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Journal of Marine Science and Technology

, Volume 19, Issue 1, pp 75–89 | Cite as

A new hybrid terrain coverage method for underwater robotic exploration

  • Tae-Seok LeeEmail author
  • Beom Hee Lee
Original article

Abstract

It is well known that it is difficult to explore underwater terrains using an autonomous underwater vehicle due to the varieties and complexities of underwater terrain elements. Since conventional underwater terrain coverage techniques are usually based on the assumption that the underwater surface is planar, they generate an unnecessary exploration path especially on steep sloped surfaces of ocean basins. This paper proposes a new type of coverage technique, the hybrid terrain coverage framework (HTCF), which considers various surface conditions in three-dimensional environments and generates an efficient exploration path for all environments. The HTCF incorporates a planar terrain coverage algorithm, a spiral path terrain coverage algorithm, and a hybrid decision module to recognize and select the most suitable technique depending on the sloped surface variations. Simulation results show that the proposed HTCF is more efficient than the conventional terrain coverage algorithm in terms of the energy consumption of the underwater vehicle.

Keywords

Coverage path planning Underwater exploration Autonomous underwater vehicle (AUV) 

Notes

Acknowledgments

This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2013R1A2A1A05005547), in part by the Brain Korea 21 Project, and in part by the Industrial Foundation Technology Development Program of MOTIE/KEIT [Development of CIRT (Collective Intelligence Robot Technologies)].

References

  1. 1.
    Oommen BJ, Iyengar SS, Rao NSV, Kashyap RL (1987) Robot navigation in unknown terrains using learned visibility graphs. Part I: the disjoint convex obstacle case. IEEE J Robotics Autom 3:672–681CrossRefGoogle Scholar
  2. 2.
    Rao NSV, Iyengar SS (1990) Autonomous robot navigation in unknown terrains: incidental learning and environmental exploration. IEEE Trans Syst Man Cybern 20(6):1443–1449CrossRefGoogle Scholar
  3. 3.
    Lumelsky V, Mukhopadhyay S, Sun K (1990) Dynamic path planning in sensor-based terrain acquisition. IEEE Trans Robotics Autom 6(4):462–472CrossRefGoogle Scholar
  4. 4.
    Hert S, Tiwari S, Lumelsky V (1996) A terrain-covering algorithm for an AUV. Auton Robots 3:91–119CrossRefGoogle Scholar
  5. 5.
    Jung YS, Lee KW, Lee SY, Choi MH, Lee BH (2009) An efficient underwater coverage method for multi-AUV with sea current disturbances. Int J Control Autom Syst 7(4):615–629CrossRefGoogle Scholar
  6. 6.
    Jung YS, Lee KW, Lee BH (2008) Advances in sea coverage methods using autonomous underwater vehicles. In: Lazinica A (ed) Recent advances in multi robot systems. Intech, Vienna, pp 69–100Google Scholar
  7. 7.
    Lee TS, Choi JS, Lee JH, Lee BH (2009) 3-D terrain covering and map building algorithm for an AUV. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA, pp 4420–4425Google Scholar
  8. 8.
    Fairfield N, Kantor G, Wetergreen D (2007) Real-time SLAM with octree evidence grids for exploration in underwater tunnels. J Field Robotics 24(1–2):3–21CrossRefGoogle Scholar
  9. 9.
    Fairfield N, Kantor G, Jonak K, Wettergreen D (2010) Autonomous exploration and mapping of flooded sinkholes. Int J Robotics Res 29(6):748–774CrossRefGoogle Scholar
  10. 10.
    Wu P, Suzuki H, Kase K (2006) Model-based simulation system for planning numerical controlled multi-axis 3D surface scanning machine. JSME Int J Ser C 48(4):748–756CrossRefGoogle Scholar
  11. 11.
    Connolly CI (1985) The determination of next best views. In: Proceedings of the IEEE International Conference of Robotics and Automation, pp 432–435Google Scholar
  12. 12.
    Massios NA, Fisher RB (1998) A best next view selection algorithm incorporating a quality criterion. In: Proceedings of the British Machine Vision Conference (BMVC), University of Southampton, UK, pp 780–789Google Scholar
  13. 13.
    Yuan X (1995) A mechanism of automatic 3D object modeling. IEEE Trans Pattern Anal Mach Intell 17(3):307–311CrossRefGoogle Scholar
  14. 14.
    Pito R (1996) A sensor based solution to the next best view problem. In: Proceedings of the International Conference on Pattern Recognition, Vienna, Austria, pp 941–945Google Scholar
  15. 15.
    Maver J, Bajcsy R (1993) Occlusions as a guide for planning the next view. IEEE Trans Pattern Anal Mach Intell 15(5):417–432CrossRefGoogle Scholar
  16. 16.
    Nygren I, Jansson M (2004) Terrain navigation for underwater vehicles using the correlator method. IEEE J Oceanic Eng 29(3):906–915CrossRefGoogle Scholar
  17. 17.
    Rosenblatt J, Williams S, Durrant-Whyte H (2002) A behavior-based architecture for autonomous underwater exploration. Inf Sci 145(1–2):69–87CrossRefzbMATHGoogle Scholar
  18. 18.
    Biggs J, Holderbaum W (2009) Optimal kinematic control of an autonomous underwater vehicle. IEEE Trans Autom Control 54(7):1623–1626CrossRefMathSciNetGoogle Scholar
  19. 19.
    Spangelo I, Egeland O (1994) Trajectory planning and collision avoidance for underwater vehicles using optimal control. IEEE J Oceanic Eng 19(4):502–511CrossRefGoogle Scholar
  20. 20.
    Kumar RP, Dasgupta A, Kumar CS (2005) Real-time optimal motion planning for autonomous underwater vehicles. Ocean Eng 32(11–12):1431–1447CrossRefGoogle Scholar
  21. 21.
    Fossen TI (1994) Guidance and control of ocean vehicles. Wiley, New YorkGoogle Scholar

Copyright information

© JASNAOE 2013

Authors and Affiliations

  1. 1.ASRI and Department of Electrical EngineeringSeoul National UniversitySeoulKorea

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