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Distributed Intelligent Systems – W3 An Introduction to Sensing An Introduction to Sensing, Action, and Control in Mobile Action, and Control in Mobile Robotics

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Page 1: Distributed Intelligent Systems – W3 An Introduction to ... · chlhannel) 200 KH (): 200 KHz (see dhdatasheet)-2 micros: 2 ch. e.g. 85 kHz → 12 μs → 4 mm resolution but possible

Distributed Intelligent Systems – W3An Introduction to Sensing An Introduction to Sensing,

Action, and Control in Mobile Action, and Control in Mobile Robotics

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Outline• General concepts

– AutonomyAutonomy– Perception-to-action loop– Sensing, actuating, computing

• e-puck– Basic features

HW hit t– HW architecture• Main example of reactive control

architecturesarchitectures– Proximal architectures– Distal architectures

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General Concepts and General Concepts and Principles for Mobile

Robotics

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AutonomyAutonomy

• Different levels/degrees of autonomyDifferent levels/degrees of autonomy– Energetic level– Sensory motor and computational levelSensory, motor, and computational level– Decisional level

• Needed degree of autonomy depends on task/environment in which the unit has to operateE i t l di t bilit i i l b t• Environmental unpredictability is crucial: robot manipulator vs. mobile robot vs. sensor node

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Autonomy – Mobile RoboticsAutonomy Mobile Robotics

Task ComplexityHuman-Guided

Robotics

State of the Art in Distributed AutonomousMobile Robotics

Research

Distributed AutonomousRobotics

?

Autonomy

IndustryAutonomous

Robotics

y

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Perception-to-Action LoopR i ( li• Reactive (e.g., nonlinear transform, single loop)

• Reactive + memory (e.g. filter, state variable, multi-loops)

• sensors • actuators

n

, p )• Deliberative (e.g. planning,

multi-loops)

Computation

Perc

eptio

n

Act

ion

P

Environment

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SensorsSensors• Propioceptive (“body”) vs. exteroceptive

(“environment”)– Ex. proprioceptive: motor speed/robot arm joint angle,

b tt ltbattery voltage– Ex. exteroceptive: distance measurement, light

intensity, sound amplitudey, p

• Passive (“measure ambient energy”) vs. active(“emit energy in the environment and measure the environmental reaction”)– Ex. passive: temperature probes, microphones, cameras– Ex. active: laser rangefinder, IR proximity sensors,

ultrasound sonars

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Classification of Typical SensorsClassification of Typical Sensors

[From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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Classification of Typical Sensors

[From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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Action - Actuators

• For different purposes: locomotion, control a part of the body (e.g. arm), heating, sound p y ( g ), g,producing, etc.

• Examples of electrical-to-mechanicalExamples of electrical to mechanical actuators: DC motors, stepper motors, servos loudspeakers etcservos, loudspeakers, etc.

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ComputationComputation• Usually microcontroller-based; memory y y

internal and potentially external to the microcontroller

• “Discretization” (analog-to-digital for values, continuous-to-discrete for time) andvalues, continuous to discrete for time) and “continuization” (digital-to-analog for values, discrete-to-continuous for time)values, discrete to continuous for time)

• Different types of control architectures: e.g., reactive (‘reflex based”) vs deliberativereactive ( reflex-based ) vs. deliberative (“planning”)

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Sensor Performance

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General Sensor Performance– Range

• Upper limit

– Dynamic range• ratio between lower and upper limits, usually in decibels

(d f d li d )(dB for power and amplitude)• e.g. voltage measurement from 1 mV to 20 V

Note: similar to the acoustic amplitude• e.g. power measurement from 1 mW to 20 W

21 UIUP =⋅=

Note: similar to the acoustic amplitude

R

Note: 20 instead of 10 because square of voltage is equal to power!![Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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General Sensor Performance– Resolution

• minimum difference between two values

General Sensor Performance

• minimum difference between two values• usually: lower limit of dynamic range = resolution• for digital sensors it is usually the A/D resolution.o d g ta se so s t s usua y t e / eso ut o .

– e.g. 5V / 255 (8 bit)

– Linearity• variation of output signal as function of the input signal• linearity is less important when signal is treated with a

tcomputer

)()(

fxfx →

)()()(?

yfxfyxfyx ⋅+⋅=⋅+⋅→⋅+⋅ βαβαβα)(yfy → )()()( yfxfyxfyx +=+→+ βαβαβα

[From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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General Sensor Performance

– Bandwidth or Frequency

General Sensor Performance

• the speed with which a sensor can provide a stream of readings

ll th i li it d di th• usually there is an upper limit depending on the sensor and the sampling rate

• lower limit is also possible, e.g. acceleration sensorp , g• frequency response: phase (delay) of the signal and

amplitude might be influenced

[Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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In Situ Sensor PerformanceIn Situ Sensor PerformanceCharacteristics that are especially relevant for real world environments• Sensitivityy

– ratio of output change to input change– however, in real world environment, the sensor has very often high

sensitivity to other environmental changes, e.g. illuminationy g , g• Cross-sensitivity (and cross-talk)

– sensitivity to other environmental parameters– influence of other active sensors– influence of other active sensors

• Error / Accuracy– difference between the sensor’s output and the true value

m = measured valuev = true value

error

[Adapted from Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

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In Situ Sensor Performance Characteristics that are especially relevant for real world environments

• Systematic error > deterministic errors• Systematic error -> deterministic errors– caused by factors that can (in theory) be modeled -> prediction– e.g. calibration of a laser sensor or of the distortion cause by the optic

of a camera

• Random error -> non-deterministic– no deterministic prediction possibleno deterministic prediction possible– however, they can be described probabilistically – e.g. gaussian noise on a distance sensor, black level noise of camera

i i (diff f )• Precision (different from accuracy!)– reproducibility of sensor results

[From Introduction to Autonomous Mobile Robots, Siegwart R. and Nourbakhsh I. R.]

σ = standard dev of the sensor noise

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k A Ed ti l e-puck: An Educational Robotic ToolRobotic Tool

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The e-puck Mobile RobotMain features

The e puck Mobile Robot

• Cylindrical, Ø 70mm• dsPIC processor

T o stepper motors• Two stepper motors• Ring of LEDs• Many sensors:

CameraSoundIR proximityIR proximity3D accelerometer

• Li-ion accumulator• Bluetooth wireless communication• Bluetooth wireless communication• Open hardware (and software)

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e-puck Overview

IR receiver(remote control)

Speaker

RS232

Accelerometer( )

Mode selector

Reset

RS232

Ring of LEDs

IR i i

Programming anddebug connector

ON-OFFIR proximity sensors

CMOSmicrophones

Wheels with stepper motor Li-Ion accumulator

CMOS camerap

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e-puck Mechanical Structuree puck Mechanical Structure

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PIC/dsPIC Familyfrom www.microchip.com

Microcontroller h kon the e-puck

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dsPIC CharacteristicsdsPIC Characteristics

e-pucki t llmicrocontroller

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e-puck Block Schema

Actuators?

Sensors?

Computation?Computation?

Communication?

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e-puck Accelerometer- Sampling of the continous time analog

accelerometer (3 axes) using the integrated A/D converterconverter

- Low to medium sampling frequency; typically a function of the application and of the accelerometer characteristicsaccelerometer characteristics

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e-puck Hearing CapabilitiesExample: acoustic source localization

- medium to high sampling frequency application

- E.g.: robot dimension 7.5 cm → microphone max inter-distance → 5.5 cm → speed of sound in air 340 m/s →

l i i i 0travel time micro-to-micro 0 (orthogonal) to 160 μs (aligned)

- max DsPIC sampling frequency (1 h l) 200 KH ( d h )channel): 200 KHz (see datasheet)

- 2 micros: 2 ch. e.g. 85 kHz → 12 μs →4 mm resolution but possible aliasing on a plane (dual localization)on a plane (dual localization)

- 3 micros: 3 ch., e.g. 56 kHz → 18 μs→ 6 mm but no aliasing on a plane (unique localization)(unique localization)

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e-puck Vision Capabilities General requirements for embedded vision:

handling of very large data flow (tens of Mbit/s)

Processing:- Pixels H x V x RGB x fps- 640 x 480 x 3 x 30 = 27Mbytes/second

Mbit/s)

- The dsPIC can execute max 15MIPS (millions of instructions/second)Memory

- One image RBG (8,8,8 bits) of 640x480 use 922kbytesO d C h 8kb f A ( d A ) f i bl- Our dsPIC has 8kbytes of RAM (Random Access Memory), for variables

- Full image acquisition impossible

e-puck microcontroller

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e-puck Vision Capabilities

- Possible workaround on e-puck: d lidownsampling

- 8 fps grayscale, 4 fps color- Image of 1800 pixels (42x42, 80x20)g p ( )

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Real and Simulated e-Puck

Real e-puck Realistically simulated e-puck (Webots)• intra robot details: discrete sensors, actuators,

transceivers, etc.• noise, nonlinearities of S&A reproduced• kinematic (e.g., speed, position) and dynamic

(e.g., mass, forces, friction, )

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E l f R ti Examples of Reactive Control ArchitecturesControl Architectures

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Reactive Architectures:Reactive Architectures: Proximal vs. Distal in Theory

• Proximal: l d– close to sensor and actuators

– very simple linear/nonlinear operators on crude d tdata

– high flexibility in shaping the behavioriffi l i i “h id d”– Difficult to engineer in a “human-guided” way;

machine-learning usually perform better

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Reactive Architectures:Reactive Architectures: Proximal vs. Distal in Theory

• Distal architectures F f d– Farer from sensor and actuators

– Self-contained behavioral blocks – Less flexibility in shaping the behavior– Easier to engineer in a “human-guided” way the

b i bl k (h d di ) diffi lbasic block (handcoding); more difficult to compose the blocks in the right way (e.g., sequence parallel )sequence, parallel, …)

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Reactive Architectures:Reactive Architectures: Proximal vs. Distal in Practice

• A whole blend!• Five “classical” examples of reactive p

control architecture for solving the same problem: obstacle avoidance.

• Two proximal: Braitenberg and Artificial Neural Network

• Three distal: rule-based and two behavior-based (Subsumption and Motor Schema)

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Ex. 1: Braitenberg’s VehiclesEx. 1: Braitenberg s Vehicles

lightsensors

symmetry axis

- - --

motors

++++

2a 2b 3a 3b

• Work on the difference (gradient) between sensors• Originally omni-directional sensors but work even better with directional sensors• + excitation, - inibition; linear controller (out = signed coefficient * in)

S t i l i i f th hi l ( )• Symmetry axis along main axis of the vehicle (----)• Originally: light sensors; works perfectly also with proximity sensors (3c?)

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Ex. 2: Artificial Neural NetworkEx. 2: Artificial Neural Network

S Soutput

S1

S2

S3 S4S5

S6

Oi

f(xi)Ni

neuron N with sigmoidtransfer function f(x)

1 S6

M1M2

wij

12)( −=xf

)( ii xfO =

Ije1

)(+ −xf

synaptic weight input

S7S8

∑ +=m

jiji IIwx 0 inhibitory conn.it t=j 1 excitatory conn.

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Ex 3: Rule-BasedEx. 3: Rule Based

Rule 1:if (proximity sensors on the left active) then

i hturn right

Rule 2:if (proximity sensors on the right active) thenif (proximity sensors on the right active) thenturn left

Rule 3:Rule 3:if (no proximity sensors active) thenmove forwards

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S b ti A hit tSubsumption Architecture

• Rodney Brooks 1986, MIT• Precursors: Braitenberg (1984), Walter (1953)• Behavioral modules (basic behaviors) represented by

Augmented Finite State machines (AFSM)• Response encoding: predominantly discrete (rule

based)• Behavioral coordination method: competitive

(priority-based arbitration via inhibition and i )suppression)

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Subsumption ArchitectureSubsumption ArchitectureSense

ModelModify the World

Create Maps

Plan

pDiscover

Avoid Collisions

Act

Avoid CollisionsMove Around

Act

Classical paradigm (serial); Subsumption (parallel); Classical paradigm (serial);emphasis on deliberativecontrol

p (p );emphasis on reactive control

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S bs mption Architect re: AFSMSubsumption Architecture: AFSM

ResetSuppressor

RS

O t tBehavioral Module

I

Inputlines

Outputlines

I hibitInhibitor: block the transmissionSuppressor: block the transmission and replaceInhibitor Suppressor: block the transmission and replacethe signal with the suppressing message

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Ex. 4: Behavior-Based with Subsumption

Obstacle avoidance1

WanderS

(1 suppresses and replaces 2)

actuatorssensors2

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Evaluation of Subsumption

+ Support for parallelism: each behavioral layer can run independently and asynchronously (including different loop time)loop time)

+ Fast execution time possible

- Coordination mechanisms restrictive (“black or white”)- Limited support for modularity (upper layers design cannot

be independent from lower layers).

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Motor SchemasMotor Schemas

R ld A ki 1987 G i T h• Ronald Arkin 1987, Georgia Tech• Precursors: Arbib (1981), Khatib (1985)• Parametrized behavioral libraries

(schemas)( )• Response encoding: continuous using

potential field analogpotential field analog• Behavioral coordination method:

cooperative via vector summation andcooperative via vector summation and normalization

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Motor SchemasMotor Schemassensors

MS1PS1S1

vector

PS2

S2

Σvector motors

PSS2S3

MS2

PS3PSS1

… PS: Perceptual SchemaPSS: Perceptual SubschemaMS M S h… MS: Motor SchemaS: sensor

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Ex. 5: Behavior-Based with Motor Schemas

Avoid-obstacle

ΣDetect-obstacles

M t G lDetect Goal actuatorssensors ΣMove-to-GoalDetect-Goal actuatorssensors

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Visualization of Vector field for Ex 5Visualization of Vector field for Ex. 5Avoid-static-obstacle

Obstacle

V SdRfGdSSdfor

≤−

>0

Obstacle

Vmagnitude =Rdfor

SdRforGRS

≤∞

≤<−

S = b t l ’ h f i fl ObstacleS = obstacle’s sphere of influenceR = radius of the obstacleG = gainD = distance robot to obstacle’s center

Vdirection = radially along a line between robot and

D = distance robot to obstacle s center

between robot and obst. center, directed away from the obstacle

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Visualization of Vector field for Ex 5Move-to-goal (ballistic)

Visualization of Vector field for Ex. 5

Output = vector = (r,φ)(magnitude, direction)

Goal(magnitude, direction)

Vmagnitude = fixed gain value

Vdirection = towards perceived goalgoal

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Visualization of Vector field for Ex 5Move-to-goal + avoid obstacle

Visualization of Vector field for Ex. 5

OLinear combination(weigthed sum) O(weigthed sum)

G

O

G

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Ex. 5: Behavior-Based with Motor Schemas

Avoid-obstacle

ΣDetect-obstacles

M t G lDetect Goal actuatorssensors ΣMove-to-GoalDetect-Goal actuatorssensors

NoiseGenerate-direction

For avoiding to get stuck in local minima (typical problem of vector field approaches)

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Evaluation of Motor Schemas

+ Support for parallelism: motor schemas are naturally parallelizable

+ Fi d b h i l bl di ibl+ Fine-tuned behavioral blending possible

- Robustness > well known problems of potential field- Robustness -> well-known problems of potential field approach -> extra introduction of noise (not clear method for exploiting that generated by sensors, …)

- Slow and computationally expensive sometimes

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Evaluation of both Architectures in Practice

• In pratice (my expertise) you tend to mix both and even moreeven more …

• The way to combine basic behavior (collaborative and/or competitive) depends from how you p ) p ydeveloped the basic behaviors (or motor schemas), reaction time required, on-board computational capabilitiescapabilities, …

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Conclusion

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Take Home Messages• Perception-to-action loop is key in robotics, several

sensor and actuator modalitiesK t i f l ifi ti t ti• Key categories for sensor classification are exteroceptivevs. propioceptive and active vs. passive

• Experimental work can be carried out with real and prealistically simulated robots

• A given behavior can be obtained with different control architecturesarchitectures

• Control architectures can be roughly classified in proximal and distal architectures

• Braitenberg vehicles and artificial neural networks are two typical examples of proximal architecture, motor schema and subsumption architecture are typicalschema and subsumption architecture are typical example of distal architecture

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Additional Literature – Week 3Books• Braitenberg V., “Vehicles: Experiments inBraitenberg V., Vehicles: Experiments in

Synthetic Psychology”, MIT Press, 1986.• Siegwart R. and Nourbakhsh I. R.,

“Introduction to Autonomous Mobile Robots”, MIT Press, 2004. A ki R C “B h i B d R b ti ” MIT• Arkin R. C., “Behavior-Based Robotics”. MIT Press, 1998.

• Everett H R “Sensors for Mobile Robots• Everett, H. R., Sensors for Mobile Robots, Theory and Application”, A. K. Peters, Ltd., 1995