Journal of Optimization Theory and Applications

, Volume 155, Issue 3, pp 986–1007 | Cite as

Coverage Maximization with Autonomous Agents in Fast Flow Environments

Article

Abstract

This work examines the cooperative motion of a group of autonomous vehicles in a fast flow environment. The magnitude of the flow velocity is assumed to be greater than the available actuation to each agent. Collectively, the agents wish to maximize total coverage area defined as the set of points reachable by any agent within T time. The reachable set of an agent in a fast flow is characterized using optimal control techniques. Specifically, this work addresses the complementary cases where the static flow field is smooth, and where the flow field is piecewise constant. The latter case arises as a proposed approximation of a smooth flow that remains analytically tractable. Furthermore, the techniques used in the piecewise constant flow case enable treatment for obstacles in the environment. In both cases, a gradient ascent method is derived to maximize the total coverage area in a distributed fashion. Simulations show that such a network is able to maximize the coverage area in a fast flow.

Keywords

Optimal sensor coverage Distributed control of multi-agent systems 

Notes

Acknowledgements

Work supported by grants NSF CAREER Award CMMI-0643679 and NSF CNS-0930946.

References

  1. 1.
    Cortés, J., Martínez, S., Bullo, F.: Spatially-distributed coverage optimization and control with limited-range interactions. ESAIM Control Optim. Calc. Var. 11, 691–719 (2005) MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Hussein, I.I., Stipanovic̀, D.M.: Effective coverage control for mobile sensor networks with guaranteed collision avoidance. IEEE Trans. Control Syst. Technol. 15, 642–657 (2007) CrossRefGoogle Scholar
  3. 3.
    Schwager, M., Rus, D., Slotine, J.: Decentralized, adaptive coverage control for networked robots. Int. J. Robot. Res. 28, 357–375 (2009) CrossRefGoogle Scholar
  4. 4.
    Zermelo, E.: Über das navigationproble bei ruhender oder veranderlicher windverteilung. Z. Angrew. Math. Mech. 11 (1931) Google Scholar
  5. 5.
    Bryson, A.E., Ho, Y.: Applied Optimal Control. Hemisphere, New York (1969) Google Scholar
  6. 6.
    Reif, J., Sun, Z.: Movement planning in the presence of flows. Algorithmica 39, 127–153 (2004) MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Ceccarelli, N., Enright, J., Frazzoli, E., Rasmussen, S., Schumacher, C.: Micro UAV path planning for reconnaissance in wind. In: American Control Conference, New York City, New York, USA, pp. 5310–5315 (2007) CrossRefGoogle Scholar
  8. 8.
    McGee, T.G., Spry, S., Hendrick, J.K.: Optimal path planning in a constant wind with a bounded turning rate. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, pp. 1–11 (2005) Google Scholar
  9. 9.
    Rysdyk, R.: Course and heading changes in significant wind. J. Guid. Control Dyn. 30, 1168–1171 (2007) CrossRefGoogle Scholar
  10. 10.
    Techy, L., Woolsey, C.A.: Minimum-time path planning for unmanned aerial vehicles in steady uniform winds. J. Guid. Control Dyn. 32, 1736–1746 (2009) CrossRefGoogle Scholar
  11. 11.
    McGee, T.G., Hendrick, J.K.: Path planning and control for multiple point surveillance by an unmanned aircraft in wind. In: American Control Conference, Minneapolis, Minnesota, USA, pp. 4261–4266 (2006) Google Scholar
  12. 12.
    Sanfelice, R.G., Frazzoli, E.: On the optimality of Dubins paths across heterogeneous terrain. In: Hybrid Systems: Computation and Control, St. Louis, Missouri, USA, pp. 457–470 (2008) CrossRefGoogle Scholar
  13. 13.
    Lozano-Perez, T., Wesley, M.: An algorithm for planning collision-free paths among polyhedral obstacles. Commun. ACM 22, 560–570 (1979) CrossRefGoogle Scholar
  14. 14.
    Rowe, N., Ross, R.: Optimal grid-free path planning across arbitrarily contoured terrain with anisotropic friction and gravity effects. IEEE Trans. Robot. 6, 540–553 (1990) CrossRefGoogle Scholar
  15. 15.
    Rowe, N., Alexander, R.: Finding optimal-path maps for path planning across weighted regions. Int. J. Robot. Res. 19, 83–95 (2000) CrossRefGoogle Scholar
  16. 16.
    LaValle, S.M.: Planning Algorithms. Cambridge University Press, New York (2006) MATHCrossRefGoogle Scholar
  17. 17.
    Paley, D.A., Peterson, C.: Stabilization of collective motion in a time-invariant flowfield. J. Guid. Control Dyn. 32, 771–779 (2009) CrossRefGoogle Scholar
  18. 18.
    Leonard, N.E., Paley, D., Lekien, F., Sepulchre, R., Fratantoni, D.M., Davis, R.: Collective motion, sensor networks and ocean sampling. Proc. IEEE 95, 48–74 (2007) CrossRefGoogle Scholar
  19. 19.
    Techy, L., Smale, D., Woolsey, C.: Coordinated aerobiological sampling of a plant pathogen in the lower atmosphere using two autonomous Unmanned Aerial Vehicles. J. Field Robot. 27, 335–343 (2010) Google Scholar
  20. 20.
    Bakolas, E., Tsiotras, P.: Minimum-time paths for a small aircraft in the presence of regionally-varying strong winds. In: AIAA Infotech@Aerospace, Atlanta, GA, pp. 2010–3380 (2010) Google Scholar
  21. 21.
    Kwok, A.: Deployment algorithms for mobile robots under dynamic constraints. Ph.D. Thesis, University of California, San Diego (2011) Google Scholar
  22. 22.
    Serres, U.: On the curvature of two-dimensional optimal control systems and Zermelo’s navigation problem. J. Math. Sci. 135, 3224–3243 (2006) MathSciNetMATHCrossRefGoogle Scholar
  23. 23.
    Gruber, P.M.: Approximation of convex bodies. In: Gruber, P.M., Wills, J.M. (eds.) Convexity and Its Applications, pp. 131–162. Birkhäuser, Boston (1983) Google Scholar
  24. 24.
    Chen, L.: Mesh smoothing schemes based on optimal Delaunay triangulations. In: 13th International Meshing Roundtable, pp. 109–120. Sandia National Laboratories (2004) Google Scholar
  25. 25.
    McLure, D.E., Vitale, R.A.: Polygonal approximation of plane convex bodies. J. Math. Anal. Appl. 51, 326–358 (1975) MathSciNetCrossRefGoogle Scholar
  26. 26.
    Susca, S., Martínez, S., Bullo, F.: Monitoring environmental boundaries with a robotic sensor network. IEEE Trans. Control Syst. Technol. 16, 288–296 (2008) CrossRefGoogle Scholar
  27. 27.
    Kwok, A., Martínez, S.: A coverage algorithm for drifters in a river environment. In: 2010 American Control Conference, Baltimore, MD, USA, pp. 6436–6441 (2010) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Opera SolutionsSan DiegoUSA
  2. 2.Mechanical and Aerospace Engineering9500 Gilman DriveLa JollaUSA

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