Transcript EMG signal - Department of Computer Science and Engineering

An EMG Enhanced Impedance and Force Control
Framework for Telerobot Operation in Space
Ning Wang¹, Chenguang Yang², Michael R. Lyu¹, and Zhijun Li³
¹Dept. of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong
²School of Computing and Mathematics, Plymouth University, United Kingdom
³Key Lab of Autonomous System and Network Control, College of Automation Science and
Engineering, South China University of Technology, Guangzhou, China
Outline
Introduction
Tele-robotics in space
Tele-impedance control
EMG signal characteristics
Working framework
Simulation & demonstration
Conclusion & future work
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What’s telerobot?
Robotics
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Deals with design, construction, operation, and application of
robots.
Interdisciplinarity: control, mechanics, artificial intelligence, etc.
Tele-operation
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Employs automated machines to take the place of humans.
Remotely operation from a distance by a human operator, rather
than following a predetermined sequence of movements.
Telerobot
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Tele-operated robot.
Telerobot operation challenge
Local human operator and remote autonomous robot
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Exchange of force and position signals, i.e., haptic feedback.
Long-range communications suffer from time delay.
Control instability!
Big challenge
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Delayed transmission of haptic signals lead to instability in robot
control.
Possible solutions?
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Wave scattering, passivity, small gain theorem, etc.
Remains a difficulty.
Telerobot operation status quo
In space
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Requiring stability.
Handling unpredictable environments.
Neural path of human being also subject to time delay.
In presence of time delay,
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Human neural control can easily maintain stability.
Humans show even superior manipulation skills in unstable interactions.
 Transfer skills from human operator to robot!
Tele-impedance
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Operation stability of humans comes from adjusting mechanical
impedance.
Transferring a human operator’s muscle impedance to a telerobot.
Principle of tele-impedance
Tele-impedance using electromyogram (EMG) (Ajoudani et al.,
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2011).
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Estimating stiffness and force from EMG signal.
Transferring impedance from human operator to robot.
Control strategy
Reference task trajectory: qr(t), t∈[0,T].
Impedance and feed-forward torque:
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with minimal feedback
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Research focus
A framework of EMG enhanced impedance and
force control for telerobot operation in space
Real-time extraction and processing of EMG.
On-line estimation of human muscle impedance and force.
Performance demonstration in simulated unstable scenario.
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EMG signal
Physiological signal generated by muscle cells.
Reflects human muscle activations and tensions.
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Long been utilized for human motor control.
Suitable for extracting force and impedance of human muscles.
How to acquire EMG data?
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Data recording
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Noninvasive electrodes.
Bi-dimensional electrical field on the skin surface.
Generated by summation of motor unit action potentials (MUAP).
Surface EMG
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Amplitude and frequency properties in EMG
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An EMG signal is typically a train of MUAP.
A band-limited signal that describes the kth EMG wave is
characterized by two sequences:
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-- amplitude;
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-- phase.
AM-FM Signal modeling
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Signal decomposition.
Primary component identification: amplitude A(n) and frequency
Ω(n).
Observations: EMG signal decomposition
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EMG & decomposed
waves in 5 frequency
bands:
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Band 1: 10-100 Hz
Band2: 100-200 Hz
Band3: 200-300 Hz
Band4: 300-400 Hz
Band5: 400-500 Hz
Observations: primary EMG components
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Instantaneous amplitude estimate A(n) and frequency
estimate Ω(n) in the decomposed EMG waves
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Working Framework
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EMG enhanced impedance and force control based teleoperation system in a typical aerospace operation scenario.
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How to estimate stiffness from EMG?
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Human muscles and tendons act as a spring-damper system
during movement.
Changing stiffness via co-activation of antagonistic muscle
pairs.
Tele-operation by adjusting co-activations and corresponding
endpoint stiffness profile (Ajoudani et al., 2011).
Discarding up to 99% of EMG signal power before estimation
(Potvin et al., 2003).  involving only 400-500 Hz (Band 5)!
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Stiffness estimation formulation
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Assuming linear mapping between muscle tensions and surface EMG
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Endpoint forces in Cartesian coordinates:
Processed EMG amplitudes in 400-500 Hz band
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At ith agonist muscle:
At jth antagonist muscle:
Parameter set:
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, and
Stiffness estimation method
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Iterative least squares (LS) approach to achieve online
estimation of parameter set
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Online endpoint force and stiffness estimation.
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Based on proportional muscle stiffness-torque relationship.
Expressions under Cartesian coordinates
Force estimation
The key idea:
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Filter most of the low frequency power of the EMG signal, i.e., use only
Band 5 EMG signal.
Nonlinearly normalized
With
is obtained by linearly normalized
to 100% of the maximum.
Involved muscles: FCR (flexor carpi radialis), ECR (extensor carpi radialis)
FCR
ECR
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Force Estimation &
Torque Calculation
Wrist Torque
Simulation
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Experimental set-up:
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Two-joint simulated robot arm
with the first joint motionless.
Right wrist of human operator
in charge of simulated robot
arm.
Motion reference trajectory at
initial position.
Implemented using Matlab
Robotics Toolbox in Simulink.
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Demonstration
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Observations on result
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Stiffness K and damping rate D:
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Stiffness K and damping rate D enlarged dramatically after
impedance increase.
Observations on result
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Angle shifting of simulated robot arm from reference
trajectory (initial position at 0 radian).
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Shifting angle reduced greatly after impedance increase.
Conclusions
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Transferring muscle impedance from human to robot
introduced for reducing instability and enhancing control
performance of tele-operation.
Real time processing of EMG signal proposed for impedance
and force estimation.
Integrated framework built for the telerobot in aerospace
applications to fully capture operator’s control skills.
Promising demonstration results shown for impedance
control in simulated scenario.
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What’s the next step?
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Complete experimental studies on physical robot arm is
planned to carry out to test and validate the framework
proposed in this paper.
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