Multi-Agent Rendezvous Algorithm with Rectilinear Decision Domain

  • Kaushik Das
  • Debasish Ghose
Part of the Communications in Computer and Information Science book series (CCIS, volume 103)


The aim of this paper is to develop a computationally efficient decentralized rendezvous algorithm for a group of autonomous agents. The algorithm generalizes the notion of sensor domain and decision domain of agents to enable implementation of simple computational algorithms. Specifically, the algorithm proposed in this paper uses a rectilinear decision domain (RDD) as against the circular decision domain assumed in earlier work. Because of this, the computational complexity of the algorithm reduces considerably and, when compared to the standard Ando’s algorithm available in the literature, the RDD algorithm shows very significant improvement in convergence time performance. Analytical results to prove convergence and supporting simulation results are presented in the paper.


Multi-agent Rendezvous Decision domain Consensus 


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  1. 1.
    Kranakis, E., Krizanc, D., Rajsbaum, S.: Mobile Agent Rendezvous: A Survey. In: Flocchini, P., Gąsieniec, L. (eds.) SIROCCO 2006. LNCS, vol. 4056, pp. 1–9. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Lin, Z., Broucke, M., Francis, B.: Local Control Strategies for Groups of Mobile Autonomous Agents. IEEE Transactions on Automatic Control 49, 622–628 (2004)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Ando, H., Oasa, Y., Suzuki, I., Yamashita, M.: Distributed Memoryless Point Convergence for Mobile Robots with Limited Visibility. IEEE Transactions on Robotics and Automation 15, 818–828 (1999)CrossRefGoogle Scholar
  4. 4.
    Lin, J., Morse, A.S., Anderson, B.D.O.: The Multi-Agent Rendezvous problem. In: Conference on Decesion & Control, Maui, Hawaii, USA, pp. 1508–1513 (2003)Google Scholar
  5. 5.
    Gartner, G.: A subexponential algorithm for abstarct optimization problems. SIAM Journal Computers 24(5), 1018–1035 (1995)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Das, K., Ghose, D.: Positional Consensus in Multi-Agent Systems using a Broadcast Control Mechanism. In: American Control Conference, St. louis, Missouri, USA, pp. 5731–5736 (2009)Google Scholar
  7. 7.
    Rockafellar, R.T.: Convex Analysis. Princeton University Press, Princeton (1972)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kaushik Das
    • 1
  • Debasish Ghose
    • 1
  1. 1.Dept. of AE, Indian Institute of ScienceGCDSLBangaloreIndia

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