model parameters, and realizes accurate estimation and compensation on the adverse effect caused by network delay.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic flow chart of a network delay estimation and compensation method of a robot remote control system according to the present invention;
FIG. 2 is a schematic diagram of a network delay estimation and compensation method of the robot remote control system according to the present invention;
FIG. 3 is a conceptual diagram of network disturbance torque in an embodiment of the present invention;
FIG. 4 is a graph of network delay in an embodiment of the present invention;
FIG. 5 is a graph of a parameter estimation from a robot dynamics model in an embodiment of the present invention;
fig. 6 is a graph of position and force estimation and tracking for a robotic remote control system in an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Referring to fig. 1 to 6, fig. 1 is a flowchart illustrating a method for estimating and compensating a network delay of a robot remote control system, the method including: step one, summarizing all negative influences caused by time delay to a disturbance moment according to a network disturbance concept; secondly, estimating parameters of the slave robot model and external environment torque by adopting an IEAOB at the slave robot end to obtain an accurate slave robot dynamic model; thirdly, estimating a network interference torque and a slave robot dynamic model parameter on line by using the IEAOB at the master robot end by using the control torque and a fed back slave robot position signal with network delay; fourthly, acquiring an estimated slave robot dynamics model at the master robot end by utilizing the slave robot model parameters obtained by the IEAOB in the third step; and fifthly, obtaining a corresponding position signal through the estimated network interference torque by the estimated slave robot model, and superposing the position signal and a fed back slave robot position signal with network delay to obtain a position signal without the influence of the network delay, thereby realizing the network delay compensation.
As shown in fig. 2, the network delay estimation and compensation method specifically includes the steps of:
step 1: as shown in FIG. 3, all negative effects of the time delay T are summarized in a disturbance torque Td：
Td＝Tc(1-e-ts)
Wherein, TcTo input control torque;
step 2: considering the slave robot end, estimating the slave robot dynamic model and the external environment moment by using the IEAOB, and specifically comprising the following steps:
1) determining a robot dynamics model as follows:
Figure BDA0002492430450000051
wherein
Figure BDA0002492430450000052
qsFor acceleration, velocity and position signals, Ms(qs,θs) In order to be the inertia, the inertia is,
Figure BDA0002492430450000053
coriolis force and centripetal force, gs(qs,θs) In order to be a gravitational torque,
Figure BDA0002492430450000054
in order to be a friction force, the friction force,
Figure BDA0002492430450000055
Tscontrolling the moment for the slave robot;
2) by expanding the system state vector
Figure BDA0002492430450000061
The robot dynamics model is extended as:
Figure BDA0002492430450000062
Figure BDA0002492430450000063
wherein, YsFor system output, GsIs an identity matrix, Hs＝[I 0 0 0 0 0]In order to observe the matrix for the states,
Figure BDA0002492430450000064
and
Figure BDA0002492430450000065
respectively process noise and measurement noise,
Figure BDA0002492430450000066
and
Figure BDA0002492430450000067
representing the ambient force, the coefficient of friction, and the rate of change of the model parameters.
3) Using robot dynamics model parameters
Figure BDA0002492430450000068
And external environment moment TeThe course of variation of (c) is modeled as a random walk (a gaussian-markov chain) which is estimated using IEAOB as follows:
Figure BDA0002492430450000069
wherein the content of the first and second substances,
Figure BDA00024924304500000610
Figure BDA00024924304500000611
Figure BDA00024924304500000612
Figure BDA00024924304500000613
wherein the content of the first and second substances,
Figure BDA00024924304500000614
a corresponding covariance matrix is represented,
Figure BDA00024924304500000615
representing the corresponding estimate;
Figure BDA0002492430450000071
wherein the content of the first and second substances,
Figure BDA0002492430450000072
Figure BDA0002492430450000073
Figure BDA0002492430450000074
Figure BDA0002492430450000075
4) the external environment torque estimated in the step 3) is used
Figure BDA0002492430450000076
And learning the model parameters
Figure BDA0002492430450000077
The feedback acts on the slave robot model to obtain an estimated slave robot dynamics model of
Figure BDA0002492430450000078
Figure BDA0002492430450000079
Wherein
Figure BDA00024924304500000710
And step 3: adopting IEAOB to interfere the network torque T at the main robot enddAnd from robot dynamics model parameters
Figure BDA00024924304500000711
Estimating, wherein the specific execution steps are similar to the step (2), and only the torque T to the external environment in the step (2) is usedeIs changed to a disturbance torque T on the networkdFinally obtaining the estimated network disturbance torque
Figure BDA00024924304500000712
Figure BDA00024924304500000713
And 4, step 4: obtaining an estimated slave robot dynamics model at the master robot end by using the robot model parameters obtained by the IEAOB in the step 3, and obtaining a corresponding position signal by enabling the estimated network interference torque to pass through the slave robot
Figure BDA00024924304500000714
Figure BDA00024924304500000715
And 5: subjecting the product obtained in step (4)
Figure BDA00024924304500000716
Position signal with network delay and feedback
Figure BDA00024924304500000717
Are superposed to obtain the final feedback position signal of
Figure BDA00024924304500000718
I.e. to compensate for the adverse effects of network delays.
Example (b):
the network delay estimation and compensation method of the robot remote control system provided by the invention is applied to the robot remote control system with single degree of freedom, namely, the master robot and the slave robot are all one-degree-of-freedom mechanical arm equipment, wherein the slave robot dynamics parameter theta iss＝Ms＝5.0e-3kgm2Coefficient of friction
Figure BDA00024924304500000719
When the IEAOB of the main robot end is initialized, the dynamic parameters and the initial values of the friction coefficient of the robot are set to be actual values, and when the IEAOB of the slave robot end is initialized, the dynamic parameters and the initial values of the friction coefficient of the robot are set to be 80% of the actual values, namely thetas＝Ms＝4.0e-3kgm2,
Figure BDA0002492430450000081
While the environmental object is placed at an angular velocity of 0.2rad/s from the initial origin of the robot. The network delay is chosen as shown in figure 4. By selecting IEAOB parameters as in table 1, the resulting estimated curves from the robot dynamics model parameters and the estimated and tracked curves of the position and force of the robot remote control system are shown in fig. 5 and 6.
Table 1 IEAOB parameters selected in the examples
Figure BDA0002492430450000082
The experimental result proves the effectiveness of the network delay estimation and compensation method of the robot remote control system, the network delay estimation and compensation method not only well estimates the parameters of the robot dynamic model and inhibits the influence caused by environmental noise, but also well estimates the network interference torque, compensates the adverse influence caused by network delay, and realizes the position tracking of the master robot and the slave robot of the robot remote control system and the tracking of artificial applied force and external environmental force. Meanwhile, the idea of the network delay estimation and compensation method of the robot remote control system can also be expanded to solve the network delay problem of other network control systems.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.

Claims (5)

1. A network delay estimation and compensation method of a robot remote control system is characterized by comprising the following steps:
s1: according to the concept of network interference, all negative effects caused by the time delay T are summarized to the interference moment TdPerforming the following steps;
s2: estimating parameters of a slave robot model and external environment torque T by adopting an extended active observer IEAOB at a slave robot end to obtain an accurate slave robot dynamic model;
s3: using control torque T at the main robot endmAnd fed back slave robot position signals with network delay
Figure FDA0002492430440000011
On-line estimation of network disturbance torque by IEAOB
Figure FDA0002492430440000012
And from the robot dynamics model parameters;
s4: acquiring an estimated slave robot dynamics model at the master robot end by using the slave robot model parameters obtained in the step S3;
s5: and obtaining a corresponding position signal by the estimated network interference torque through the estimated slave robot model, and then superposing the position signal and a fed back slave robot position signal with network delay to obtain a position signal without the influence of the network delay, thereby realizing the network delay compensation.
2. The method of claim 1, wherein in step S1, the time delay t is t ═ t-1+t2Wherein t is1Network communication delay from a master robot end to a slave robot end, namely control channel delay; t is t2The method comprises the steps of delaying network communication from a robot end to a main robot end, namely delaying a feedback channel;
the disturbance torque Td＝Tc(1-e-ts)，TcFor input of the control torque, s is a laplace frequency domain transform symbol.
3. The method of claim 2, wherein the IEAOB is applied to the parameters of the robot dynamics model at the slave robot end in step S2
Figure FDA0002492430440000013
And external environment moment TeAnd estimating, wherein the specific steps of estimating comprise:
s21: the slave robot dynamics model was determined to be:
Figure FDA0002492430440000014
wherein, thetasIs the inertial parameter of the robot and is,
Figure FDA0002492430440000015
qsacceleration, velocity and position signals, Ms(qs,θs) In order to be the inertia, the inertia is,
Figure FDA0002492430440000016
coriolis force and centripetal force, gs(qs,θs) In order to be a gravitational torque,
Figure FDA0002492430440000017
in order to be a friction force, the friction force,
Figure FDA0002492430440000018
Figure FDA0002492430440000019
Tsin order to control the moment from the robot,
Figure FDA00024924304400000110
is a function of the coulomb friction coefficient,
Figure FDA00024924304400000111
is a viscous friction coefficient;
s22: by expanding the system state vector
Figure FDA00024924304400000112
The slave robot dynamics model is extended to:
Figure FDA0002492430440000021
Figure FDA0002492430440000022
wherein, YsFor system output, GsIs an identity matrix, Hs＝[I 0 0 0 0 0]In order to observe the matrix for the states,
Figure FDA0002492430440000023
and
Figure FDA0002492430440000024
respectively process noise and measurement noise,
Figure FDA0002492430440000025
and
Figure FDA0002492430440000026
respectively representing the change rates of the external environment force, the friction coefficient and the model parameter;
s23: will be derived from the robot dynamics model parameters
Figure FDA0002492430440000027
And external environment moment TeThe variation process of (2) is simulated into a random walk process, namely a Gaussian-Markov chain, and the IEAOB is adopted to estimate the random walk process as follows:
Figure FDA0002492430440000028
wherein the content of the first and second substances,
Figure FDA0002492430440000029
Figure FDA00024924304400000215
Figure FDA00024924304400000210
Figure FDA00024924304400000211
wherein the content of the first and second substances,
Figure FDA00024924304400000212
a corresponding covariance matrix is represented,
Figure FDA00024924304400000213
representing the corresponding estimate;
Figure FDA00024924304400000214
wherein the content of the first and second substances,
Figure FDA0002492430440000031
Figure FDA0002492430440000032
Figure FDA0002492430440000033
Figure FDA0002492430440000034
s24: the external environment torque estimated in the step S23
Figure FDA0002492430440000035
And from robot dynamics model parameters
Figure FDA0002492430440000036
The feedback acts on the slave robot model to obtain an accurate slave robot dynamics model of
Figure FDA0002492430440000037
Figure FDA0002492430440000038
Wherein
Figure FDA0002492430440000039
4. The method of claim 3, wherein in step S3, IEAOB is used to estimate and compensate the network delay of the robot remote control system, and the main robot end is applied with the network disturbance torque TdAnd from robot dynamics model parameters
Figure FDA00024924304400000310
Estimating to obtain the estimated network interference torque as
Figure FDA00024924304400000311
5. The method of claim 4, wherein in step S5, the network disturbance torque estimated in step S3 is passed through the corresponding position signal obtained from the robot dynamics model estimated in step S4
Figure FDA00024924304400000312
Slave robot position signal with network delay for combining it with feedback
Figure FDA00024924304400000313
Are superposed to obtain the final feedback position signal of
Figure FDA00024924304400000314
I.e. to compensate for the adverse effects of network delays.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113297798A (en) * 2021-06-10 2021-08-24 重庆邮电大学工业互联网研究院 Robot external contact force estimation method based on artificial neural network
CN114012729A (en) * 2021-11-16 2022-02-08 哈尔滨理工大学 Three-side teleoperation system and method for foot-type mobile robot fused with interaction force estimation algorithm

Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001025986A (en) * 1999-07-12 2001-01-30 Mitsubishi Electric Corp Remote control device of robot
WO2002048806A1 (en) * 2000-12-14 2002-06-20 Kabushiki Kaisha Yaskawa Denki Feedback control device
CN103235509A (en) * 2013-03-29 2013-08-07 北京控制工程研究所 Rotating member disturbance compensation method based on momentum wheel
CN104238356A (en) * 2014-09-26 2014-12-24 贵州大学 Observation method based on extended state observer for time delay system
JP2015082758A (en) * 2013-10-23 2015-04-27 ＮＥＵＳＯＦＴ Ｊａｐａｎ株式会社 Remote operation reception system, remote operation system, and program
US20150316906A1 (en) * 2012-12-20 2015-11-05 Tianjin University An active front-end rectifier filter delay compensation method based on model predictive control
CN105459118A (en) * 2016-01-07 2016-04-06 北京邮电大学 Wave variable four-channel bilateral control method based on master-end force buffer
CN106054599A (en) * 2016-05-25 2016-10-26 哈尔滨工程大学 Master-slave underwater robotic arm delay control method
EP3117967A1 (en) * 2015-07-15 2017-01-18 ETH Zurich Transparency control method for robotic devices and a control device therefor
CN106647281A (en) * 2017-01-18 2017-05-10 燕山大学 Method for compensating finite interference time of remote operation system based on terminal slide model
CN106899991A (en) * 2017-03-08 2017-06-27 哈尔滨工业大学深圳研究生院 Adaptive optimal ad hoc network method based on multirobot and gaussian signal model
CN106938470A (en) * 2017-03-22 2017-07-11 华中科技大学 A kind of device and method of Robot Force control teaching learning by imitation
CN106985139A (en) * 2017-04-12 2017-07-28 西北工业大学 Robot for space active disturbance rejection control method for coordinating with compensating is observed based on extended mode
CN107908107A (en) * 2017-11-13 2018-04-13 大连理工大学 Disturbance rejection control method of the time lag sampling system based on fallout predictor
CN108646569A (en) * 2018-07-09 2018-10-12 燕山大学 The control method of remote control system under discrete-time state

Patent Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001025986A (en) * 1999-07-12 2001-01-30 Mitsubishi Electric Corp Remote control device of robot
WO2002048806A1 (en) * 2000-12-14 2002-06-20 Kabushiki Kaisha Yaskawa Denki Feedback control device
US20150316906A1 (en) * 2012-12-20 2015-11-05 Tianjin University An active front-end rectifier filter delay compensation method based on model predictive control
CN103235509A (en) * 2013-03-29 2013-08-07 北京控制工程研究所 Rotating member disturbance compensation method based on momentum wheel
JP2015082758A (en) * 2013-10-23 2015-04-27 ＮＥＵＳＯＦＴ Ｊａｐａｎ株式会社 Remote operation reception system, remote operation system, and program
CN104238356A (en) * 2014-09-26 2014-12-24 贵州大学 Observation method based on extended state observer for time delay system
EP3117967A1 (en) * 2015-07-15 2017-01-18 ETH Zurich Transparency control method for robotic devices and a control device therefor
CN105459118A (en) * 2016-01-07 2016-04-06 北京邮电大学 Wave variable four-channel bilateral control method based on master-end force buffer
CN106054599A (en) * 2016-05-25 2016-10-26 哈尔滨工程大学 Master-slave underwater robotic arm delay control method
CN106647281A (en) * 2017-01-18 2017-05-10 燕山大学 Method for compensating finite interference time of remote operation system based on terminal slide model
CN106899991A (en) * 2017-03-08 2017-06-27 哈尔滨工业大学深圳研究生院 Adaptive optimal ad hoc network method based on multirobot and gaussian signal model
CN106938470A (en) * 2017-03-22 2017-07-11 华中科技大学 A kind of device and method of Robot Force control teaching learning by imitation
CN106985139A (en) * 2017-04-12 2017-07-28 西北工业大学 Robot for space active disturbance rejection control method for coordinating with compensating is observed based on extended mode
CN107908107A (en) * 2017-11-13 2018-04-13 大连理工大学 Disturbance rejection control method of the time lag sampling system based on fallout predictor
CN108646569A (en) * 2018-07-09 2018-10-12 燕山大学 The control method of remote control system under discrete-time state

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
AAMIR SHAHZAD;HUBERT ROTH: "Bilateral telecontrol of AutoMerlin mobile robot with fix communication delay", 《2016 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR)》 *
LINPING CHAN∗, FAZEL NAGHDY, DAVID STIRLING: "Extended active observer for force estimation and disturbance rejection of robotic manipulators", 《ROBOTICS AND AUTONOMOUS SYSTEMS》 *
R. CORTESAO, J. PARK, AND O. KHATIB: "Real-time adaptive control for haptic telemanipulation with Kalman active observers", 《IEEE TRANS.ROBOT》 *
李玉玲: "时延双边遥操作机器人***控制方法研究", 《中国博士学位论文全文数据库信息科技辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113297798A (en) * 2021-06-10 2021-08-24 重庆邮电大学工业互联网研究院 Robot external contact force estimation method based on artificial neural network
CN114012729A (en) * 2021-11-16 2022-02-08 哈尔滨理工大学 Three-side teleoperation system and method for foot-type mobile robot fused with interaction force estimation algorithm

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