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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Berman, S. Halasz, A. Kumar, V. Pratt, S. |
| Copyright Year | 2007 |
| Description | Author affiliation: Dept. of Mech. Eng. & Appl. Mech., Pennsylvania Univ., Philadelphia, PA (Berman, S.; Halasz, A.; Kumar, V.) |
| Abstract | We present a methodology for characterizing and synthesizing swarm behaviors using both a macroscopic model that represents a swarm as a continuum and a microscopic model that represents individual robots. We develop a systematic approach for synthesizing behaviors at the macroscopic level that can be realized on individual robots at the microscopic level. Our methodology is inspired by a dynamical model of ant house hunting [1], a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. At the macroscopic level, we are able to synthesize controllers that result in the deployment of a robotic swarm in a predefined ratio between distinct sites. We then derive hybrid controllers for individual robots using only local interactions and no communication that respect the specifications of the global continuous behavior. Our simulations demonstrate that our synthesis procedure yields a correct microscopic model from the macroscopic description with guarantees on performance at both levels |
| Sponsorship | IEEE Robotics and Automation Soc |
| Starting Page | 2318 |
| Ending Page | 2323 |
| File Size | 798732 |
| Page Count | 6 |
| File Format | |
| ISBN | 1424406013 |
| ISSN | 10504729 |
| DOI | 10.1109/ROBOT.2007.363665 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-04-10 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Microscopy Robotics and automation Communication system control Robot control Robot sensing systems Recruitment Noise robustness State-space methods Differential equations Solid modeling |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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