Swarm Intelligence

, Volume 10, Issue 1, pp 1–31

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

  • Eduardo Castello
  • Tomoyuki Yamamoto
  • Fabio Dalla Libera
  • Wenguo Liu
  • Alan F. T. Winfield
  • Yutaka Nakamura
  • Hiroshi Ishiguro
Article

DOI: 10.1007/s11721-015-0117-7

Cite this article as:
Castello, E., Yamamoto, T., Libera, F.D. et al. Swarm Intell (2016) 10: 1. doi:10.1007/s11721-015-0117-7

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.

Keywords

Adaptive foraging Cooperative behaviour Autonomous systems 

Supplementary material

Supplementary material 1 (mov 80849 KB)

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Eduardo Castello
    • 1
  • Tomoyuki Yamamoto
    • 2
  • Fabio Dalla Libera
    • 1
  • Wenguo Liu
    • 3
  • Alan F. T. Winfield
    • 3
  • Yutaka Nakamura
    • 1
  • Hiroshi Ishiguro
    • 1
  1. 1.Graduate School of Engineering ScienceOsaka UniversityToyonaka, OsakaJapan
  2. 2.Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka UniversityOsakaJapan
  3. 3.Bristol Robotics Lab (BRL)University of the West of EnglandBristolUK

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