1. 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

2. Outline Introduction Tele-robotics in space Tele-impedance control
EMG signal characteristics
Working framework
Simulation & demonstration
Conclusion & future work

3. What’s telerobot? Robotics Tele-operation Telerobot
Deals with design, construction, operation, and application of robots.
Interdisciplinarity: control, mechanics, artificial intelligence, etc.
Tele-operation
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
Tele-operated robot.

4. Telerobot operation challenge
Local human operator and remote autonomous robot
Exchange of force and position signals, i.e., haptic feedback.
Long-range communications suffer from time delay.
Big challenge
Delayed transmission of haptic signals lead to instability in robot control.
Possible solutions?
Wave scattering, passivity, small gain theorem, etc.
Remains a difficulty.
Control instability!

5. Telerobot operation status quo
In space
Requiring stability.
Handling unpredictable environments.
Neural path of human being also subject to time delay.
In presence of time delay,
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
Operation stability of humans comes from adjusting mechanical impedance.
Transferring a human operator’s muscle impedance to a telerobot.

6. Principle of tele-impedance
Tele-impedance using electromyogram (EMG) (Ajoudani et al., 2011).
Estimating stiffness and force from EMG signal.
Transferring impedance from human operator to robot.

7. Control strategy Reference task trajectory: qr(t), t∈[0,T].
Impedance and feed-forward torque:
with minimal feedback
Support vectors: data points closest to the hyperplane

8. Research focus
Real-time extraction and processing of EMG.
On-line estimation of human muscle impedance and force.
Performance demonstration in simulated unstable scenario.
A framework of EMG enhanced impedance and force control for telerobot operation in space

9. EMG signal Physiological signal generated by muscle cells.
Reflects human muscle activations and tensions.
Long been utilized for human motor control.
Suitable for extracting force and impedance of human muscles.

10. How to acquire EMG data? Data recording Surface EMG
Noninvasive electrodes.
Bi-dimensional electrical field on the skin surface.
Generated by summation of motor unit action potentials (MUAP).
Surface EMG
When the EEG is measured using non-invasive electrodes arrayed on an individual’s scalp it is referred to as scalp EEG; and when it is measured using electrodes placed on the surface of the brain or within its depths it is referred to as intracranial EEG.
The EEG is a multichannel recording of the electrical activity generated by collections of neurons within the brain.
Different channels reflect the activity within different brain regions.
The scalp EEG is an average of the multifarious activities of many small zones of the cortical surface beneath the electrode.

11. Amplitude and frequency properties in EMG
An EMG signal is typically a train of MUAP.
A band-limited signal that describes the kth EMG wave is characterized by two sequences:
-- amplitude;
-- phase.
AM-FM Signal modeling
Signal decomposition.
Primary component identification: amplitude A(n) and frequency Ω(n).

12. Observations: EMG signal decomposition
EMG & decomposed waves in 5 frequency bands:
Band 1: Hz
Band2: Hz
Band3: Hz
Band4: Hz
Band5: Hz

13. Observations: primary EMG components
Instantaneous amplitude estimate A(n) and frequency estimate Ω(n) in the decomposed EMG waves

14. Working Framework
EMG enhanced impedance and force control based tele- operation system in a typical aerospace operation scenario.

15. How to estimate stiffness from EMG?
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 Hz (Band 5)!
Support vectors: data points closest to the hyperplane

16. Stiffness estimation formulation
Assuming linear mapping between muscle tensions and surface EMG
Endpoint forces in Cartesian coordinates: , and
Processed EMG amplitudes in Hz band
At ith agonist muscle:
At jth antagonist muscle:
Parameter set:

17. Stiffness estimation method
Iterative least squares (LS) approach to achieve online estimation of parameter set
Online endpoint force and stiffness estimation.
Based on proportional muscle stiffness-torque relationship.
Expressions under Cartesian coordinates
Support vectors: data points closest to the hyperplane

18. Force estimation The key idea: FCR Wrist Torque ECR
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)
Support vectors: data points closest to the hyperplane
Force Estimation &
Torque Calculation
FCR
ECR
Wrist Torque

19. Simulation Experimental set-up:
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.
Among the incidence of neurologic symptoms, seizures are considered to be the most frequently encountered events, second only to dizziness

20. Demonstration
Among the incidence of neurologic symptoms, seizures are considered to be the most frequently encountered events, second only to dizziness

21. Observations on result
Stiffness K and damping rate D:
Stiffness K and damping rate D enlarged dramatically after impedance increase.
Among the incidence of neurologic symptoms, seizures are considered to be the most frequently encountered events, second only to dizziness

22. Observations on result
Angle shifting of simulated robot arm from reference trajectory (initial position at 0 radian).
Shifting angle reduced greatly after impedance increase.
Among the incidence of neurologic symptoms, seizures are considered to be the most frequently encountered events, second only to dizziness

23. Conclusions
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.

24. What’s the next step?
Complete experimental studies on physical robot arm is planned to carry out to test and validate the framework proposed in this paper.

25. Thank you very much!
Q & A