CN107053179B - A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning - Google Patents
A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning Download PDFInfo
- Publication number
- CN107053179B CN107053179B CN201710263232.XA CN201710263232A CN107053179B CN 107053179 B CN107053179 B CN 107053179B CN 201710263232 A CN201710263232 A CN 201710263232A CN 107053179 B CN107053179 B CN 107053179B
- Authority
- CN
- China
- Prior art keywords
- mechanical arm
- fuzzy
- admittance
- reinforcement learning
- operator
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1628—Programme controls characterised by the control loop
- B25J9/163—Programme controls characterised by the control loop learning, adaptive, model based, rule based expert control
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J13/00—Controls for manipulators
- B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
- B25J13/085—Force or torque sensors
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Human Computer Interaction (AREA)
- Feedback Control In General (AREA)
Abstract
The mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning that the invention discloses a kind of, using fuzzy reinforcement algorithm, the real-time adjustable strategies of training admittance parameter by way of on-line study, moment of face that change admittance control strategy after convergence is applied according to operator, current joint velocity and acceleration control motor actively comply with the control intention of operator, task is actively followed with complete mechanical arm, without establishing corresponding task and environmental model, there is faster convergence rate and stable actual effect.This method can significantly reduce the working strength of operator, improve positioning accuracy, help to reduce mechanical arm structure size and self weight, the control that man-machine power interaction models can respond operator well is intended to, with good adaptive ability, machine power interactive experience more remarkable fluency can be made one, closer to power interaction impression when operating in daily life to actual object.
Description
Technical field:
The invention belongs to interaction control technology fields, are specifically related to a kind of mechanical arm based on Fuzzy Reinforcement Learning
Compliant Force Control method.
Background technique:
Before carrying out robot assisted Minimally Invasive Surgery, medical staff needs to be formulated according to the personal feature of patient corresponding
Operation plan is selected the incision site of Minimally Invasive Surgery and is set the initial attitude of each mechanical arm with this.In the process of implementation, it needs
Each mechanical arm is drawn to mini-incision position and manually adjusts the joint angles of surgical arm, is i.e. operator directly applies mechanical arm
Add external force, is intended to adjust accordingly each connecting rod pose of mechanical arm according to operation.In general, mechanical arm is using retarder as machinery
The transmission link of shoulder joint power, when gearing friction can make to lead diarthrodial pose adjustment and becomes difficult big retarding.
There are mainly two types of solutions common at present: one is electromagnetic brake is installed after retarder, passing through control
The disengaging and actuation of retarder and rear end power delivery section are realized in the movement of electromagnetic brake, i.e., so-called passively to comply with control
Mode.If mechanical arm is pulled in this way, the gravity of mechanical arm itself is all undertaken by operator, and working strength increases
Big and uncontrollable operation precision.Further, since each joint and its driving motor are detached from during pulling, in order to obtain machine
Tool arm articulation angle adjusted, it is also necessary to the additional change in location for increasing encoder record joint.Electromagnetic brake and
The introducing of auxiliary coder will increase the structure size and own wt of mechanical arm, and the frequent actuation of electromagnetic brake also will affect
The absolute positional accuracy of robot.It is on the other side another be achieved in that in the case where joint motor is in slave mode, root
Control according to the stress condition estimation operator of mechanical arm is intended to, and drives mechanical arm auxiliary operation person to complete by joint motor
Expected pose adjustment, i.e., it is so-called actively to comply with control mode.Current control method of actively complying with uses at mechanical arm end
It holds position of the installing force approach sensor control robot arm end effector in cartesian space mobile, is often concerned with end
The location track of ending tool rather than pose adjustment, fixed power interaction position is also not easy to robot linkage posture in addition
Independent adjustment, therefore the position of actively putting for being not particularly suited for minimally invasive surgical operation robot requires.In addition, there is also one for such mode
Fixed problem is then difficult to take into account control precision and operating experience according to fixed control parameter model, according to variable control
Parameter model processed is difficult to ensure the submissive and smooth of man-machine interactive operation again.
Summary of the invention:
To solve the above problems, the invention proposes a kind of mechanical arm Compliant Force Control side based on Fuzzy Reinforcement Learning
Method.
In order to achieve the above objectives, technical scheme is as follows:
A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning, includes the following steps:
S1: admittance Controlling model is established.
S2: the motion state of mechanical arm, the moment of face that operator applies and environment return value are obtained.
S3: in order to obtain the admittance model parameter strategy being adapted with current environment, according to what is obtained in step S2
Relevant information carries out the on-line training of admittance model parameter strategy by Fuzzy Reinforcement Learning, until algorithmic statement, to
Hope the change admittance Controlling model for obtaining and being adapted with current environment.
S4: the admittance parameter adjustable strategies after convergence trained in step S3 are applied to become among admittance Controlling model,
The feedback speed of moment of face and joint of mechanical arm that admittance Controlling model after changing parameter applies according to operator calculates joint
Current speed value is simultaneously sent to joint drive motor.
As a preferred embodiment of the above technical solution, the Fuzzy Reinforcement Learning in the step S3 specifically comprises the following steps:
S31: the moment of face that the motion state of mechanical arm and operator are applied is as state variable, in each state variable
Domain within the scope of divide multiple fuzzy sets, establish corresponding fuzzy rule and provide discrete movement set.
S32: calculating the degree of membership of each state variable according to current state input, carry out fuzzy division to state space,
Calculating has activated weight corresponding to fuzzy rule.
S33: discrete movement value is selected according to current admittance model parameter strategy.
S34: the admittance parameter adjustable strategies after convergence trained in step S3 are applied to become among admittance Controlling model,
The feedback speed of the moment of face and joint of mechanical arm that are applied according to operator calculates joint current speed value and is sent to joint and drives
Dynamic motor, so that realizes micro-wound surgical operation mechanical arm actively puts bit function.
S35: current admittance model parameter strategy is updated according to the environment return value currently obtained.
S36: repeating above-mentioned S32-S35 step, until algorithmic statement.
As a preferred embodiment of the above technical solution, further include following steps:
S0: torque sensor is integrated in each joint of mechanical arm, the torque sensor is for detecting connecing between man-machine
Touch square.
As a preferred embodiment of the above technical solution, in the step S2:
The gravity compensation model of identified off-line mechanical arm by the way of linear regression, to obtain the outer of operator's application
Torque.
As a preferred embodiment of the above technical solution, in the step S2:
The motion state of the mechanical arm includes the speed and acceleration of each joint of mechanical arm.
The beneficial effects of the present invention are:
Relative to passively mode is complied with, the working strength of operator can be significantly reduced, improves positioning accuracy, helps to subtract
Small structure size and self weight.
Mode is actively complied with relative to preset parameter model, there is good adaptive ability, when contact torque increases
When, power interactive controlling model can actively reduce the automatic virtual blocks parameter of environment, change the movement velocity of mechanical arm faster, energy
Enough movement tendencies for quickly following human arm, can be more labor-saving to the operating experience of people；Conversely, when contact force (amplitude) is gradually reduced
When, power interactive controlling model can increase automatic virtual blocks parameter value correspondingly to improve the control precision of human-computer interaction, auxiliary operation
Person's positioning, reduces overshoot.
Mode is actively complied with relative to time-varying parameter model, man-machine power interaction models can respond well operator's
Control is intended to, and makes one machine power interactive experience more remarkable fluency, when being operated in closer daily life to actual object
Power interaction impression.
Detailed description of the invention:
The following drawings are only intended to schematically illustrate and explain the present invention, not delimit the scope of the invention.Wherein:
Fig. 1 is a kind of master of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning of one embodiment of the invention
Dynamic Shared control flow chart；
Fig. 2 is the Fuzzy Reinforcement Learning flow chart of one embodiment of the invention.
Specific embodiment:
As shown in Figure 1, a kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning of the invention, including it is as follows
Step:
S1: admittance Controlling model is established.
S2: the motion state of mechanical arm, the moment of face that operator applies and environment return value are obtained.The mechanical arm
Motion state includes the speed and acceleration in each mechanical arm active rotation joint.
S3: in order to obtain the admittance model parameter strategy being adapted with current environment, according to what is obtained in step S2
Relevant information carries out the on-line training of admittance model parameter strategy by Fuzzy Reinforcement Learning, until algorithmic statement, to
Hope the change admittance Controlling model for obtaining and being adapted with current environment.Fuzzy Reinforcement Learning in the step S3 specifically includes following step
It is rapid:
S31: the moment of face that the motion state of mechanical arm and operator are applied is as state variable (I), in each state
The domain X of variableiMultiple fuzzy sets are divided in range, are established corresponding fuzzy rule and are provided discrete movement set A={ u1,
u2,…,un, wherein μiFor discrete transfer corresponding to current activated fuzzy rule (being determined as current fuzzy division)
Make.
S32: I is inputted according to current stateiCalculate the degree of membership μ of each state variablei(Ii), mould is carried out to state space
Paste divides, and calculating has activated fuzzy rule fiCorresponding weight wi, wherein fiIndicate i-th of fuzzy rule, wiIt is corresponding
Fuzzy rule activity, i.e., the weight of the corresponding discrete movement of current each fringe component.
S33: discrete movement value u is selected according to current admittance model parameter strategyi。
S34: the discrete movement value in step S33 is integrated into final movement output valve U and the value is used for admittance control
Simulation.
S35: current admittance model parameter strategy is updated according to the environment return value currently obtained.
S36: repeating above-mentioned S32-S35 step, until algorithmic statement.
S4: the admittance parameter adjustable strategies after convergence trained in step S3 are applied to become among admittance Controlling model,
The feedback speed of the moment of face and joint of mechanical arm that are applied according to operator calculates joint current speed value and is sent to joint and drives
Dynamic motor, so that realizes micro-wound surgical operation mechanical arm actively puts bit function.
The control method of the present embodiment needs to integrate torque sensor in each joint of mechanical arm, and the torque sensor is used
Contact torque between detection is man-machine.In the present embodiment by the way of linear regression identified off-line mechanical arm gravity compensation
Model, to obtain the moment of face of operator's application.Different from the teaching mode of conventional industrial robot, micro-wound surgical operation machine
What the preoperative pendulum position process of device people needed to adjust is the spatial attitude of each connecting rod of mechanical arm rather than end effector is in world coordinates
It is the spatial position in (cartesian coordinate system), and usually such power interaction implementation is used and is installed in mechanical arm execution end
The mode of six-dimension force sensor realizes reciprocal force information collection, but does so the effect that people interacts with robot progress power that will limit
Position is unfavorable for the independent adjustment of each connecting rod pose of surgery mechanical arm.Torque sensor is integrated into respectively in order to solve the above problem
Among mechanical arm active rotation joint, the power interaction locations of mechanical arm and external environment can in this manner extended to whole
Mechanical arm, moment inspecting and power interactive controlling are also more directly reliable.
In joint space, proposes and a kind of combined with nitrification enhancement based on fuzzy theory in conjunction with practical application
Become admittance Controlling model framework.In human-computer interaction process, make since people plays guidance among entire power interactive controlling circuit
With because the operating characteristic of this person can have larger impact to power interaction effect.In addition, Manipulator Dynamics characteristic can be with control mould
The variation of shape parameter and change, human-computer interaction can also be had an impact.In order to by human factor in interactive process and dynamic
Mechanical change is passed through online in view of actively complying among Controlling model using the intensified learning method based on multistep time difference
The mode of study handles the problem of above-mentioned factor is brought.Meanwhile the introducing of fuzzy theory helps to solve intensified learning state sky
Between general problem, so that Compliant Force Control algorithm is received continuous state and input and generate the output of continuous control parameter.This
Outside for the moment of face that extraction operation person applies, the gravity compensation model of identified off-line mechanical arm by the way of linear regression.
The man-machine power interaction control method proposed has faster convergence rate and stabilization without establishing corresponding task and environmental model
Actual effect.
Active Compliance Control process is as shown in Figure 1, Fuzzy Reinforcement Learning combines trained receipts according to current motion state
Discrete movement selection strategy after holding back obtains current admittance Controlling model parameter, and admittance Controlling model is applied according to operator
The control that moment of face and current joint speed control motor actively comply with operator is intended to, to complete power interactive task.It is fuzzy strong
The single step training process that chemistry is practised is as shown in Fig. 2, calculate the degree of membership of each state variable simultaneously according to current state input first
Fuzzy division is carried out to state space, according to discrete movement weight corresponding to the fuzzy rule currently triggered and explores strategy choosing
It selects discrete movement value and integrates and export final admittance model parameter value.The new admittance model for changing parameter is used for current people
Machine interactive process obtains expectation in human-computer interaction process to obtain current environmental feedback and according to value of feedback corrective action weight
The performance indicator obtained tends to be maximum, and the continuous iteration above process is until algorithmic statement.
The present embodiment is described further the above method by taking simple joint power interactive controlling as an example, as shown in Figure 1:
The speed currently measured with mechanical arm rotary jointAnd accelerationAnd suffered moment of face τhIt is defeated as state
Enter variable, equidistantly divides 7 fuzzy sets, corresponding number of fuzzy rules within the scope of the domain of each state variable respectively
For 343 (7 × 7 × 7).It is exported using the automatic virtual blocks parameter in admittance Controlling model as the movement of intensified learning, if discrete dynamic
Making collection element number is 3, then corresponding fuzzy rule weight number is 1029 (7 × 7 × 7 × 3).Man-machine Compliant Force interactive controlling
Realize to include Strategies Training and interactive application two parts.During Strategies Training, using the minimum acceleration model of people as
Optimality criterion, the human-computer interaction task executed needed for constantly repeating, nitrification enhancement can according to being interacted with operator and
The experience of generation persistently modifies the decision strategy of intelligent body until convergence.In human-computer interaction application process, it is based on intensified learning
Power interactive controlling algorithm fuzzy division is carried out to trigger corresponding fuzzy rule, after convergence according to current state input
Change admittance policy selection each activated the optimal action value component of fuzzy rule, finally by the corresponding activation of fuzzy rule
Degree (is indicated) each action value component of integration by the T norm of corresponding fuzzy set degree of membership, ultimately generates current time admittance control
Parameter value c used by simulation.The moment of face τ that admittance Controlling model after changing parameter applies according to operatorhAnd machinery
The feedback speed of shoulder jointCalculate joint current speed valueAnd it is sent to joint drive motor.
A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning described in the present embodiment, using fuzzy reinforcing
Learning algorithm, the real-time adjustable strategies of training admittance parameter, the change admittance control strategy after convergence by way of on-line study
The moment of face, the current joint velocity and acceleration control motor that are applied according to operator actively comply with the control meaning of operator
Figure actively follows task with complete mechanical arm, without establishing corresponding task and environmental model, has faster convergence rate
With stable actual effect.This method can significantly reduce the working strength of operator, improve positioning accuracy, facilitate reduction machine
Tool arm configuration size and self weight, man-machine power interaction models can respond well operator control be intended to, have it is good from
Adaptability can make one machine power interactive experience more remarkable fluency, closer to when operating in daily life to actual object
Power interaction impression.
Obviously, the above embodiments are merely examples for clarifying the description, and does not limit the embodiments.It is right
For those of ordinary skill in the art, can also make on the basis of the above description it is other it is various forms of variation or
It changes.There is no necessity and possibility to exhaust all the enbodiments.And it is extended from this it is obvious variation or
It changes still within the protection scope of the invention.
Claims (5)
1. a kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning, which comprises the steps of:
S1: admittance Controlling model is established；
S2: the motion state of mechanical arm, the moment of face that operator applies and environment return value are obtained；
S3: in order to obtain the admittance model parameter strategy being adapted with current environment, according to the correlation obtained in step S2
Information carries out the on-line training of admittance model parameter strategy by Fuzzy Reinforcement Learning until algorithmic statement, is obtained with expectation
The change admittance Controlling model being adapted with current environment；
S4: the admittance parameter adjustable strategies after convergence trained in step S3 are applied to become among admittance Controlling model, according to
The feedback speed of moment of face and joint of mechanical arm that operator applies calculates joint current speed value and is sent to joint drive electricity
Machine, so that realizes micro-wound surgical operation mechanical arm actively puts bit function.
2. the mechanical arm Compliant Force Control method according to claim 1 based on Fuzzy Reinforcement Learning, which is characterized in that institute
The Fuzzy Reinforcement Learning stated in step S3 specifically comprises the following steps:
S31: the moment of face that the motion state of mechanical arm and operator are applied is as state variable, in the opinion of each state variable
Multiple fuzzy sets are divided within the scope of domain, are established corresponding fuzzy rule and are provided discrete movement set；
S32: the degree of membership of each state variable is calculated according to current state input, fuzzy division is carried out to state space, is calculated
Weight corresponding to fuzzy rule is activated；
S33: discrete movement value is selected according to current admittance model parameter strategy；
S34: the discrete movement value in step S33 is integrated into final movement output valve U and the value is used for admittance control mould
Type；
S35: current admittance model parameter strategy is updated according to the environment return value currently obtained；
S36: repeating above-mentioned S32-S35 step, until algorithmic statement.
3. the mechanical arm Compliant Force Control method according to claim 1 based on Fuzzy Reinforcement Learning, which is characterized in that also
Include the following steps:
S0: integrating torque sensor in each joint of mechanical arm, and the torque sensor is used to detect the contact force between man-machine
Square.
4. the mechanical arm Compliant Force Control method according to claim 3 based on Fuzzy Reinforcement Learning, which is characterized in that institute
It states in step S2:
The gravity compensation model of identified off-line mechanical arm by the way of linear regression, to obtain the external force of operator's application
Square.
5. the mechanical arm Compliant Force Control method according to claim 1 based on Fuzzy Reinforcement Learning, which is characterized in that institute
It states in step S2:
The motion state of the mechanical arm includes the speed and acceleration of each joint of mechanical arm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710263232.XA CN107053179B (en) | 2017-04-21 | 2017-04-21 | A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710263232.XA CN107053179B (en) | 2017-04-21 | 2017-04-21 | A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107053179A CN107053179A (en) | 2017-08-18 |
| CN107053179B true CN107053179B (en) | 2019-07-23 |
Family
ID=59600893
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710263232.XA Active CN107053179B (en) | 2017-04-21 | 2017-04-21 | A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107053179B (en) |
Families Citing this family (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109960246B (en) | 2017-12-22 | 2021-03-30 | 华为技术有限公司 | Action control method and device |
| CN109352648B (en) * | 2018-10-12 | 2021-03-02 | 北京地平线机器人技术研发有限公司 | Mechanical mechanism control method and device and electronic equipment |
| CN109512518A (en) * | 2018-12-19 | 2019-03-26 | 苏州大学 | A kind of operating robot man-machine coordination motion blur model reference learning control method |
| CN109605377B (en) * | 2019-01-21 | 2020-05-22 | 厦门大学 | Robot joint motion control method and system based on reinforcement learning |
| CN110298854B (en) * | 2019-05-17 | 2021-05-11 | 同济大学 | Flight snake-shaped arm cooperative positioning method based on online self-adaption and monocular vision |
| JP2021037582A (en) * | 2019-09-03 | 2021-03-11 | ファナック株式会社 | Control device of robot having arm |
| CN111390877B (en) * | 2020-04-23 | 2021-07-13 | 电子科技大学 | Exoskeleton device and exoskeleton admittance variation control method |
| CN111881772B (en) * | 2020-07-06 | 2023-11-07 | 上海交通大学 | Multi-mechanical arm cooperative assembly method and system based on deep reinforcement learning |
| CN112936278B (en) * | 2021-02-07 | 2022-07-29 | 深圳市优必选科技股份有限公司 | Man-machine cooperation control method and device for robot and robot |
| CN113568313B (en) * | 2021-09-24 | 2021-12-17 | 南京航空航天大学 | Variable admittance auxiliary large component assembly method and system based on operation intention identification |
| CN114344077A (en) * | 2021-12-07 | 2022-04-15 | 华南理工大学 | Flexible upper limb rehabilitation robot system based on SEMG movement intention recognition |
| CN114569410B (en) * | 2022-05-06 | 2022-09-13 | 卓道医疗科技(浙江)有限公司 | Control method and device for rehabilitation robot training mode and storage medium |
| CN114800513B (en) * | 2022-05-10 | 2024-03-29 | 上海交通大学 | System and method for automatically generating robot shaft hole assembly program based on single dragging teaching |
| CN114932557B (en) * | 2022-06-24 | 2023-07-28 | 合肥工业大学 | Self-adaptive admittance control method based on energy consumption under kinematic constraint |
| CN116619393B (en) * | 2023-07-24 | 2023-11-14 | 杭州键嘉医疗科技股份有限公司 | Mechanical arm admittance variation control method, device and equipment based on SVM |
| CN117084794B (en) * | 2023-10-20 | 2024-02-06 | 北京航空航天大学 | Respiration follow-up control method, device and controller |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101484086A (en) * | 2006-05-19 | 2009-07-15 | 马科外科公司 | Method and apparatus for controlling a haptic device |
| JP2009208170A (en) * | 2008-02-29 | 2009-09-17 | Toyota Motor Corp | Power assisting device and its control method |
| CN105242533A (en) * | 2015-09-01 | 2016-01-13 | 西北工业大学 | Variable-admittance teleoperation control method with fusion of multi-information |
| CN105583824A (en) * | 2016-01-26 | 2016-05-18 | 清华大学 | Force control traction and swinging multi-degree-of-freedom mechanical arm control device and method |
-
2017
- 2017-04-21 CN CN201710263232.XA patent/CN107053179B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101484086A (en) * | 2006-05-19 | 2009-07-15 | 马科外科公司 | Method and apparatus for controlling a haptic device |
| JP2009208170A (en) * | 2008-02-29 | 2009-09-17 | Toyota Motor Corp | Power assisting device and its control method |
| CN105242533A (en) * | 2015-09-01 | 2016-01-13 | 西北工业大学 | Variable-admittance teleoperation control method with fusion of multi-information |
| CN105583824A (en) * | 2016-01-26 | 2016-05-18 | 清华大学 | Force control traction and swinging multi-degree-of-freedom mechanical arm control device and method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107053179A (en) | 2017-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107053179B (en) | A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning | |
| Li et al. | Asymmetric bimanual control of dual-arm exoskeletons for human-cooperative manipulations | |
| CN108883533B (en) | Robot control | |
| Su et al. | Fuzzy approximation-based task-space control of robot manipulators with remote center of motion constraint | |
| Dimeas et al. | Fuzzy learning variable admittance control for human-robot cooperation | |
| Su et al. | Neural network enhanced robot tool identification and calibration for bilateral teleoperation | |
| CN106945043A (en) | A kind of master-slave mode telesurgery robot multi-arm cooperative control system | |
| CN106041912B (en) | Master-slave mode snake-shaped robot system and its position control method | |
| Wang et al. | Hybrid adaptive control strategy for continuum surgical robot under external load | |
| Garrido et al. | Modular design and control of an upper limb exoskeleton | |
| CN110703604B (en) | Exoskeleton dynamic model parameter identification method and exoskeleton device | |
| CN108227506A (en) | A kind of robot admittance control system based on adaptive optimization method | |
| CN113001540A (en) | Man-machine interaction intelligent control method of load-mobile exoskeleton and exoskeleton system | |
| Saini et al. | Adaptive control of a master-slave based robotic surgical system with haptic feedback | |
| Wang et al. | A PSO-optimized fuzzy reinforcement learning method for making the minimally invasive surgical arm cleverer | |
| CN110181517B (en) | Double teleoperation training method based on virtual clamp | |
| Ying-Long et al. | Remote human-robot collaborative impedance control strategy of pharyngeal swab sampling robot | |
| Zidane et al. | Trajectory control of a laparoscopic 3-PUU parallel manipulator based on neural network in SIMSCAPE SIMULINK environment | |
| CN113500597B (en) | Multi-end teleoperation sliding mode impedance control method based on force translation mechanism | |
| Ai et al. | Master-slave control technology of isomeric surgical robot for minimally invasive surgery | |
| Saini et al. | Intelligent control of a master-slave based robotic surgical system | |
| Qu et al. | Research on fuzzy adaptive impedance control of lower extremity exoskeleton | |
| Hu et al. | Enhance transparency of force feedback interaction series mechanism by SMC strategy | |
| CN115755592B (en) | Multi-mode control method for adjusting motion state of three-degree-of-freedom exoskeleton and exoskeleton | |
| Wang et al. | Cerebellum-inspired model-free tracking control and visual servoing of a rigid-flexible hybrid robotic endoscope with RCM constraints |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20180611 Address after: 215000 Room 302, 15 building, 8 Jinfeng Road, Huqiu District, Suzhou, Jiangsu. Applicant after: Suzhou health multirobot company limited Address before: 150090 08, room 15, No. 368 Changjiang Road, Nangang District, Harbin, Heilongjiang. Applicant before: HARBIN SIZHERUI INTELLIGENT MEDICAL EQUIPMENT CO., LTD. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |