ROBOTICS 01PEEQW Basilio Bona DAUIN Politecnico di Torino Mobile - - PowerPoint PPT Presentation

ROBOTICS 01PEEQW

Basilio Bona DAUIN – Politecnico di Torino

Mobile & Service Robotics Sensors for Robotics – 1

Onboard sensors

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Onboard sensors

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Onboard sensors

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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:

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

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Definitions

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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 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

• f the environment (better performance, but may influence

the environment)

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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 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

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Sensors types

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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, 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 Reflective beacons EC – SA

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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 (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

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Sensor characteristics

Dynamic range Resolution Linearity

Bandwidth or frequency Transfer function

Reproducibility/precision Accuracy Systematic errors Hysteresis Temperature coefficient Noise and disturbances: signal/noise ratio Cost

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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 Resolution = lower limit of dynamic range Digital sensors: it depends on the bit number of the A/D converter Example 8 bit=255 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

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Accuracy and precision

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True value Measurement accuracy precision

Precise but not accurate Precise and accurate Accurate but not precise Not accurate and not precise

Accuracy and Precision

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Precision = Repeatability = Reproducibility

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Noise

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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

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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

• f distribution adds to the measure an error term that is

proportional to the square root of the bandwidth of the measuring system

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/ 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 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

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White noise

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Noise types

Noise are of many types: Shot noise Thermal noise Flicker noise Burst noise Avalanche noise To know the noise type is important for modeling purposes

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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 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

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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 With good approximation the amplitude of the signal has a Gaussian probability density function

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Thermal noise

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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

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Flicker noise

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example of pink noise spectrum