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Top-Down vs. Bottom-Up Model-Based Methodologies for Distributed Control: A Comparative Experimental Study

  • Grégory MermoudEmail author
  • Utkarsh Upadhyay
  • William C. Evans
  • Alcherio Martinoli
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)

Abstract

Model-based synthesis of distributed controllers for multi-robot systems is commonly approached in either a top-down or bottom-up fashion. In this paper, we investigate the experimental challenges of both approaches, with a special emphasis on resource-constrained miniature robots. We make our comparison through a case study in which a group of 2-cm-sized mobile robots screen the environment for undesirable features, and destroy or neutralize them. First, we solve this problem using a top-down approach that relies on a graph-based representation of the system, allowing for direct optimization using numerical techniques (e.g., linear and non-linear convex optimization) under very unrealistic assumptions (e.g., infinite number of robots, perfect localization, global communication, etc.). We show how one can relax these assumptions in the context of resource-constrained robots, and explain the resulting impact on system performance. Second, we solve the same problem using a bottom-up approach, i.e., we build up computationally efficient and accurate models at multiple abstraction levels, and use them to optimize the robots’ controller using evolutionary algorithms. Finally, we outline the differences between the top-down and bottom-up approaches, and experimentally compare their performance.

Keywords

Mobile Robot Multiagent System Chemical Reaction Network Finite State Machine Central Planner 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Berman, S., Halasz, A., Hsieh, M.A., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Trans. on Robotics 25(4), 927–937 (2009)CrossRefGoogle Scholar
  2. 2.
    Caprari, G., Estier, T., Siegwart, R.: Fascination of down scaling - Alice the sugar cube robot. J. of Micromechatronics 1(3), 177–190 (2002)CrossRefGoogle Scholar
  3. 3.
    Fletcher, R.: Semi-definite matrix constraints in optimization. SIAM J. on Control and Optimization 23(4), 493–513 (1985)MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Grant, M., Boyd, S.: CVX: Matlab software for disciplined convex programming, version 1.21 (May 2010), http://cvxr.com/cvx
  5. 5.
    Ijspeert, A., Martinoli, A., Billard, A., Gambardella, L.: Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots 11(2), 149–171 (2001)zbMATHCrossRefGoogle Scholar
  6. 6.
    Li, H., Cao, Y., Petzold, L.R., Gillespie, D.T.: Algorithms and software for stochastic simulation of biochemical reacting systems. Biotechnology Progress 24(1), 56–61 (2008)CrossRefGoogle Scholar
  7. 7.
    Lochmatter, T., Roduit, P., Cianci, C., Correll, N., Jacot, J., Martinoli, A.: Swistrack - a flexible open source tracking software for multi-agent systems. In: Proc. of the 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS 2008), pp. 4004–4010 (2008)Google Scholar
  8. 8.
    Martinoli, A., Easton, K., Agassounon, W.: Modeling swarm robotic systems: A case study in collaborative distributed manipulation. Int. J. Robotics Research 23(4-5), 415–436 (2004)CrossRefGoogle Scholar
  9. 9.
    Matthey, L., Berman, S., Kumar, V.: Stochastic strategies for a swarm robotic assembly system. In: Proc. of the 2009 IEEE Int. Conf. on Robotics and Automation (ICRA 2009), pp. 1953–1958 (May 2009)Google Scholar
  10. 10.
    Mermoud, G., Brugger, J., Martinoli, A.: Towards multi-level modeling of self-assembling intelligent micro-systems. In: Proc. of the 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), vol. 1, pp. 89–96 (May 2009)Google Scholar
  11. 11.
    Mermoud, G., Matthey, L., Evans, W., Martinoli, A.: Aggregation-mediated collective perception and action in a swarm of miniature robots. In: Luck, M., Sen, S., van der Hoewk, W., Kaminka, G. (eds.) Proc. of the 9th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, pp. 599–606 (May 2010)Google Scholar
  12. 12.
    Ross, S.M.: Introduction to Probability Models, 9th edn. Academic Press, Inc., Orlando (2006)zbMATHGoogle Scholar
  13. 13.
    Winfield, A., Liu, W., Nembrini, J., Martinoli, A.: Modelling a wireless connected swarm of mobile robots. Swarm Intelligence 2(2), 241–266 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  • Grégory Mermoud
    • 1
    Email author
  • Utkarsh Upadhyay
    • 1
  • William C. Evans
    • 1
  • Alcherio Martinoli
    • 1
  1. 1.School of Architecture, Civil and Environmental Engineering, Distributed Intelligent Systems and Algorithms LaboratoryÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland

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