Swarm RoboticsAnton Galkin

24779 Nano/Micro-RoboticsDepartment of Mechanical Engineering

Carnegie Mellon UniversityPittsburgh, PA 15289

Email: agalkin@andrew.cmu.edu

Swarm Basics

• Motivation: BiomimicryAnts, birds, fish

• Decentralized local interactionsno global information

• Behavior-based intelligencesimple, inexpensive

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

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

Biomimicry

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

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

Directed Activity

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

Research methods

A typical scene from a human swarm day

“Using a Collection of Humans as an Execution Testbed for Swarm Algorithms”

“Red Herring” Applet

• Java 2 SDK 1.4.2.05

Swarm Modeling - Equations

• Attractant-repellant model-function of distance to considered agent-positive = attractant-negative = repellant

• Final choice: linear relationship

10 20 30 40 50 60

-1.5

-1

-0.5

0.5

1

1.5

10 20 30 40 50 60

-3

-2

-1

1

2

3

10 20 30 40 50 60

-10

-5

5

10

15

20

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

Swarm Modeling - Aggregation

Relative cluster size vs

Population

Number of Clusters vs

Population

0

0.2

0.4

0.6

0.8

1

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Static vs. Dynamic Equilibrium

• Static equilibrium-stable positions-no motion-geometrically optimal-(eqdist >> r)

• Dynamic equilibrium-constantly in motion-(eqdist > r)

Predator Avoidance

• New “predator” agent-always repells

• inverse F-x relationship

• Interesting agent behavior

10 20 30 40 50 60

-5

-4

-3

-2

-1

-10/x

herding splitting avoiding vacuole

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

References1. Emma Alenius1, Åge J. Eide2, Jan Johansson1, Jimmy Johansson1, Johan Land1 and Thomas Lindblad1,

“Experiments on Clustering using Swarm Intelligence and Collective Behavior” 1Royal Institute of Technology, S-10691 Stockholm, 2Ostfold College, N-1757 Halden,

2. By Guy Theraulaz1, Jacques Gautrais1, Scott Camazine2 and Jean-Louis Deneubourg3, “The Formation of Spatial Patterns in Social Insects: From Simple Behaviors to Complex Structures,” 1CNR-FRE 2382, Centre de Recherches sur la Cognition Animale, Universite Paul Sabatier, 118 route de Narbonne, 31062 Toulouse Cedex 4, France; 2Medical, Science and Nature Images, 310 West Main Street, Boalsburg, PA 16827-1327, USA; 3CENOLI, CP 231, Universite Libre de Bruxelles, Boulevard du Triomphe, 1050 Brussels, Belgium; 6 May 2003

3. G. Dudek1, M. Jenkinj E. Milios2, and D. Wilkest3, “A Taxonomy for Swarm Robots,” 1Research Centre for Intelligent Machines, McGill University, Montrkal, Qukbec, Canada; 2Department of Computer Science, York University, North York, Ontario, Canada; 3Department of Computer Science, University of Toronto, Toronto, Ontario, Canada, 26 July 1993

4. C. Ronald Kube, Hong Zhang, “Collective Robotic Intelligence,” Department of Computing Science, University of Alberta, Edmonton, Alberta Canada T6G 2J9, 1 Sept 1992

5. Debashish Chowdhury1, Katsuhiro Nishinari2, and Andreas Schadschneider3, “Self-organized patterns and traffic flow in colonies of organisms: from bacteria and social insects to vertebrates,” 1Department of Physics, Indian Institute of Technology, Kanpur 208016, India; 2Department of Applied Mathematics and Informatics, Ryukoku University, Shiga 520-2194, Japan; 3Institute for Theoretical Physics, Universit¨at zu K¨oln, 50937 K¨oln, Germany, 9 January 2004

6. Erol Sahin, “Swarm Robotics: From Sources of Inspiration to Domains of Application,” KOVAN – Dept. of Computer Eng., Middle East Technical University, Ankara, 06531, Turkey, erol@ceng.metu.edu.tr, E. Sahin and W.M. Spears (Eds.): Swarm Robotics WS 2004, LNCS 3342, pp. 10–20, 2005.

7. Julia K Parrish1,2, Steven V. Viscido2, Daniel Gru Nbaum3, “Self-Organized Fish Schools: An Examination of Emergent Properties,” 1School of Aquatic and Fishery Sciences, Box 355020, University of Washington, Seattle, Washington, 98195-5020; 2Zoology Department, University of Washington; and 3School of Oceanography, University of Washington, Biol. Bull. 202: 296–305., June 2002

8. Y. LIU, K. M. PASSINO, Communicated by M. A. Simaan, “Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors,” Journal of Optimization Theory and Applications: Vol. 115, No. 3, pp. 603–628, December 2002

9. Daniel W. Palmer, Mark Kirschenbaum, Jon P. Murton, Michael A. Kovacina*, Daniel H. Steinberg**, Sam N. Calabrese, Kelly M. Zajac, Chad M. Hantak, Jason E. Scatz, “Using a Collection of Humans as an Execution Testbed for Swarm Algorithms,” John Carroll University, University heights, OH 44118, *Orbital Research Inc, Highland Heights, OH 44143, **Dim Sum Thinking Inc, University Heights, OH 44118,