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Adaptive foraging in dynamic environments using scale-free interaction networks

Ilja Rausch (UGent) , Pieter Simoens (UGent) and Yara Khaluf (UGent)
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Abstract
Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.
Keywords
COLLECTIVE DECISION-MAKING, SWARM, swarm robotics, foraging, collective decision-making, scale-free, networks, dynamic environments, adaptive swarm

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Please use this url to cite or link to this publication:

MLA
Rausch, Ilja, et al. “Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks.” FRONTIERS IN ROBOTICS AND AI, vol. 7, 2020, doi:10.3389/frobt.2020.00086.
APA
Rausch, I., Simoens, P., & Khaluf, Y. (2020). Adaptive foraging in dynamic environments using scale-free interaction networks. FRONTIERS IN ROBOTICS AND AI, 7. https://doi.org/10.3389/frobt.2020.00086
Chicago author-date
Rausch, Ilja, Pieter Simoens, and Yara Khaluf. 2020. “Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks.” FRONTIERS IN ROBOTICS AND AI 7. https://doi.org/10.3389/frobt.2020.00086.
Chicago author-date (all authors)
Rausch, Ilja, Pieter Simoens, and Yara Khaluf. 2020. “Adaptive Foraging in Dynamic Environments Using Scale-Free Interaction Networks.” FRONTIERS IN ROBOTICS AND AI 7. doi:10.3389/frobt.2020.00086.
Vancouver
1.
Rausch I, Simoens P, Khaluf Y. Adaptive foraging in dynamic environments using scale-free interaction networks. FRONTIERS IN ROBOTICS AND AI. 2020;7.
IEEE
[1]
I. Rausch, P. Simoens, and Y. Khaluf, “Adaptive foraging in dynamic environments using scale-free interaction networks,” FRONTIERS IN ROBOTICS AND AI, vol. 7, 2020.
@article{8673064,
  abstract     = {Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.},
  articleno    = {86},
  author       = {Rausch, Ilja and Simoens, Pieter and Khaluf, Yara},
  issn         = {2296-9144},
  journal      = {FRONTIERS IN ROBOTICS AND AI},
  keywords     = {COLLECTIVE DECISION-MAKING,SWARM,swarm robotics,foraging,collective decision-making,scale-free,networks,dynamic environments,adaptive swarm},
  language     = {eng},
  pages        = {14},
  title        = {Adaptive foraging in dynamic environments using scale-free interaction networks},
  url          = {http://dx.doi.org/10.3389/frobt.2020.00086},
  volume       = {7},
  year         = {2020},
}

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