Last time we saw: Last time we saw:• DC motors

– inefficiencies, operating voltage and current, stall voltage and current and torque

– current and work of a motor• Gearing gear ratios

– gearing up and down– combining gears

• Pulse width modulation • Servo motors

Lecture Outline Lecture Outline• What are sensors?• Types of sensors (many examples)• Sensor complexity• Signals -> symbols• Levels of processing• Poor and good design of perception• Biological perception and lessons• Sensor fusion

Why is Robotics hard? Why is Robotics hard?• Sensors are limited and crude• Effectors are limited and crude• State (internal and external, but

mostly external) is partially-observable

• Environment is dynamic (changing over time)

• Environment is full of potentially-useful information

What are sensors? What are sensors?• Sensors constitute the perceptual

system of a robot• Sensors do not provide state• Sensors are physical devices that

measure physical quantities• Examples:

– Physical property -> sensor:– contact -> switch– distance -> ultrasound, radar, infra red

Examples of sensors Examples of sensors• More examples:

– Physical property -> sensor:– light level -> photo cells, cameras– sound level -> microphones– strain -> strain gauges– rotation -> encoders– magnetism -> compasses– smell -> chemical– temperature -> thermal, infra red

More examples of sensors More examples of sensors• Even more examples:

– Physical property -> sensor:– inclination -> inclinometers– rate of change of inclination -> gyroscopes– pressure -> pressure gauges– altitude -> altimeters– and many more…

• Note: the same property can be measured with different sensors

Types of Sensors Types of Sensors• Sensors range from simple to

complex in the amount of information they provide– simple: an on/off switch (1 bit of input)

– complex: the human retina (> 100 million photosensitive elements!)

• A sensor provides “raw” information, which usually needs to be processed

Sensor Complexity Sensor Complexity• The output of a simple sensor can

be used directly, without processing (e.g., if switch closed, stop, else go)

• The output of a complex sensor must be processed

• We can ask: “Given the sensory reading I am getting, what was the world like to make the sensor give me this reading?” => reconstruction

Signals -> Symbols (State) Signals -> Symbols (State)• Sensors do not provide

state/symbols, just signals• A great deal of computation may be

required to convert the signal from a sensor into useful state for the robot

• This process bridges the areas of electronics, signal processing, and computation

Levels of Processing Levels of Processing• to find out if a switch is open or closed,

we need to measure voltage going through the circuit => electronics

• using a microphone to separate voice from noise and recognize => signal processing

• using a surveillance camera, find people in the image and recognize criminals, perhaps by comparing them to a large database => computation

Requirements Requirements• The more processing that needs to

be done, the more computation is required

• Thus perception requires:– sensors (power and electronics)

– computation (more power and electronics)

– connectors (to connect it all)

Poor Designs of Perception Poor Designs of Perception• It is not a good idea to separate

– what the robot senses– how it senses it– how it processes it– and how it uses it

• If these are separated, the resulting robot design is typically large, bulky, and ineffective.

History of Poor Designs History of Poor Designs• Historically, perception has been

treated poorly:– perception in isolation– perception as “king”– perception as reconstruction

• Traditionally these approaches came from computer vision, which provides the most complex data

Good Designs of Perception Good Designs of Perception• Instead, it is best to think about

these as a single complete design:– the task the robot has to perform– the best sensors for that task– the best mechanical design that will

allow the robot to get the necessary sensory information to perform that task (e.g., the body shape of the robot, the placement of the sensors, etc.)

A New & Better Way A New & Better Way• Perception in the context of action

and the task• Action-oriented perception• Expectation-based perception use

knowledge about the world as constraints on sensor interpretation

• Focus-of-attention methods provide constraints on where to look

• Perceptual classes partition the world into useful categories

Biological Perception Biological Perception• Nature solves this problem cleverly: it

evolves special sensors with special geometric and mechanical properties.

• Consider facetted eyes of flies, polarized light sensors on birds, horizon/line sensors on bugs, the shape of the human ear, etc.

• Biological sensors use clever mechanical designs that maximize the sensor's properties, i.e., its range and correctness.

Proprioception Proprioception• Origin of received sensory

information divides perception into– Proprioception: sensing internal state

(e.g., muscle tension, limb position)– Exteroception: sensing external state

(e.g., vision, audition, smell, etc.)• Examples of proprioception

– path integration (dead-reckoning)– balancing– all movement...

Affordances Affordances• Affordances are “potentialities for

action inherent in an object or scene” (Gibson 1979, psychology)

• The focus is the interaction between the robot and its environment

• Perception is biased by what needs to be done (the task)

• E.g.: a chair can be something to sit in, avoid, throw, etc.

Lessons from Biology Lessons from Biology• As a robot designer, you may not get

the chance to make up new sensors• But you will always have the chance

(and the need) to design interesting ways of using the available sensors

• Utilize the interaction with the world and always keep in mind the task

• Food for thought: how would you detect people in an environment?

Example: detecting people Example: detecting people• temperature: pyro-electric sensors

detect special temperature ranges• movement: if everything else is

static or slower/faster• color: if people wear uniquely

colored clothing in your environment• shape: now you need to do complex

vision processing

Example: measuring distance Example: measuring distance• ultrasound sensors (sonar) give you

distance directly (time of flight)• infra red provides return signal intensity• two cameras (i.e., stereo) can give you

distance/depth• use perspective projection with 1 camera• use a laser and a camera, triangulate• use structured light; overlying grid

patterns on the world• ...

Sensor Fusion Sensor Fusion• A powerful strategy is to combine

different sensors => Sensor Fusion • Sensor fusion is complex because

sensors have:– different characteristics– different accuracy– different complexity

• Computation is necessary to com-bine them effectively (in real-time)

Biological Sensor Fusion Biological Sensor Fusion• The brain processes information

from many sensors (vision, touch, smell, hearing, sound)

• The processing areas are distinct in the brain (and for vision, they are further subdivided into the “what” and “where” pathways)

• Much complex processing is involved in combining the information