Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms
Abstract
A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.
- Authors:
- Publication Date:
- Research Org.:
- Idaho National Lab. (INL), Idaho Falls, ID (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1110996
- Report Number(s):
- INL/JOU-11-22074
Journal ID: ISSN 1551--3203
- DOE Contract Number:
- DE-AC07-05ID14517
- Resource Type:
- Journal Article
- Journal Name:
- IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
- Additional Journal Information:
- Journal Volume: 9; Journal Issue: 4; Journal ID: ISSN 1551--3203
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 42 ENGINEERING; MANET EVSM swarm
Citation Formats
Derr, Kurt, and Manic, Milos. Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms. United States: N. p., 2013.
Web. doi:10.1109/TII.2012.2228870.
Derr, Kurt, & Manic, Milos. Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms. United States. https://doi.org/10.1109/TII.2012.2228870
Derr, Kurt, and Manic, Milos. 2013.
"Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms". United States. https://doi.org/10.1109/TII.2012.2228870.
@article{osti_1110996,
title = {Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms},
author = {Derr, Kurt and Manic, Milos},
abstractNote = {A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.},
doi = {10.1109/TII.2012.2228870},
url = {https://www.osti.gov/biblio/1110996},
journal = {IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS},
issn = {1551--3203},
number = 4,
volume = 9,
place = {United States},
year = {Fri Nov 01 00:00:00 EDT 2013},
month = {Fri Nov 01 00:00:00 EDT 2013}
}