An Example of robots with their sensors

Transcription

1 ROBOTICA 03CFIOR DAUIN Politecnico di Torino

2 Mobile & Service Robotics Sensors for Robotics 1

3 An Example of robots with their sensors 3

4 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 4

5 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 into a signal which can be measured electrically The sensors are classified according to the following criteria: 1. Primary Input quantity (aka measurand) 2. Transduction principles 3. Measured property (as temperature, flow, displacement, proximity, acceleration, etc.) 4. Material and technology 5. Application 5

6 Sensors types Proprioceptive sensors (PC) They measure quantities coming from the robot itself, e.g., motor speed, wheel lloads, 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) 6

7 Sensors types Analog Sensors: they measure continuous variables ibl and provide the information as a physical reading (mercury thermometers and old stylevoltmetersare goodexamples of analogsensors) 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 7

8 Sensors classification Category Sensors Type Tactile sensors/proximity s/p o sensors Active wheel sensors Heading sensors with respect to a fixed RF Absolute cartesian sensors Contact sensors (on/off), bumpers Proximity sensors (inductive/capacitive) Distance sensors (inductive/capacitive) Potentiometric encoders Optical, magnetic, Hall-effect, inductive, capacitive encoders, syncro and resolvers Compasses EC - SP PC - SP PC - SA EC - SP Gyroscopes PC - SP Inclinometers GPS (outdoor only) Optical or RF beacons Ultrasonic beacons EC SP/A EC SA EC SA EC SA Refelctive beacons EC SA 8

9 Sensors classification Category Sensors Type Active distance sensors (active ranging) Motion and velocity sensors (speed relative to fixed or mobile objects) Vision sensors: distance from stereo vision, feature analysis, segmentation, object recognition Reflective sensors Ultrasonic sensors Laser range finders, Laser scanners Optical triangulation (1D) Structured light (2D) Doppler radar Doppler sound CCD and CMOS cameras Integrated packages for visual ranging g Integrated packages for object tracking 9

10 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 10

11 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= range 20 V > 20/255 = 0.08 Bandwidth Difference bt between upper and lower frequencies Large bandwidth means large transfer rate Lower bandwidth is possible in acceleration sensors 11

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13 Accuracy and precision 13

14 Accuracy and Precision Precision i = Repeatability = Reproducibility Precise but not accurate Accurate but not precise Not accurate and not precise Precise and accurate 14

15 Noise 15

16 Noise 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 precisely known If the noise is smaller than the measurement fluctuations ti and the noise introduced by the electronic components, it is not influent On the contrary it can degrade the entire chain plant sensorcontroller and make it unusable 16

17 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 / 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 Hz 17

18 Noise types Noise are of many types; theseinclude Shot noise Thermal noise Flicker noise Burst noise Avalanche noise To know the noise type is important for modeling purposes 18

19 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 d electrons remove these random fluctuations Shot noise is distinct from current fluctuations in thermal equilibrium, which happen without any applied voltage and without t any average current flowing. These thermal equilibrium current fluctuations are known as thermal noise The shot noise spectrum is flat 19

20 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 id an electrical l 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 bbl density function 20

21 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 21

22 White noise 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 purposesp 22