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Lattice Formation in Mobile Autonomous Sensor Arrays

  • Eric Martinson
  • David Payton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3342)

Abstract

The purpose of this work is to enable an array of mobile sensors to autonomously arrange themselves into a regularly spaced lattice formation such that they may collectively be used as an effective phased-array sensor. Existing approaches to this problem encounter issues with local minima which allow the formation of lattice patterns that are locally regular but have discontinuities or defects that would be undesirable in a narrow-band beamforming application. By exploiting a common reference orientation, such as might be obtained from a magnetic compass, we have been able to create control laws that operate on orthogonal axes and thereby minimize the occurrence of local minima. The result is that we can now form lattice patterns with greater uniformity over extended distances, with significantly less energy or movement per robot. Despite the need for a shared directional reference, our methods are also robust to significant error in the reference readings.

Keywords

Mobile Robot Formation Error Sensor Range Line Force Primary Axis 
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.
    Gorman, P.: The Defense of Fombler’s Ford, part of the DARPA Command Post of the FutureGoogle Scholar
  2. 2.
    Parker, L., Kannan, B., Fu, X., Tang, Y.: Heterogeneous Mobile Sensor Net Deployment Using Robot Herding and Line-of-Sight Formations. IROS (2003)Google Scholar
  3. 3.
    Platt, R., Fagg, A., Grupen, R.: Nullspace Composition of Control Lays for Grasping. IROS (2002)Google Scholar
  4. 4.
    Balch, T., Hybinette, M.: Social Potentials for Scalable Multi-Robot Formations. ICRA, 73–80 (2000)Google Scholar
  5. 5.
    Fredslund, J., Mataric, M.: A General Algorithm for Robot Formations Using Local Sensing and Minimal Communication. IEEE Transactions on Robotics and Automation 18(5) (October 2002)Google Scholar
  6. 6.
    Suzuki, I., Yamashita, M.: Distributed Anonymous Mobile Robots: Formation of Geometric Patterns. SIAM Journal on Computing 28(4), 1347–1363 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Flocchini, P., Prencipe, G., Santora, N., Widmayer, P.: Pattern Formation by Autonomous Robots Without Chirality. In: Proc. of International Colloquium on Structural Information and Communication Complexity, pp. 147–162 (2001)Google Scholar
  8. 8.
    Spears, W., Spears, D., Hamann, J., Heil, R.: Distributed, Physics-Based Control of Swarms of Vehicles. Distributed, Physics-Based Control of Swarms of Vehicles, Autonomous Robots 17(2-3) (August 2004) (in press)Google Scholar
  9. 9.
    Arkin, R.C.: Behavior-Based Robotics. MIT Press, Cambridge (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Eric Martinson
    • 1
  • David Payton
    • 2
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.HRL LaboratoriesMalibuUSA

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