Remember Me
Or use your Academic/Social account:


Or use your Academic/Social account:


You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.


Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message


Verify Password:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Liu, W.; Winfield, A. F. (2009)
Languages: English
Types: Unknown
In this paper, we extend a macroscopic probabilistic model of a swarm of foraging robots from the homogeneous to the heterogeneous case. In the swarm, each robot is capable of adjusting its searching time and resting time thresholds following the rules described in our previous paper [1]. In\ud order to model the difference between robots, private/public resting time and searching time thresholds\ud are introduced, a number of equations are then developed to work out the relationship between these private time thresholds and public time thresholds based on previously developed difference equations [2]. The extended macroscopic probabilistic model has been tested using the simulation tools Player/Stage. The results from the macroscopic probabilistic model match with those from the simulation with reasonable accuracy, not only in the final net energy of the swarm but also in the instantaneous\ud net energy. Although the model is specific to adaptive foraging, we believe the methodology can be extended to other systems in which the heterogeneity of the system is coupled with its time parameters.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Wenguo Liu, Alan F. T. Winfield, Jin Sa, Jie Chen, and Lihua Dou. Towards energy optimization: Emergent task allocation in a swarm of foraging robots. Adaptive Behavior, 15(3):289-305, 2007.
    • [2] Wenguo Liu, Alan F. T. Winfield, and Jin Sa. Modelling swarm robotic system: A case study in collective foraging. In Myra S. Wilson, Frèdèric Labrosse, Ulrich Nehmzow, Chris Melhuish, and Mark Witkowski, editors, Proceeding of Towards Autonomous Robotic Systems, pages 25 - 32, Aberystwyth, UK, September 2007.
    • [3] Alcherio Martinoli, Auke Jan Ijspeert, and F. Mondada. Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 29:51-63, 1999.
    • [4] Alcherio Martinoli, Auke Jan Ijspeert, and Luca Maria Gambardella. A probabilistic model for understanding and comparing collective aggregation mechansims. In ECAL '99: Proceedings of the 5th European Conference on Advances in Artificial Life, pages 575-584, London, UK, 1999. Springer-Verlag.
    • [5] D. J. T. Sumpter and S. C. Pratt. A modelling framework for understanding social insect foraging. Behavioral Ecology and Sociobiology, 53:131-144, 2003.
    • [6] Ken Sugawara and Masaki Sano. Cooperative acceleration of task performance: foraging behavior of interacting multi-robots system. Phys. D, 100(3-4):343-354, 1997.
    • [7] K. Sugawara, M. Sano, I. Yoshihara, K. Abe, and T. Watanabe. Foraging behaviour of multi-robot system and emergence of swarm intelligence. In Systems, Man, and Cybernetics, 1999. IEEE SMC '99 Conference Proceedings. 1999 IEEE International Conference on, volume 3, pages 257-262 vol.3, 1999.
    • [8] K. Sugawara and T. Watanabe. A study on foraging behavior of simple multi-robot system. In IECON 02 [Industrial Electronics Society, IEEE 2002 28th Annual Conference of the], volume 4, pages 3085-3090 vol.4, 2002.
    • [9] Kristina Lerman and Aram Galstyan. A general methodology for mathematical analysis of multi-agent
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article