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Swarm Robotics: From
sources of inspiration to
domains of application
What is Swarm Robotics?
• Yet another novel approach to the control of
large group of robots!
• Study of multi-robot coordination strategies
inspired from social insects.
• Engineering self-organization in physically
embodied swarms.
• Application of Swarm Intelligence to the
control of a group of robots.
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Attempting to define the term Swarm
Robotics?
Need to identify
– aspects that make swarm robotics approach novel
and desirable.
– aspects that distinguishes swarm robotics from
other related studies.
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What’s novel and desirable in the Swarm
Robotics approach?
• Emphasis on the system-level functioning
properties observed in social insect systems:
– Robustness
– Flexibility
– Scalability
• Essential for deploying large numbers of
robots.
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Robustness
• Social insects can continue to operate despite
large disturbances.
–
–
–
–
Redundancy
Decentralized coordination
Simplicity of the individuals
Distributed sensing
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Flexibility
• Social insects can offer modularized solutions
to tasks of different nature by utilizing different
coordination mechanisms.
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Flexibility – same swarm, different tasks
• Foraging
• Prey retrieval
• Chain formation
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Scalability
• Social insects are observed to be able to
operate under a wide range of group sizes.
That is, coordination mechanisms are rather
independent of the number of individuals in
the group.
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Putting swarm robotics in place
• Where is Swarm Robotics placed in relation
to other related studies?
• Aspects that distinguish swarm robotics
studies from:
– other flavors of multi-robot studies
– other related studies such as Swarm Intelligence,
Sensing networks, etc..
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0 - Individuals should be robots!
• Individuals should be autonomous robots.
– Individuals should
• be situated and autonomous
• be able to physically interact
• Mobility of individuals is sufficient, but not required.
• Metamorphic robotic systems?
– Yes
• Sensor networks?
– No
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1 - Large number of robots
• The study should be relevant for the
coordination of large numbers of robots.
– Why relevancy?
– How large is “large”?
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2 - Few homogeneous groups of robots
• The robotic system should consist of few
homogeneous groups and that the number of
robots in each group should be large.
– Teams are not swarms.
– Hierarchical robotic systems (for instance swarms
with a “designated queen”) are less `swarm
robotic’.
– What’s a homogeneous group?
– How about individual adaptation?
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3 - Relatively incapable of inefficient robots
• The robotic system should utilize relatively incapable
or inefficient robots with respect to the task at hand.
– The robots should have difficulties in carrying the task on
their own.
– The deployment of a group of robots should improve the
performance of system.
– The deployment of a group of robots should improve the
robustness of the system.
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4 - Robots with only local sensing and
communication abilities
• For coordinating their actions, the robots
should utilize only local sensing and
communication capabilities.
– Locality promotes scalability.
– Existence of global communication channels not
used for coordination among the robots does not
violate.
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Criteria for Swarm Robotic systems
• A swarm robotic system should consist of
–
–
–
–
large numbers of robots,
few homogeneous groups of robots,
robots that are relatively incapable or inefficient,
robots with only local sensing and communication
abilities.
• Not a checklist for evaluating a study.
• But as yardsticks to evaluate how `swarm
robotic’ a given study is.
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Finally… a definition
Swarm robotics is the study of how large number of
relatively simple physically embodied agents can
be designed such that a desired collective behavior
emerges from the local interactions among agents
and between the agents and the environment.
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Sources of inspiration
• Self-organizing natural systems
– Social insect systems: ants, termites, wasps,
bees, cockroaches, locusts…
– Animals with social behaviors: penguins, birds,
fish, sheep...
– Unicellular organisms: Amoebae, bacteria,
viruses…
• Artificial self-organizing systems
– Amorphous computing
– Self-assembly of materials
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Aggregation of amoebae into slime mould
• When food is abundant,
amoebae (D. discoideum) acts
independently of others, feeding
and multiplying (Bonner;1967,
Goldbeter;1996).
• When food supply is depleted
amoebae release cAMP ( a
chemo-attractant for amoeba)
into the extra-cellular
environment.
• Amoebae aggregate forming a
slug, a multi-cellular organism
which can move and sporulate.
Summarized from Self-Organization in Biological
Systems by Scott Camazine, Jean-Louis
Deneubourg, Nigel R. Franks, James Sneyd,
Guy Theraulaz, and Eric Bonabeau
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Aggregation mechanism
• Amoebae secrete cAMP leading to spiral
waves that streams the cells to the
center
– Positive feedback mechanisms
• Release cAMP with a certain period
(oscillatory mode).
• If hit by a cAMP pulse, amplify it (relay
mode).
– Negative feedback mechanism
• High cAMP concentrations briefly
desensitize the receptors.
• Amoebae moves in the direction of
increasing cAMP concentration (at 1/10
of the cAMP wave speed).
• Cell-to-cell adhesion makes amoeba
clumps persistent.
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Take-home lesson/inspiration
• The mechanism discussed aggregate 10,000100,000 cells!
• In a recent study (Dorigo et al.;2004), it is shown that
aggregating tens of robots (equipped with simulated
speakers and microphones) is very challenging.
• Stigmergy seems to be a key element for scalability!
• Stigmergy in a swarm of robots
– Natural stigmergy: Using water, chemicals, etc..
– Stigmergy using embedded systems: e.g. Gnats (Balch;)
– Stigmergy using robots: Use some of the robots as the
“medium” while others aggregate.
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Quorum sensing in bacteria
• Bacteria seem to have interesting
communication mechanisms to
increase their survival.
• V. fischeri produces light when its
population reach a critical size.
• V. cholarae delays the production of
virulence factor until they reach a
certain mass, to ensure a successful
infection against the infection system.
• Recent studies show that bacteria use
certain auto-inducers to detect their
density in the environment.
B.L. Bassler, How
bacteria talk to
each other:
regulation of gene
expression by
quorum sensing.
Current Opinions in
Microbiology 1999
Dec;2(6):582-7.
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Take home lesson/inspiration
• Quorum sensing will be an essential problem
for swarm robotic systems.
• The density of individuals is an important
parameter in natural swarms which can lead
to bifurcations in swarm behavior.
• Density measurement w/o stigmergy is likely
to be an interesting challenge.
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Information exchange in bacteria
• Bacterial colonies can be more resistant to antibiotics
than bacteria living in suspension!
• Hypothesis: Bacteria form a “genomic web”
communicating with each other:
– Inducive communication: a chemical signal triggers a certain
action in other bacteria.
– Informative communication: the message received is
interpreted by the cell, and its response is determined by its
history as well as its current state.
E. Ben-Jacob, Bacterial self-organization: co-enhancement of complexification and
adaptability in a dynamic environment. Phil. Trans. R. Soc. Lond. A, 361, pp 1283–
1312, 2003.
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Take home lesson/inspiration
• In real life, some individuals of a swarm
robotic systems will probably discover certain
hazards the hard way.
• Individuals should be able to pass last-minute
signals and information to the rest of the
swarm.
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Amorphous computing
• Challenge: “How can prespecified, coherent behavior be
engineered from the cooperation
of vast numbers of unreliable
parts interconnected in unknown
and time-varying ways?”
• Medium: “a system of irregularly placed, asynchronous, locally
interacting computing elements”.
• Inspiration and approach: morphogenetic processes in biological
systems such as tissue growth.
Amorphous Computing, Abelson et al, Communications of the ACM, Volume
43, Number 5, May 2001.
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Take home lesson/inspiration
• Amorphous computing nodes [if and when
they become available] can be “active
intelligent pheromones” of swarm robotic
systems.
• Swarm robotic systems, when immobile, are
amorphous computing mediums and can
utilize their programming paradigms.
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Self-assembly
• Self-assembly: self-organization by making physical bond
formation
– Individuals lose some of their motility. This creates some interesting
dynamics. Social insects and breakable bonds in chemistry
• Self-assembly of materials is described as the “autonomous
organization of components into patterns or structures without
[external] intervention.” Whitesides and Grzybowski (Science; 2002)
• Self-assembly is a promising method for fabricating regular
structures: nano-scale self-assembly is promising for building
large numbers of micro- electro-mechanical systems (MEMS),
improving the robotic assembly processes.
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Take home lesson/inspiration
• Use of templates for scaffolding the selfassembly/organization process to reduce
defects in the structure.
• Catalytic agents to improve the self-assembly
process.
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