ubicom book slides 1 ubiquitous computing: smart devices

UbiCom Book Slides

1Ubiquitous computing: smart devices,

environments and interaction

Chapter 6

Tagging, Sensing & Controlling

Stefan Poslad

http://www.eecs.qmul.ac.uk/people/stefan/ubicom

Overview• Introduction • Tagging the Physical World• Sensors and Sensor Networks• Micro Sensing & MEMS• Micro Actuation & MEMS• Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks)• Robots

Ubiquitous computing: smart devices, environments and interaction 2

Chapter 6: Overview

The slides for this chapter are also expanded and split into several parts in the full pack

• Part A: Tagging physical world & augmented reality• Part B: Sensors, Sensor Nets• Part C: MEMS• Part D: Embedded Systems• Part E: Control Systems & Robots


Overview

Chapter 6 focuses on:• internal system properties: context-awareness & autonomy• external interaction with the physical environment.


IntroductionTo enable Smart (Physical) Environments, devices should:• Spread more into the physical environment, becoming part

of more user activities in physical environment • Be cheap to operate: autonomous energy etc• Be low maintenance: automatic• Be able to interact with physical environment context• Be sometimes small enough so as to …• Be able to be encapsulated and embedded • Be cheap to manufacture


UbiCom Internal System Properties


Distributed

implicit HCI

Context-Aware

Autonomous Intelligent

Physical Environments

PhysicalPhenomena

UbiComp System

ICT

CPICPI (Sense, Adapt)

Smart Physical Environments

7Ubiquitous computing: smart devices, environments and interaction

Micro sensors actuators &

Augmented Reality

Annotation

Macro Control Systems

MEMS

Sensor Nets

RFID

Embedded RT

Locators

Integrated Circuits

Nano-technology

Operating systems

Process Control

Nanobots

Robots

Smart Physical Environments

Virtual Tags


Smart (Physical) Environments

SensorsTags

Physical Environment Devices Context-aware systems

Controllers

Physical

RFID

Virtual

Active

Passive

Actuators micro macro

Sensor Nets Nano

TypesOS

MEMS

MTOSASOS

RTOS

Types

Programmable

PID

Adaptive

Robot

Arms Mobile

LegsWheels

Nanobot

Skins

Paint

Dust

Matter

Site, Anchor Etc.

Link

Data Mgt

CPI

Natural Physical Environment

Dimensions

Augmented Reality

Smart Devices

Contexts

Overview

• Introduction• Tagging the Physical World • Sensors and Sensor Networks• Micro Actuation and Sensing: MEMS• Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks)• Robots


Tagging (or Annotating) the Physical World

Outline of this section• Applications• Life-cycle for Tagging Physical Objects• Tags: Types and Characteristics• Physical and Virtual Tag Management• RFID Tags• Personalised and Social Tags


Tagging: Applications

• Locate items, e.g.?• Retrieve annotations associated with physical objects

(augmented reality) e.g. ?• Security, e/g/. . • Tracking, e.g.,• Automated Routing: of physical objects, e.g., ?• Automated Physical Access: e.g., ?


Tagging Applications: Automated Physical Access


Tagging Applications: Asset Tracking


Tagging Applications: Security


Physical versus Virtual Tags


Physical Object

Physical Tag, e.g., RFID

Stefan’s car

Virtual Tag

Virtual View of physical objects, e.g., digital Photo

Life-cycle for Tagging Physical Objects


Capturing:

Anchoring : .Organising:

Accessing :

Presenting

Managing :

Design issues for Anchoring Tags on Physical Objects


Different ways to characterise and classify tagging • By how to augment physical world objects for use in virtual

(computer) environments• By use of Onsite versus Offsite and attached versus

detached classification of tags

Augment physical environments for use in virtual environments

• Augment the user:

• Augment the physical object:

• Augment the surrounding environment:


Onsite versus Offsite & Attached versus Detached Annotation


2 dimensions:• User of the annotation is

– onsite (co-located or local) with physical object versus– offsite (not co-located or remote).

• Annotation is – attached (or augments) physical object it refers to versus – being detached (not augmented or not collocated) with the physical

object.

Onsite versus Offsite & Attached versus Detached Annotation


Offsite

Onsite

Attached Detached

Design issues for Anchoring

Tags on Physical Objects

Ubiquitous computing: smart devices, environments and interaction

Analogue Digital

Tags

Physical Virtual (annotation)

Attached versus detached

Onsite versus off-site

Physical-Virtual Tag Link

Cardinality

AR

Augment User

Augment Physical Object

Augment Physical Environment

RFID

Static vs. Dynamic

Physical Environment Objects

Static States Dynamic States

Sensors

Design issues for Tagging Physical environment

• Tags read outdoors in noisy, wet, dark or bright environments.

• Annotation data storage, distribution & integration with data• Data management must start as soon as the data is

captured (readers). • Multiple tags & readers per unit Vol..

– Challenges?

• Redundant annotations: similar items are captured, many times over. – Solutions?

• Applications and businesses need to define the level of aggregation, reporting, analysis


RFID Tags• A type of on-site tag, attached to physical object• RFID (Radio Frequency Identifier) Tags, attached to objects

to enable identification of objects in the world over a wireless link.

RFID Tags versus Bar codes?


RFID Tags: Applications• ???


Types of RFID Tag• RFID tags may be classified into whether or not they:

– Active:– Passive:.

• Active tags are more expensive and require more maintenance but have a longer range compared to passive tags.

• Typical RFID system main components: • tag itself, reader, data storage, post-processing• RFID tag versus RF Smart Card?


Active RFID Tags• Active RFID tags used on large, more expensive assets• . • Typically operate at 0.455, 2.45 or 5.8 GHz frequencies• Have a read range of 20 M to 100 M, • Cost?• Complex active tags could also incorporate sensors.

How? Why?

• 2 types of active tags: transponders and beacons


Active RFID Transponders• Active transponders are woken up when they receive a

signal from a reader.• Transponders conserve battery life. How?

• Important application of active transponders is in toll payment collection, checkpoint control and other systems.


Active RFID Beacons• Main difference c.f. Transponder is long range, global?

beacon reader• Beacons are used in Real-Time Location Systems (RTLS) • Longer range RTLS could utilise GPS or mobile phone

GSM trilateration – See Chapter 7

• In RTLS, a beacon emits a signal with its unique identifier at pre-set intervals –


Active RFID Transponder Application: toll booths


Passive RFID Tags• Contain no power source and no active transmitter• Power to transmit comes from where?• Cheaper than active tags, cost? • Shorter (read access) range than active tags, typically ??• Passive RFID transponder consists of a microchip attached

to an antenna, e.g., same as smart card• Lower maintenance• Passive Transponders can be packaged in many different

ways, – ????


Passive RFID Tags• Passive tags typically operate at lower frequencies than

active tags–

• Low-frequency tags are ideal for applications where the tag needs to be read through certain soft materials and water at a close range. Why?


Passive Tags: Near Field• 2.different approaches to transfer power from the reader

to passive tags: near field and far field

Near field • Passive RFID interaction based upon electromagnetic

induction. • Explain how this works here


Passive Tags: Far Field

• Why can’t electromagnetic induction be used?

• So how does far field RFID interaction work?


Business Use of Annotation

• Physical artefact annotation is often driven by business goals.

• Uniquely identify objects from manufacture during business processes


Personal use of Annotation

• Tags are less specific, deterministic, multi-modal (using multiple sensory channels) using multimedia.

• Subjective annotations are used in multiple contexts, multiple applications and multiple activities by users.

• Semantic gap challenge: between the low-level object features extracted and their high-level meaning with respect to a context of use

• Several projects to tag personal views of physical world– MyLifeBits

– Semacode

– Google Earth? But Is it personalised?

– etc


Personal use of Annotation: Semacode

• Semacode (2005) propose a scheme to define labels that can be automatically processed from captured images and linked to a Web-based spatial information encyclopaedia.

• How does a semacode encodes URLs?? • How to create a semacodes?• How do read a Semacaode • Some management may be needed to control malicious

removal, movement and attachment.

Ubiquitous computing: smart devices, environments and interaction36

Semacode Use


Convert URL to visual code

Web Page

Photograph (Read Code)

get

post

Attach to physical world

Convert URL to visual code

Web Page

Photograph (Read Code)

get

post

Attach to physical world

Phone

Overview• Introduction• Tagging the Physical World• Sensors and Sensor Networks • Micro Actuation and Sensing: MEMS• Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks)• Robots


Sensors: introduction• Sensors are transducers that convert some physical

phenomenon into an electrical signal

• Wireless sensors:

• Sensors can be networked – sensor nets


Sensor ApplicationsGive some examples of sensor use• Cars• Computers• Retail, logistics:• Household tasks• Buildings• Environment monitoring• Industrial sensing & diagnostics


Sensors TypesSensors can be characterised according to:

• Passive (tags) vs. active

• Single sensors vs sensor arrays vs sensor nets

• Read-only program vs. re-programmable


Sensors versus Tags

• ???



S

S

S

S

SS

S

SSS

S S

S

S

Sensor net

Internet

Access Node

Storage

Distribution field of phenomena that can be detected measured

PhysicalPhenomena

User

Sensors that detect event

Sensors that notify access node

Sensor net

Sensor net

Sensor Nets • Main components of a typical sensor network system are

networked sensors nodes serviced by sensor access node. • Slightly different but compatible view of a sensor network is

to view sensors as being of three types of node): – common nodes– sink nodes– gateway (access)

• In scenario given earlier, some sensors in the network can act as sink nodes within the network in addition to the access node.

• Concepts of sensor node & sensor net can be ambiguous: – A sensor can act as a node in a network of sensors versus there is

a special sensor network server often called a sensor (access) node


Sensor Net: Functions

• The main functions of sensor networks can be layered in a protocol stack according to:– physical network characteristics,

– data network characteristics

– data processing and sensor choreography

• Use small network protocol stack for sensor nets. Why?• Other conceptual protocol layered stacks could also be

used instead to model sensor operation,


Sensor Net: Functions


Event definition & processing

Collaborativeprocessing

Datastorage

Data discovery

Sensor distribution & density

RF , Opticaltransmission characteristics

Sensor to NetworkPhysical environment characteristics

AddressingRouting Intra vs. inter node

In-situprocessing

Internetwork

Data uncertainty

Sensor ElectronicsDSP Power

management

Data processing

Sensors: Electronics

Trans-ducer

AnalogueFilterAmplifier

ADC DSP

Modulator Trans-mitter

Switch

Antenna

ReceiverDemod-ulator

Power management

Battery

Transceiver

StorageProcessing

Sensor

Power

Sensor Net Design: Signal Detection & Processing

Positioning & coverage of networks is important. Why?


Sensor Net Design: Positioning & Coverage

• Given: sensor field (either known sensor locations, or spatial density)– Where to add new nodes for max coverage?– How to move existing nodes for max coverage?

• Can Control– Area coverage:– Detectability:– Node coverage:


Sensor Net Design: Improved SNR Through Using Denser Sensor Nets

• Sensor has finite range determined by base-line (floor) noise level

• Denser sensor field improves detection of signal source within range. How?


Overview

• Overview: Sensor Net Components & Processes• Physical Network: Environment, Density &

Transmission• Data Network: Addressing and Routing • Data Processing: Distributed Data Storage & Data

Queries


Senor Net Design: Sensor Data Routing

• Networking sensors versus networking computers?• Sensors form P2P network with a mesh topology network • Sensors are massively distributed and work in real-time• No universal routing protocols or central registry. • Each node acts a router and application host.


Sensor Routing

• Make sensor address resolution efficient• Data centric routing,

– Directed Diffusion– Flooding– Gossiping

• Routing classification– Network structure: flat, hierarchical, hybrid– By interaction protocol


Sensor Networks vs. Ad Hoc Networks

???


Sensor Net Topologies

• ??


Senor Net Design: In-Network Processing

• Why perform In-Network Processing?

Sensor Node

Sensors


Sensor Net: Data Storage & Retrieval

• What designs/ architectures can we use for sensor net data storage an retrieval?


Sensor Database System

• Characteristics of a Sensor Network:

• Can existing database techniques be reused?



Sensor Net: Technologies, Kits & Standards

• Sun Spot: Java• Berkeley Motes: TinyOS, C• SPINE (Signal Processing in Node Environment)• OGC Standards: SensorML etc


Overview• Introduction• Tagging the Physical World• Sensors and Sensor Networks• Micro Actuation and Sensing: MEMS • Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks)• Robots


Micro Actuation and Sensing: MEMS

• Fabrication• Micro-Actuators• Micro-Sensors• Smart Surfaces, Skin, Paint, Matter and Dust• Downsizing to Nanotechnology and Quantum Devices


Trend: Miniaturisation

• Electronic components become smaller, faster, cheaper to fabricate, lower power & lower maintenance, they can be more easily deployed on a massive and pervasive scale.

• MicroElectro Mechanical Systems (MEMS) are based upon IC Chip design

• Possibilities for miniaturization extend into all aspects of life, & potential for embedding computing & comms technology quite literally everywhere is becoming a reality.

• IT as an invisible component in everyone's surroundings• Extending the Internet deep into the physical environment


Trend: IC Transistor Density

• Gordon Moore (1965), Intel co-founder made a prediction, now popularly known as Moore's Law, which states that the number of transistors on an IC chip doubles ~ every 2 y

• Does it mean that software processing capability will also increases in this way?

• IC Chip density = Software Performance?


MEMS: Introduction• MEMS (Micro-electromechanical systems): micron- to

millimetre-scale electronic devices fabricated as discrete devices or in large arrays

• MEMS perform 2 basic types of functions: sensors or actuators.

• Both act as transducers converting one signal into another.

• MEMS actuators: electrical signal -> physical phenomena to move or control mechanisms.

• MEMS Sensors work in reverse to actuators


Electrostatic motor

Actuator

Hinge

MEMS ExamplesGyroscope


MEMS: Fabrication• MEMS comprising mechanical and discrete electronic

components• MEMS design is different from macro devices • MEMS design are based upon IC chips design• Silicon based materials have:

– Well understood electrical properties– Good mechanical properties


MEMS: Fabrication• Design a new circuit = design of interconnections among

millions of relatively simple and identical components. • Diversity and complexity of the interconnections -> diversity

of electronic components including memory chips and CPUs.

• Multiplicity, batch fabrication, is inherent. • Miniaturisation of IC based MEMS processing has

important advantages over macro electromechanical devices and systems?


MEMS : Fabrication• Micromachines are fabricated just like ICs. • MEMS type ICs can be fabricated in different ways using:

– Bulk micro-machining– Surface micro-machining– LIGA deep structures.


Micro-Actuator• Mechanisms involved in micro-actuation whilst conceptually

similar to equivalent macro mechanisms may function fundamentally differently,

• Are engineered in a fundamentally different way using IC


Micro-actuator: Applications• Micro-mirrors, e.g., ??• Micro-fluid pumps, e.g., ??• Miniature RF transceivers, e.g., ??• Miniature Storage devices, e.g., ??• Etc


Micro-sensors• Sensors are a type of transducer• Microsensors can work quite differently from equivalent

macro sensor, • Sensors enable adaptation• Often embedded into system as part of a control loop


MEMS: Applications• Micro-accelerometers,

– E.g., ??

• Micro-gyroscopes – E.g.,

• Detecting Structural Changes– E.g.,


Smart Device Form Factors: Smart Dust, Skins & Clay

• 3 forms proposed by Weiser (1 tabs, 2 pads & 3 boards) can be extended to include 3 more forms:

4. Smart Dust:

5. Smart Skins:

6. Smart Clay:


Smart Dust: MEMS• MEMS can be sprayed into physical environment• E.g., Smart Dust project (Pister, UC,Berkely)• (see Chapter 2)


Smart Skins: MEMS

• MEMS can be permanently attached to some fixed substrate forming – smart surfaces– smart skin

• E.g. Paint that is able to sense vibrations

• See also Organic Displays (Chapter 5)


Smart Clay: MEMS

• Claytronics project• Can behave as malleable programmable matter • Are MEMS ensembles • Self-assembled into any arbitrary 3D shape• Goal to achieve a synthetic reality.


MEMS: Challenges• Establishing ownership of all of these micro items. • Coping with data overload• Different Low-level patterns of signals may be ambiguous

and variable. • Handling context switches between these augmented

environment events via assisted senses and the unassisted ones.

• Are micro-devices either easy to dispose of or hard to dispose of?– What is we swallow / breath them in?

• How to manage MEMS?– See Chapter 12


Nanocomputing• Nanocomputing can be defined as the manipulation,

precision placement, measurement, modelling, and manufacture to create systems with less than 100 nm

• Also referred to as nanotechnology • Is based upon a broader range of materials, mechanisms &

sizes down to molecular level• MEMS Vs. Nanocomputing?


Nanocomputing• The drive to switch transistors faster and to be low-

powered has been to make them smaller. • When electronic components approach nanometer sizes,

odd things begin to happen. What?

• This raised an early concern about the feasibility of nanotechnology.

Other challenges are:• thermal noise• positioning and the control of structures at this level


Nanocomputing• Nanotechnology at first proposed to use a bottom-up

approach to design, to be able to assemble custom-made molecular structures for specific applications,

• A major challenge to this design process is the complexity and novelty in understanding and being able to model materials at this level.

• More research is needed to understand how combinations of materials, in particular compounds, gives materials at the molecular level certain physical and functional properties..


Overview• Introduction• Tagging the Physical World• Sensors and Sensor Networks• Micro Actuation and Sensing: MEMS• Embedded Systems and Real-time Systems • Control Systems (For Physical World Tasks)• Robots


Embedded Systems: Introduction• Is a component in a larger system • Is programmable• Performs a single, dedicated task. • May or may not be visible as a computer to a user of that

system • May or may not have a visible control interface• E.g., ??? • May be local or remote,

– e.g., ??

• fixed or mobile – e.g??


Embedded System Characteristics (Embedded vs. MTOS Systems)

Traditionally, embedded systems differ from MTOS systems

OS of Embedded systems differ vs. MTOS system

1. Specialised to single task enactment (ASOS)

2. Actions on physical world tasks are often scheduled with respect to real-time constraints (RTOS)

3. Safety-criticality is considered more important


Embedded vs. MTOS Systems

• Often have constraints concerning power consumption• Often are designed to operate over a wide-range of

physical environmental conditions compared to PC – e.g.,

• Often operate under moderate to severe real-time constraints.

• System failures can have life-threatening consequences.– E.g.,


Embedded vs. MTOS Systems• Each embedded computing devices may be designed for

its own rigidly defined operational bounds – e.g.,

• Linking embedded systems to external systems • Designs often engineered for a trade-off • Fewer system resources then PC. How?• Embedded systems not always easy to programme. Why?• Most embedded designs (hardware & software) are unique • Use a far simpler & cheaper OS & hardware. Why?


Embedded Systems: Hardware• Microprocessors

• Microcontroller

• FPGA (Field Programmable Gate Arrays):


Real-Time System (RTS)

• Real-time systems (RTS) can be considered to be resource-constrained

• Often RTS perform safety-critical tasks

• RTS reacts to external events that interrupt it:

• RTS uses mechanisms for priority scheduling of interrupts • RTOS may also use additional process control:

– .


RTS Design Concerns

• There are a range of real-time design concerns to support critical response time of a task:–

• Need to optimise – both response time and data transfer rate – optimising these when there are simultaneous tasks.

• Key factors that affect the response time are?– process context-switching– interrupt latency


RTS: Hard vs. Soft• Timeliness is single most important aspect of RT system. • RTS system is one where timing of result is just as

important as the result itself. • A correct answer produced too late is just as bad as an

incorrect answer or no answer at all. • RTS correctness of computations not only depends upon

the logical correctness of the computation but also upon time to produce results.

• If the timing constraints are not met, system failure occurs• Timing constraints can vary between different real-time

systems. • Therefore, RTS can fall into one of three categories: soft,

hard or firm.. Ubiquitous computing: smart devices, environments and interaction 89

RTS: Soft

• Single computation arriving late may not be significant to the operation of the system,

–

• Although many late arrivals might be significant• Timing requirements can be defined by using an average

response time.


RTOS: Hard

• Timing requirements are vital. • Response that’s late is incorrect and system failure results. • Activities must complete by specified deadline, always. • Different types of deadlines. What?

• If a deadline is missed the task fails– E.g., ??

• This demands that the system has the ability to predict how long computations will take in advance.


Safety-Critical Systems

• Instructors could add some text here or delete this slide.


Overview• Introduction• Tagging the Physical World• Sensors and Sensor Networks• Micro Actuation and Sensing: MEMS• Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks) • Robots


Links to other Topics

• Control systems / robots can be simple, operate in static deterministic environments.

• To operate in more dynamic non- deterministic environments, they can make use of AI techniques (Chapters 8-10).

• HCI aspects of (biologically inspired) robots such as affective computing etc (Chapter 5)


Control Systems (For Physical World Tasks)

• Simply type of control – Activated only when defined thresholds are crossed,– e.g., .

• Disadvantages?–

• Solutions?–


Control Systems: Feedback Control

• 2 basic kinds of feedback: – negative

– positive

Negative feedback • Seeks to reduce some change in a system output or state• Based upon derivative of output • Which is then used to modify input to regulate output.• Several types of feedback control: D, P, I, PID

Positive feedback• Acts to amplify a system state or output


Control Systems: Derivative (D) Feedback Control


Controller DAC & Drive

ADC

Plant∑

Reference Value r(t)

Output o(t)Error

e(t)=r(t)–f(t)

Feedback

+

-

Input i(t)

Control System

Transducerf(t)

Control Systems: Proportional (P) Feedback Control

• In simple proportional (-ve feedback) control system• Action taken to negatively feedback a signal to the plant, • Is in proportion to the degree the system diverges from the

reference value• This leads to a much smoother regulation

– e.g.,.


Proportional g.e(t)e(t)

P Controller

Control Systems: PID Controllers• Sometimes P type controller output is not regulated

correctly– e.g., ??

• To solve this problem either integral or differential control or both can be added to the control.

• PID controller is so named because it combines Proportional, Integral and Derivative type control

• Proportional (P) controller is just the error signal multiplied by a constant and fed out to a hardware drive.


Control Systems: PID Controllers

• Integral (I) controller deals with past behaviour of control. –

• Derivative (D) type controller is used to predict the plant behaviour

• P, PI, PD or PID control are often simple enough, to be hard-coded into controllers

• Usually support some adjustment controls, – e.g.,

• PID controllers can be designed to be programmable


PID Controllers


e(t)

Integral

Derivative

∑+

+

-f(t)

Proportional

PID Controller

Programmable Controllers: Microcontrollers

• Hardware architecture of microcontrollers is much simpler than general purpose processor mother-boards in PCs?

• I/O control support can be simpler as there may not be any video screen output or keyboard input.

• Micro-controllers can range in complexity• Originally, programmed in assembly language, later in C • Control programs often developed in an emulator on a PC• More recent microcontrollers can be integrated with on-chip

debug circuitry accessed by an in-circuit emulator


Complex Control Systems

• PID control Useful for coarse-gained, static control– E.g., palletising, coarse-controlled locomotion, etc

• PID control not suitable for ?– fine-grained– dynamic control– uncertainties in control


Complex Control

• Several sources of uncertainty?

• Techniques for controlling uncertain systems?


Overview• Introduction• Tagging the Physical World• Sensors and Sensor Networks• Micro Actuation and Sensing: MEMS• Embedded Systems and Real-time Systems• Control Systems (For Physical World Tasks)• Robots


Robots• Early 1960s, robots started to be used to automate

industrial tasks particularly in manufacturing

Why Automate?


Main Robot ComponentsRobots consist of: • End effectors or actuators:• Locomotion:• Drive:• Controller• Sensor


Robots: Localisation• Localisation is used to determine a robot’s position in

relation to its physical environment. • Localisation can be local or global. • Local localisation is often simpler in which a robot corrects

its position in relation to its initial or other current reference location.

• Global localisation is discussed more in context-aware systems part.


Robots: Types

3 Main Types• Robot manipulator or robot arm• Mobile robots• Biologically inspired robots


Robot Manipulators• A manipulator consists of a linked chain of rigid bodies that

are linked in an open kinematic chain at joints. • rigid body can have up to 6 Degrees Of Freedom (DOF) of

movement. • This comprises 3 translational DOF

– ???


Robot Manipulators• Also comprises 3 rotational DOF

– ???

• Joints are designed to restrict some DOF. • Human operators may be in the control loop of robot

manipulators. Why?


Robot Manipulators: Design

• Motion planning needed• Control algorithms?• Regulation of contact force• Manipulators need to cope with variations in components

and objects being manipulated. Solutions?– Use adaptive AI techniques (Chapter 8)

– Put human in the control loop


Mobile Robots• Mobile robots use various kinds of locomotion systems

– ?

• Simplest types of mobile robots to control – ??

• In dynamic non-deterministic environments, control is more complicated–

• A more complex, well-known & highly successful use of mobile robots was Mars Explorer Robots–


Mobile Robots• No. of DOF is often less compared to a robot manipulator.

–

Need ways to navigate obstacles?• Simple approach: use collision detection

• More complex approach: anticipate & avoid collisions– Need environment models (AI, Chapter 8)– Need to replan paths to reach goal destinations (AI, Chapter 8)


Biologically Inspired Robots: Legged locomotion

• Biologically inspired robots are more complex type of robot – Combines legged locomotion capabilities & manipulator

• 2 main focuses to these robots: – Legged locomotion (in combination with manipulator)– Human-Robot Interaction


Biologically Inspired Robots: Legged locomotion

• The use of legs enables legged robots to travel over irregular terrain

• Biped robots often have more DOF than either the mobile robot or robot manipulator

• Particular design challenge for biped robots is stability–


Biologically Inspired Robots: Human Robot Interaction

• Human robot Interaction:– a specialisation of HCI, see Chapter 5

• Robots can assist humans and extend sensing capabilities of (less able?) humans – Posthuman model.

• Robots can fulfil social roles– i.e., affective computing (Chapter 5)– e.g., artificial pets

• Social guided learning – Learning by imitation or by tutelage

• Use of more human oriented interface & interaction – E.g., speech recognition


Nanobots

• Nanobots can be manufactured as MEMS or at molecular level.

• Microscopic world is governed by the same physical laws as the macroscopic world

• But relative importance of the physical laws change in how it affects the mechanics and the electronics at this scale


Nanobots

• Nature in terms of micro-organisms can be harnessed in order to provide a host body for nanobots to move about – e.g.,

• Shrinking device size to these nano dimensions leads to many interesting challenges:


Developing UbiCom Robot Applications

• Industrial types of robots

• Low cost consumer type robots

• Robots toolkits that are programmable.


Ultrasonic Sensor

Light Sensor

Motor A

Motor B

Motor C




• Task: robot manipulates a Rubik’s Cube to its solved state • Goal: robot performs whole task or guides humans to do it

Design involves • Design: of the robot mechanics

–

• Design: how and when the robot senses state of the world– e.g. ,

• Planning algorithm: to link individual actions

• Overall architecture: to integrate different sub-tasks– e.g.,



Several practical issues for physical robots tasks execution• Sensor accuracy• Position accuracy • Variable amounts of friction during movement• Some elasticity in the robot arm

• Low-level design to tell robots to carry out specific tasks• Tasks need to be designed to fit the robots capabilities• In open physical world, much non-determinism to handle• -> There does not yet exist, flexible general purpose

UbiCom robots, which can act as autonomous assistants or servants for mass human use.


Overview• Introduction • Tagging the Physical World • Sensors and Sensor Networks • Micro Actuation and Sensing: MEMS • Embedded Systems and Real-time Systems • Control Systems (For Physical World Tasks) • Robots


Summary & Revision

For each chapter• See book web-site for chapter summaries, references,

resources etc.• Identify new terms & concepts• Apply new terms and concepts: define, use in old and

new situations & problems• Debate problems, challenges and solutions• See Chapter exercises on web-site


Exercises: Define New Concepts

• Annotation


Exercise: Applying New Concepts


Supplementary Slides

• Exercises & Solutions


Sensor ApplicationsEx: Give some examples of sensor use• Cars: air pressure, brake-wear, car-doors, engine etc• Lap-top: accelerometers – switch off computer disks when

dropped• Retail, logistics: RFIDs• Heaters: thermostats• Infrastructure protection / Intrusion detection (active sensors)• Environment monitoring• Industrial sensing & diagnostics• Battlefield awareness• Sensors can be characterised according to:

– passive (tags) vs. active– Single sensors vs sensor arrays vs sensor nets– Read-only program vs. re-programmable


ubicom book slides 1 ubiquitous computing: smart devices

Technology

smart devices

physical objects tags

physical environment

physical world objects

characteristics physical

virtual environments

security ubiquitous

analysis ubiquitous