ROBOTICSROBOTICS

01PEEQW01PEEQW01PEEQW01PEEQW

Basilio BonaBasilio Bona

DAUIN DAUIN –– Politecnico di TorinoPolitecnico di Torino

Mobile & Service RoboticsMobile & Service Robotics

Sensors for Robotics Sensors for Robotics –– 11

An Example of robots with their sensors

3ROBOTICS 01PEEQWBasilio Bona

Another example

Omnivision Camera (360°)

Pan-Tilt-Zoom (PTZ) camera

Sonars

IMU=Inertial Measurement Unit

4

Laser Scanner

Encoders inside differential wheels

Bumpers

Passive support wheel

ROBOTICS 01PEEQWBasilio Bona

� A sensor is a device that produces a measurable response to a

change in a physical quantity related to the robot or the

environment

� Usually sensors convert the physical quantity into a signal which

can be measured electrically

� The sensors are classified according to the following criteria:

Definitions

� The sensors are classified according to the following criteria:

1. Primary Input quantity (aka measurand)

2. Measured property (as temperature, flow, displacement,

proximity, acceleration, etc.)

3. Transduction principles

4. Material and technology

5. Application

5ROBOTICS 01PEEQWBasilio Bona

Sensors types

� Proprioceptive sensors (PC)

� They measure quantities coming from the robot itself, e.g.,

motor speed, wheel loads, robot heading, battery charge

status, etc.

� Exteroceptive sensors (EC)

� They measure quantities coming from the environment: e.g.,

walls distance, earth magnetic fields, intensity of the walls distance, earth magnetic fields, intensity of the

ambient light, obstacle positions, etc.

� Passive sensors (SP)

� They use the energy coming from the environment

� Active sensors (SA)

� They use the energy they produce and measure the reaction

of the environment (better performance, but may influence

the environment)

Basilio Bona 6ROBOTICS 01PEEQW

� Analog Sensors: they measure continuous variables and provide the

information as a physical reading (mercury thermometers and old

style voltmeters are good examples of analog sensors)

� Digital Sensors: they measure continuous or discrete variables, but

the provided information is always digital, i.e., discretized

Sensors types

� Continuous Sensors: although the name is somehow misleading,

continuous sensors (analog or digital) provide a reading that is on a

continuous range, as opposite to ON/OFF sensors

� Binary Sensors : they give only two levels of information ON/OFF or

YES/NO: a lamp that switches on when a certain temperature level is

attained, is an analog binary sensor

7ROBOTICS 01PEEQWBasilio Bona

Sensors classification

Category Sensors Type

Tactile sensors/proximity sensors

Contact sensors (on/off), bumpers EC - SP

Proximity sensors (inductive/capacitive)

EC - SA

Distance sensors (inductive/capacitive)

EC - SA

Active wheel sensors

Potentiometric encoders PC - SP

Optical, magnetic, Hall-effect, inductive, capacitive encoders, PC - SAinductive, capacitive encoders, syncro and resolvers

PC - SA

Heading sensors with respect to a fixed RF

Compasses EC - SP

Gyroscopes PC - SP

Inclinometers EC – SP/A

Absolute cartesian sensors

GPS (outdoor only) EC – SA

Optical or RF beacons EC – SA

Ultrasonic beacons EC – SA

Refelctive beacons EC – SA

Basilio Bona 8ROBOTICS 01PEEQW

Sensors classification

Category Sensors Type

Active distance sensors

(active ranging)

Reflective sensors EC - SA

Ultrasonic sensors EC - SA

Laser range finders, Laser scanners EC - SA

Optical triangulation (1D) EC - SA

Structured light (2D) EC - SA

Motion and velocity sensors Doppler radar EC - SAMotion and velocity sensors (speed relative to fixed or mobile objects)

Doppler radar EC - SA

Doppler sound EC - SA

Vision sensors: distance from stereo vision, feature analysis, segmentation, object recognition

CCD and CMOS cameras EC - SA

Integrated packages for visual ranging

EC - SA

Integrated packages for object tracking

EC - SA

Basilio Bona 9ROBOTICS 01PEEQW

Sensor characteristics

� Dynamic range

� Resolution

� Linearity

� Bandwidth or frequency

� Transfer function

� Reproducibility/precision� Reproducibility/precision

� Accuracy

� Systematic errors

� Hysteresis

� Temperature coefficient

� Noise and disturbances: signal/noise ratio

� Cost

Basilio Bona 10ROBOTICS 01PEEQW

Sensor characteristics

� Dynamic range

� Ratio between lower and upper measurement limits, expressed in dB

� Example: voltage sensor min=1 mV, max 20V: dynamic range 86dB

� Range = upper limit of dynamic range

� Resolution

� Minimum measurable difference between two values� Minimum measurable difference between two values

� Resolution = lower limit of dynamic range

� Digital sensors: it depends on the bit number of the A/D converter

� Example 8 bit=25510 range 20 V -> 20/255 = 0.08

� Bandwidth

� Difference between upper and lower frequencies

� Large bandwidth means large transfer rate

� Lower bandwidth is possible in acceleration sensors

Basilio Bona 11ROBOTICS 01PEEQW

Basilio Bona 12ROBOTICS 01PEEQW

Accuracy and precision

13ROBOTICS 01PEEQWBasilio Bona

Precise but

not accurate

Accurate but

not precise

Accuracy and Precision

Precision = Repeatability = Reproducibility

Precise and

accurate

Not accurate and

not precise

14ROBOTICS 01PEEQWBasilio Bona

NoiseNoiseNoiseNoise

15ROBOTICS 01PEEQW

Noise

� All sensors are subject to noise

� Due to random fluctuations or electromagnetic interference, an

undesired component is added to the measured signal that

cannot be precisely known

� If the noise is smaller than the measurement fluctuations and the

noise introduced by the electronic components, it is not influent

� If not, it can degrade the entire chain plant-sensor-controller and

make it unusable

Basilio Bona 16ROBOTICS 01PEEQW

Noise

� Noise is often spread on a large frequency spectrum and many

noise sources produce the so-called white noise, where the

power spectral density is equal at every frequency

� The noise is often characterized by the spectral density of the

noise Root Mean Square (RMS), given as

� Since it is a density, to obtain the RMS value one shall integrate

the spectrum density in the frequency band of interest. This type

of distribution adds to the measure an error term that is

proportional to the square root of the bandwidth of the

measuring system

Basilio Bona 17ROBOTICS 01PEEQW

/V Hz

� White noise is a random signal (or process) with a flat power

spectral density

� The signal contains equal power within a fixed bandwidth at any

center frequency

� An infinite-bandwidth white noise signal is a purely theoretical

construction

White noise

� The bandwidth of white noise is limited in practice by the

mechanism of noise generation, by the transmission medium and

by finite observation capabilities

� A random signal is considered “white noise” if it is observed to

have a flat spectrum over the widest possible bandwidth

� White noise is often used for modeling purposes

18ROBOTICS 01PEEQWBasilio Bona

Noise types

Noise are of many types:

� Shot noise

� Thermal noise

� Flicker noise

� Burst noise� Burst noise

� Avalanche noise

To know the noise type is important for modeling purposes

Basilio Bona 19ROBOTICS 01PEEQW

Shot noise

� Shot noise, often called quantum noise, is always associated to

random fluctuations of the electric current in electrical

conductors, due to the current being carried by discrete charges

(electrons) whose number per unit time fluctuates randomly

� This is often an issue in p-n junctions. In metal wires this is much

less important, since correlation between individual electrons

remove these random fluctuationsremove these random fluctuations

� Shot noise is distinct from current fluctuations in thermal

equilibrium, which happen without any applied voltage and

without any average current flowing. These thermal equilibrium

current fluctuations are known as thermal noise

� The shot noise spectrum is flat

Basilio Bona 20ROBOTICS 01PEEQW

� Thermal noise, also called Johnson–Nyquist noise, is the

electronic noise generated by the thermal agitation of the

charge carriers (usually the electrons) inside an electrical

conductor at equilibrium, which happens regardless of any

applied voltage

� Thermal noise is approximately white

Thermal noise

� Thermal noise is approximately white

� With good approximation the amplitude of the signal has a

Gaussian probability density function

21ROBOTICS 01PEEQWBasilio Bona

� Flicker noise, also called 1/f noise or pink noise is characterized

by a frequency spectrum such that the power spectral density is

inversely proportional to the frequency

� It is always present in active components of electronic circuits

and in many passive ones

� It is proportional to the current amplitude, so if the current is

sufficiently low, the thermal noise will predominate

Flicker noise

sufficiently low, the thermal noise will predominate

22ROBOTICS 01PEEQWBasilio Bona

example of pink noise spectrum