Technical Cognitive Systems

Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Technical Cognitive Systems
Michael Beetz
Intelligent Autonomous Systems
Technische Universitat Munchen
Summer Term 2012
Part XII
Actuators
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Outline
Robot Bases
Hardware Components
Robot Arms
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
3
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Outline
Robot Bases
Hardware Components
Robot Arms
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
4
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
(Wheeled) Locomotion
Goal:Bring the robot to a desired pose (x;y;):
(position in x-axis,position in y-axis,angle with x-axis)
) 3 Degrees of Freedom (DOF)
.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
5
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
(Wheeled) Locomotion
Goal:Bring the robot to a desired pose (x;y;):
(position in x-axis,position in y-axis,angle with x-axis)
) 3 Degrees of Freedom (DOF)
Many robots have less controllable degrees of freedom.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
6
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Wheel Types
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
7
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Bases

Ackermann steering

dierential-drive

turnable wheel

omniwheel

mecanum-wheel
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
8
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Ackerman-Steering
 car-like steering
 robust
 but hard to control (parking!)
Issue:Outer wheels moves on a circle of dierent radius than inner wheel.)
steering angle should be dierent!
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
9
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Dierential-Drive

Turns on spot

Good choice for round robots
 Parking is easier
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
10
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Turnable wheels

Omnidirectional (can drive forwards,sideways and turn)

On change of direction,requires'reconguration'of its wheels.

!Controllers should not oscillate
PR2:Double wheel construction to reduce friction while turning the wheel
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
11
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
\Omniwheels"
 Omnidirectional (can drive forwards,sideways and turn)
 Wheels have free rollers at 90
o
 Three wheels are enough
 Hard to make them run smooth
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
12
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Mecanum-Wheels

Omnidirectional (can drive forwards,sideways and turn)

Wheels have free rollers at 45
o

No reconguration is involved

Depending on wheels,requires at ground
Linearity )
A (linear) combination of cartesian movements can be achieved with the linear
combination of the respective wheel velocities.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
13
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Control of robot bases
Cartesian pose in the plane:
x =
0
@
x
y

1
A
Wheel positions:
q =
0
@
q
1
:::
q
n
1
A
The forward kinematics,relating q to x is nonlinear and hard to work with.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
14
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Control of robot bases
It's easier to use its derivatives:
Cartesian Velocity
_x =
0
@
_x
_y
_

1
A
Wheel Velocities:
_q =
0
@
_q
1
:::
_q
n
1
A
These velocities are related by a matrix:The Jacobian matrix J
_q = J_x
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
15
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Example:Omniwheel Kinematics
 orange:free roller turning
 green:wheel turning
 blue:resulting movement
Forward:( _q
1
;_q
2
;_q
3
) = (0;cos(30
o
);cos(30
o
))
...
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
16
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Outline
Robot Bases
Hardware Components
Motors
Encoders
Gearboxes
Robot Arms
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
17
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Stepper Motor

very standardized

strong at low speeds

used in printers

moves repeatably

feedforward by nature

\looses steps"when friction/inertia/...is too high...

...or is moving at its resonance frequency
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
18
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Brushed DC Motor
 cheap
 used in toys
 turns without motor controller
 commutation with brushes (may wear out)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
19
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Brushless DC Motor
 cheap as well

used e.g.in CDROM drives

electronic commutation necessary (with sensors or sensorless)

coils can be inside or outside (better cooling when outside)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
20
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Pulse Width Modulation (PWM)

transistors are ecient when either on or o

(not so ecient in between)

switch the power on and o at a high freqency (and let the motor do the
low-pass ltering)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
21
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Optical Encoders
 Robots typically use wheels for movement.
 If we measure how much the wheels travel,we can approximate our
position.
 Typical Solution:Optical Encoders on the shaft of the wheels (or motors).
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
22
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Absolute Optical Encoders
Characteristics:

They give the absolute position at all times.(Really only useful if the
encoder does not turn more than once).

Expensive

Requires log
2
n tracks and sensors for a resolution of n ticks/rev.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
23
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Absolute Optical Encoders (2)
Two main types:

Binary code (b)

Gray code (a).Advantage:Only one bit changes at a time.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
24
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
One-track absolute encoder

absolute encoder with only one track

still needs log
2
n sensors...

...mounted at precise positions!
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
25
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Incremental Optical Encoders
 They are simpler:Only one track.
 They only give information about direction and ammount of rotation in
steps (known as'ticks')
 That's OK:We can keep a counter in memory for the position.
 The direction is known using two IR receivers,placed as to receive the light
from the emitter with a 90 degree phase dierence.It also allows to
quadruple the resolution of the disc.(400 ticks from a disc with 100
stripes).
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
26
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Incremental Optical Encoders (2)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
27
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Incremental Optical Encoders (2)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
28
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Planetary (=Epicyclic) Gearbox

good gear ratios

good torque transmission

very little play
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
29
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Harmonic-Drive Gearbox

very high gear ratio

acceptable torque transmission

highly nonlinear friction

(practically) no play
each rotation of the inner ellipse moves the outer gear one tooth forward.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
30
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Cycloidal Gearbox

very high gear ratio
 high torque transmission
 used in industrial robots
 prone to vibrations
 (practically) no play
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
31
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Outline
Robot Bases
Hardware Components
Robot Arms
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
32
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
33
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
34
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
35
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
36
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF

placing an object (only position is important):
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
37
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF

placing an object (only position is important):3 DOF
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
38
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF

placing an object (only position is important):3 DOF

placing an object (position and orientation):
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
39
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF

placing an object (only position is important):3 DOF

placing an object (position and orientation):6 DOF
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
40
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF
 placing an object (only position is important):3 DOF
 placing an object (position and orientation):6 DOF
 imitating a human arm:
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
41
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Robot Arms
How many degrees of freedom do we need?(It depends on the task!)
 pointing a camera:2 DOF

placing an object (only position is important):3 DOF

placing an object (position and orientation):6 DOF

imitating a human arm:7 DOF (from shoulder ball joint)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
42
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Industrial Robots vs.Mobile Service Robots
 big & heavy
 high speed & accuracy
 powerful

dangerous
 small & lightweight
 lower speed & accuracy
reqirements

better mass/payload ratio

must be safer
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
43
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
An Example Lighweight Robot
The Kuka LWR
 light (15kg)
 still strong (14kg payload { on
streched arm)
 brushless DC motors
 harmonic drive gearboxes
 impedance control to make it
soft and safe
What if impedance control fails?
!Still safer than industrial robots (it has less inertia).
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
44
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Control Challenge

Industrial robots have sti joints
() PD control is enough)

The LWR has elastic joints.

modeled as two coupled
spring-damper-mass systems
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
45
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Control Challenge II

contains 4 energy buers

4 values are required to describe the
dynamic state of a joint.

Eectively,requires more
measurements per joint to control it
well,e.g.a torque sensor after
reduction.
 The state chosen for the LWR is
(q,_q,,_)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
46
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
State Feedback Controller
NOTE:This controller compensates the dynamics of the actual joint
The State Feedback Control Law used in this robot is of the form:

M
= K
P
q
e
K
D
_q
e
K
T

e
K
S
_
e
+Feedforward
Where (q
e
;_q
e
;
e
;_
e
) are the errors of the actual positions/torques w.r.t.the
desired positions/torques.
Feedforward term includes:gravity compensation,friction compensation,inertia,
coriolis,etc.
!The torque sensor also allows to estimate gearbox friction and to precisely
measure external torques.
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
47
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Impedance Control Interface
NOTE:The user interface,assuming an idealized joint
Control Strategies:
 Position Control (May lead to strong forces)
 Force Control (May lead to far position osets)
 Impedance Control:Force and Position are coupled:
 = K(q
desired
q
actual
) D_q mq
K:Stiness
D:Damping
m:Mass (not controlled)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
48
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
PR2 Arms
Second Example:PR2 Arms
 7 DOF arms
 mechanical gravity
compensation

2 innite joints

weak BLDC motors (< 10W)

belt transmissions

payload about 2 kg

exible joints but missing extra
sensors
!lower performance,lower
gains,damping...
 but still good enough!
Intrinsically safe,no matter what the controllers do!
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
49
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Gravity Compensation
 Just the law of the lever
 = L  F
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
50
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Gravity Compensation

Just the law of the lever

When the arm is inclined,we need to project the force...
 = cos()  L  F
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
51
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Gravity Compensation

Just the law of the lever
 When the arm is inclined,we need to project the force...

...or the lever!
 = cos()  L  F
)Easy to calculate (just discard the vertical component of the center of mass!)
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
52
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
References

Wheeled Locomotion
{ Handbook of Robotics
{ Some more extensive lecture notes:
www.cfar.umd.edu/~fer/cmsc828/classes/cse390-05-03.pdf
 Components
{ Wikipedia
 Robot arms
{ (Advanced):Albu-Schaer,Alin:Regelung von Robotern mit elastischen
Gelenken am Beispiel der DLR-Leichtbaumarme
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
53
Department of Informatics
Intelligent Autonomous Systems Technische Universität München
Credits
The contents of this lecture was composed of material from various sources,
including:

Wikipedia

Course EML2322L from University of Florida:
http://www2.mae.u .edu/designlab/Class Projects/Background
Information/Background Information.htm
 http://electricly.com
Robot Bases Hardware Components Robot Arms
Michael Beetz
Summer Term 2012
Technical Cognitive Systems
54