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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
Directing Chemotaxis-Based Spatial Self-Organization via Biased, Random Initial Conditions
arXiv (Cornell University)
09 Mar 2018
Abstract
Inspired by the chemotaxis interaction of living cells, we have developed an
agent-based approach for self-organizing shape formation. Since all our
simulations begin with a different uniform random configuration and our agents
move stochastically, it has been observed that the self-organization process
may form two or more stable final configurations. These differing
configurations may be characterized via statistical moments of the agents'
locations. In order to direct the agents to robustly form one specific
configuration, we generate biased initial conditions whose statistical moments
are related to moments of the desired configuration. With this approach, we are
able to successfully direct the aggregating swarms to produced a desired
macroscopic shape, starting from randomized initial conditions with controlled
statistical properties.
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Details
- Title
- Directing Chemotaxis-Based Spatial Self-Organization via Biased, Random Initial Conditions
- Creators
- Sean GrimesLinge BaiAndrew W. E McDonaldDavid E Breen
- Publication Details
- arXiv (Cornell University)
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021868724904721