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CMPUT 412 Sensing

CMPUT 412 Sensing. Csaba Szepesvári University of Alberta. 1. Defining sensors and actuators. Actuators. Sensors. Environment. Sensations (and reward). actions. Controller = agent. 2. Perception. Sensors Uncertainty Features. 3. How are sensors used?. 4.

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CMPUT 412 Sensing

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  1. CMPUT 412 Sensing Csaba Szepesvári University of Alberta 1

  2. Defining sensors and actuators Actuators Sensors Environment Sensations (and reward) actions Controller = agent 2

  3. Perception Sensors Uncertainty Features 3

  4. How are sensors used? 4

  5. HelpMate, Transition Research Corp. 5

  6. B21, Real World Interface 6

  7. Robart II, H.R. Everett 7

  8. Savannah, River Site Nuclear Surveillance Robot 8

  9. BibaBot, BlueBotics SA, Switzerland Omnidirectional Camera Pan-Tilt Camera IMU Inertial Measurement Unit Sonar Sensors Emergency Stop Button Laser Range Scanner Wheel Encoders Bumper 9

  10. Taxonomy of sensors 10

  11. Classification of Sensors  Where is the information coming from?  Inside: Proprioceptive sensors  motor speed, wheel load, heading of the robot, battery status  Outside: Exteroceptive sensors  distances to objects, intensity of the ambient light, unique features  How does it work? Requires energy emission?  No: Passive sensors  temperature probes, microphones, CCD  Yes: Active sensors  Controlled interaction -> better performance  Interference  Simple vs. composite (sonar vs. wheel sensor) 11

  12. General Classification (1) 12

  13. General Classification (2) 13

  14. Sensor performance 14

  15. How Do (Simple) Sensors Work? Electrical current Analog to digital conversion Physical process Environment 00101011010100 11010111010101 input output Analog signals Digital signals 15

  16. Mathematical Models  Signal in => signal out: response  Memoryless: Vout= S( Ein, Noiset)  With memory: Vout= f( Vout, Ein, Noiset) Sampling rate, aliasing, dithering Electrical current Analog to digital conversion Physical process Environment 00101011010100 11010111010101 input output 16

  17. Nominal Sensor Performance  Valid inputs  Emin: Minimum detectable energy  Emax: Maximum detectable energy  Dynamic range = Emax/Emin, or 10 log(Emax/Emin) [dB]  power measurement or volt? (V2~ power)  Operating range (Nmin, Nmax): Emin· Nmin· Nmax· Emax  No aliasing in the operating range (e.g., distance sens.)  Response  Sensor response: S(Ein)=?  Linear? (or non-linear)  Hysteresis  Resolution (¢):  E1-E2· ¢ ) S(E1)¼ S(E2); often ¢=min(Emin , ¢A/D )  Timing  Response time (range): delay between input and output [ms]  Bandwidth: number of measurements per second [Hz] 17

  18. In Situ Sensor Performance: Sensitivity Characteristics .. especially relevant for real world environments  Sensitivity:  How much does the output change with the input?  Memoryless sensors: min{ [d/dE S] (Ein) | Ein}  Sensors with memory: min{ f(V,Ein)/Ein| V, Ein }  Cross-sensitivity  sensitivity to environmental parameters that are orthogonal to the target parameters  e.g. flux-gate compass responds to ferrous buildings, orthogonal to magnetic north  Error: ²(t) = S(t) - S(Ein(t))  Systematic: ²(t) = D(Ein(t))  Random: ²(t) is random, e.g., ²(t) ~ N(¹,¾2)  Accuracy (systemacity): 1-|D(Ein)|/Ein, e.g., 97.5% accuracy  Precision (reproducability): Rangeout/ Var(²(t))1/2 18

  19. In Situ Sensor Performance: Errors Characteristics .. especially relevant for real world environments  Error: ²(t) = S(t) - S(Ein(t))  Systematic: ²(t) = D(Ein(t))  Predictable, deterministic  Examples:  Calibration errors of range finders  Unmodeled slope of a hallway floor  Bent stereo camera head due to an earlier collision  Random: ²(t) is random, e.g., ²(t) ~ N(¹,¾2)  Unpredictable, stochastic  Example:  Thermal noise ~ hue calibration, black level noise in a camera  Accuracy – accounts for systemic errors  1-|D(Ein)|/Ein, e.g., 97.5% accuracy  Precision – high precision ~ low noise  Rangeout/ Var(²(t))1/2 19

  20. Challenges in Mobile Robotics Systematic vs. random errors Error distributions 20

  21. Systematic vs. Random?  Sonar sensor:  Sensitivity to: material, relative positions of sensor and target (cross-sensitivity)  Specular reflections (smooth sheetrock wall; in general material, angle)  Systematic or random? What if the robot moves?  CCD camera:  changing illuminations  light or sound absorbing surfaces  Cross-sensitivity of robot sensor to robot pose and robot-environment dynamics  rarely possible to model -> appear as random errors  systematic errors and random errors might be well defined in controlled environment. This is not the case for mobile robots !! 21

  22. Error Distributions  A convenient assumption: ²(t) ~ N(0,§)  WRONG!  Sonar (ultrasonic) sensor  Sometimes accurate, sometimes overestimating  Systematic or random? “Operation modes”  Random => Bimodal: - mode for the case that the signal returns directly - mode for the case that the signals returns after multi- path reflections.  Errors in the output of a stereo vision system (distances)  Characteristics of error distributions  Uni- vs. Multi-modal,  Symmetric vs. asymmetric  Independent vs. dependent (decorrelated vs. correlated) 22

  23. About Some Sensors Wheel Encoders Active Ranging 23

  24. Wheel Encoders 24

  25. Wheel/Motor Encoders (1) Principle: Photo detection + optical grid Direction of motion: Quadrature encoder Output: Read values with polling or use interrupts Resolution: 2000 (->10K) cycles per revolution (CPR).  for higher resolution: interpolation, sine waves Accuracy: no systematic error (accuracy~100%)      Rotating optical grid time 25

  26. Wheel/Motor Encoders (2)  Measures position or speed of the wheels or steering  Use: odometry, position estimation, detect sliding of motors scanning reticle fields scale slits Direction change: 26

  27. Active Range Sensors 27

  28. Range Sensors  Large range distance measurement -> “range sensors”  Why?  Range information is key for localization and environment modeling  Cheap  Relatively accurate  How?  Time of flight  Active sensing (sound, light) 28

  29. Time of flight - principles  Time delay of arrival (TDOA)  TDOA – impulses  Sound, light  TDOA – phase shift  Light  Geometry  Triangulation – single light beam  Light  Triangulation – structured light  Light  Light sensor; 1D or 2D camera 29

  30. Time Delay of Arrival  d = v t  d – distance travelled (computed)  v – speed of propagation (known)  t – time of flight (measured) D Source & sensor Target 2D = v t 30

  31. TDOA: Limitations  What distances can we measure?  Must wait for the last package to arrive before sending out the next one => Speed of propagation determines maximum range!  Speeds  Sound: 0.3 m/ms  Electromagnetic signals (light=laser): 0.3 m/ns, 1M times faster!  3 meters takes..  Sound: 10 ms  Light: 10 ns .. But technical difficulties => expensive and delicate sensors 31

  32. TDOA: Errors  Time measurement  Exact time of arrival of the reflected signal  Time of flight measure (laser range sensors)  Opening angle of transmitted beam (ultrasonic range sensors)  Interaction with the target (surface, specular reflections)  Variation of propagation speed  Speed of mobile robot and target (if not at stand still) 32

  33. Ultrasonic Sensor 33

  34. Ultrasonic (US) Sensor  transmit a packet of US pressure waves  The speed of sound v (340 m/s) in air is q °R v = MT  °: adiabatic index (sound wave->compression->heat)  R: moral gas constant [J/(mol K)]  M: molar mass [kg/mol]  T: temperature [K] 34

  35. Operation Wave packet Transmitted sound threshold Analog echo signal & threshold Digital echo signal integrator Time of flight (output) Integrated time & output signal Blanking time 35

  36. Ultrasonic Sensor Piezo transducer  Frequencies: 40 - 180 kHz  Sound source: piezo/electrostatic transducer  transmitter and receiver separated or not separated  Propagation: cone  opening angles around 20 to 40 degrees  regions of constant depth  segments of an arc (sphere for 3D) measurement cone 0° -30° 30° Electrostatic transducer -60° 60° Amplitude [dB] Typical intensity distribution of an ultrasonic sensor 36

  37. Example 37

  38. Imaging with an US Issues:  Soft surfaces Sound surfaces that are far from being perpendicular to the direction of the sound -> specular reflection  a) 360° scan b) results from different geometric primitives 38

  39. Characteristics  Range: 12cm – 5 m  Accuracy: 98%-99.1%  Single sensor operating speed: 50Hz  3m -> 20ms ->50 measurements per sec  Multiple sensors:  Cycle time->0.4sec -> 2.5Hz ->limits speed of motion (collisions) 39

  40. Laser Range Sensor 40

  41. Laser Range Sensor: Physics Laser= •Low divergence •Well-defined wavelength 41

  42. Time of flight measurement methods Pulsed laser  Direct measurement of elapsed time  Receiver: Picoseconds accuracy  Accuracy: centimeters Beat frequency between a frequency modulated continuous wave and its received reflection Phase shift measurement  Technically easier than the above two methods 42

  43. Distance from phase-shift Target Reflected beam (r(x)) Amplitude [V] Transmitted beam (s(x)) r(x) = s(2d¡ x) Phase [m] z d ¸ z = 2d+ k0¸ r(z) = 0, s(2d¡ z) = 0, sinced < ¸ =2;k0= 0) z = 2d ) µ= 2¼2d µ ¸ 4¼ 2d¡ z = k¸ , ¸ d = Ambiguity! d and d+¸/2 give the same µ 43

  44. Laser Range Sensor  Phase-Shift Measurement D Transmitter P Target L Phase Transmitted Beam Reflected Beam Measurement  l = c/f  2 D L D L       2 c: speed of light (0.3 m/ns) f: the modulating frequency D’: distance covered by the emitted light for f = 5 Mhz (as in the AT&T sensor), l = 60 meters 44

  45. Laser Range Sensor Confidence in the range (phase estimate) is inversely proportional to the square of the received signal amplitude.  Hence dark, distant objects will not produce such good range estimated as closer brighter objects …  45

  46. Laser Range Sensor Typical range image of a 2D laser range sensor with a rotating mirror. The length of the lines through the measurement points indicate the uncertainties.  46

  47. Triangulation Ranging  Geometry -> distance  Unknown object size: project a known light pattern onto the environment and use triangulation  Known object size: triangulation without light projecting 47

  48. Laser Triangulation (1D) D Laser / Collimated beam P Target L L D  f Transmitted Beam x x Reflected Beam Lens Position-Sensitive Device (PSD) or Linear Camera 48

  49. Sharp IR Rangers 49

  50. Conclusions  Why & how?  Sensing: Essential to deal with contingencies in the world  Sensors: Make sensing possible  Anatomy of sensors:  Physics, A/D, characteristics  Wheel encoders  Distance sensors  Time of flight  Triangulation 50

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