Advertisement

Application of Collective Robotic Search Using Neural Network Based Dual Heuristic Programming (DHP)

  • Nian Zhang
  • Donald C. WunschII
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

An important application of mobile robots is searching a region to locate the origin of a specific phenomenon. A variety of optimization algo-rithms can be employed to locate the target source, which has the maximum intensity of the distribution of some detected function. We propose a neural network based dual heuristic programming (DHP) algorithm to solve the collective robotic search problem. Experiments were carried out to investigate the effect of noise and the number of robots on the task performance, as well as the expenses. The experimental results were compared with those of stochastic optimization algorithm. It showed that the performance of the dual heuristic programming (DHP) is better than the stochastic optimization method.

Keywords

Hide Layer Mobile Robot Stochastic Optimization Route Length Critic Network 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gelenbe, E., Schmajuk, N., Staddon, J., Reif, J.: Autonomous Search By Robots and Animals: A Survey. Robotics and Autonomous Systems 22, 23–34 (1997)CrossRefGoogle Scholar
  2. 2.
    Arkin, R., Hobbins, J.: Dimensions of Communication and Social Organization in Multi-agent Robotic Systems. Proceedings of Simulation of Adaptive Behavior (1994)Google Scholar
  3. 3.
    Goss, S., Deneubourg, J.: Harvesting by a Group of Robots. In: Proceedings of European Conference of Artificial Life (1992)Google Scholar
  4. 4.
    Mataric, M.: Interaction and Intelligent Behavior. MIT AI Lab Tech Report AITR-1495 (1994)Google Scholar
  5. 5.
    Steels, L.: Cooperation between Distributed Agents through Self-Organization. In: European Workshop on Modeling Autonomous Agents in a Multi-Agent World (1990)Google Scholar
  6. 6.
    Goldsmith, S.Y., Robinett, R.: Collective Search by Mobile Robots Using Alpha-Beta Coordination. 1998 Collective Robotics Workshop. Agent World, Paris (1998)Google Scholar
  7. 7.
    Spires, S.V., Goldsmith, S.Y.: Exhaustive Geographic Search with Mobile Robots along Space-Filling Curves. In: Drogoul, A., Fukuda, T., Tambe, M. (eds.) CRW 1998. LNCS, vol. 1456, Springer, Heidelberg (1998)CrossRefGoogle Scholar
  8. 8.
    Zhang, N., Novokhodko, A., Wunsch II, D.C., Dagli, C.H.: A Comparative Study of Neural Networks Based Learning Strategies on Robotic Search Problems. In: Proceedings of the SPIE Application and Science of Computational Intelligence Conference, Orlando, Florida, vol. 4390 (2001)Google Scholar
  9. 9.
    Prokhorov, D.V., Wunsch II, D.C.: Adaptive Critic Designs. IEEE Trans. on Neural Networks 8(5), 997–1007 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nian Zhang
    • 1
  • Donald C. WunschII
    • 2
  1. 1.Dept. of Electrical and Computer EngineeringSouth Dakota School of Mines & TechnologyRapid CityUSA
  2. 2.Dept. of Electrical and Computer EngineeringUniversity of Missouri-RollaRollaUSA

Personalised recommendations