Title: Swarm Robotics Anton Galkin 24779 Nano/Micro-Robotics Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, PA 15289 Email: agalkin@andrew.cmu.edu
1Swarm RoboticsAnton Galkin24779
Nano/Micro-RoboticsDepartment of Mechanical
EngineeringCarnegie Mellon UniversityPittsburgh,
PA 15289Email agalkin_at_andrew.cmu.edu
2Swarm Basics
- Motivation Biomimicry Ants, birds, fish
- Decentralized local interactions no global
information - Behavior-based intelligence simple, inexpensive
3Advantages 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
4Advantages 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
5Biomimicry
- Inspiration-social insects-schools of
fish-flocks of birds
6Biomimicry - Pattern vs. Function
- Human perception can be misleading
- Evolutionarily neutral-funnel or torus swarm
shapes - OR - - Adaptive to group dynamics-coordinated movement
directed activity
7Directed Activity
8Swarm 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
9Research methods
A typical scene from a human swarm day Using a
Collection of Humans as an Execution Testbed for
Swarm Algorithms
10Red Herring Applet
11Swarm 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
12Swarm Modeling - Aggregation
- Relative cluster size vs
- Population
- Number of Clusters vs
- Population
13Static vs. Dynamic Equilibrium
- Static equilibrium-stable positions-no
motion-geometrically optimal-(eqdist gtgt r) - Dynamic equilibrium-constantly in
motion-(eqdist gt r)
14Predator Avoidance
- New predator agent-always repells
- inverse F-x relationship
- Interesting agent behavior
-10/x
herding splitting
avoiding
vacuole
15Conclusions
- 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
16References
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