Swarm Robotics Anton Galkin 24779 Nano/Micro-Robotics Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, PA 15289 Email: [e-mail]

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Swarm RoboticsAnton Galkin24779
Nano/Micro-RoboticsDepartment of Mechanical
EngineeringCarnegie Mellon UniversityPittsburgh,
PA 15289Email agalkin_at_andrew.cmu.edu
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Swarm Basics

• Motivation Biomimicry Ants, birds, fish
• Decentralized local interactions no global
  information
• Behavior-based intelligence simple, inexpensive

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Advantages to Swarming (Nature)

• Enhanced protection
• Greater ease of travel
• Predator confusion
• Increased capability (perform tasks previously
  impossible or impractical)-carrying heavy
  objects-building structures many orders of
  magnitude greater than agent

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Advantages to Swarming (Robotics)

• Redundancy Failure tolerance-single agent
  failure is not catastrophic
• Decreased complexity (usually)
• Decreased cost (usually)
• Versatility, ease of adaptability
• Scalability
• Rapid wide-area coverage
• Increased capability-perform non-linear
  tasks-perform prohibitively expensive, complex
  or time consuming tasks more easily

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Biomimicry

• Inspiration-social insects-schools of
  fish-flocks of birds

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Biomimicry - Pattern vs. Function

• Human perception can be misleading
• Evolutionarily neutral-funnel or torus swarm
  shapes - OR -
• Adaptive to group dynamics-coordinated movement
  directed activity

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Directed Activity
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Swarm Modeling

• Lagrangian method
• Swarm Aggregation
• Attractant-repellant model-autonomous agents
  modeled as inertial mass subject to forces from
  other agents-long range attraction-short-range
  repulsion
• Rule size or numerical preference

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Research methods
A typical scene from a human swarm day Using a
Collection of Humans as an Execution Testbed for
Swarm Algorithms
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Red Herring Applet

• Java 2 SDK 1.4.2.05

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Swarm Modeling - Equations

• Attractant-repellant model-function of distance
  to considered agent-positive
  attractant-negative repellant
• Final choice linear relationship

atan(x-20)
sqrt(x-1)-4.5 x/2-10
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Swarm Modeling - Aggregation

• Relative cluster size vs
• Population
• Number of Clusters vs
• Population

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Static vs. Dynamic Equilibrium

• Static equilibrium-stable positions-no
  motion-geometrically optimal-(eqdist gtgt r)
• Dynamic equilibrium-constantly in
  motion-(eqdist gt r)

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Predator Avoidance

• New predator agent-always repells
• inverse F-x relationship
• Interesting agent behavior

-10/x
herding splitting
avoiding
vacuole
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Conclusions

• Simple aggregation models can lead to complex
  autonomous agent behavior
• Applied fish school dynamics?-localized
  interactions-minimalist intelligence/sensor
  array-inexpensive, disposable robots
• Collective swarm intelligence

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References

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