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

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.

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!

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.

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.

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

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

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.

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.

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).

Observations: EMG signal decomposition EMG & decomposed waves in 5 frequency bands: 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 Instantaneous amplitude estimate A(n) and frequency estimate Ω(n) in the decomposed EMG waves

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

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 400-500 Hz (Band 5)! Support vectors: data points closest to the hyperplane

Stiffness estimation formulation Assuming linear mapping between muscle tensions and surface EMG Endpoint forces in Cartesian coordinates: , and Processed EMG amplitudes in 400-500 Hz band At ith agonist muscle: At jth antagonist muscle: Parameter set:

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

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

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

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

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

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

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.

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.

Thank you very much! Q & A