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Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach

Abstract

Developing self-organised swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular response threshold model and proposes a new adaptive response threshold model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions drawn through real-robot and simulation results.

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Notes

  1. The response curve corresponds to the plot of the response probability \(P_{f}\) as a function of the stimulus \(S_{(t)}\).

  2. Sum of the heights equal to 1

    Fig. 18
    figure 18

    Normalised n distribution for the simulations that a “died” within the first 5 min of the simulation time; b “died” within the second 5 min of the simulation time; c “died” within the third 5 min of the simulation time; d “died” within the fourth 5 min of the simulation time; e that remained “alive” during the whole simulation time . a 1st quarter, b 2nd quarter, c 3th quarter, d 4th quarter, e successful simulations

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Acknowledgments

All real-robot experiments described in the research were conducted within the Swarm Robotics Group at BRL (Bristol Robotics Laboratory). This research was supported by “Program for Leading Graduate Schools” of the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Correspondence to Eduardo Castello.

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Castello, E., Yamamoto, T., Libera, F.D. et al. Adaptive foraging for simulated and real robotic swarms: the dynamical response threshold approach. Swarm Intell 10, 1–31 (2016). https://doi.org/10.1007/s11721-015-0117-7

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  • DOI: https://doi.org/10.1007/s11721-015-0117-7

Keywords

  • Adaptive foraging
  • Cooperative behaviour
  • Autonomous systems