Humanoid Robot
Development of a simulation environment of
an entertainment humanoid robot
Pedro Daniel Dinis Teodoro
Orientador: Professor Miguel Afonso Dias de Ayala Botto
Co-orientador: Professor Jorge Manuel Mateus Martins
Lisboa-September-2007
INTRODUCTION
Goal
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
State of Art
Objectives
The creation of the strong foundations for future
This thesis was
developed inincollaboration
with Robosavvy Ltd
developments
humanoid robots.
and boosted the creation of the Humanoid Robotics Laboratory
of IDMEC-Center of Intelligent Systems, at Instituto Superior
Técnico (http://humanoids.dem.ist.utl.pt/ ).
2
INTRODUCTION
Goal
State of Art
Objectives
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Current commercially available humanoid robots are designed
to perform motions using open-loop control.
These robots are usually not able to move on uneven terrain
and it is difficult or impossible to get them to perform
movements that require instantaneous reaction to momentary
instability.
3
INTRODUCTION
Goal
State of Art
Objectives
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
1. The establishment of a real-time protocol communication
between the PC, using Matlab/Simulink® and the robot
2. The identification of internal and external properties of the
humanoid robot.
3. LQR implementation for
stabilizing the humanoid robot
on a high bar.
4
SET UP
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
robot
Hardware
Architecture
Software
Architecture
Bioloid humanoid robot from Robotis.com
CM5 Controller
AX12+ Servo
•1
MBps
•the
maincommunication
controller of theSpeed.
humanoid.
•Full
feedback
Position (300o),data
Speed, DC
•57600
bps to on
receive/transmite
current,
Voltageand
andPC
Temperature.
through servos
•Can be set as an endless wheel.
•High Torque servos (1Nm).
5
SET UP
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
robot
Hardware
Architecture
Software
Architecture
Humanoid control architecture
6
SET UP
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
robot
Hardware
Architecture
Software
Architecture
C-MEX S-function written in C to
communicate with the CM-5 throughout
UART (universal asynchronous receiver /
transmitter).
C program for Atmega128 for completing
the serial communication bridge.
We have now a way to identify the
parameters of the humanoid models
making online experiments.
7
IDENTIFICATION
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Mechanical
Properties
DC Servo
Properties
DC Servo
Identification
Mathematical
Model
Close Loop Pos
Open Loop Speed
Measured
signals
Schematic of joint and link for two different humanoid
configurations.
8
IDENTIFICATION
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Mechanical
Properties
DC Servo
Properties
DC Servo
Identification
Mathematical
Model
Close Loop Pos
Open Loop Speed
Measured
signals
Possible internal block diagram control of the servos.
9
IDENTIFICATION
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Mechanical
Properties
DC Servo
Properties
DC Servo
Identification
Mathematical
Model
Close Loop Pos
Open Loop Speed
Measured
signals
Experiments suggest that the
servos do not have internally
any angular velocity feedback
control.
Servos have an internal
feedback position control loop
10
IDENTIFICATION
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Mechanical
Properties
DC Servo
Properties
DC Servo
Identification
Mathematical
Model
Close Loop Pos
Open Loop Speed
Measured
signals
Dead-zone effect due to
Experiments show that the
stiction. In our case this was output estimated velocity error
clearly quantified to be around is proportional to the voltage
7-10% of the full range.
supplied to the servo.
11
IDENTIFICATION
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Mechanical
Properties
DC Servo
Properties
DC Servo
Identification
Mathematical
Model
Close Loop Pos
Open Loop Speed
Measured
signals
The dynamic characteristics of
the servo are well captured by
the BJ model.
TF 
Box Jenkins (2,1,2,1) was
found to best approximate the
desired dynamical behavior of
the servo.
0.06217 z
z 2  1.469 z  0.5544
12
SIMULATOR
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
model
SimMechanics
simulator
Virtual Reality
animation
The humanoid is treated as a three body serial chain in an
inverted pendulum configuration.
13
SIMULATOR
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
model
SimMechanics
simulator
Virtual Reality
animation
The system is underactuated, being the motion of the legs and
torso prescribed in order to stabilize the full body of the
humanoid above the high bar.
14
SIMULATOR
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
The humanoid
model
SimMechanics
simulator
Virtual Reality
animation
Parent-Child hierarchy
15
CONTROL
Guide
Equations of
motion
State-Space
model
Linear Quadratic
Regulator



 
The
equations
m 
l l cos  q for a 
m  l   of
 motion
 


generic
n-link underactuated
 I , i  j
inverted
pendulum
deduced from
m m , i j
the
Euler-Lagrange
equations.


 g
m l cos q 
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
n
ij
k
k
k j
k
a
a j
b c
k
b i c  j  j  i
b c
d 1
d  min( b ,c )
k
ji
ij
n
i
k

k i a 1
a


k a
b 1
b

n
hi  Ci   m ij ,
j i
 min( b ,c ) 
Ci    mk lblc   q d 
k i b i c 1 j  i
 d 1 
n
k
k
2
 b c

sin   qd  min( b ,c ) , iff min( b, c)  1
 d 1

n
 b c

 

m
l
l

k b c   qd  min( b ,c ) 
b i c i  j  i
 d 1

k
m ij  
k i
 m11 m12
m
 21 m22


mn1 mn 2
m1n   q1  1   h1   1 
m2 n  q 2   2  h2   2 



       
       
mnn  q n   n   h3   n 
k
 b c

sin   qd  min( b ,c ) , iff min( b, c)  1
 d 1

and
lc i  k
li   i
 li i  k
16
CONTROL
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Equations of
motion
State-Space
model
Linear Quadratic
Regulator
in order to use linear control algorithms, the system dynamics
is linearized, using a first order Taylor's expansion at the
vertical unstable equilibrium, q=[π/2,0,0]T and q =[0,0,0]T.
x  Ax  Bu
0


0


0
A
74
.
2389

  81.5857

 7.3471
0
0
0
 117.8764
348.7777
 230.9113
0
0




0
0




0
0
B

  176.2065 97.5517 
 527.7831  467.2635


 467.2635 697.9566 
0
0
0
0.0028
 76.0776
215.8591
1
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0









State-space vector



x  q1  , q2 , q3 , q1 , q 2 , q3 
2


Link 1 (Arms)
Link 2 (Torso)
Link 3 (Legs)
l (mm)
143.6
115.8
184.0
lc (mm)
68.7
57.5
116.3
m (g)
367.6
981.5
576.4
Physical properties
I (gcm2)
7890.7
32898.6
11328.0
17
CONTROL
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
Equations of
motion
State-Space
model
Linear Quadratic
Regulator
analyzing the zeros and poles
of the system, it can be
concluded that system is a
non-minimum phase one
Linear Quadratic Regulator
was chosen, which provides
a linear state feedback
control law for the system
u  k T x
Wothout
Angle
compensation
With
Angle
compensation
18
RESULTS
Ideal
servo
Servo
resolution
Gyro
resolution
Servo
dead-zone
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
19
CONCLUTIONS
Achieved
Control
Recommendations
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
This project has successfully achieved the creation of the strong
foundations for future developments in humanoid robots.
20
CONCLUTIONS
Achieved
Control
Recommendations
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
LQR strategy was successfully applied in the stabilization of
the humanoid on a high-bar although only in simulation and
without the nonlinearities of the servos.
21
CONCLUTIONS
Achieved
Control
Recommendations
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
New
gyroscope
New
nonlinear
model
containing
the dynamics
and
nonlinearities
of the servos
New
nonlinear
controller
(e.g. Sliding
mode
control)
22
VIDEO
Guide
Introduction
Set Up
Indentification
Simulator
Control
Results
Conclusions
Video
23
Humanoid Robot
Development of a simulation environment of
an entertainment humanoid robot
Pedro Daniel Dinis Teodoro
Orientador: Professor Miguel Afonso Dias de Ayala Botto
Co-orientador: Professor Jorge Manuel Mateus Martins
Lisboa-September-2007