CN110007601A - A kind of control device and equipment of bilateral teleoperation system - Google Patents
A kind of control device and equipment of bilateral teleoperation system Download PDFInfo
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Abstract
The invention discloses the control devices and equipment of a kind of bilateral teleoperation system, it include: estimation module, the motion state measured value of the target environment output of motion status simulation reference value and bilateral teleoperation system for exporting target environment model is input in algorithm for estimating model, exports the optimal estimation value of motion state；Feedforward compensation value output module exports feedforward compensation value for inputting the optimal estimation value in static object environmental model；Determining module, the force feedback value for being exported using the target environment are subtracted the feedforward compensation value, determine target force value of feedback；Feedback module, for the target force value of feedback to be sent to the slave robot of the bilateral teleoperation system, by the operator that the target force value of feedback is fed back to the bilateral teleoperation system from robot.Device provided by the present invention and equipment improve the precision of bilateral teleoperation medical system robotic tracking's target environment motion state.
Description
Technical Field
The invention relates to the technical field of robot control, in particular to a control device and equipment of a bilateral teleoperation system.
Background
In a complex modern working environment, partial work is finished by a robot instead of manual work, but due to the limitation of the development level of the fields such as sensors, artificial intelligence and the like, the robot cannot effectively finish high-precision tasks in the complex environment. The robot control technology based on the bilateral teleoperation system embeds artificial intelligent decision of the main control end into the control of the robot, greatly improves the working efficiency of the robot, for example, under the environment of medical operation, human beings can have the capability of performing operation on human organs outside the human bodies, and the medical application of the bilateral teleoperation becomes very critical along with the research of force perception equipment. The stability and the real-time tracking performance are main pain points of a bilateral teleoperation system, when the system is applied to equipment such as medical treatment and the like which need high-precision identification, the tracking precision performance of the system cannot meet the requirement, the application range of the robot is limited to a great extent, the robot cannot be guaranteed to safely complete an operation under the high-precision requirement, and particularly when an operation is performed on a human organ, the pulsation of the organ can influence the operation process.
In the prior art, a multi-step predictive control algorithm is used for enhancing the estimation performance of the motion state of a robot, so that the motion state of the tail end of a surgical robot and the motion state of a target environment are synchronized to eliminate the influence of the motion state and the target environment. In the prior art, a Kalman filtering algorithm is used for estimating the motion state of a robot, and the robot is predicted in multiple steps, so that the motion of the tail end of the robot can track the motion of a heart. In the training process, multi-sample data is needed, if the sample ergodicity of the training data is insufficient, the performance of the model is reduced, so that the compensation effect is influenced, and the whole process is offline compensation control in application. However, this method has many limitations and cannot completely eliminate the influence of the dynamic environment on the operation of the robot.
From the above, it can be seen that how to eliminate the influence of the uncertainty force caused by the target environment on the bilateral teleoperation system is a problem to be solved at present.
Disclosure of Invention
The invention aims to provide a control device and equipment of a bilateral teleoperation system, which aim to solve the problem that a dynamic operation environment can generate uncertain force on a robot of the bilateral telemedicine operation system.
To solve the above technical problem, the present invention provides a control device for a bilateral teleoperation system, comprising: the estimation module is used for inputting a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model and outputting an optimal estimation value of the motion state; the feedforward compensation value output module is used for inputting the optimal estimated value of the motion state into a static target environment model and outputting a feedforward compensation value of the bilateral teleoperation system; the determining module is used for subtracting the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value; the feedback module is used for sending the target force feedback value to a slave robot of the bilateral teleoperation system, and the slave robot feeds the target force feedback value back to an operator of the bilateral teleoperation system; the target environment model is a motion state model of the target environment constructed by utilizing sensor simulation; the static target environment model is a model simulating soft tissue of the target environment in a static state.
Preferably, the estimation module is specifically configured to:
and inputting the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment of the bilateral teleoperation system into a maximum likelihood estimation algorithm model, and outputting the optimal estimation value of the motion state.
Preferably, the estimation module is specifically configured to:
and inputting the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment of the bilateral teleoperation system into a Kalman filter, and outputting the optimal motion state estimation value.
Preferably, the estimation module comprises:
the first construction unit is used for constructing a state equation of the target environment model and a measurement equation of the target environment after the motion state simulation reference value and the motion state measurement value are input into the Kalman filter;
the second construction unit is used for constructing an update equation of the time and the state of the Kalman filter according to the state equation and the measurement equation;
the updating unit is used for determining a Kalman gain updating value, a state updating value and an error covariance updating value according to the updating equation;
and the determining unit is used for determining the optimal estimation value of the motion state according to the Kalman gain update value, the state update value and the error covariance update value.
Preferably, the first building unit is specifically configured to:
after the motion state simulation reference value and the motion state measurement value are input into the Kalman filter, a state equation X (k) ═ A · X (k-1) + B · V (k-1) of the target environment model and a measurement equation Y (k) ═ C · X (k) + I · T (k) of the target environment are constructed;
wherein, x (k) is a simulated reference vector of the motion state of the slave robot system of 2 × 1 order, including a position value and a velocity value of the slave robot; x (k-1) is a simulated reference vector of the motion state of the slave robot system at the last moment, V (k) is a 2X 1-order system process noise vector, and A and B are system parameter matrixes;
y (k) is a measurement vector of the motion state of the target environment, X (k) is a prior value of the state of the system at the current moment, T (k) is dynamic system measurement noise, and C is a measurement parameter matrix known by the dynamic system.
Preferably, the second building unit is specifically configured to:
an update equation for constructing the Kalman filter time and state from the state equation and the measurement equation:
wherein,for the prior state optimum at time k-1,a priori values of the system state for the predictions at time k,estimating a covariance prior, P, for time kk-1Estimating covariance, K, for K-1 time errorkThe value of the kalman gain update for time k,updating the value of the state for time k, PkUpdate the error covariance value at time k, Q and H are the state transition matrices,is the unit vector at time K-1,
preferably, the determining unit is specifically configured to:
and performing autoregressive operation on the Kalman gain update value, the state update value and the error covariance update value to determine the optimal estimation value of the motion state.
Preferably, the feedback module comprises:
the first feedback unit is used for inputting the target force feedback value to a slave robot of the bilateral teleoperation system and then inputting the force feedback value output by the slave robot to a communication channel of the bilateral teleoperation system;
the second feedback unit is used for inputting the force feedback value output by the communication channel to the robot of the bilateral teleoperation system; and sending the force feedback value output by the main robot to the operator.
Preferably, the target environment is a dynamic cardiac environment.
The invention also provides a control device of the bilateral teleoperation system, which comprises:
the main robot is used for receiving a motion state value of an operator and feeding back a force feedback value sent by the main robot to the operator;
the communication channel is used for receiving the motion state value sent by the main robot and feeding back the force feedback value sent by the slave robot to the main robot;
the slave robot is used for receiving the motion state value sent by the communication channel and feeding back a target force feedback value sent by the target environment to the communication channel;
a memory for storing a computer program;
the processor is used for executing the computer program, inputting a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model, and outputting an optimal estimation value of the motion state; inputting the optimal estimated value of the motion state into a static target environment model, and outputting a feedforward compensation value of the bilateral teleoperation system; subtracting the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value; and sending the target force feedback value to a slave robot of the bilateral teleoperation system, and feeding the target force feedback value back to an operator of the bilateral teleoperation system by the slave robot.
The control device of the bilateral teleoperation system provided by the invention utilizes the sensor to simulate and construct the motion state of the target environment of the bilateral teleoperation system to obtain the target environment module; and obtaining a static target environment model by simulating soft tissues of the target environment in a static state. The control device provided by the invention comprises an estimation module, a feedforward compensation module, a determination module and a feedback module. And the estimation module inputs the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment into a preselected estimation algorithm model to obtain the optimal estimation value of the motion state. And the feedforward compensation value output module inputs the optimal motion state estimation value into the static target environment model to obtain a feedforward compensation value of the bilateral teleoperation system. The determination module subtracts the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value. The feedback module sends the target force feedback value to a slave robot of the bilateral teleoperation system so as to feed the target force feedback value back to an operator of the bilateral telemedicine operation system through the slave robot.
The control device of the bilateral teleoperation system provided by the invention obtains the optimal estimated value of the motion state of the slave robot by utilizing the motion state simulation reference value output by the target environment model, the motion state measured value output by the target environment and the preselected estimation algorithm model, acts the optimal estimated value on soft tissues in the static state of the target environment to obtain an uncertain force feedback model generated by the contact action of the slave robot and the target environment, and the output value of the model acts on the output value of the target environment as feed-forward compensation, thereby improving the precision of the bilateral telemedicine operating system robot for tracking the motion state of the target environment.
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In order to more clearly illustrate the embodiments or technical solutions of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
Fig. 1 is a block diagram of a control apparatus of a bilateral teleoperation system according to an embodiment of the present invention;
fig. 2 is another structural block diagram of a control device of a bilateral teleoperation system according to an embodiment of the present invention.
Fig. 3 is a block diagram of a control device of a bilateral teleoperation system according to an embodiment of the present invention.
Detailed Description
The core of the invention is to provide a control device and equipment of a bilateral teleoperation system, which improve the precision of the bilateral telemedicine operation system robot in tracking the motion state of the target environment.
In order that those skilled in the art will better understand the disclosure, the invention will be described in further detail with reference to the accompanying drawings and specific embodiments. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a block diagram illustrating a control device of a bilateral teleoperation system according to an embodiment of the present invention. The specific device may include: the system comprises an estimation module 100, a feedforward compensation value output module 200, a determination module 300 and a feedback module 400. The estimation module 100 is configured to input a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model, and output the optimal estimation value of the motion state. The feedforward compensation value output module 200 is configured to input the optimal motion state estimation value into a static target environment model, and output a feedforward compensation value of the bilateral teleoperation system. The determining module 300 is configured to determine a target force feedback value by subtracting the feedforward compensation value from the force feedback value output by the target environment. Feedback module 400 is configured to send the target force feedback value to a slave robot of the bilateral teleoperation system, where the slave robot feeds the target force feedback value back to an operator of the bilateral teleoperation system. The target environment model is a motion state model of the target environment constructed by utilizing sensor simulation; the static target environment model is a model simulating soft tissue of the target environment in a static state.
In the embodiment of the invention, the optimal estimation value of the motion state can be estimated by utilizing a maximum likelihood estimation algorithm; other estimation algorithm models such as a Kalman filter can also be used for estimating the optimal estimation value of the motion state.
In the prior art, a motion state model of a target environment is established in a visual sensing mode, and the motion state of a robot is acted through real-time motion compensation, so that the robot can be synchronous with the motion state of the target environment, even if the motion state of the robot and the motion state of the target environment are relatively static. Therefore, when the robot contacts the target environment, the target environment can generate interference force on the robot, and uncertain forces such as inertia force and the like can be generated when the robot moves. According to the method, the state value obtained by constructing the motion model of the target environment and the state value obtained by the robot acting on the feedback of the target environment are used as input data of the estimation algorithm model, the optimal estimation value of the motion state of the robot is obtained through the estimation algorithm model, and the optimal estimation value of the motion state output by the robot acts on the static soft tissue model to obtain the force feedback model, so that the interference force generated when the robot is in contact with the target environment is overcome, the robot can track the motion state of the target environment on line, the interference problem caused by uncertain force factors is reduced, and the precision of tracking the motion state of the target environment is improved.
Based on the foregoing embodiments, in this embodiment, a kalman filter is taken as an example to further explain a process of implementing control compensation between the robot end and the target environment by the control device of the bilateral teleoperation system provided in this embodiment. Referring to fig. 2, fig. 2 is another structural block diagram of a control device of a dual-side remote operating system according to an embodiment of the present invention.
In this embodiment, the target environment may be a dynamic cardiac environment; the target environment model is a heart environment motion state model constructed by utilizing a sensor; the static target environment model is a soft tissue model of a dynamic heart environment in a static state.
The estimation module 100 comprises a first building element, a second building element, an updating element and a determining element.
The first construction unit is configured to input the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment into the kalman filter, and construct an equation of state X (k) ═ a · X (k-1) + B · V (k-1) of the target environment model and an equation of measurement y (k) ═ C · X (k) + I · t (k) of the target environment;
wherein, x (k) is a simulated reference vector of the motion state of the slave robot system of 2 × 1 order, including a position value and a velocity value of the slave robot; x (k-1) is a simulated reference vector of the motion state of the slave robot system at the last moment, V (k) is a 2X 1-order system process noise vector, A and B are system parameter matrixes, and parameter values are constants determined by robot model selection;
y (k) is a measurement vector of the motion state of the target environment, X (k) is a prior value of the state of the system at the current moment, T (k) is dynamic system measurement noise, and C is a measurement parameter matrix known by the dynamic system.
The second construction unit is configured to construct an update equation of the time and the state of the kalman filter according to the state equation and the measurement equation:
wherein,for the prior state optimum at time k-1,a priori values of the system state for the predictions at time k,estimating a covariance prior, P, for time kk-1Estimating covariance, K, for K-1 time errorkThe value of the kalman gain update for time k,updating the value of the state for time k, PkUpdate the error covariance value at time k, Q and H are the state transition matrices,is the unit vector at time K-1,
the updating unit is used for determining a Kalman gain updating value, a state updating value and an error covariance updating value according to the updating equation.
The determining unit is used for performing autoregressive operation on the Kalman gain update value, the state update value and the error covariance update value to determine the optimal estimated value of the motion state at the moment k
The estimation module 400 comprises a first feedback unit and a second feedback unit.
The first feedback unit is used for inputting the target force feedback value to a slave robot of the bilateral teleoperation medical system and then inputting the force feedback value output by the slave robot to a communication channel of the bilateral teleoperation medical system;
the second feedback unit is used for inputting the force feedback value output by the communication channel to the robot of the bilateral teleoperation medical system; and sending the force feedback value output by the main robot to the operator.
The embodiment of the invention is mainly applied to the robot with the medical operation background, and the enhanced force feedback model is constructed at the robot end and is controlled and compensated. The optimal estimated value is obtainedThe force feedback value is used as a compensation value, the compensation value is used as a feedforward value of the bilateral teleoperation system to act on the system, interference force generated when the robot is in contact with a dynamic target environment is overcome, the robot can track the motion state of the target environment on line, the interference problem caused by the uncertainty force factor is reduced, the precision of tracking the motion state of the target environment is improved, and the influence of uncertainty force on a cardiac surgery is reduced.
It should be noted that the above embodiments are not diagnostic methods for diseases, but control techniques for robots. The control device of the bilateral teleoperation system provided by the invention is not limited to the description in the surgical environment in the above embodiments. The invention relates to the field of robots, in particular to a technology for realizing compensation of a force feedback model constructed by describing the motion state of a robot, which can meet the requirements of accurate output and tracking of the tail end operation of the robot.
Referring to fig. 3, fig. 3 is a block diagram illustrating a control device of a dual-side remote operating system according to an embodiment of the present invention; the specific equipment may include:
the main robot is used for receiving a motion state value of an operator and feeding back a force feedback value sent by the main robot to the operator;
the communication channel is used for receiving the motion state value sent by the main robot and feeding back the force feedback value sent by the slave robot to the main robot;
the slave robot is used for receiving the motion state value sent by the communication channel and feeding back a target force feedback value sent by the target environment to the communication channel;
a memory for storing a computer program;
the processor is used for executing the computer program, inputting a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model, and outputting an optimal estimation value of the motion state; inputting the optimal estimated value of the motion state into a static target environment model, and outputting a feedforward compensation value of the bilateral teleoperation system; subtracting the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value; and sending the target force feedback value to a slave robot of the bilateral teleoperation system, and feeding the target force feedback value back to an operator of the bilateral teleoperation system by the slave robot.
The above details describe the control device and the device of the bilateral teleoperation system provided by the present invention. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (10)
1. A control device for a bilateral teleoperation system, comprising:
the estimation module is used for inputting a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model and outputting an optimal estimation value of the motion state;
the feedforward compensation value output module is used for inputting the optimal estimated value of the motion state into a static target environment model and outputting a feedforward compensation value of the bilateral teleoperation system;
the determining module is used for subtracting the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value;
the feedback module is used for sending the target force feedback value to a slave robot of the bilateral teleoperation system, and the slave robot feeds the target force feedback value back to an operator of the bilateral teleoperation system;
the target environment model is a motion state model of the target environment constructed by utilizing sensor simulation; the static target environment model is a model simulating soft tissue of the target environment in a static state.
2. The control device of claim 1, wherein the estimation module is specifically configured to:
and inputting the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment of the bilateral teleoperation system into a maximum likelihood estimation algorithm model, and outputting the optimal estimation value of the motion state.
3. The control device of claim 1, wherein the estimation module is specifically configured to:
and inputting the motion state simulation reference value output by the target environment model and the motion state measurement value output by the target environment of the bilateral teleoperation system into a Kalman filter, and outputting the optimal motion state estimation value.
4. The control apparatus of claim 3, wherein the estimation module comprises:
the first construction unit is used for constructing a state equation of the target environment model and a measurement equation of the target environment after the motion state simulation reference value and the motion state measurement value are input into the Kalman filter;
the second construction unit is used for constructing an update equation of the time and the state of the Kalman filter according to the state equation and the measurement equation;
the updating unit is used for determining a Kalman gain updating value, a state updating value and an error covariance updating value according to the updating equation;
and the determining unit is used for determining the optimal estimation value of the motion state according to the Kalman gain update value, the state update value and the error covariance update value.
5. The control device according to claim 4, characterized in that the first building unit is specifically configured to:
after the motion state simulation reference value and the motion state measurement value are input into the Kalman filter, a state equation X (k) ═ A · X (k-1) + B · V (k-1) of the target environment model and a measurement equation Y (k) ═ C · X (k) + I · T (k) of the target environment are constructed;
wherein, x (k) is a simulated reference vector of the motion state of the slave robot system of 2 × 1 order, including a position value and a velocity value of the slave robot; x (k-1) is a simulated reference vector of the motion state of the slave robot system at the last moment, V (k) is a 2X 1-order system process noise vector, and A and B are system parameter matrixes;
y (k) is a measurement vector of the motion state of the target environment, X (k) is a prior value of the state of the system at the current moment, T (k) is dynamic system measurement noise, and C is a measurement parameter matrix known by the dynamic system.
6. The control device according to claim 5, characterized in that the second building unit is specifically configured to:
an update equation for constructing the Kalman filter time and state from the state equation and the measurement equation:
wherein,for the prior state optimum at time k-1,a priori values of the system state for the predictions at time k,estimating a covariance prior, P, for time kk-1Estimating covariance, K, for K-1 time errorkThe value of the kalman gain update for time k,updating the value of the state for time k, PkUpdate the error covariance value at time k, Q and H are the state transition matrices,is the unit vector at time K-1,
7. the control device according to claim 6, wherein the determination unit is specifically configured to:
and performing autoregressive operation on the Kalman gain update value, the state update value and the error covariance update value to determine the optimal estimation value of the motion state.
8. The control apparatus of claim 1, wherein the feedback module comprises:
the first feedback unit is used for inputting the target force feedback value to a slave robot of the bilateral teleoperation system and then inputting the force feedback value output by the slave robot to a communication channel of the bilateral teleoperation system;
the second feedback unit is used for inputting the force feedback value output by the communication channel to the robot of the bilateral teleoperation system; and sending the force feedback value output by the main robot to the operator.
9. The control device of any one of claims 1 to 8, wherein the target environment is a dynamic cardiac environment.
10. A control device for a bilateral teleoperated system, comprising:
the main robot is used for receiving a motion state value of an operator and feeding back a force feedback value sent by the main robot to the operator;
the communication channel is used for receiving the motion state value sent by the main robot and feeding back the force feedback value sent by the slave robot to the main robot;
the slave robot is used for receiving the motion state value sent by the communication channel and feeding back a target force feedback value sent by the target environment to the communication channel;
a memory for storing a computer program;
the processor is used for executing the computer program, inputting a motion state simulation reference value output by the target environment model and a motion state measurement value output by the target environment of the bilateral teleoperation system into a preselected estimation algorithm model, and outputting an optimal estimation value of the motion state; inputting the optimal estimated value of the motion state into a static target environment model, and outputting a feedforward compensation value of the bilateral teleoperation system; subtracting the feedforward compensation value from the force feedback value output by the target environment to determine a target force feedback value; and sending the target force feedback value to a slave robot of the bilateral teleoperation system, and feeding the target force feedback value back to an operator of the bilateral teleoperation system by the slave robot.
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Citations (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2010048160A2 (en) * | 2008-10-20 | 2010-04-29 | The Johns Hopkins University | Environment property estimation and graphical display |
| US20100204713A1 (en) * | 2006-02-03 | 2010-08-12 | The European Atomic Energy Community (Euratom) | Medical robotic system |
| US20110264303A1 (en) * | 2010-02-17 | 2011-10-27 | Scott Raymond Lenser | Situational Awareness for Teleoperation of a Remote Vehicle |
| CN102710214A (en) * | 2012-06-11 | 2012-10-03 | 桂林电子科技大学 | Nonlinear interference control method and control system for permanent magnet linear synchronous motor |
| CN202711088U (en) * | 2012-08-24 | 2013-01-30 | 广东工业大学 | Teleoperation robot force feedback control system |
| CN103869773A (en) * | 2014-03-04 | 2014-06-18 | 南昌大学 | Master end machine-based method for correcting feedback force by force feedback controller |
| CN104142627A (en) * | 2014-07-03 | 2014-11-12 | 浙江工业大学 | Networked brushless direct current motor time-delay compensation and control method using active-disturbance-rejection control technology |
| CN104238361A (en) * | 2014-09-03 | 2014-12-24 | 南京理工大学 | Adaptive robust position control method and system for motor servo system |
| CN104252134A (en) * | 2014-09-17 | 2014-12-31 | 南京理工大学 | Method for controlling position of self-adaptive robust of motor servo system based on extended state observer |
| US20150008861A1 (en) * | 2013-07-05 | 2015-01-08 | Fanuc Corporation | Motor control device provided with feedforward control |
| CN104716883A (en) * | 2015-04-09 | 2015-06-17 | 南京埃斯顿自动化股份有限公司 | Improvement method for low-speed performance of permanent magnet synchronous motor |
| WO2015168675A1 (en) * | 2014-05-02 | 2015-11-05 | Thales Visionix, Inc. | Improved registration for vehicular augmented reality using auto-harmonization |
| US20160008988A1 (en) * | 2014-03-03 | 2016-01-14 | California Institute Of Technology | Robotics Platforms Incorporating Manipulators Having Common Joint Designs |
| CN105382841A (en) * | 2015-11-17 | 2016-03-09 | 西北工业大学 | Teleoperation double-side control method based on dual-channel communication |
| CN105890596A (en) * | 2016-03-30 | 2016-08-24 | 南京航空航天大学 | Visual navigation optimal information estimation method with adjustable output speed |
| US20170167341A1 (en) * | 2015-12-10 | 2017-06-15 | General Electric Company | System and method for emission control in power plants |
| CN106933103A (en) * | 2017-04-20 | 2017-07-07 | 清华大学深圳研究生院 | A kind of control method of the remote operating bilateral controller of finite time convergence control |
| JP2017208012A (en) * | 2016-05-20 | 2017-11-24 | 三菱重工業株式会社 | Control device, underwater vehicle, control method, and program |
| CN107450326A (en) * | 2017-08-09 | 2017-12-08 | 清华大学深圳研究生院 | Contragradience finite time bilateral teleoperation control method and computer-readable recording medium |
| CN107932506A (en) * | 2017-11-15 | 2018-04-20 | 电子科技大学 | A kind of force feedback bilateral teleoperation stable control method |
| CN108012576A (en) * | 2016-01-22 | 2018-05-08 | 东芝三菱电机产业系统株式会社 | The speed control unit of motor |
| CN108133491A (en) * | 2017-12-29 | 2018-06-08 | 重庆锐纳达自动化技术有限公司 | A kind of method for realizing dynamic target tracking |
| CN108519739A (en) * | 2018-04-20 | 2018-09-11 | 电子科技大学 | Based on the multi-time Delay remote control system stability control method to power and position prediction |
| CN108549226A (en) * | 2018-04-13 | 2018-09-18 | 燕山大学 | A kind of continuous finite-time control method of remote control system under time-vary delay system |
| CN108762077A (en) * | 2018-05-31 | 2018-11-06 | 浙江工业大学 | A kind of mobile robot moving horizon estimation method with communication constraint |
| CN108803344A (en) * | 2018-07-25 | 2018-11-13 | 西北工业大学 | A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch |
| CN109003292A (en) * | 2018-06-25 | 2018-12-14 | 华南理工大学 | A kind of motion target tracking method based on switch Kalman filter |
| CN109085749A (en) * | 2018-08-07 | 2018-12-25 | 浙江大学 | A kind of non-linear remote operating bilateral control method based on adaptive fuzzy inverting |
| CN109240086A (en) * | 2018-10-16 | 2019-01-18 | 浙江大学 | A kind of adaptive robust control method of non-linear bilateral teleoperation system |
-
2019
- 2019-05-06 CN CN201910372071.7A patent/CN110007601B/en not_active Expired - Fee Related
Patent Citations (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100204713A1 (en) * | 2006-02-03 | 2010-08-12 | The European Atomic Energy Community (Euratom) | Medical robotic system |
| WO2010048160A2 (en) * | 2008-10-20 | 2010-04-29 | The Johns Hopkins University | Environment property estimation and graphical display |
| US20110264303A1 (en) * | 2010-02-17 | 2011-10-27 | Scott Raymond Lenser | Situational Awareness for Teleoperation of a Remote Vehicle |
| CN102710214A (en) * | 2012-06-11 | 2012-10-03 | 桂林电子科技大学 | Nonlinear interference control method and control system for permanent magnet linear synchronous motor |
| CN202711088U (en) * | 2012-08-24 | 2013-01-30 | 广东工业大学 | Teleoperation robot force feedback control system |
| US20150008861A1 (en) * | 2013-07-05 | 2015-01-08 | Fanuc Corporation | Motor control device provided with feedforward control |
| US20160008988A1 (en) * | 2014-03-03 | 2016-01-14 | California Institute Of Technology | Robotics Platforms Incorporating Manipulators Having Common Joint Designs |
| CN103869773A (en) * | 2014-03-04 | 2014-06-18 | 南昌大学 | Master end machine-based method for correcting feedback force by force feedback controller |
| WO2015168675A1 (en) * | 2014-05-02 | 2015-11-05 | Thales Visionix, Inc. | Improved registration for vehicular augmented reality using auto-harmonization |
| CN104142627A (en) * | 2014-07-03 | 2014-11-12 | 浙江工业大学 | Networked brushless direct current motor time-delay compensation and control method using active-disturbance-rejection control technology |
| CN104238361A (en) * | 2014-09-03 | 2014-12-24 | 南京理工大学 | Adaptive robust position control method and system for motor servo system |
| CN104252134A (en) * | 2014-09-17 | 2014-12-31 | 南京理工大学 | Method for controlling position of self-adaptive robust of motor servo system based on extended state observer |
| CN104716883A (en) * | 2015-04-09 | 2015-06-17 | 南京埃斯顿自动化股份有限公司 | Improvement method for low-speed performance of permanent magnet synchronous motor |
| CN105382841A (en) * | 2015-11-17 | 2016-03-09 | 西北工业大学 | Teleoperation double-side control method based on dual-channel communication |
| US20170167341A1 (en) * | 2015-12-10 | 2017-06-15 | General Electric Company | System and method for emission control in power plants |
| CN108012576A (en) * | 2016-01-22 | 2018-05-08 | 东芝三菱电机产业系统株式会社 | The speed control unit of motor |
| CN105890596A (en) * | 2016-03-30 | 2016-08-24 | 南京航空航天大学 | Visual navigation optimal information estimation method with adjustable output speed |
| JP2017208012A (en) * | 2016-05-20 | 2017-11-24 | 三菱重工業株式会社 | Control device, underwater vehicle, control method, and program |
| CN106933103A (en) * | 2017-04-20 | 2017-07-07 | 清华大学深圳研究生院 | A kind of control method of the remote operating bilateral controller of finite time convergence control |
| CN107450326A (en) * | 2017-08-09 | 2017-12-08 | 清华大学深圳研究生院 | Contragradience finite time bilateral teleoperation control method and computer-readable recording medium |
| CN107932506A (en) * | 2017-11-15 | 2018-04-20 | 电子科技大学 | A kind of force feedback bilateral teleoperation stable control method |
| CN108133491A (en) * | 2017-12-29 | 2018-06-08 | 重庆锐纳达自动化技术有限公司 | A kind of method for realizing dynamic target tracking |
| CN108549226A (en) * | 2018-04-13 | 2018-09-18 | 燕山大学 | A kind of continuous finite-time control method of remote control system under time-vary delay system |
| CN108519739A (en) * | 2018-04-20 | 2018-09-11 | 电子科技大学 | Based on the multi-time Delay remote control system stability control method to power and position prediction |
| CN108762077A (en) * | 2018-05-31 | 2018-11-06 | 浙江工业大学 | A kind of mobile robot moving horizon estimation method with communication constraint |
| CN109003292A (en) * | 2018-06-25 | 2018-12-14 | 华南理工大学 | A kind of motion target tracking method based on switch Kalman filter |
| CN108803344A (en) * | 2018-07-25 | 2018-11-13 | 西北工业大学 | A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch |
| CN109085749A (en) * | 2018-08-07 | 2018-12-25 | 浙江大学 | A kind of non-linear remote operating bilateral control method based on adaptive fuzzy inverting |
| CN109240086A (en) * | 2018-10-16 | 2019-01-18 | 浙江大学 | A kind of adaptive robust control method of non-linear bilateral teleoperation system |
Non-Patent Citations (8)
| Title |
|---|
| BUZZI, J等: "On the Value of Estimating Human Arm Stiffness during Virtual Teleoperation with Robotic Manipulators", 《FRONTIERS IN NEUROSCIENCE》 * |
| HASHEMZADEH, F等: "A New Method for Bilateral Teleoperation Passivity under Varying Time Delays", 《MATHEMATICAL PROBLEMS IN ENGINEERING》 * |
| JING GUO等: "A Scaled Bilateral Teleoperation System for Robotic-Assisted Surgery with Time Delay", 《JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS》 * |
| 刘奕: "基于Android系统的远程机器人控制技术", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 吴立凯: "大时延空间遥操作系统自适应控制方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 应旻: "基于力反馈预测的遥操作双边控制技术研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 张艳霞: "基于力觉和视觉的机械手伺服控制研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
| 景包睿: "二自由度机械臂建模与控制研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118036200A (en) * | 2024-01-24 | 2024-05-14 | 德宝艺苑网络科技(北京)有限公司 | Force circulation bidirectional feedback simulation equipment |
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