CN114932557A - Adaptive admittance control method based on energy consumption under kinematic constraint - Google Patents

Adaptive admittance control method based on energy consumption under kinematic constraint Download PDF

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CN114932557A
CN114932557A CN202210729992.6A CN202210729992A CN114932557A CN 114932557 A CN114932557 A CN 114932557A CN 202210729992 A CN202210729992 A CN 202210729992A CN 114932557 A CN114932557 A CN 114932557A
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damping
energy consumption
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admittance
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CN114932557B (en
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黄云志
钱鑫泉
韩亮
何磊
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Hefei University of Technology
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    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00Programme-controlled manipulators
    • B25J9/16Programme controls
    • B25J9/1602Programme controls characterised by the control system, structure, architecture
    • BPERFORMING OPERATIONS; TRANSPORTING
    • B25HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25JMANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
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Abstract

The invention discloses an adaptive admittance control method based on energy consumption under kinematic constraint, belonging to the field of human-computer loop interaction. The minimum criterion of energy consumption in the human-computer ring interaction process is provided, an admittance control law is designed on the basis of comprehensively considering the interaction force and the movement speed of the robot, damping parameters are updated, and the flexibility and the safety of the human-computer ring interaction are improved. The quality parameter range of the admittance controller is given according to the kinematic constraint of the robot, the speed, the acceleration and the variable acceleration limit of the robot are considered, and the safety of the robot system is improved. The admittance controller converts the acting force into the pose correction quantity of the tail end of the mechanical arm, and the pose correction quantity is superposed on the input of the robot system, and the robot motion control is realized through the position controller. The method can make the robot well conform to the intention of an operator, reduce the man-machine interaction force, improve the precision of the contact force with the environment, prevent the instability of the interaction process caused by the undersize admittance parameters, and improve the flexibility and the safety of the man-machine loop interaction.

Description

Adaptive admittance control method based on energy consumption under kinematic constraint
Technical Field
The invention relates to the technical field of man-machine loop interaction, in particular to an energy consumption-based adaptive admittance control method under kinematic constraint.
Background
Human-computer interaction is that an operator pulls a mechanical arm to complete specific movement, and most commonly human-computer teaching is performed. The cooperative mechanical arm can independently realize track reproduction from receiving teaching, and various human-computer loop interaction modes exist in the whole process. In order to make the interaction process more compliant and safe, the robot needs to have the ability to adapt to the intentions of the operator and the risks that may arise in the interaction.
The traditional admittance control method has the problems of poor flexibility and poor safety. In the prior art, a human-computer cooperation system control method based on intention identification of chinese patent CN112276944A estimates the intention of a human by using a neural network identification system, and although the method reduces the interaction force of human-computer cooperation, the method does not consider the constraint conditions of the mechanical arm itself, and cannot ensure the safety of the mechanical arm system. The method for controlling the compliance force of the mechanical arm based on the fuzzy reinforcement learning of the Chinese patent CN107053179B adopts the fuzzy reinforcement learning algorithm and completes the active following task of the mechanical arm by online learning and training the real-time adjustment strategy of admittance parameters, but the method has low convergence speed and reduces the compliance of man-machine cooperation. The adaptive man-machine cooperative control method based on the optimal admittance parameters, disclosed by the chinese patent CN113352322A, finds the optimal admittance parameters and introduces the assisting power into the admittance control equation in an integral reinforcement learning manner, but the method requires a large amount of data training and is only suitable for specific tasks.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a self-adaptive admittance control method considering energy consumption under kinematic constraint, and the flexibility and the safety of a human-computer loop interaction process are improved.
In order to achieve the purpose, the invention adopts the following technical scheme that:
an adaptive admittance control method based on energy consumption under kinematic constraint considers a man-machine ring interaction process, establishes a capability consumption minimum criterion according to interaction force and a robot movement speed, designs an admittance control law, and updates damping parameters.
Preferably, the damping update formula of the man-machine interaction admittance controller is as follows:
Figure BDA0003712820640000021
wherein b is the updated damping value, b 0 Is an initial damping value, e is a natural constant, α is a parameter, f h Is the force exerted on the robotic arm, and v is the velocity of the robotic arm in Cartesian space.
Preferably, the damping coefficient of the admittance controller for human-computer interaction is updated based on the minimum energy consumption criterion in the human-computer loop interaction process, and the specific method is as follows:
s11, energy consumption in the process of man-machine interaction can be represented by the following formula;
Figure BDA0003712820640000022
wherein f is h Is the force applied to the robotic arm, v is the velocity of the robotic arm in cartesian space;
s12, considering the relation between energy consumption and damping, minimizing the energy consumption in the interactive process, and calculating the partial derivative of the energy to the damping;
Figure BDA0003712820640000023
wherein f is h Is the acting force applied on the mechanical arm, and v is the velocity of the mechanical arm in Cartesian space;
s13, obtaining the damping coefficient b of the admittance controller according to the operation force f applied on the mechanical arm h And the relational expression of the motion speed v of the mechanical arm in the Cartesian space, wherein the damping updating formula is as follows:
Figure BDA0003712820640000024
wherein b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, α is the parameter;
s14, knowing the speed of the robot arm itself
Figure BDA0003712820640000029
Acceleration of a vehicle
Figure BDA0003712820640000028
And variable acceleration
Figure BDA00037128206400000210
According to the operating force f h And a damping coefficient b for the damping coefficient,
setting the value range of the quality parameter m
Figure BDA0003712820640000025
The subscript min represents a minimum value, i.e., a lower limit, and the subscript max represents a maximum value, i.e., an upper limit.
Preferably, the damping update formula of the machine-ring interactive admittance controller is as follows:
Figure BDA0003712820640000026
wherein,
Figure BDA0003712820640000027
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is ambient velocity, b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, and α is the parameter.
Preferably, the damping coefficient of the admittance controller in the machine loop interaction is updated based on the energy consumption minimum criterion, and the specific method is as follows:
s21, energy consumption in the process of interaction between the mechanical arm and the environment can be represented by the following formula;
Figure BDA0003712820640000031
wherein,
Figure BDA0003712820640000032
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is the ambient velocity;
s22, in order to minimize the energy consumption in the interaction process, the partial derivative of the energy to the damping is obtained;
Figure BDA0003712820640000033
wherein,
Figure BDA0003712820640000034
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space, v e Is the ambient velocity;
s23 deviation of damping coefficient b from admittance controller with contact force
Figure BDA0003712820640000035
And speed deviation
Figure BDA0003712820640000036
The machine-ring interactive admittance controller damping updating expression is as follows:
Figure BDA0003712820640000037
wherein,
Figure BDA0003712820640000038
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space, v e Is the ambient velocity, b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, α is the parameter;
s24, knowing the robot arm speed
Figure BDA00037128206400000317
Acceleration of a vehicle
Figure BDA00037128206400000316
And variable acceleration
Figure BDA0003712820640000039
According to the deviation of the applied force
Figure BDA00037128206400000310
Damping coefficient b and ambient velocity
Figure BDA00037128206400000311
Ambient acceleration
Figure BDA00037128206400000312
And ambient variation of acceleration
Figure BDA00037128206400000313
Setting the value range of the quality parameter m
Figure BDA00037128206400000314
Wherein,
Figure BDA00037128206400000315
the subscript min represents the minimum, i.e., lower limit, and the subscript max represents the maximum, i.e., upper limit.
The invention has the advantages that:
(1) the invention provides an energy consumption minimum criterion in the human-computer ring interaction process, an admittance control law is designed on the basis of comprehensively considering interaction force and the movement speed of a robot, damping parameters are updated, the damping coefficient exponentially decreases along with the force applied by an operator and the movement speed of a mechanical arm at the beginning stage of the human-computer ring interaction, and the flexibility of the human-computer ring cooperation is improved; the damping coefficient is kept at a smaller value in the motion process, so that the energy consumption in the cooperation process is reduced; when the mechanical arm needs to execute fine work or stop motion emergently, the damping coefficient can exponentially rise, and the control precision and the safety of the mechanical arm are improved.
(2) The invention also gives the mass parameter range of the admittance controller according to the kinematic constraint of the robot, considers the limits of the speed, the acceleration and the variable acceleration of the robot arm, prevents the instability of the motion of the robot arm caused by the undersize admittance parameter and ensures the motion safety of the robot arm system.
(3) The self-adaptive admittance control method ensures that the mechanical arm can identify the movement intention of an operator in the human-computer loop cooperation process, and improves the flexibility of the mechanical arm system.
Drawings
FIG. 1 is a block diagram of the adaptive admittance control of the present invention.
FIG. 2 is a diagram of the trajectory tracking effect of the adaptive admittance of the present invention.
Fig. 3 is a graph showing the change of the damping coefficient in the X direction according to the present invention.
Fig. 4 is a graph showing the variation of the damping coefficient in the Y direction according to the present invention.
The English meaning in the drawings is as follows:
desired traj-desired track, actual traj-track.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, a method for controlling adaptive admittance based on energy consumption in human-computer interaction under kinematic constraint includes the following steps:
s1: and modeling the mechanical arm. Building a kinematic model of the mechanical arm in Simulink;
s2: generation of the desired trajectory. Planning a track on an XY plane of a task space, wherein the track is used as an expected track for the track tracking of the mechanical arm in the later period;
s3: and (4) acquiring a force signal. According to the actual motion trail x and the expected motion trail x of the mechanical arm d Calculating the force f between the robot arm and the operator h And (3) feedback control for the admittance controller, wherein k in formula (1) is the set environmental stiffness parameter.
f h =k(x-x d ) (1)
S4: and (4) considering the energy consumption in an impedance expression, minimizing the energy consumption in the man-machine interaction process, and solving the relation between an energy function and damping, wherein the relation is shown in formulas (2) and (3).
Figure BDA0003712820640000051
Figure BDA0003712820640000052
S5: and updating the damping coefficient. Collecting the speed v of the mechanical arm in the Cartesian space, and obtaining the acting force f according to the step S3 h On-line calculation of damping coefficient
Figure BDA0003712820640000053
Wherein b is 0 Is an initial damping value, e is a natural constant, α is a parameter, f h Is the force exerted on the robotic arm, and v is the velocity of the robotic arm in cartesian space.
S6: and (4) admittance control. Impedance parameters m, b and force f h Substituting into formula
Figure BDA0003712820640000054
And calculating the displacement correction quantity of the tail end of the mechanical arm. Wherein
Figure BDA0003712820640000055
Respectively the acceleration and velocity of the mechanical arm in cartesian space.
S7: and controlling the motion of the mechanical arm. Superposing the displacement correction quantity delta x calculated by the admittance controller on the initial target position x d Obtaining a reference position x of the mechanical arm r As shown in equation (4). x is the number of r And obtaining the expected motion angle of each joint of the mechanical arm through inverse kinematics solution, and realizing motion control on the mechanical arm through a position controller.
x r =x d +Δx (4)
Fig. 2 is a diagram of the track following effect of adaptive admittance control, the solid line is the desired track desired traj, the dotted line is the following track actual traj, and the desired track and the following track of the present invention coincide. Fig. 3 and 4 are graphs showing changes in the damping coefficient in the X and Y directions, respectively.
The invention is not to be considered as limited to the specific embodiments shown and described, but is to be understood to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined by the appended claims.

Claims (5)

1. An adaptive admittance control method based on energy consumption under kinematic constraint is characterized in that a human-computer loop interaction process is considered, a capability consumption minimum criterion is established according to interaction force and robot movement speed, an admittance control law is designed, and damping parameters are updated.
2. The adaptive admittance control method based on energy consumption under kinematic constraint according to claim 1, wherein the damping of the human-computer interaction admittance controller updates the formula as follows:
Figure FDA0003712820630000011
where b is the updated damping value, b 0 Is an initial damping value, e is a natural constant, α is a parameter, f h Is the force exerted on the robotic arm, and v is the velocity of the robotic arm in cartesian space.
3. The adaptive admittance control method based on energy consumption under kinematic constraint according to claim 1 or 2, wherein the damping coefficient of the admittance controller for human-computer interaction is updated based on the energy consumption minimum criterion of the human-computer loop interaction process, and the specific method is as follows:
s11, energy consumption in the process of man-machine interaction can be represented by the following formula;
Figure FDA0003712820630000012
wherein, f h Is the force applied to the robotic arm, v is the velocity of the robotic arm in cartesian space;
s12, considering the relation between energy consumption and damping, minimizing the energy consumption in the interactive process, and calculating the partial derivative of the energy to the damping;
Figure FDA0003712820630000013
wherein, f h Is the force applied to the robotic arm, v is the velocity of the robotic arm in cartesian space;
s13, obtaining the damping coefficient b of the admittance controller according to the operation force f applied on the mechanical arm h And the relational expression of the motion speed v of the mechanical arm in the Cartesian space, wherein the damping updating formula is as follows:
Figure FDA0003712820630000014
where b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, α is the parameter;
s14, knowing the speed of the robot arm itself
Figure FDA0003712820630000015
Acceleration of a vehicle
Figure FDA0003712820630000016
And variable acceleration
Figure FDA0003712820630000017
In response to the operating force f h And a damping coefficient b for the damping of the vibration,
setting the value range of the quality parameter m
Figure FDA0003712820630000018
The subscript min represents a minimum value, i.e., a lower limit, and the subscript max represents a maximum value, i.e., an upper limit.
4. The adaptive admittance control method based on energy consumption under kinematic constraint according to claim 1, wherein the damping update formula of the machine-loop interactive admittance controller is as follows:
Figure FDA0003712820630000021
wherein,
Figure FDA0003712820630000022
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is ambient velocity, b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, and α is the parameter.
5. The adaptive admittance control method based on energy consumption under kinematic constraint according to claim 1 or 4, wherein the damping coefficient of the admittance controller in the machine loop interaction is updated based on the energy consumption minimum criterion, and the specific method is as follows:
s21, energy consumption in the process of interaction between the mechanical arm and the environment can be represented by the following formula;
Figure FDA0003712820630000023
wherein,
Figure FDA0003712820630000024
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is the ambient velocity;
s22, in order to minimize the energy consumption in the interaction process, the partial derivative of the energy to the damping is obtained;
Figure FDA0003712820630000025
wherein,
Figure FDA0003712820630000026
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is the ambient velocity;
s23 deviation of damping coefficient b from admittance controller with contact force
Figure FDA0003712820630000027
And speed deviation
Figure FDA0003712820630000028
The relationship expression of (2) and the admittance controller damping updating expression of machine ring interaction are as follows:
Figure FDA0003712820630000029
wherein,
Figure FDA00037128206300000210
f e is the actual contact force between the mechanical arm and the environment, f d Is the expected contact force between the robot arm and the environment, v is the velocity of the robot arm in Cartesian space e Is the ambient velocity, b is the updated damping value, b 0 Is the initial damping value, e is the natural constant, α is the parameter;
s24, knowing the robot arm speed
Figure FDA00037128206300000211
Acceleration of a vehicle
Figure FDA00037128206300000212
And variable acceleration
Figure FDA00037128206300000213
According to the applied force deviation
Figure FDA00037128206300000214
Damping coefficient b and ambient velocity
Figure FDA00037128206300000215
Ambient acceleration
Figure FDA00037128206300000216
And ambient variation of acceleration
Figure FDA00037128206300000217
Setting quality parametersValue range of m
Figure FDA00037128206300000218
Wherein,
Figure FDA0003712820630000031
the subscript min represents the minimum, i.e., lower limit, and the subscript max represents the maximum, i.e., upper limit.
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Citations (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5023808A (en) * 1987-04-06 1991-06-11 California Institute Of Technology Dual-arm manipulators with adaptive control
WO2015137877A1 (en) * 2014-03-14 2015-09-17 National University Of Singapore Gait rehabilitation apparatus
CN105242533A (en) * 2015-09-01 2016-01-13 西北工业大学 Variable-admittance teleoperation control method with fusion of multi-information
US20160067061A1 (en) * 2014-08-15 2016-03-10 Honda Motor Co., Ltd Integral admittance shaping for an exoskeleton control design framework
CN107053179A (en) * 2017-04-21 2017-08-18 哈尔滨思哲睿智能医疗设备有限公司 A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning
CN109249394A (en) * 2018-09-27 2019-01-22 上海电气集团股份有限公司 Robot control method and system based on admittance control algorithms
CN109366488A (en) * 2018-12-07 2019-02-22 哈尔滨工业大学 A kind of superimposed oscillation power Cartesian impedance control method of object manipulator assembly
CN109910005A (en) * 2019-03-04 2019-06-21 上海电气集团股份有限公司 Change admittance control method and system for robot
CN110597072A (en) * 2019-10-22 2019-12-20 上海电气集团股份有限公司 Robot admittance compliance control method and system
CN110977974A (en) * 2019-12-11 2020-04-10 遨博(北京)智能科技有限公司 Admittance control method, device and system for avoiding singular position type of robot
CN111230873A (en) * 2020-01-31 2020-06-05 武汉大学 Teaching learning-based collaborative handling control system and method
CN111281743A (en) * 2020-02-29 2020-06-16 西北工业大学 Self-adaptive flexible control method for exoskeleton robot for upper limb rehabilitation
US20200206943A1 (en) * 2019-01-02 2020-07-02 Research & Business Foundation Sungkyunkwan University Apparatus and method for controlling robot
CN111660307A (en) * 2020-05-27 2020-09-15 华中科技大学 Robot operation high-assistance precision virtual clamp control method and system
CN111660306A (en) * 2020-05-27 2020-09-15 华中科技大学 Robot variable admittance control method and system based on operator comfort
CN112276944A (en) * 2020-10-19 2021-01-29 哈尔滨理工大学 Man-machine cooperation system control method based on intention recognition
JP2021117918A (en) * 2020-01-29 2021-08-10 株式会社人機一体 Drive unit having admittance control
CN113352322A (en) * 2021-05-19 2021-09-07 浙江工业大学 Adaptive man-machine cooperation control method based on optimal admittance parameters
CN113568313A (en) * 2021-09-24 2021-10-29 南京航空航天大学 Variable admittance auxiliary large component assembly method and system based on operation intention identification
CN113733105A (en) * 2021-10-18 2021-12-03 哈尔滨理工大学 Cooperative mechanical arm fuzzy variable admittance control system and method based on human intention recognition
WO2022007358A1 (en) * 2020-07-08 2022-01-13 深圳市优必选科技股份有限公司 Impedance control method and apparatus, impedance controller, and robot
CN114406983A (en) * 2021-12-06 2022-04-29 中国科学院深圳先进技术研究院 Adaptive admittance control method and related device for lower limb exoskeleton robot

Patent Citations (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5023808A (en) * 1987-04-06 1991-06-11 California Institute Of Technology Dual-arm manipulators with adaptive control
WO2015137877A1 (en) * 2014-03-14 2015-09-17 National University Of Singapore Gait rehabilitation apparatus
US20160067061A1 (en) * 2014-08-15 2016-03-10 Honda Motor Co., Ltd Integral admittance shaping for an exoskeleton control design framework
CN105242533A (en) * 2015-09-01 2016-01-13 西北工业大学 Variable-admittance teleoperation control method with fusion of multi-information
CN107053179A (en) * 2017-04-21 2017-08-18 哈尔滨思哲睿智能医疗设备有限公司 A kind of mechanical arm Compliant Force Control method based on Fuzzy Reinforcement Learning
CN109249394A (en) * 2018-09-27 2019-01-22 上海电气集团股份有限公司 Robot control method and system based on admittance control algorithms
CN109366488A (en) * 2018-12-07 2019-02-22 哈尔滨工业大学 A kind of superimposed oscillation power Cartesian impedance control method of object manipulator assembly
US20200206943A1 (en) * 2019-01-02 2020-07-02 Research & Business Foundation Sungkyunkwan University Apparatus and method for controlling robot
CN109910005A (en) * 2019-03-04 2019-06-21 上海电气集团股份有限公司 Change admittance control method and system for robot
CN110597072A (en) * 2019-10-22 2019-12-20 上海电气集团股份有限公司 Robot admittance compliance control method and system
CN110977974A (en) * 2019-12-11 2020-04-10 遨博(北京)智能科技有限公司 Admittance control method, device and system for avoiding singular position type of robot
JP2021117918A (en) * 2020-01-29 2021-08-10 株式会社人機一体 Drive unit having admittance control
CN111230873A (en) * 2020-01-31 2020-06-05 武汉大学 Teaching learning-based collaborative handling control system and method
CN111281743A (en) * 2020-02-29 2020-06-16 西北工业大学 Self-adaptive flexible control method for exoskeleton robot for upper limb rehabilitation
CN111660307A (en) * 2020-05-27 2020-09-15 华中科技大学 Robot operation high-assistance precision virtual clamp control method and system
CN111660306A (en) * 2020-05-27 2020-09-15 华中科技大学 Robot variable admittance control method and system based on operator comfort
WO2022007358A1 (en) * 2020-07-08 2022-01-13 深圳市优必选科技股份有限公司 Impedance control method and apparatus, impedance controller, and robot
CN112276944A (en) * 2020-10-19 2021-01-29 哈尔滨理工大学 Man-machine cooperation system control method based on intention recognition
CN113352322A (en) * 2021-05-19 2021-09-07 浙江工业大学 Adaptive man-machine cooperation control method based on optimal admittance parameters
CN113568313A (en) * 2021-09-24 2021-10-29 南京航空航天大学 Variable admittance auxiliary large component assembly method and system based on operation intention identification
CN113733105A (en) * 2021-10-18 2021-12-03 哈尔滨理工大学 Cooperative mechanical arm fuzzy variable admittance control system and method based on human intention recognition
CN114406983A (en) * 2021-12-06 2022-04-29 中国科学院深圳先进技术研究院 Adaptive admittance control method and related device for lower limb exoskeleton robot

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
杨静宜等: "基于特征深度学习的机器人协调操作感知控制", 《计算机仿真》, vol. 38, no. 1, pages 307 - 311 *

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