Mobile & Service Robotics Mobile & Service Robotics Sensors for Sensors for Robotics Sensors for Sensors for Robotics Robotics – 1 Robotics 1
An Example of robots with their sensors 2 Basilio Bona – Robotica 03CFIOR ‐ 2011
Another example Omnivision Camera (360 ° ) Pan-Tilt-Zoom (PTZ) camera IMU= Inertial Measurement Unit Sonars Laser Scanner Encoders inside differential wheels Bumpers Passive support wheel 3 Basilio Bona – Robotica 03CFIOR ‐ 2011
Definition � A sensor is a device that produces a measurable response to a change in a physical condition (such as temperature) or to a change in a chemical concentration � Usually commonly used sensors convert the physical quantity � Usually commonly used sensors convert the physical quantity into a signal which can be measured electrically � The sensors are classified according to the following criteria: � The sensors are classified according to the following criteria: 1. Primary Input quantity (aka measurand) 2. 2 Transduction principles Transduction principles 3. Measured property (as temperature, flow, displacement, proximity, acceleration, etc.) proximity, acceleration, etc.) 4. Material and technology 5 5. Application Application 4 Basilio Bona – Robotica 03CFIOR ‐ 2011
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 of the environment (better performance, but may influence the environment) 5 Basilio Bona – Robotica 03CFIOR ‐ 2011
Sensors types � A � Analog Sensors: they measure continuous variables and provide the l S th ti i bl d id th information as a physical reading (mercury thermometers and old style voltmeters are good examples of analog sensors) 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 p y g , , � Continuous Sensors: although the name is somehow misleading, � 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 6 Basilio Bona – Robotica 03CFIOR ‐ 2011
Sensors classification Category Sensors Type Contact sensors (on/ off), bumpers EC - SP Proximity sensors y Tactile sensors/ proximity Tactile senso s/ p o imit EC - SA EC SA (inductive/ capacitive) sensors Distance sensors EC - SA (inductive/ capacitive) Potentiometric encoders PC - SP Optical, magnetic, Hall-effect, Active wheel sensors inductive, capacitive encoders, inductive, capacitive encoders, PC PC - SA SA syncro and resolvers Compasses EC - SP Heading sensors with respect to g p Gyroscopes Gyroscopes PC PC - SP SP a fixed RF Inclinometers EC – SP/ A GPS (outdoor only) EC – SA Optical or RF beacons EC – SA Absolute cartesian sensors Ultrasonic beacons EC – SA Refelctive beacons Refelctive beacons EC – SA EC SA 7 Basilio Bona – Robotica 03CFIOR ‐ 2011
Sensors classification Category Sensors Type Reflective sensors EC - SA Ultrasonic sensors EC - SA Active distance sensors Laser range finders, Laser scanners EC - SA (active ranging) (active ranging) Optical triangulation (1D) EC - SA Structured light (2D) EC - SA Motion and velocity sensors Doppler radar EC - SA (speed relative to fixed or Doppler sound EC - SA mobile objects) CCD and CMOS cameras EC - SA Vision sensors: distance from Integrated packages for visual EC - SA stereo vision, feature analysis, ranging g g segmentation object segmentation, object recognition Integrated packages for object EC - SA tracking 8 Basilio Bona – Robotica 03CFIOR ‐ 2011
Sensor characteristics � D � Dynamic range i � Resolution � Linearity � Bandwidth or frequency � Transfer function � Reproducibility/precision � Accuracy � Systematic errors � Systematic errors � Hysteresis � Temperature coefficient � Temperature coefficient � Noise and disturbances: signal/noise ratio � C � Cost t 9 Basilio Bona – Robotica 03CFIOR ‐ 2011
Sensor characteristics � Dynamic range � Ratio between lower and upper measurement limits, expressed in dB dB � Example: voltage sensor min=1 mV, max 20V: dynamic range 86dB � � Range = upper limit of dynamic range 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 10 range 20 V ‐ > 20/255 = 0.08 � Bandwidth � � Diff Difference between upper and lower frequencies b t d l f i � Large bandwidth means large transfer rate � Lower bandwidth is possible in acceleration sensors Lower bandwidth is possible in acceleration sensors 10 Basilio Bona – Robotica 03CFIOR ‐ 2011
Accuracy and precision 11 Basilio Bona – Robotica 03CFIOR ‐ 2011
Accuracy and Precision Precision = Repeatability = Reproducibility P i i R bili R d ibili Accurate but Precise but not precise t i not accurate Not accurate and Precise and not precise accurate 12 Basilio Bona – Robotica 03CFIOR ‐ 2011
Noise Noise 13 Basilio Bona – Robotica 03CFIOR ‐ 2011
Noise � All sensors are subject to noise, since, due to random � All sensors are subject to noise since due to random fluctuations or electromagnetic interference, they add to the measured signal an undesired component that cannot be measured signal an undesired component that cannot be precisely known � If the noise is smaller than the measurement fluctuations and � If th i i ll th th t fl t ti d the noise introduced by the electronic components, it is not influent influent � On the contrary it can degrade the entire chain plant ‐ sensor ‐ controller and make it unusable 14 Basilio Bona – Robotica 03CFIOR ‐ 2011
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 V / Hz � Since it is a density, to obtain the RMS value one shall integrate y, g 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 15 Basilio Bona – Robotica 03CFIOR ‐ 2011
Noise types Noise are of many types; these include Noise are of many types; these include � Shot noise � Thermal noise � Thermal noise � Flicker noise � Burst noise � Avalanche noise To know the noise type is important for modeling purposes 16 Basilio Bona – Robotica 03CFIOR ‐ 2011
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 d l � This is often an issue in p ‐ n junctions. In metal wires this is much less important, since correlation between individual h l l b d d l 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 ith t t fl i Th th l equilibrium current fluctuations are known as thermal noise � Th � The shot noise spectrum is flat h t i t i fl t 17 Basilio Bona – Robotica 03CFIOR ‐ 2011
Thermal noise � 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 h i ( ll h l ) i id l i l conductor at equilibrium, which happens regardless of any applied voltage applied voltage � Thermal noise is approximately white pp y � With good approximation the amplitude of the signal has a Gaussian probability density function b b l d f 18 Basilio Bona – Robotica 03CFIOR ‐ 2011
Flicker noise � 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 19 Basilio Bona – Robotica 03CFIOR ‐ 2011
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