Limits...
Multiscale modelling and analysis of collective decision making in swarm robotics.

Vigelius M, Meyer B, Pascoe G - PLoS ONE (2014)

Bottom Line: Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model.Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process.Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

View Article: PubMed Central - PubMed

Affiliation: FIT Centre for Research in Intelligent Systems, Monash University, Melbourne, Australia.

ABSTRACT
We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

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Picture of a kilobot.Picture courtesy of K-Team (http://www.k-team.com/).
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pone-0111542-g008: Picture of a kilobot.Picture courtesy of K-Team (http://www.k-team.com/).

Mentions: For our experiments, we used a swarm of ten Kilobots moving freely on a flat table. The Kilobots are confined to a square area of which was marked out using layered tape. Initially the Kilobots were distributed homogeneously over the table such that they are outside the communication distance of each other. Each Kilobot is initialized to either “red'' or “green'' state, with a probability of . The state of each bot is indicated by a red or green LED. The Kilobots achieve consensus using a behavioural algorithm similar to the density estimation algorithm presented above [24]. The communication distance is transmitted using an integer value . By experiment, we found that corresponds to cm and we assume that inside this range the relationship is linear. Fig. 7 illustrates the basic setup. An actual Kilobot is shown in Fig. 8. The swarm parameters are given in Table 2.


Multiscale modelling and analysis of collective decision making in swarm robotics.

Vigelius M, Meyer B, Pascoe G - PLoS ONE (2014)

Picture of a kilobot.Picture courtesy of K-Team (http://www.k-team.com/).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4219724&req=5

pone-0111542-g008: Picture of a kilobot.Picture courtesy of K-Team (http://www.k-team.com/).
Mentions: For our experiments, we used a swarm of ten Kilobots moving freely on a flat table. The Kilobots are confined to a square area of which was marked out using layered tape. Initially the Kilobots were distributed homogeneously over the table such that they are outside the communication distance of each other. Each Kilobot is initialized to either “red'' or “green'' state, with a probability of . The state of each bot is indicated by a red or green LED. The Kilobots achieve consensus using a behavioural algorithm similar to the density estimation algorithm presented above [24]. The communication distance is transmitted using an integer value . By experiment, we found that corresponds to cm and we assume that inside this range the relationship is linear. Fig. 7 illustrates the basic setup. An actual Kilobot is shown in Fig. 8. The swarm parameters are given in Table 2.

Bottom Line: Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model.Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process.Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

View Article: PubMed Central - PubMed

Affiliation: FIT Centre for Research in Intelligent Systems, Monash University, Melbourne, Australia.

ABSTRACT
We present a unified approach to describing certain types of collective decision making in swarm robotics that bridges from a microscopic individual-based description to aggregate properties. Our approach encompasses robot swarm experiments, microscopic and probabilistic macroscopic-discrete simulations as well as an analytic mathematical model. Following up on previous work, we identify the symmetry parameter, a measure of the progress of the swarm towards a decision, as a fundamental integrated swarm property and formulate its time evolution as a continuous-time Markov process. Contrary to previous work, which justified this approach only empirically and a posteriori, we justify it from first principles and derive hard limits on the parameter regime in which it is applicable.

Show MeSH