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Proceedings Paper

Dynamic behavior of multirobot systems using lattice gas automata
Author(s): Keith M. Stantz; Stewart M. Cameron; Rush D. Robinett; Michael W. Trahan; John S. Wagner
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Paper Abstract

Recent attention has been given to the deployment of an adaptable sensor array realized by multi-robotic systems (or swarms). Our group has been studying the collective, autonomous behavior of these such systems and their applications in the area of remote-sensing and emerging threats. To accomplish such tasks, an interdisciplinary research effort at Sandia National Laboratories are conducting tests in the fields of sensor technology, robotics, and multi- agents architectures. Our goal is to coordinate a constellation of point sensors using unmanned robotic vehicles (e.g., RATLERs, Robotic All-Terrain Lunar Exploration Rover- class vehicles) that optimizes spatial coverage and multivariate signal analysis. An overall design methodology evolves complex collective behaviors realized through local interaction (kinetic) physics and artificial intelligence. Learning objectives incorporate real-time operational responses to environmental changes. This paper focuses on our recent work understanding the dynamics of many-body systems according to the physics-based hydrodynamic model of lattice gas automata. Three design features are investigated. One, for single-speed robots, a hexagonal nearest-neighbor interaction topology is necessary to preserve standard hydrodynamic flow. Two, adaptability, defined by the swarm's rate of deformation, can be controlled through the hydrodynamic viscosity term, which, in turn, is defined by the local robotic interaction rules. Three, due to the inherent nonlinearity of the dynamical equations describing large ensembles, stability criteria ensuring convergence to equilibrium states is developed by scaling information flow rates relative to a swarm's hydrodynamic flow rate. An initial test case simulates a swarm of twenty-five robots maneuvering past an obstacle while following a moving target. A genetic algorithm optimizes applied nearest-neighbor forces in each of five spatial regions distributed over the simulation domain. Armed with this knowledge, the swarm adapts by changing state in order to avoid the obstacle. Simulation results are qualitatively similar to a lattice gas.

Paper Details

Date Published: 22 July 1999
PDF: 11 pages
Proc. SPIE 3693, Unmanned Ground Vehicle Technology, (22 July 1999); doi: 10.1117/12.354461
Show Author Affiliations
Keith M. Stantz, Sandia National Labs. (United States)
Stewart M. Cameron, Sandia National Labs. (United States)
Rush D. Robinett, Sandia National Labs. (United States)
Michael W. Trahan, Sandia National Labs. (United States)
John S. Wagner, Sandia National Labs. (United States)


Published in SPIE Proceedings Vol. 3693:
Unmanned Ground Vehicle Technology
Grant R. Gerhart; Robert W. Gunderson; Chuck M. Shoemaker, Editor(s)

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