Common Sensing Techniques
for Reactive Robots
(12-11-7)
Sungmin Lee (이성민)
Division of Electronic Engineering, Chonbuk National University
Intelligent Systems & Robotics Lab.
http://robotics.jbnu.ac.kr
Chapter objectives
•
Describe difference between active and passive sensors
•
Describe the types of behavioral sensor fusion
•
Define each of the following terms in one or two sentences:
proprioception, exteroception, exproprioception,proximity sensor, logical
sensor, false positive, false negative, hue, saturation, computer vision
•
Describe the problems of specular reflection, cross talk, foreshortening,
and if given a 2D line drawing of surfaces, illustrate where each of these
problems would be likely to occur
•
If given a small interleaved RGB image and a range of color values for a
region, be able to 1) threshold on color and 2) construct a color
histogram
Contents
1. Logical sensors
2. Behavioral Sensor Fusion
3. Attributes of a sensor
4. Sensor Categories
5. Computer vision
6. Case study
7. Summary
Motivation
• Sensing is tightly coupled with acting in reactive systems,
so need to know about sensors
• What sensors are out there?
– Ultrasonics, cameras are traditional favorites
– Sick laser ranger is gaining fast in popularity
• How would you describe them (attributes)?
• How would you decide which one to pick and use for an
application?
Logical Sensors
• A unit of sensing or module (supplies a particular percept).
• It consists of the signal processing and the software
processing.
• Can be easily implemented as a perceptual schema.
• Different sensors/perceptual schemas can produce the
same percept - motor schema doesn’t care!
– Behavior can pick what’s available
• Example: ring of IRs, ring of sonars
– If sensor fails, then another can be substituted without
deliberation or explicit modeling
– Conflicts in allocation can be solved by using logical
sensors (deliberation is required to assign)
Active vs. Passive (Example)
• Active sensors
- Sensor emits some form of
energy and then measures the
impact as a way of understanding
the environment
- Ex. Ultrasonics, laser
• Passive sensors
- Sensor receives energy already in
the environment
- Ex. Camera
• Passive consume less energy,
but often signal-noise problems
• Active often have restricted
environments
Thermal
sensor
Stereo
Camera
pair
Laser
ranger
Sonars
Bump
sensor
Behavioral Sensor Fusion
Sensor fusion is a broad term used for any process that
combines information from multiple sensors into a single percept.
In some cases multiple sensors are used when a particular sensor
is too imprecise or noisy to give reliable data. Adding a second
sensor can give another “vote” for the percept.
False positive
When a sensor leads the robot to believe that a percept is
present, but it is not, the error is called a false positive.
False negative
The robot has made a positive identification of percept, but it was
false. Likewise, an error where the robot misses a percept is
known as a false negative.
Sensing Model
11
Sensor/Transducer
Behavior
Action
Sensing in Reactive Paradigm
Behavior
Behavior
Behavior
Each behavior has its own dedicated sensing. One behavior
literally does not know what another behavior is doing or
perceiving.
Behavioral Sensor Fusion:
-sensor fission
Perceptual
Schemas
Motor
Schemas
This sensor fission in part as a take off on the connotations of
the word “fusion” in nuclear physics. In nuclear fusion, energy is
created by forcing atoms and particles together, while in fission,
energy is creating by separating atoms and particles.
Behavioral Sensor Fusion:
-action-oriented sensor fusion
Perceptual
Schema
Motor
Schema
This type of sensor fusion is called action-oriented sensor fusion
to emphasize that the sensor data is being transformed into a
behavior-specific representation in order to support a particular
action, not for constructing a world model.
Behavioral Sensor Fusion:
-sensor fashion
Perceptual
Schema
Motor
Sc hema
Sensor fashion, an alliterative name intended to imply the robot
was changing sensors with changing circumstances just as
people change styles of clothes with the seasons.
Designing a Sensor Suite
-Attributes of a sensor
•Field of view, range : does it cover the “right” area
•Accuracy & repeatability : how well does it work?
•Responsiveness in target domain : how well does it work for
this domain?
•Power consumption : may suck the batteries dry too fast
•Reliability : can be a bit flakey, vulnerable
•Size : always a concern!
•Computational Complexity : can you process it fast enough?
•Interpretation Reliability : do you believe what it’s saying?
Designing a Sensor Suite
-Attributes of a sensor suite
Should be considered for the entire sensing suite :
•Simplicity
•Modularity
•Redundancy
- physical redundancy
(there are several instances of physically identical sensors on the
robot.)
-logical redundancy
(another sensor using a different sensing modality can
produce the same percept or releaser.)
- fault tolerance
Sensor Categories
• Proprioceptive
– Inertial Navigation System(INS)
– Global Positioning System(GPS)
• Exteroceptive
– Proximity
• Range
• Contact
– Computer Vision
Proprioceptive Sensors(1)
-Inertial navigation system (INS)
MQ-9 Reaper
Measure movements electronically through miniature
accelerometers INS can provide accurate dead reckoning to 0.1
percent of the distance traveled.
However, this technology is unsuitable for mobile robots for
several reasons.(cost, Size, etc)
Proprioceptive Sensors(2)
-Global Positioning System (GPS)
GPS systems work by receiving signals from satellites orbiting the
Earth.
GPS is not complete solutions to the dead reckoning problem in
mobile robots.
GPS does not work indoors(environmental limit)
Proximity Sensors(1)
-Sonar or ultrasonic
•Sonar refers to any system for using sound to
measure range. (use a sonar for underwater
vehicles ).
•Ground vehicles commonly use sonar with an
ultrasonic frequency.
•Ultrasonic sensors generate high frequency
sound waves and evaluate the echo which is
received back by the sensor. Sensors calculate
the time interval between sending the signal
and receiving the echo to determine the
distance to an object.
•Ultrasonic is possibly the most common
sensor on commercial robots operating
Polaroid
ultrasonic
transducer
Proximity Sensors(1)
- Three problems with sonar range readings
foreshortenin
g
specular reflection
cross-talk
chairs, tables– legs, edges too thin for resolution
Proximity Sensors(1)
- sonar maps
lab
hallway
Maps produced by a mobile robot using sonars in: a.) a lab and b.) a
hallway. (The black line is the path of the robot.)
Proximity Sensors(1)
- Attributes of ultrasonic
• Power consumption
– High
• Reliability
– Lots of problems
• Size
– Size of a Half dollar, board is similar size and can be creatively
packaged
• Computational Complexity
– Low; doesn’t give much information
• Interpretation Reliability
– poor
Proximity Sensors(1)
- Ultrasonic Summary
• Physics : active sensor, works on time of flight
• Advantages : range, inexpensive ($30 US), small
• Disadvantages : specular reflection, crosstalk, foreshortening,
high power consumption, low resolution
Proximity Sensors(2)
- Infrared ray (IR)
Sharp GP2Y0A21YK
They emit near-infrared energy and measure whether any
significant amount of the IR light is returned.
These often fail in practice because the light emitted is often
“washed out” by bright ambient lighting or is absorbed by dark
materials (i.e., the environment has too much noise).
Proximity Sensors(3)
- Bump and feeler sensors
Roomba 500
Bump
Popular class of robotic sensing is tactile, or touch, done with
bump and feeler sensors.
The sensitivity of a bump sensor can be adjusted for different
contact pressures
Computer Vision
- Definition
Computer vision refers to processing data from any modality
which uses the electromagnetic spectrum which produces an
image.
face recognition
Computer Vision
- Attributes
•Physics : light reflecting off of surfaces, respond to wavelenght
•Field of view, range : depends on lens; lens typical have a
different VFOV and HFOV (vertical, horizontal)
•Accuracy & repeatability : good
•Responsiveness in target domain : depends on lighting source,
inherent constrast between objects of interest
•Power consumption : low
•Reliability : good
•Size : can be miniaturized
•Interpretation Reliability : good
Computer Vision
- CCD cameras
• A charge-coupled device (CCD) is a
device for the movement of electrical
charge, usually from within the device to
an area where the charge can be
manipulated, for example conversion into
a digital value.
•
•
•
•
CCD sensors typically produce less NOISE.
CCD sensors typically are more light-sensitive.
CMOS sensors use far less power.
CMOS sensor cost less to produce.
Computer Vision
- Color planes
• RGB (red, green, blue) is the NTSC output
– Poor color constancy in “real world”
• H,S,I (hue, saturation, intensity) has theoretical color
constancy
– But not with conversion from RGB to HSI
• Alternatives SCT (Spherical Coordinate Transform)
That color space was designed to transform RGB data to a
color space that more closely duplicates the response of
the human eye.
Original image
RGB
HSI
SCT
Computer Vision
- Common Vision Algorithms
• For reactive applications:
– Color segmentation
• Imprint on a color region, then follow it
(or remember it)
– Color histogramming
• Imprint on a region with a distribution of color,
then follow it (or remember it)
Range from Vision
-Stereo camera pairs
• Using two cameras to extract
range data is often referred to as
range from stereo, stereo
disparity, binocular vision, or just
plain “stereo.” One way to extract
depth is to try to superimpose a
camera over each eye.
Ways of extracting depth from a pair of cameras
• Each camera finds the same
point in each image, turns itself
to center that point in the
image, then measures the
relative angle. The cameras are
known as the stereo pair.
Range from Vision
-Light stripers
Light striping, light stripers or structured light detectors work by
projecting a colored line (or stripe), grid, or pattern of dots on the
environment. Then a regular vision camera observes how the pattern is
distorted in the image.
Range from Vision
-Laser ranging(Sick)
Accuracy & repeatability - Excellent results
Responsiveness in target domain
Power consumption
- High; reduce battery run time by half
Reliability - good
Size - A bit large
Computational Complexity
– Not bad until try to “stack up”
• Interpretation Reliability
– Much better than any other ranger
•
•
•
•
•
•
•
SICK PLS100
flat surface
an obstacle
a negative obstacle
Range from Vision
-Laser Ranger Summary
• 180o plane
• Advantages: high accuracy, coverage
• Disadvantages: 2D, resistant to miniaturization, cost
($13,000 US)
NASA/CMU Nomad robot
(Carnegie Mellon University )
Case Study :
Hors d’Oeuvres, Anyone? (Borg Shark and Puffer Fish)
Camera pair (redundant):
Face color
Digital thermometer:
“Face” temperature check
Laser range:
Count treat
removal
If blocked,
Puffed up
Sensor fusion:
Reduced
False positives,
False negatives
From 27.5% to 0%
Sonars:
Avoid obstacles,
State diagrams for the Borg Shark
sonar
shaft
encoders
thermal
map:
evidence grid
waypoint
navigation:
move to goal,
avoid
full
tray
at waypoint
OR
food removed
time limit
serving exceeded
awaiting
refill:
finding faces
vision
vision
serving food:
finding faces,
counting treat
removal
food
depleted
thermal
laser range
sonar
Summary
• Design of a sensor suite requires careful consideration
– Almost all robots will have proprioception, but exteroception
needs to be closely matched to the task and the environment
• Most common exteroceptive sensors on mobile robots are:
– Ultrasonics
– Computer vision
– Laser range
• Color vision can be hard, almost all vision is computationally
expensive unless focus on affordances
– Borg shark and Puffer fish with color plus heat
– Polly and texture
Thank you