Abstract
A novel framework for designing and implementing morphogenetic robotic swarms by using very simple, particle-like mobile robots is presented. This simple model extension naturally enables growth and self-assembly of robotic swarms by local information transmission and stochastic differentiation, and moreover, self-repair by stochastic re-differentiation of robots. Swarm Chemistry has been used as the basic model for the proposed design framework. For a given swarm, specifications for its macroscopic properties are indirectly and implicitly woven into a list of different kinetic parameter settings for each swarm component. Once a robot is activated, it simply reacts kinetically to nearby robots and independently re-differentiates with small probability. This architecture demonstrates dynamic production and maintenance of patterns, which allows robust, adaptive behaviors under variable environmental conditions, including self-repair with no central controller. If the number of robots is increased too much, the swarm loses coherence and the design embedded in the original recipe is not reproduced correctly.
| Original language | English |
|---|---|
| Article number | 5508726 |
| Pages (from-to) | 43-49 |
| Number of pages | 7 |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 5 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2010 Aug |
| Externally published | Yes |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Artificial Intelligence
Cite this
Robust morphogenesis of robotic swarms. / Sayama, Hiroki.
In: IEEE Computational Intelligence Magazine, Vol. 5, No. 3, 5508726, 08.2010, p. 43-49.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Robust morphogenesis of robotic swarms
AU - Sayama, Hiroki
PY - 2010/8
Y1 - 2010/8
N2 - A novel framework for designing and implementing morphogenetic robotic swarms by using very simple, particle-like mobile robots is presented. This simple model extension naturally enables growth and self-assembly of robotic swarms by local information transmission and stochastic differentiation, and moreover, self-repair by stochastic re-differentiation of robots. Swarm Chemistry has been used as the basic model for the proposed design framework. For a given swarm, specifications for its macroscopic properties are indirectly and implicitly woven into a list of different kinetic parameter settings for each swarm component. Once a robot is activated, it simply reacts kinetically to nearby robots and independently re-differentiates with small probability. This architecture demonstrates dynamic production and maintenance of patterns, which allows robust, adaptive behaviors under variable environmental conditions, including self-repair with no central controller. If the number of robots is increased too much, the swarm loses coherence and the design embedded in the original recipe is not reproduced correctly.
AB - A novel framework for designing and implementing morphogenetic robotic swarms by using very simple, particle-like mobile robots is presented. This simple model extension naturally enables growth and self-assembly of robotic swarms by local information transmission and stochastic differentiation, and moreover, self-repair by stochastic re-differentiation of robots. Swarm Chemistry has been used as the basic model for the proposed design framework. For a given swarm, specifications for its macroscopic properties are indirectly and implicitly woven into a list of different kinetic parameter settings for each swarm component. Once a robot is activated, it simply reacts kinetically to nearby robots and independently re-differentiates with small probability. This architecture demonstrates dynamic production and maintenance of patterns, which allows robust, adaptive behaviors under variable environmental conditions, including self-repair with no central controller. If the number of robots is increased too much, the swarm loses coherence and the design embedded in the original recipe is not reproduced correctly.
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U2 - 10.1109/MCI.2010.937323
DO - 10.1109/MCI.2010.937323
M3 - Article
AN - SCOPUS:77955080755
VL - 5
SP - 43
EP - 49
JO - IEEE Computational Intelligence Magazine
JF - IEEE Computational Intelligence Magazine
SN - 1556-603X
IS - 3
M1 - 5508726
ER -