Transcript Real-Time - San Diego State University

SDSU
Real-Time Control of a Multi-Fingered
Robot Hand Using EMG Signals
Master’s Thesis
By
Luenin Barrios
Supervisor: Marko Vuskovic
Department of Computer Science
San Diego State University
June 29, 2010
SDSU
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Outline
Introduction to Research
Multi-Fingered Robot Hands and Prostheses
Measurement of EMG Signals
Feature Extraction and Classification
Synergy and Robot Control System
Hardware Description
Implementation
Observations and Results
Summary
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Introduction
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Goal of Research:
To implement a program that uses the EMG
Classifier output to control the grasp motions of
the SDSU robot hand in real-time
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Grasp modes:
Chris Miller Master’s Thesis 2008.
SDSU
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Prosthetic Hands Overview
Early Models
Restrictions and Limitations
Degrees of Freedom
EMG Signal Control
Otto Bock Grasp Pincher
TAP Version 3 Prototype
SDSU Robot Hand
SDSU
Overall Schematic
Signal
Processing
EMG
Amplification
Device
Saksit Siriprayoonsak 2005
A/D
Converter
Time Sample
Extraction
Classifier
Feature
Extraction
Transformation
Chris Miller 2008
Prosthetic
Hand
Controller
This Project
SDSU
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EMG Signals
Electromyography
EMG potentials: 50 μV and up to 20 to 30 mV
Source:www.univie.ac.at/cga/courses/be522/emg/fiber.gif
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Forearm Muscle Anatomy
Chris Miller Master’s Thesis 2008
EMG Amplifier Device
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Saksit Siriprayoonsak 2005
4 Bipolar Channels
1 Reference Channel
Surface Electrodes
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EMG Amplifier Device Con’td
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EMG Classifier Program
Signal Detection
Bonato Method
Onset of Movement
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Classifier
Signal Processing
Feature Extraction
Methods:
Waveform Length (Farry et al., 1996)
Spectral Moments (Vuskovic et al., 2005)
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EMG Signal Processing
Feature Extraction Method 1
Waveform Length
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EMG Signal Processing
Feature Extraction Method 2
Spectral Moments
I-coefficients
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Feature Classification
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Mahalanobis Distance(Mahalanobis, 1936)
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Sample Feature Vector Space
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Feature Log Transformation
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Box Cox Transformation (1964)
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Robot Joint Control System
PID Controller and Actuator
SDSU
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Joint Control System
Acutuator Model
Variable
Description
Unit
Ra
Terminal or Armature
Resistance
3.38 Ohm
Ka
Torque Constant
8.11 mNm/A
Jm
Rotor Inertia
1.27 gcm2
Kg
Gear Transmission Ratio Thumb
Gear Transmission Ratio Finger
1:26
1:19
Ga
Driver Gain
1
Kb
Speed or Proportionality
Constant
1180 rpm/V
V0
Nominal Voltage
12 Volt
ω0
No Load Speed
13900 rpm
SDSU
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Joint Control System
PID Controller
e = qmd - qm; // Get controller error
qmdot = (qm-qmold)/_Ts; // Get derivative of error
ei = eiold + e * _Ts;
// Get integral of error
u = _Kp*e - _Kv*qmdot + _Ki*ei; // Control law
qmold = qm;
eiold = ei;
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Synergetic Motion
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Synergetic Mapping
θj = fj (m, D) where j = 0, 1…5 and m = 1…4
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Approximation Function (Vuskovic and Marjanski)
am,j = γm,j
cm,j = αm,j
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Synergetic Training
Joint Angle
θ1 (cm,1 + D1) = am,1 bm,1 - am,1 D1
Object Shapes and Sizes
Spherical
Cylindrical
Point
Lateral
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Calibration and Training
Sample Training for Point Objects:
Sample Positions for Lateral, Cylindrical and Spherical
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Robot Hardware
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Servo To Go Board
Signal Transition Box
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Servo To Go Interface Board
Encoder Input A/B signal
 Analog Input/Output
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Signal Transition Box
Central hub for signals/cables
 Relays information
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Example: Joint 0
P3 DAC
2
EnIn A 14
EnIn B 17
DB50
EnOut A 35
EnOut B 34
DB25
AnalogIn
2
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EMG Robot Hand
User Interface/Motion Command Interpreter
Client/Server
TCP/IP
Real-time EMG/User Commands
Grasp modes: Cylindrical, Spherical, Point, Lateral
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EMG Robot Hand Cont’d
Examples:
Command: g 0 45
Command: o 3 9
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Overall Runtime Flow Chart
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Overall Runtime Flow Chart Cont’d
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System Execution
Step 2
Step 1
Step 3
Step 4
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Summary
Multi-fingered Robot Hands and EMG Signals
 Collection of EMG Signals
 Feature Extraction and Classification
 PID Controller
 Synergetic Motion
 Overall System Diagram and Transition Box
 Real-time control of Robot Hand using EMG
Signals
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Conclusions/Future Work
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Feasibility of EMG Signal for Hand Control
Synergetic Grasp Motions
Classifier for real-time control
Combine projects so they reside on same
machine
Improve arm/amplifier device contact
Wireless electrodes/sensory network
Improve time delays in Classifier
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Questions/Comments?