1、An EMG Enhanced Impedance and Force Control Framework for Telerobot Operation in Space,Ning Wang, Chenguang Yang, Michael R. Lyu, and Zhijun Li Dept. of Computer Science & Engineering, The Chinese University of Hong Kong, Hong Kong School of Computing and Mathematics, Plymouth University, United Kin
2、gdom Key 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 Conc
3、lusion & future work,2,Whats telerobot?,3,Robotics 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 b
4、y a human operator, rather than following a predetermined sequence of movements. Telerobot Tele-operated robot.,Telerobot operation challenge,4,Local human operator and remote autonomous robot Exchange of force and position signals, i.e., haptic feedback. Long-range communications suffer from time d
5、elay.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,5,In space Requiring stability. Handling unpredictable en
6、vironments. 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 o
7、f humans comes from adjusting mechanical impedance. Transferring a human operators muscle impedance to a telerobot.,Principle of tele-impedance,6,Tele-impedance using electromyogram (EMG) (Ajoudani et al., 2011). Estimating stiffness and force from EMG signal. Transferring impedance from human opera
8、tor to robot.,Reference task trajectory: qr(t), t0,T. Impedance and feed-forward torque:with minimal feedback,Control strategy,7,Research focus,8,Real-time extraction and processing of EMG. On-line estimation of human muscle impedance and force. Performance demonstration in simulated unstable scenar
9、io.,EMG signal,9,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 Noninvasive electrodes. Bi-dimensional el
10、ectrical field on the skin surface. Generated by summation of motor unit action potentials (MUAP). Surface EMG,10,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;
11、 - phase. AM-FM Signal modeling Signal decomposition. Primary component identification: amplitude A(n) and frequency (n).,11,Observations: EMG signal decomposition,12,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 H
12、z,Observations: primary EMG components,13,Instantaneous amplitude estimate A(n) and frequency estimate (n) in the decomposed EMG waves,Working Framework,14,EMG enhanced impedance and force control based tele-operation system in a typical aerospace operation scenario.,How to estimate stiffness from E
13、MG?,15,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
14、 before estimation (Potvin et al., 2003). involving only 400-500 Hz (Band 5)!,Stiffness estimation formulation,16,Endpoint forces in Cartesian coordinates: , and Processed EMG amplitudes in 400-500 Hz band At ith agonist muscle: At jth antagonist muscle: Parameter set:,Assuming linear mapping betwee
15、n muscle tensions and surface EMG,Stiffness estimation method,17,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,F
16、orce estimation,18,The key idea: Filter most of the low frequency power of the EMG signal, i.e., use only Band 5 EMG signal. Nonlinearly normalizedWith is obtained by linearly normalized to 100% of the maximum.Involved muscles: FCR (flexor carpi radialis), ECR (extensor carpi radialis),Simulation,19
17、,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.,Demonstration,20,Observations on result,21,Stif
18、fness K and damping rate D: Stiffness K and damping rate D enlarged dramatically after impedance increase.,Observations on result,22,Angle shifting of simulated robot arm from reference trajectory (initial position at 0 radian). Shifting angle reduced greatly after impedance increase.,Conclusions,23
19、,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
20、capture operators control skills. Promising demonstration results shown for impedance control in simulated scenario.,Whats the next step?,24,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,
