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Topics: Introduction to Robotics CS 491/691(X)

Topics: Introduction to Robotics CS 491/691(X). Lecture 5 Instructor: Monica Nicolescu. Review. Sensors Simple, complex Proprioceptive, exteroceptive Switches Light sensors Polarized light sensors Resistive position sensors Potentiometers Reflective optosensors. Reflective Optosensors.

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Topics: Introduction to Robotics CS 491/691(X)

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  1. Topics: Introduction to RoboticsCS 491/691(X) Lecture 5 Instructor: Monica Nicolescu

  2. Review • Sensors • Simple, complex • Proprioceptive, exteroceptive • Switches • Light sensors • Polarized light sensors • Resistive position sensors • Potentiometers • Reflective optosensors CS 491/691(X) - Lecture 5

  3. Reflective Optosensors • Include a source of light emitter (light emitting diodes LED) and a light detector (photodiode or phototransistor) • Two arrangements, depending on the positions of the emitter and detector • Reflectance sensors: Emitter and detector are side by side; Light reflects from the object back into the detector • Break-beam sensors: The emitter and detector face each other; Object is detected if light between them is interrupted CS 491/691(X) - Lecture 5

  4. Calibration • Ambient / background light can interfere with the sensor measurement • The ambient light level should be subtracted to get only the emitter light level • Calibration: the process of adjusting a mechanism so as to maximize its performance • Ambient light can change  sensors need to be calibrated repeatedly • Detecting ambient light is difficult if the emitter has the same wavelength • Adjust the wavelength of the emitter CS 491/691(X) - Lecture 5

  5. Infra Red (IR) Light • IR light works at a frequency different than ambient light • IR sensors are used in the same ways as the visible light sensors, but more robustly • Reflectance sensors, break beams • Sensor reports the amount of overall illumination, • ambient lighting and the light from light source • More powerful way to use infrared sensing • Modulation/demodulation: rapidly turn on and off the source of light CS 491/691(X) - Lecture 5

  6. Modulation/Demodulation • Modulated IR is commonly used for communication • Modulation is done by flashing the light source at a particular frequency • This signal is detected by a demodulator tuned to that particular frequency • Offers great insensitivity to ambient light • Flashes of light can be detected even if weak CS 491/691(X) - Lecture 5

  7. Infrared Communication • Bit frames • All bits take the same amount of time to transmit • Sample the signal in the middle of the bit frame • Used for standard computer/modem communication • Useful when the waveform can be reliably transmitted • Bit intervals • Sampled at the falling edge • Duration of interval between sampling determines whether it is a 0 or 1 • Common in commercial use • Useful when it is difficult to control the exact shape of the waveform CS 491/691(X) - Lecture 5

  8. Proximity Sensing • Ideal application for modulated/demodulated IR light sensing • Light from the emitter is reflected back into detector by a nearby object, indicating whether an object is present • LED emitter and detector are pointed in the same direction • Modulated light is far less susceptible to environmental variables • amount of ambient light and the reflectivity of different objects CS 491/691(X) - Lecture 5

  9. Break Beam Sensors • Any pair of compatible emitter-detector devices can be used to make a break-beam sensor • Examples: • Incadescent flashlight bulb and photocell • Red LEDs and visible-light-sensitive photo-transistors • IR emitters and detectors • Where have you seen these? • Break beams and clever burglars in movies • In robotics they are mostly used for keeping track of shaft rotation CS 491/691(X) - Lecture 5

  10. Shaft Encoding • Shaft encoders • Measure the angular rotation of a shaft or an axle • Provide position and velocity information about the shaft • Speedometers: measure how fast the wheels are turning • Odometers: measure the number of rotations of the wheels CS 491/691(X) - Lecture 5

  11. Measuring Rotation • A perforated disk is mounted on the shaft • An emitter–detector pair is placed on both sides of the disk • As the shaft rotates, the holes in the disk interrupt the light beam • These light pulses are counted thus monitoring the rotation of the shaft • The more notches, the higher the resolution of the encoder • One notch, only complete rotations can be counted CS 491/691(X) - Lecture 5

  12. General Encoder Properties • Encoders are active sensors • Produce and measure a wave function of light intensity • The wave peaks are counted to compute the speed of the shaft • Encoders measure rotational velocity and position CS 491/691(X) - Lecture 5

  13. Color-Based Encoders • Use a reflectance sensors to count the rotations • Paint the disk wedges in alternating contrasting colors • Black wedges absorb light, white reflect it and only reflections are counted CS 491/691(X) - Lecture 5

  14. Uses of Encoders • Velocity can be measured • at a driven (active) wheel • at a passive wheel (e.g., dragged behind a legged robot) • By combining position and velocity information, one can: • move in a straight line • rotate by a fixed angle • Can be difficult due to wheel and gear slippage and to backlash in geartrains CS 491/691(X) - Lecture 5

  15. Quadrature Shaft Encoding • How can we measure direction of rotation? • Idea: • Use two encoders instead of one • Align sensors to be 90 degrees out of phase • Compare the outputs of both sensors at each time step with the previous time step • Only one sensor changes state (on/off) at each time step, based on the direction of the shaft rotation  this determines the direction of rotation • A counter is incremented in the encoder that was on CS 491/691(X) - Lecture 5

  16. Which Direction is the Shaft Moving? Encoder A = 1 and Encoder B = 0 • If moving to position AB=00, the position count is incremented • If moving to the position AB=11, the position count is decremented State transition table: • Previous state = current state  no change in position • Single-bit change  incrementing / decrementing the count • Double-bit change  illegal transition CS 491/691(X) - Lecture 5

  17. Uses of QSE in Robotics • Robot arms with complex joints • e.g., rotary/ball joints like knees or shoulders • Cartesian robots, overhead cranes • The rotation of a long worm screw moves an arm/rack back and fort along an axis • Copy machines, printers • Elevators • Motion of robot wheels • Dead-reckoning positioning CS 491/691(X) - Lecture 5

  18. Ultrasonic Distance Sensing • Sonars:so(und) na(vigation) r(anging) • Based on the time-of-flight principle • The emitter sends a “chirp” of sound • If the sound encounters a barrier it reflects back to the sensor • The reflection is detected by a receiver circuit, tuned to the frequency of the emitter • Distance to objects can be computed by measuring the elapsed time between the chirp and the echo • Sound travels about 0.89 milliseconds per foot CS 491/691(X) - Lecture 5

  19. Sonar Sensors • Emitter is a membrane that transforms mechanical energy into a “ping” (inaudible sound wave) • The receiver is a microphone tuned to the frequency of the emitted sound • Polaroid Ultrasound Sensor • Used in a camera to measure the distance from the camera to the subject for auto-focus system • Emits in a 30 degree sound cone • Has a range of 32 feet • Operates at 50 KHz CS 491/691(X) - Lecture 5

  20. Echolocation • Echolocation = finding location based on sonar • Numerous animals use echolocation • Bats use sound for: • finding pray, avoid obstacles, find mates, communication with other bats Dolphins/Whales: find small fish, swim through mazes • Natural sensors are much more complex than artificial ones CS 491/691(X) - Lecture 5

  21. Specular Reflection • Sound does not reflect directly and come right back • Specular reflection • The sound wave bounces off multiple sources before returning to the detector • Smoothness • The smoother the surface the more likely is that the sound would bounce off • Incident angle • The smaller the incident angle of the sound wave the higher the probability that the sound will bounce off CS 491/691(X) - Lecture 5

  22. Improving Accuracy • Use rough surfaces in lab environments • Multiple sensors covering the same area • Multiple readings over time to detect “discontinuities” • Active sensing • In spite of these problems sonars are used successfully in robotics applications • Navigation • Mapping CS 491/691(X) - Lecture 5

  23. Laser Sensing • High accuracy sensor • Lasers use light time-of-flight • Light is emitted in a beam (3mm) rather than a cone • Provide higher resolution • For small distances light travels faster than it can be measured  use phase-shift measurement • SICK LMS200 • 360 readings over an 180-degrees, 10Hz • Disadvantages: • cost, weight, power, price • mostly 2D CS 491/691(X) - Lecture 5

  24. Visual Sensing • Cameras try to model biological eyes • Machine vision is a highly difficult research area • Reconstruction • What is that? Who is that? Where is that? • Robotics requires answers related to achieving goals • Not usually necessary to reconstruct the entire world • Applications • Security, robotics (mapping, navigation) CS 491/691(X) - Lecture 5

  25. Principles of Cameras • Cameras have many similarities with the human eye • The light goes through an opening (iris - lens) and hits the image plane (retina) • The retina is attached to light-sensitive elements (rods, cones – silicon circuits) • Only objects at a particular range are in focus (fovea) – depth of field • 512x512 pixels (cameras), 120x106 rods and 6x106 cones (eye) • The brightness is proportional to the amount of light reflected from the objects CS 491/691(X) - Lecture 5

  26. Image Brightness • Brightness depends on • reflectance of the surface patch • position and distribution of the light sources in the environment • amount of light reflected from other objects in the scene onto the surface patch • Two types of reflection • Specular (smooth surfaces) • Diffuse (rough sourfaces) • Necessary to account for these properties for correct object reconstruction  complex computation CS 491/691(X) - Lecture 5

  27. Early Vision • The retina is attached to numerous rods and cones which, in turn, are attached to nerve cells (neurons) • The nerves process the information; they perform "early vision", and pass information on throughout the brain to do "higher-level" vision processing • The typical first step ("early vision") is edge detection, i.e., find all the edges in the image • Suppose we have a b&w camera with a 512 x 512 pixel image • Each pixel has an intensity level between white and black • How do we find an object in the image? Do we know if there is one? CS 491/691(X) - Lecture 5

  28. Edge Detection • Edge = a curve in the image across which there is a change in brightness • Finding edges • Differentiate the image and look for areas where the magnitude of the derivative is large • Difficulties • Not only edges produce changes in brightness: shadows, noise • Smoothing • Filter the image using convolution • Use filters of various orientations • Segmentation: get objects out of the lines CS 491/691(X) - Lecture 5

  29. Model-Based Vision • Compare the current image with images of similar objects (models) stored in memory • Models provide prior information about the objects • Storing models • Line drawings • Several views of the same object • Repeatable features (two eyes, a nose, a mouth) • Difficulties • Translation, orientation and scale • Not known what is the object in the image • Occlusion CS 491/691(X) - Lecture 5

  30. Vision from Motion • Take advantage of motion to facilitate vision • Static system can detect moving objects • Subtract two consecutive images from each other  the movement between frames • Moving system can detect static objects • At consecutive time steps continuous objects move as one • Exact movement of the camera should be known • Robots are typically moving themselves • Need to consider the movement of the robot CS 491/691(X) - Lecture 5

  31. Stereo Vision • 3D information can be computed from two images • Compute relative positions of cameras • Compute disparity • displacement of a point in 3D between the two images • Disparity is inverse proportional with actual distance in 3D CS 491/691(X) - Lecture 5

  32. Biological Vision • Similar visual strategies are used in nature • Model-based vision is essential for object/people recognition • Vestibular occular reflex • Eyes stay fixed while the head/body is moving to stabilize the image • Stereo vision • Typical in carnivores • Human vision is particularly good at recognizing shadows, textures, contours, other shapes CS 491/691(X) - Lecture 5

  33. Vision for Robots • If complete scene reconstruction is not needed we can simplify the problem based on the task requirements • Use color • Use a combination of color and movement • Use small images • Combine other sensors with vision • Use knowledge about the environment CS 491/691(X) - Lecture 5

  34. Examples of Vision-Based Navigation Running QRIO Sony Aibo – obstacle avoidance CS 491/691(X) - Lecture 5

  35. Readings • F. Martin: Chapter 6 • M. Matarić: 9 CS 491/691(X) - Lecture 5

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