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CMPUT 412Sensing

Csaba SzepesváriUniversity of Alberta

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Defining sensors and actuators

Environment

actions

Sensations(and reward)

Controller= agent

Sensors Actuators

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Perception

SensorsUncertaintyFeatures

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How are sensors used?

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HelpMate, Transition Research Corp.

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B21, Real World Interface

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Robart II, H.R. Everett

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Savannah, River Site Nuclear Surveillance Robot

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BibaBot, BlueBotics SA, Switzerland

Pan-Tilt Camera

Omnidirectional Camera

IMUInertial Measurement Unit

Sonar Sensors

Laser Range Scanner

Bumper

Emergency Stop Button

Wheel Encoders

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Taxonomy of sensors

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Classification of Sensors Where is the information coming from?

Inside: Proprioceptive sensors motor speed, wheel load, heading of the robot,

battery status Outside: Exteroceptive sensors

distances to objects, intensity of the ambient light, unique features

How does it work? Requires energy emission? No: Passive sensors

temperature probes, microphones, CCD Yes: Active sensors

Controlled interaction -> better performance Interference

Simple vs. composite (sonar vs. wheel sensor)

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General Classification (1)

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General Classification (2)

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Sensor performance

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How Do (Simple) Sensors Work?

Analog signals Digital signals

Physical process

Environment

Electrical current

Analog to digital

conversion

0010101101010011010111010101input output

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Mathematical Models

Signal in => signal out: response Memoryless: Vout = S( Ein , Noiset) With memory: Vout = f( Vout, Ein , Noiset)

Physical process

Environment

Electrical current

Analog to digital

conversion

0010101101010011010111010101input output

Sampling rate, aliasing, dithering

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Nominal Sensor Performance Valid inputs

Emin: Minimum detectable energy Emax: Maximum detectable energy Dynamic range = Emax/Emin , or 10 log(Emax/Emin ) [dB]

power measurement or volt? (V2 ~ power) Operating range (Nmin, Nmax): Emin · Nmin · Nmax · Emax

No aliasing in the operating range (e.g., distance sens.) Response

Sensor response: S(Ein)=? Linear? (or non-linear) Hysteresis

Resolution (¢): E1-E2· ¢ ) S(E1)¼ S(E2); often ¢=min(Emin , ¢A/D )

Timing Response time (range): delay between input and output [ms] Bandwidth: number of measurements per second [Hz]

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In Situ Sensor Performance: Sensitivity

Characteristics .. especially relevant for real world environments Sensitivity:

How much does the output change with the input? Memoryless sensors: min{ [d/dE S] (Ein) | Ein } Sensors with memory: min{ f(V,Ein)/Ein | V, Ein }

Cross-sensitivity sensitivity to environmental parameters that are orthogonal

to the target parameters e.g. flux-gate compass responds to ferrous buildings,

orthogonal to magnetic north Error: ²(t) = S(t) - S(Ein(t))

Systematic: ²(t) = D(Ein(t)) Random: ²(t) is random, e.g., ²(t) ~ N(¹,¾2)

Accuracy (systemacity): 1-|D(Ein)|/Ein, e.g., 97.5% accuracy

Precision (reproducability): Rangeout/ Var(²(t))1/2

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In Situ Sensor Performance: Errors

Characteristics .. especially relevant for real world environments Error: ²(t) = S(t) - S(Ein(t))

Systematic: ²(t) = D(Ein(t)) Predictable, deterministic Examples:

Calibration errors of range finders Unmodeled slope of a hallway floor Bent stereo camera head due to an earlier collision

Random: ²(t) is random, e.g., ²(t) ~ N(¹,¾2) Unpredictable, stochastic Example:

Thermal noise ~ hue calibration, black level noise in a camera

Accuracy – accounts for systemic errors 1-|D(Ein)|/Ein, e.g., 97.5% accuracy

Precision – high precision ~ low noise Rangeout/ Var(²(t))1/2

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Challenges in Mobile Robotics

Systematic vs. random errorsError distributions

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Systematic vs. Random? Sonar sensor:

Sensitivity to: material, relative positions of sensor and target (cross-sensitivity)

Specular reflections (smooth sheetrock wall; in general material, angle)

Systematic or random? What if the robot moves? CCD camera:

changing illuminations light or sound absorbing surfaces

Cross-sensitivity of robot sensor to robot pose and robot-environment dynamics rarely possible to model -> appear as random errors systematic errors and random errors might be well defined

in controlled environment. This is not the case for mobile robots !!

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Error Distributions A convenient assumption: ²(t) ~ N(0,§) WRONG!

Sonar (ultrasonic) sensor Sometimes accurate, sometimes overestimating Systematic or random? “Operation modes” Random => Bimodal:

- mode for the case that the signal returns directly- mode for the case that the signals returns after multi-path reflections.

Errors in the output of a stereo vision system (distances)

Characteristics of error distributions Uni- vs. Multi-modal, Symmetric vs. asymmetric Independent vs. dependent (decorrelated vs. correlated)

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About Some Sensors

Wheel EncodersActive Ranging

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Wheel Encoders

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Wheel/Motor Encoders (1) Principle: Photo detection + optical grid Direction of motion: Quadrature encoder Output: Read values with polling or use interrupts Resolution: 2000 (->10K) cycles per revolution (CPR).

for higher resolution: interpolation, sine waves Accuracy: no systematic error (accuracy~100%)

time

Rotating optical grid

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Wheel/Motor Encoders (2) Measures position or speed

of the wheels or steering Use: odometry,

position estimation, detect sliding of motors

scanning reticle fields

scale slits

Direction change:

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Active Range Sensors

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Range Sensors Large range distance measurement

-> “range sensors” Why?

Range information is key for localization and environment modeling

Cheap Relatively accurate

How? Time of flight Active sensing (sound, light)

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Time of flight - principles Time delay of arrival (TDOA)

TDOA – impulses Sound, light

TDOA – phase shift Light

Geometry Triangulation – single light beam

Light Triangulation – structured light

Light Light sensor; 1D or 2D camera

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Time Delay of Arrival

d = v t d – distance travelled (computed) v – speed of propagation (known) t – time of flight (measured)

2D = v t

D

TargetSource & sensor

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TDOA: Limitations What distances can we measure?

Must wait for the last package to arrive before sending out the next one=> Speed of propagation determines maximum range!

Speeds Sound: 0.3 m/ms Electromagnetic signals (light=laser):

0.3 m/ns, 1M times faster! 3 meters takes..

Sound: 10 ms Light: 10 ns

.. But technical difficulties => expensive and delicate sensors

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TDOA: Errors Time measurement

Exact time of arrival of the reflected signal Time of flight measure (laser range sensors)

Opening angle of transmitted beam (ultrasonic range sensors)

Interaction with the target (surface, specular reflections)

Variation of propagation speed Speed of mobile robot and target (if not at

stand still)

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Ultrasonic Sensor

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Ultrasonic (US) Sensor transmit a packet of US pressure waves The speed of sound v (340 m/s) in air is

°: adiabatic index (sound wave->compression->heat)

R: moral gas constant [J/(mol K)] M: molar mass [kg/mol] T: temperature [K]

v =q

° RM T

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Operation

Transmitted sound

Analog echo signal &

threshold

Digital echo signal

Integrated time & output signal

integrator Time of flight (output)

threshold

Wave packet

Blanking time

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Ultrasonic Sensor Frequencies: 40 - 180 kHz Sound source: piezo/electrostatic transducer

transmitter and receiver separated or not separated Propagation: cone

opening angles around 20 to 40 degrees regions of constant depth segments of an arc (sphere for 3D)

Typical intensity distribution of an ultrasonic sensor

-30°

-60°

0°

30°

60°

Amplitude [dB]

measurement cone

Piezo transducer

Electrostatic transducer

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Example

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Imaging with an USIssues: Soft surfaces Sound surfaces that are far from being

perpendicular to the direction of the sound -> specular reflection

a) 360° scan b) results from different geometric primitives

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Characteristics

Range: 12cm – 5 m Accuracy: 98%-99.1% Single sensor operating speed: 50Hz

3m -> 20ms ->50 measurements per sec Multiple sensors:

Cycle time->0.4sec -> 2.5Hz->limits speed of motion (collisions)

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Laser Range Sensor

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Laser Range Sensor: Physics

Laser=•Low divergence•Well-defined wavelength

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Time of flight measurement methods

Pulsed laser Direct measurement of elapsed time Receiver: Picoseconds accuracy Accuracy: centimeters

Beat frequency between a frequency modulated continuous wave and its received reflection

Phase shift measurement Technically easier than the above two

methods

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Distance from phase-shift

¸

z

Phase [m]

Transmittedbeam (s(x))

Reflected beam (r(x))

Target

d

r(x) = s(2d¡ x)

r(z) = 0 , s(2d¡ z) = 0, 2d¡ z = k¸ , z = 2d+ k0̧

sinced < ¸=2;k0= 0) z = 2d ) µ= 2¼2d¸

d= µ¸4¼

Amplitude [V]

Ambiguity! d and d+¸/2 give the same µAmbiguity! d and d+¸/2 give the same µ

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Laser Range Sensor Phase-Shift Measurement

c: speed of light (0.3 m/ns)

f: the modulating frequency

D’: distance covered by the emitted light

for f = 5 Mhz (as in the AT&T sensor), = 60 meters

PhaseMeasurement

Target

D

L

Transmitter

Transmitted BeamReflected Beam

P

2

2 LDLD = c/f

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Laser Range Sensor

Confidence in the range (phase estimate) is inversely proportional to the square of the received signal amplitude. Hence dark, distant objects will not produce such good range estimated as closer brighter objects …

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Laser Range Sensor Typical range image of a 2D laser range sensor with a rotating mirror. The length

of the lines through the measurement points indicate the uncertainties.

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Triangulation Ranging Geometry -> distance

Unknown object size: project a known light pattern onto the environment and use triangulation

Known object size: triangulation without light projecting

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Laser Triangulation (1D)

Target

D

L

Laser / Collimated beam

Transmitted Beam

Reflected Beam

P

Position-Sensitive Device (PSD)

or Linear Camera

x

Lens

x

LfD

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Sharp IR Rangers

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Conclusions

Why & how? Sensing: Essential to deal with

contingencies in the world Sensors: Make sensing possible

Anatomy of sensors: Physics, A/D, characteristics

Wheel encoders Distance sensors

Time of flight Triangulation