Robotics 5

8/6/2019 Robotics 5

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EXPERT SYSTEMS AND SOLUTIONS

Email: [email protected]

[email protected]

Cell: 9952749533www.researchprojects.info

PAIYANOOR, OMR, CHENNAI

Call For Research Projects Final

year students of B.E in EEE, ECE, EI,

M.E (Power Systems), M.E (Applied

Electronics), M.E (Power Electronics)

Ph.D Electrical and Electronics.

Students can assemble their hardware in our

Research labs. Experts will be guiding the projects.

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Multiagent Control ofModular Self-Reconfigurable

Robots

Tad HoggHP Labs

Hristo BojinovJeremy Kubica

Arancha Casal

PARC·s modular robotics group

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topics� modular robots

� multi-agent control� results

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modular robots� collections of modules

² each module is a robot

� self-reconfigurable² modules can change connections

² so overall robot changes shape

� ´modular self-reconfigurableµ robots² MSR

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why change shape?

� adjust shape to task

� e.g., locomotion² wheel, spider, snake, «

� e.g., manipulation² match ´fingerµ size to object size

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² Proteo

² Prismatic

² future possibilities

� multi-agent control

� results

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Proteo� rhombic dodecahedron

� space filling

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Proteo� modules move over neighbors

each edge of cube is a diagonal of RD face

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² Proteo

² Prismatic



� results

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prismatic MSR robots� modules connect via arms

� extending arms moves neighbors� examples² Crystalline robot (Dartmouth)

� moves in 2 dimensions

² TeleCube (PARC)� moves in 3 dimensions

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TeleCube� cubes

� 6 independent arms

� 2:1 length ratio

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neighbors cooperate to move

expandcontract

physical movevirtual move

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² Proteo

² Prismatic



� results

actual modules

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devices for ´smart matterµ� micro-electomechanical (MEMS)

� bacteria

� molecular

� quantum

sensor + computer + actuatorsensor + computer + actuator

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micromachines (MEMS)� made with photolithography

² e.g., programmable force fields (open loop)

� hard to assemble

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biological machines� biotechnology: program bacteria

² e.g., T. Knight, R. Weiss at MIT AI Lab

� limited abilities

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programs for bacteria� gene regulatory networks

� engineered changes give someprogram control over behavior

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molecular machines� ribosomes:

² make proteins in cells

� protein motors² move material in cells

² ATP synthase rotor� size: 10nm

DNA mRNA

protein

See Nature, 386, 299 (1997)

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molecular machines� carbon nanotubes and buckyballs

² strong, light, flexible, electronic devices

² easy to make

² hard to arrange

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molecular machines� complex molecules for robot parts

� currently:² only theory

² hard to make

² hard to assemble

� potential: cheap, fast, strong parts

example medical applications:

R. Freitas, Jr., Nanomedicine, 1999

example designs:

E. Drexler , R. Merkle, A. Globus

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quantum computers� potential: much faster algorithms

² e.g., factoring

� very difficult to build

amplitudes while solving a

10-variable 3-SAT instance

with 3 solutions

quantum search heuristic

Java demo: www.hpl.hp.com/shl/projects/quantum/demo

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quantum machines� potential: detail control over materials

² e.g., interfere two ways to absorb light =>transparent

� very difficult to build

See T. Hogg and G. Chase, Quantum smart matter , 1996www.arxiv.org/abs/quant-ph/9611021

S. Lloyd and L. Viola, Control of open quantum systems dynamics, 2000

www.arxiv.org/abs/quant-ph/0008101

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quantum machines� example: coin weighing puzzle

² quantum sensor finds bad coin in single try

See B. Terhal, J. Smolin, Single quantum querying of a database, 1997

www.arxiv.org/abs/quant-ph/9705041

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devices: summary� smaller devices

² harder to make

² harder to connect, assemble

² greater potential capability

� but need many, cheap devices

� statistical or systems view

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challenge: How

to build?� physical/engineering constraints

² unreliable parts

² misconnected

� limits early technology

� economics² build cheaply

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challenge: How

to use?� information/computational constraints

² limited, changing info from environment

² computational complexity� e.g., planning optimal device use

� limits even mature technology

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� results

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control challenge� coordinate many modules

� sensor & actuator errors� decompose programming task to only need

² local info (small scale)

² high-level task description (large scale)

� e.g., grasp object of unspecified shape

cf., H. Simon: nearly decomposable systems

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control before hardw

are?� many, small modules don·t yet exist

² hence, hardware details unknown

² but can study general issues

� control may simplify hardware design² e.g., manage in spite of defects

² identify compute/communicate tradeoffs

sensor + computer + actuatorsensor + computer + actuator

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physics vs. sizegravity

friction

Brownian motion

thermal noise

decoherence

faster

smaller

harder to build

MEMS

molecular

quantum

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multi-agent control� matches control to physics

² different agents for each scale

� matches control to available info² rapid response to local info

² manager agents: overall coordination� without need for details

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motivation: biologysocial insects, multicellular organisms, ecology

reliable behavior from unreliable parts

cf. incentive issuesnoncooperative agents

economics, common law, «

examplestermite moundsembryo growth

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motivation: teams� robot soccer

� insect-like robot teams² e.g., foraging

� MSR robots have² tighter physical constraints

² direct access to neighbor locations� e.g., no need for vision to find neighbors

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� results² computational ecology

² Proteo

² Telecube

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computational ecology� dynamical behavior of simple agents

² asynchronous, local decisions

² delays, imperfect information

² ´mean-fieldµ statistical theory² see B. Huberman, The Ecology of Computation, 1988

� apply to actual robot behaviors² see K. Lerman et al. in Artificial Life, 2001

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techniques� finite-state machine for each module

² simple script, some randomness

� local communication² create gradients through structure

� ´scentsµ

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² Proteo

² Telecube

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example: grow

ing a chain� modes:

² SLEEP, SEARCH(red), SEED( yellow), FINAL(white)

² initially: all in SLEEP, randomly pick one SEED

� seed:² picks growth direction

² emits scent� attracts modules

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grow

ing a chain

SLEEP

SEARCH SEED

FINAL

descend gradient

+ propagate scent emit scent=0

if neighbor is seed

if neighbor

became seed

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scent

� set S=min(neighbors)+1

� move around neighbor until lower

value found� if seed found: become new seed

SEEDS = 0

S-1S

S+1

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structures� recursive branching

² multilevel arms

� grow around object² using contact sensors

See H. Bojinov et al., Multiagent Control of

Self-reconfigurable Robots, 2000

www.arxiv.org/abs/cs.RO/0006030

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² Proteo

² Telecube

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locomotion� make snake shape� move toward goal

² barrier� follow wall� find gap

� higher-level control: general direction² building on low-level agent behavior

see: Kubica et al, Proc. ICRA 2001

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object manipulation� exert forces to move

object

² based on contact withobject

² ´scentµ recruits

other modules� modules on surfaceform rigid shell

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summary� simple agents perform basic tasks

² reconfiguration

² locomotion

² manipulate objects

� apply to different hardware types

² Proteo: surface motions² TeleCube: internal motions

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future directions� quantify capabilities

� design more complex behaviors

� implement on hardware

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quantify capabilities� examples of agent-based control

² are only specific instances

� quantify² how robust?

² how accurate?

² what cost?� e.g., power use

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agent design� combine with higher-level agents

² e.g., switch among low-level behaviors

� automate agent design² e.g., genetic algorithms (FXPAL)

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test on hardw

are� various existing robots

² few, fairly large modules

� large number of tiny modules² don·t yet exist

� wait for hardware vs. simulate?² understand likely hardware capabilities

² e.g., MEMS, «

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conclusions� agent-based control for MSR robots

² gives robust low-level behaviors

² simplifies higher-level task control

� biological system models² suggest module rules

² useful even if not biologically accurate

www.hpl.hp.com/shl/people/tad

Robotics 5

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