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Swarm Intelligence – W12:Segregation, Aggregation, and Decision Processes I:
Social Insects, Sensor Networks, and Multi-Robot
Systems
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Outline• An introduction to WSN• Collective decisions
– Common pattern– Natural examples– Mixed society examples– Artificial examples
• Aggregation and Segregation– Natural examples– Artificial examples
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An Introduction to Wireless Sensor
Networks
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Selected Slides from MOBICOM 2002 Tutorial T5
Wireless Sensor NetworksDeborah Estrin & Mani [email protected], [email protected]
UCLA
Akbar [email protected]
University of Wisconsin, MadisonCopyright © 2002
Intro
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Embedded Networked Sensing Potential• Micro-sensors, on-board
processing, and wireless interfaces all feasible at very small scale– can monitor
phenomena “up close”
• Will enable spatially and temporally denseenvironmental monitoring
• Embedded Networked Sensing will reveal previously unobservable phenomena
Seismic Structure response
Contaminant Transport
Marine Microorganisms
Ecosystems, Biocomplexity
Source: D. Estrin, UCLA
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Motivating Applications
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App#1: Seismic• Interaction between ground motions and
structure/foundation response not well understood.– Current seismic networks not spatially
dense enough to monitor structure deformation in response to ground motion, to sample wavefield without spatial aliasing.
• Science– Understand response of buildings and
underlying soil to ground shaking – Develop models to predict structure response
for earthquake scenarios.• Technology/Applications
– Identification of seismic events that cause significant structure shaking.
– Local, at-node processing of waveforms.– Dense structure monitoring systems.
• ENS will provide field data at sufficient densities to develop predictive models of structure, foundation, soil response.Source: D. Estrin, UCLA
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Field Experiment
⎜⎯⎯⎯⎯⎯⎯⎯ 1 km ⎯⎯⎯⎯⎯⎯⎜
• 38 strong-motion seismometers in 17-story steel-frame Factor Building.• 100 free-field seismometers in UCLA campus ground at 100-m spacing
Source: D. Estrin, UCLA
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App#2: Contaminant Transport• Science
– Understand intermedia contaminant transport and fate in real systems.
– Identify risky situations before they become exposures. Subterranean deployment.
• Multiple modalities (e.g., pH, redox conditions, etc.)
• Micro sizes for some applications (e.g., pesticide transport in plant roots).
• Tracking contaminant “fronts”.• At-node interpretation of
potential for risk (in field deployment).
Soil Zone
Groundwater
Volatization
SpillPath
Air Emissions
Dissolution
Water Well
Source: D. Estrin, UCLA
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Contaminantplume
ENS Research Implications
• Environmental Micro-Sensors– Sensors capable of
recognizing phases in air/water/soil mixtures.
– Sensors that withstand physically and chemically harsh conditions.
– Microsensors.• Signal Processing
– Nodes capable of real-time analysis of signals.
– Collaborative signal processing to expend energy only where there is risk.Source: D. Estrin, UCLA
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App#3:Ecosystem MonitoringScience• Understand response of wild populations (plants and animals) to habitats
over time.• Develop in situ observation of species and ecosystem dynamics.
Techniques• Data acquisition of physical and chemical properties, at various
spatial and temporal scales, appropriate to the ecosystem, species and habitat.
• Automatic identification of organisms(current techniques involve close-range human observation).
• Measurements over long period of time,taken in-situ.
• Harsh environments with extremes in temperature, moisture, obstructions, ...
Source: D. Estrin, UCLA
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WSN Requirements for Habitat/Ecophysiology Applications
• Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm - 100 m), and temporal sampling intervals (1 ms -days), depending on habitats and organisms.
• Naive approach → Too many sensors →Too many data.– In-network, distributed information processing
• Wireless communication due to climate, terrain, thick vegetation.
• Adaptive Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment.
• Mobility for deploying scarce resources (e.g., high resolution sensors).
Source: D. Estrin, UCLA
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Enabling Technologies and Challenges
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Enabling Technologies
Embedded Networked
Sensing& Actuation
Control system w/Small form factorUntethered nodes
ExploitcollaborativeSensing, action
Tightly coupled to physical world
Embed numerous distributed devices to monitor and interact with physical world
Network devices to coordinate and perform higher-level tasks
Exploit spatially and temporally dense, in situ, sensing and actuation
Source: D. Estrin, UCLA
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Sensor Node Energy Roadmap
20002000 20022002 20042004
10,00010,000
1,0001,000
100100
1010
11
.1.1
Ave
rage
Pow
er (m
W)
• Deployed (5W)
• PAC/C Baseline (.5W)
• (50 mW)
(1mW)
RehostingRehosting to Low to Low Power COTSPower COTS(10x)(10x)
--SystemSystem--OnOn--ChipChip--Adv Power Adv Power ManagementManagementAlgorithms (50x)Algorithms (50x)
Source: ISI & DARPA PAC/C Program
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Communication/Computation Technology Projection
Assume: 10kbit/sec. Radio, 10 m range.Assume: 10kbit/sec. Radio, 10 m range.
Large cost of communications relative to computation Large cost of communications relative to computation continuescontinues
1999 (Bluetooth
Technology)2004
(150nJ/bit) (5nJ/bit)1.5mW* 50uW
~ 190 MOPS(5pJ/OP)
Computation
Communication
Source: ISI & DARPA PAC/C Program
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New Design Themes• Long-lived systems that can be untethered and unattended
– Low-duty cycle operation with bounded latency– Exploit redundancy and heterogeneous tiered systems
• Leverage data processing inside the network– Thousands or millions of operations per second can be done using energy of
sending a bit over 10 or 100 meters (Pottie00)– Exploit computation near data to reduce communication
• Self configuring systems that can be deployed ad hoc– Un-modeled physical world dynamics makes systems appear ad hoc– Measure and adapt to unpredictable environment– Exploit spatial diversity and density of sensor/actuator nodes
• Achieve desired global behavior with adaptive localized algorithms– Cant afford to extract dynamic state information needed for centralized
control
Source: D. Estrin, UCLA
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From Embedded Sensing to Embedded Control
• Embedded in unattended “control systems”– Different from traditional Internet, PDA, Mobility applications – More than control of the sensor network itself
• Critical applications extend beyond sensing to control and actuation– Transportation, Precision Agriculture, Medical monitoring and
drug delivery, Battlefied applications– Concerns extend beyond traditional networked systems
• Usability, Reliability, Safety• Need systems architecture to manage interactions
– Current system development: one-off, incrementally tuned, stove-piped
– Serious repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scalability...
Source: D. Estrin, UCLA
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Sample Layered Architecture
Resource constraints call for more tightly integrated layers
Open Question:
Can we define anInternet-like architecture for such application-specific systems??
In-network: Application processing, Data aggregation, Query processing
Adaptive topology, Geo-Routing
MAC, Time, Location
Phy: comm, sensing, actuation, SP
User Queries, External Database
Data dissemination, storage, caching
Source: D. Estrin, UCLA
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WSN and SI
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Two Main Application Categories
C1: Low-duty cycle continuous sampling (e.g., temperature/humidity field monitoring over years)
C2: Event-based monitoring (e.g., human, animal species monitoring, tracking) →probably the most appropriate one for SI algorithms since collaboration in a dynamic environment emphasized
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WSN and SI• SI principles might have impact because:
– Volume/mass constraints and therefore limited resources at the individual node level
– Large number of nodes– Autonomy– Collaboration among nodes– Self-organization
• And might not have impact because:– Most of them assume underlying mobile systems– Most of them do not exploit direct communication– Most of them rely on full distributedness and often
centralization and synchronization means energy saving
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Pointers on WSN• Mobicom 02 tutorial:
http://nesl.ee.ucla.edu/tutorials/mobicom02/• Course list:
http://www-net.cs.umass.edu/cs791_sensornets/additional_resources.htm• TinyOS:
http://www.tinyos.net/• Smart Dust Project
http://robotics.eecs.berkeley.edu/~pister/SmartDust/• UCLA Center for Embedded Networking Center
http://www.cens.ucla.edu/• Intel research Lab at Berkeley
http://www.intel-research.net/berkeley/• NCCR-MICS at EPFL and other Swiss institutions
http://www.mics.org
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Collective Decisions
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Modeling
Individual behaviorsand local interactions
Global structuresand collective
decisions
• Local rules and appopriate amplification and/or coordination mechanisms can lead to collective decisions
• Modeling to understand the underlying mechanisms and generate ideas for artificial systems
Ideas forartificialsystems
Understanding Collective Decisions
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Choice occurs randomly
(Deneubourg et al., 1990)
Example 1: Selecting a Path (W2)
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Example 2: Selecting a Food Source (W2)
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• Leurre: European project focusing on mixed insect-robot societies (http://leurre.ulb.ac.be)
• Relevant Leurre partners for the presented work:
• ULB (coordinator, modeling, experimental work with insects/robots)
• Uni Rennes/CNRS (chemistry)• EPFL – ASL/LSRO (mobile robots design
and development)• EPFL – SWIS (vision-based tracking and
modeling)
Example 3: Selecting a Shelter
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[Halloy et al., Science, Nov. 2007]
The LEURRE Project: OverviewGoal of the project: quantitatively characterize the self-organized collective decision-making process of a cockroach society by unravelling and influencing the local interaction rules
• A simple decision-making scenario: 1 arena, 2 shelters
• Shelters of the same and different darkness
• Groups of pure cockroaches (16), mixed robot+cockroaches (12+4)
• Infiltration using chemical camouflage and statistical behavioral model
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The LEURRE Project: Sample ResultsLegend: mixed, pure; shelter 1, shelter 2
A . Symmetric shelters (infiltration) B. Asymmetric shelters (active control)
Macroscopic Model
Experiment
Experiment
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The EPFL Tools for the LEURRE Project3. Multi-level modeling
Ss Sa
1
1
( 1) ( ) ( ) ( 1) ( ) ( ) ( )
( ) ( ) ( 1) ( )
join joinj j r s j j
leave leavej j
N k N k p N k j p j N k p j N k
p j N k j p j N k j−
+
⎡ ⎤+ = + − −⎣ ⎦− + +
Module-based microscopic model
Agent-based microscopic model
Macroscopic model
[Correll et al., ALife J., in prep.]
Ss SaSs Sa
Target system
1. Robots as a flexible, interactive, societal “microscope”
[Asadpour et al., ARS J., 2006]
2. A marker-less vision-based tracking tool: SwisTrack
[Correll et al., IROS 2006]http://sourceforge.net/projects/swistrack
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Example 4: Selecting a Shelter[Garnier et al., IEEE-SIS 2005]
• Alice II robots, 1 m arena size• 10 robots group size• Aggregation algorithm inspired by
cockroaches• 14+ cm shelter is able to host the whole
populationDifferent shelters
Shelter size [cm]
Equal sheltersDifferent shelters
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Example 5: Selecting a Direction
Converging on the direction of rotation (clockwise or anticlockwise):• 11 Alice I robots• local com, infrared based• Idea: G. Theraulaz (and A. Martinoli); implementation: G. Caprari, W. Agassounon
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Object Aggregation and Segregation -
Natural Systems
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Cemetery Organization in the Ant Messor Sancta
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Reduction of the spread of infection? Chretien (1996)
Corpse Aggregation in the Ant Messor Sancta
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It defines a class of mechanisms exploited by social insects to coordinate and control their activity via indirect interactions and signs left in the environment.
• Stigmergic mechanisms can be classified in two different categories: quantitative (or continuous) stigmergy and qualitative (or discrete) stigmergy
Stimulus
Answer
S1
R1
S2
R2
S3
R3
time
S 4
R4
S 5
R5
Stop
Definition
Stigmergy
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A Microscopic Model of Corpse Aggregation
(Deneubourg et al. 1991)Characteristics of the algorithm for individual behavior
• When an ant encounters a corpse, it will pick it up with a probability which increases with the degree of isolation of the corpse
• When an ant is carrying a corpse, it will drop it with a probability which increases with the number of corpses in the vicinity
• Modulation of pick up/drop probabilities as a function of the pheromone clouds around the cluster -> quantitative (continuous)stigmergy
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The probability that an agent which is not carrying an item will pick up an item
ppick-up =k1
k1 + f
Probability that an agent carrying an item will drop the item
pdrop =f
k2 + f
f: fraction of neighborhood sites occupied by object o (perceived stimulus)k1, k2: threshold constants
Algorithm for individual behavior
( )2
)2(
A Microscopic Model of Corpse Clustering
f
ppick-up1
0f = k1
0.25
f
1
00.25
pdrop
f = k2
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Example in StarLogo (Termites)
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Key ideas:• A sorting problem implies two or more objects
to be manipulated• Sorting = aggregation of the same type of
object and separation of different types of objects in different clusters
• Clustering = special case of aggregation and separation with one object type (so no separation)
From Clustering to Sorting
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t = 15
t = 0
Short term memoryat t = 15
Probability of picking up an object of type i:
Probability of dropping an object of type i which is being carried:
f(oi): fraction of neighboring sites occupied by objects of the same typeof the object oi
k1, k2 : threshold constants
ppick-up(oi)= ( k1 / (k1 + f(oi) ) ) 2
pdrop(oi)= ( f(oi) / ( k2 + f(oi) ) 2
Individual behavioral algorithm
Clustering & Sorting
In this algorithm f(oi) are estimated based on a short-term memory tuned to the underlying random walk!
Perceptual field of an ant
(Deneubourg et al. 1991)
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Results of the C&S Algorithm
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What do these Simple Microscopic Models Explain?
• Explanation of cemetery organization in Lasius niger, Pheidole pallidula, and Messor Sancta ant species ok
• Explanation of brood sorting in Leptothorax ants (concentric annular sorting) unexplained! → a further mechanism should be involved → work with robots at UWE (Nigel Franks, Chris Melhuish) provide a better explanation (though not a definitive one probably)
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Object Aggregation and Segregation -
Artificial Systems
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The mission:From local actions to global tasks: stigmergy and collective robotics.
The plan:• Give the robot some means of moving some discrete items.• Give it a start by enabling it to make small clusters.• Think of some way of estimating local density so that it can use
the Deneubourg algorithm to make progressively larger clusters.
Solution:• Use quantitative stigmergy via physical cluster size as positive
feedback; positive feedback “embedded” in the system design
Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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The behavior:
Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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How does it works?• adding a puck to a cluster increases its size • taking a puck from a cluster reduces its size
• probability of leaving a puck on a cluster increases with the size of the cluster• probability of taking a puck from a cluster decreases with the size of the
cluster→ so rate of growth increases with size and the feedback is always positive
• The sum of the rates of growth over all clusters will be zero (conservation of pucks)
• Therefore the rate of growth of at least the smallest cluster must be negative• Probability of creating a new cluster in the middle of the arena and split a
cluster is zero• So the # of clusters is monotonically decreasing (but not strictly
monotonically decreasing) over time
Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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Why a single cluster?
• Clusters of 2 or less pucks are irreversibly eliminated (“sharper”, more discontinuous function than that used by ants in the lower number of objects)
• Noise play a major role: good randomness in trajectories and “mixing up” through local interactions → asymmetric noise on the cluster size → the higher the ratio robots/pucks the higher the noise amplitude on cluster size
• Without microscopic noise the clustering process would risk to get stuck in a multi-cluster situation (as in the case of ant experiments)
Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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Conclusion• Robots can form clusters using a simpler algorithm than that proposed
by Deneubourg for ants: – strictly local sensing without memory still work– no need of modulation of pick-up/dropping probabilities via an additional
modality (in ants, chemical communication); local stochasticity (summarized in agent probabilistic response in biological models) achieved “for free” via direct “mechanical interaction” with the objects
• The ant algorithm is robust: even when run on completely different platforms such as robots, it shows coherent behavior; it is robust to different underlying randomness forms, individual failures, interferences, and environmental disturbances.
• The algorithm could be improved in its efficiency … moving beyond natural frontiers on specific tasks!
Puck Clustering (Beckers, Holland, and Deneubourg, 1994)
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Puck Clustering Modeling• Same microscopic modeling method explained in week 8;
no free parameters• From controller blueprint + geometrical considerations,
calculate the probabilities of finding teammates, clusters of a given size, walls and those of shaving pucks from a given cluster
• Consider typical delays for obstacle avoidance or manipulation maneuvers
• Different from stick pulling: environment is characterized by more states, more variety of interaction with a cluster than with a stick (dependent on the approaching angle)
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Cluster Interaction ProbabilitiesReal robots Interaction Probabilities
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Results using a Microscopic Model• [Martinoli, Ijspeert, Gambardella, 1999]• No macroscopic model, no extension for segregation (yet …)
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Frisbee Clustering and Sorting(Holland and Melhuish, 1998)
Bio-mimicking experiment: Leptothorax ants
• Live in cracks in rocks• Sort their brood• Perform nest migrations:
- find a new nest site- move the queen and brood there- build a surrounding wall- sort the brood
• Because of the 2D habitat and the interesting behaviors, ideal subjects for representing in a land-robotic form ... but they build with particles of grit thesame width as their bodies (“blind bulldozing”)... so we need ‘building materials’the same width as the robots – Frisbees
• Arena’s area is 1760 times the area of a robot: same order of magnitude as the ratio of the area of a Leptothorax nest to a single ant.
• Qualitative/feasibility rather than quantitative/efficiency study
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The Robots
• 23 cm in diameter
• 3h battery autonomy
• Motorola 68332, 16 Mb RAM
• 4 continuous emitting IR proximity sensors
• Microswitch on the gripper (threshold between 1 and 2 frisbees), locking-unlocking frisbee operations controlled by an actuator
• Double optical sensor (color detection) for center and periphery color detection of the carried frisbee
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Experimental set-up
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 1: behavior for basic clustering
Rule 1:if (gripper pressed & Object ahead) then
make random turn away from object
Rule 2:if (gripper pressed & no Object ahead) then
reverse small distance make random turn left or right
Rule 3:go forward
Modified Beckers et al. basic behavior for rigid walls/robots!
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 1: Results for basic clustering (10 robots, 44 frisbees)
8h 25 min, end criterium:90% offrisbees gathered(40)
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 1: comparison with performance Beckers et al. 94
• Beckers et al. 1994: 81 pucks and 3 robots in 1h 45 min
• Holland et al. 1998: 40 frisbees and 10 robots in 8h 25 min
Why? No quantitative comparison, modeling up to date …
• Different arena’s area, different robot speed
• Cluster of 1 object (Holland ‘98) vs. cluster of 2 objects (Beckers ‘94) irreversibly removed
• Noise in cluster shape (compacity of the clusters)
• …
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Could we form clusters at the edges of the arena?
"It must be emphasized that a very large arena was necessary in Deneubourg et al's experiments to obtain "bulk" clusters: in effect, ants are attracted towards the edges of the experimental arena if these are too close to the nest, resulting in clusters almost exclusively along the edges."Eric Bonabeau, 1998
Test:• Vary the algorithm ok• Vary the sensors ok• Vary the arena size?
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Boundary clustering, algorithm
Rule 1:if (gripper pressed & Object ahead) then
with probability pmake random turn away from object
else with probability (1-p)reverse small distance (dropping the frisbee)make random turn left or right
Rule 2:if (gripper pressed & no Object ahead) then
reverse small distance (dropping the frisbee)make random turn left or right
Rule 3:go forward
Algorithm modified:
Rule 1 probabilistic!
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Boundary clustering, parameter bifurcationprobabilityof retention p RESULTS
1.0 leads to a central cluster after 6 hours 35 minutes
0.95 leads to a central cluster, stopped when 2 main central clusters formed. Stopped ~2.5hours
0.9 leads to a central cluster, stopped when 2 main central clusters formed. Stopped ~5hours
0.88 1 cluster formed at edge. 40/44 at 9hrs 5m continued to be stable up to 11hrs 20min.
0.85 1 major cluster formed at edge and approx. 15 singletons around the periphery. stopped after 1110hours
0.8 1 major cluster formed at edge and approx. 15 singletons around the periphery. Stopped after 11hrs
0.5 All pucks taken to periphery (frame 8, 0hr40mins)but no single cluster formed Stopped at 11 hrs
0.0 All pucks taken to periphery (frame 3 0hr15m) but no single cluster formed. Stopped at 3hrs.
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Boundary clustering, parameter bifurcation
p = 0.0p = 0.5p = 0.8
p = 0.88p = 0.9p = 1.0
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Boundary clustering, sensory modification
Drop puck/Leave puck
Drop puck/Leave puck
wall
Mean 100.3 0
Pick up/Retain puck
Obstacle detection only with central sensor (instead of the 3 frontal sensors)!
p =100.3/180 = 0.56
But robot movements not really uniformlydistributed (trajectories, no wall following ) …
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 2: Boundary clustering, sensory modification
Similar to p = 0.88 in the algorithmic version!
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 3: The pull back algorithm
Rule 1:if (gripper pressed & Object ahead) then
make random turn away from object
Rule 2:if (gripper pressed & no Object ahead) then
if plain thenlower pin and reverse for pull-back distanceraise pin
endifreverse small distance make random turn left or right
• Initial idea: change the compacity (pull back distance is a sensitive parameter) for speeding up aggregation process
• Robot behavior different with ring or plain frisbees
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 3: The pull back algorithm, results (pullback distance 2.6 frisbees, 6 robots)
t = 0h00 t = 1h45 t = 8h05
Annular sorting like in Leptothorax ants!
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Exp. 3: The pull back algorithm, results (pullback distance 2.6 frisbees, 6 robots, end criterium: first cluster of 20 frisbees)
Trial 1 2 3 4 5
Time in hours 7.58 2.75 25.3 11.7 4.50
Number of plains
11 12 11 10 12
High std dev
Low std dev
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Conclusion• Similarly to Beckers et al. experiment, robots can form clusters using
a simpler algorithm than that proposed by Deneubourg for ants (no memory, only mechanical interaction)
• Similarly to Beckers et al. experiment, the ant algorithm is robust but its efficiency can be improved (cost?, what sensor modalities? com?) Several design choices for achieving the same microscopic (and therefore macroscopic) system behavior are shown: SW (control parameter) vs. HW (different number of sensors) → system modeling!
• Clustering & Sorting more interesting than clustering only from application point of view!
• Our multi-level modeling methodology could provide great guidance for designing an efficient sorting robotic system!
Frisbee Gathering and Sorting(Holland and Melhuish, 1998)
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Robot Aggregation and Segregation
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Robot Aggregation using Wireless Communication - Setup
[Correll & Martinoli, ICRA-CBW07]
• Eg. 2 clusters of 3 robots, 6 of 1 robot• Webots experiments using Alice II with
radio, 12 robots total• Arena diameter: 1 m• Communication range: 10 cm• Simplified com model
Real robotic node: Alice II with Zigbee-compliant com module
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Robot Aggregation using Wireless Communication – Macroscopic Model
Control algorithm (inspired by cockroach experiments of Jeanson et al., 2004)
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Robot Aggregation using Wireless Communication – Sample Results
Microscopic Module-Based model (Webots)• 1500 runs• sample time 10 s• 3 h (10800 s) per run
Macroscopic model
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Conclusion
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Take Home Messages• WSN represent a very promising technology for a number of
application• Commonalities and synergies between swarm robotics, WSN,
and SI are potential there but need to further outlined and formalized
• Collective decisions can be taken by exploiting self-organization as key coordination mechanism (positive feedback necessary to reach consensus)
• Collective decisions can be taken by natural, artificial, and mixed systems consisting of mobile or static nodes
• Aggregation and segregation are two basic behaviors which are highly parallelizable and widely spread out in natural systems
• Aggregation/segregation mechanisms are also underlying some collective decisions
• Modeling techniques help as usual to understand natural systems and transport ideas to artificial platforms
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Additional Literature – Week 12Papers• Martinoli A., Ijspeert A. J., and Mondada F., “Understanding Collective Aggregation
Mechanisms: From Probabilistic Modelling to Experiments with Real Robots”. Special issue on Distributed Autonomous Robotic Systems, Dillmann R., Lüth T., Dario P., and Wörn H., editors, Robotics and Autonomous Systems, 29(1): 51-63, 1999.
• Martinoli A., Ijspeert A. J., and Gambardella L. M., “A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechanisms”. In Floreano D., Mondada F., and Nicoud J.-D., editors, Proc. of the Fifth European Conf. on Artificial Life, September, 1999, Lausanne, Switzerland, Lectures Notes in Computer Science, Springer Verlag, pp. 575-584.
• Wilson M., Melhuish C.; Sendova-Franks A. B.; Scholes S., “Algorithms for Building Annular Structures with Minimalist Robots Inspired by Brood Sorting in Ant Colonies”, Autonomous Robots, 17(2): 115-136, 2004.
• Scholes S., Wilson M., Sendova-Franks Ana B., and Melhuish C. “Comparisons in Evolution and Engineering: The Collective Intelligence of Sorting” Adaptive Behavior 12: 147-159, 2004.
• S. Garnier, C. Jost, R. Jeanson, J. Gautrais, A. Grimal, M. Asadpour, G. Caprari, and G. Theraulaz. The embodiment of cockroach aggregation behavior in a group of micro-robots. Artificial Life. Accepted for publication.
• Correll N. and Martinoli A., “Modeling Self-Organized Aggregation of a Swarm of Miniature Robots. Proc. of the IEEE ICRA 2007 Workshop on Collective Behaviors inspired by Biological and Biochemical Systems, April 2007, Rome, Italy.