RO OBOTIC CA – 01CFIDV 02CFIC CY MOBILE ROBOTICS Sensors An Introduction Basilio Bona DAUIN – Politecnico di Torino Basilio Bona – DAUIN – Politecnico di Torino 001/1 RO OBOTIC CA – 01CFIDV 02CFIC CY An Example Basilio Bona – DAUIN – Politecnico di Torino 001/2 An Example RO OBOTIC CA – 01CFIDV 02CFIC CY Omnivision Camera (360°) Pan-Tilt-Zoom (PTZ) camera IMU=Inertial Measurement Unit Sonars Laser Scanner Encoders Bumpers Passive wheel Basilio Bona – DAUIN – Politecnico di Torino 001/3 Sensors Type RO OBOTIC CA – 01CFIDV 02CFIC CY Proprioceptive P i ti sensors (PC) – They measure quantities coming from the system, e.g., motor speed, wheel load, heading of the robot, battery charge status, etc. Exteroceptive sensors (EC) – They measure quantities coming from the environment al robot: e.g., wall distance, magnetic fields, intensity of the ambient light, obstacle position, etc. Passive sensors (SP) – They y use the energy gy coming g from the environment (not ( to be confused with the energy required to move) Active sensors (SA) – Emit their proper energy and measure the reaction of the environment – Better performance, but may influence the environment Basilio Bona – DAUIN – Politecnico di Torino 001/4 RO OBOTIC CA – 01CFIDV 02CFIC CY Sensors Type Analog Sensors Digital g Sensors Continuous Sensors Binary Sensors (ON/OFF) Basilio Bona – DAUIN – Politecnico di Torino 001/5 Sensors Classification RO OBOTIC CA – 01CFIDV 02CFIC CY Category Tactile sensors/proximity senso s/p o imit sensors Active wheel sensors Heading g sensors with respect p to a fixed RF Absolute cartesian sensors Sensors Type Contact sensors (on/off), bumpers EC - SP Proximity y sensors (inductive/capacitive) EC - SA Distance sensors (inductive/capacitive) EC - SA Potentiometric encoders PC - SP Optical, magnetic, Hall-effect, inductive, capacitive encoders, syncro and resolvers PC - SA Compasses EC - SP Gyroscopes PC - SP Inclinometers EC – SP/A GPS (outdoor only) EC – SA Optical or RF beacons EC – SA Ultrasonic beacons EC – SA Refelctive beacons EC – SA Basilio Bona – DAUIN – Politecnico di Torino 001/6 Sensors Classification RO OBOTIC CA – 01CFIDV 02CFIC CY Category Sensors 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 segmentation, recognition Type 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 Doppler radar EC - SA Doppler sound EC - SA CCD and CMOS cameras EC - SA Integrated packages for visual ranging g g EC - SA Integrated packages for object tracking EC - SA Basilio Bona – DAUIN – Politecnico di Torino 001/7 RO OBOTIC CA – 01CFIDV 02CFIC CY Sensor Characteristics Transducer = Sensor Dynamic range and range Resolution Linearity – Dynamic range – Bandwidth or frequency – Transfer function Reproducibility/precision Accuracy Systematic errors Hysteresis Temperature coefficient Noise and disturbances: signal/noise ratio Basilio Bona – DAUIN – Politecnico di Torino 001/8 Sensor Characteristics RO OBOTIC CA – 01CFIDV 02CFIC CY Dynamic range – Ratio between lower and upper limits expressed in dB – Example. E ample Voltage sensor senso min=1 min 1 mV mV, max ma 20V: 20V dynamic range 86dB g = upper pp limits – Range Resolution – Minimum measurable difference between two values – Lower limits of dynamic range = resolution – Digital sensors: it depends on the bit number of the A/D converter – Example 8 bit=25510 range 20 V -> 20/255 Bandwidth – Large bandwidth means large transfer rate – Lower bandwidth is possible in acceleration sensors Basilio Bona – DAUIN – Politecnico di Torino 001/9 RO OBOTIC CA – 01CFIDV 02CFIC CY Accuracy and Precision Basilio Bona – DAUIN – Politecnico di Torino 001/10 Accuracy and Precision RO OBOTIC CA – 01CFIDV 02CFIC CY Precision = Repeatability = Reproducibility Precise but not accurate Not accurate and precise not p Accurate but not precise Precise and accurate RO OBOTIC CA – 01CFIDV 02CFIC CY Noise Basilio Bona – DAUIN – Politecnico di Torino 001/12 RO OBOTIC CA – 01CFIDV 02CFIC CY Noise Types Shot noise Thermal noise Flicker noise Burst noise Avalanche noise Basilio Bona – DAUIN – Politecnico di Torino 001/13 RO OBOTIC CA – 01CFIDV 02CFIC CY Shot noise Basilio Bona – DAUIN – Politecnico di Torino 001/14 RO OBOTIC CA – 01CFIDV 02CFIC CY Thermal noise Basilio Bona – DAUIN – Politecnico di Torino 001/15 RO OBOTIC CA – 01CFIDV 02CFIC CY Flicker Noise Basilio Bona – DAUIN – Politecnico di Torino 001/16 RO OBOTIC CA – 01CFIDV 02CFIC CY Flicker Noise Basilio Bona – DAUIN – Politecnico di Torino 001/17 RO OBOTIC CA – 01CFIDV 02CFIC CY Burst and Avalanche noise Basilio Bona – DAUIN – Politecnico di Torino 001/18 RO OBOTIC CA – 01CFIDV 02CFIC CY Noise Color Basilio Bona – DAUIN – Politecnico di Torino 001/19 RO OBOTIC CA – 01CFIDV 02CFIC CY Noise Floor – (Rumore di fondo) Basilio Bona – DAUIN – Politecnico di Torino 001/20 RO OBOTIC CA – 01CFIDV 02CFIC CY Sensors and Mobile Robotics U Usually ll the th signal i l noise i iis modeled d l d according di tto a statistic distribution, but … – Causes of random errors are often unknown or poorly – A Gaussian or symmetric distribution is often used, but this can be wrong Example: – Ultrasound (sonar) sensors may overestimate the perceived di t distance, therefore th f they th do d nott have h a symmetrical t i l error distribution p reflected beams arrive together g with direct – Often multiple beams – Stereo vision can correlate two images in a wrong way, generating results that are without any sense Basilio Bona – DAUIN – Politecnico di Torino 001/21
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