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Swarm Basics

Swarm Robotics Anton Galkin 24779 Nano/Micro-Robotics Department of Mechanical Engineering Carnegie Mellon University Pittsburgh, PA 15289 Email: agalkin@andrew.cmu.edu. Swarm Basics. Motivation: Biomimicry Ants, birds, fish Decentralized local interactions no global information

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Swarm Basics

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  1. Swarm RoboticsAnton Galkin24779 Nano/Micro-RoboticsDepartment of Mechanical EngineeringCarnegie Mellon UniversityPittsburgh, PA 15289Email: agalkin@andrew.cmu.edu

  2. Swarm Basics • Motivation: Biomimicry Ants, birds, fish • Decentralized local interactions no global information • Behavior-based intelligence simple, inexpensive

  3. 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

  4. 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

  5. Biomimicry • Inspiration:-social insects-schools of fish-flocks of birds

  6. 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

  7. Directed Activity

  8. 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

  9. Research methods A typical scene from a human swarm day “Using a Collection of Humans as an Execution Testbed for Swarm Algorithms”

  10. “Red Herring” Applet • Java 2 SDK 1.4.2.05

  11. 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

  12. Swarm Modeling - Aggregation Relative cluster size vs Population Number of Clusters vs Population

  13. Static vs. Dynamic Equilibrium • Static equilibrium-stable positions-no motion-geometrically optimal-(eqdist >> r) • Dynamic equilibrium-constantly in motion-(eqdist > r)

  14. Predator Avoidance • New “predator” agent-always repells • inverse F-x relationship • Interesting agent behavior -10/x herding splitting avoiding vacuole

  15. 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

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

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