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Toward autonomous robotic containment booms: visual servoing for robust inter-vehicle docking of surface vehicles

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Abstract

Inter-vehicle docking is the problem of coordinating multiple robots to actively form and maintain physical contact. It is an important capability for autonomous surface vehicles (ASVs) and is an essential part of a wide class of missions. This article considers one such mission: the emergency response and environmental protection problem of containing a floating pollutant. We propose a solution in which multiple robots autonomously navigate so as to surround the surface matter. Before doing so, the robots dock with one another to secure specialized attachments designed to ensnare the contaminant. We describe the prototypical physical robot system developed to perform this task, and we detail the system architecture, sensing and computational hardware, control system, and visual processing pipeline. While employing multiple ASVs maximizes spatial reconfigurability, it depends on the inter-robot docking capabilities being particularly reliable. But achieving robust docking is a significant technical challenge because the water continually induces external disturbances on the control system. These disturbances are non-stationary and almost impossible to predict for unknown environments. Our system relies primarily on visual servoing within a control framework in which a variety of sensors are fused. Accurate disturbance measurements are obtained through traditional sensor modeling and filtering techniques. As the environment is a priori unknown, varies from trial to trial, and has proven difficult to model, we apply a model-free reinforcement learning algorithm, SARSA(λ), along with specialized initial conditions which ensure stable operation, and an exploration guidance approach that increases the speed of convergence. We adopt a two-loop control scheme for visual servoing to successfully make use of feature descriptors with various (and variable) computational times. We demonstrate this approach to the docking problem with autonomous ground vehicles and ASVs. The results from several situations are compared, showing that disturbance rejection coupled with SARSA(λ) is an effective approach.

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References

  1. Aull M (2009) Visual servoing for an autonomous rendezvous and capture system. Intell Serv Robot 2(3): 131–137

    Article  Google Scholar 

  2. BBC (2007) South Korea fights huge oil spill. http://news.bbc.co.uk/2/hi/asia-pacific/7135896.stm. Accessed 10 Dec 2007

  3. Benjamin M, Curcio J, Leonard JJ, Newman P (2006) Navigation of unmanned marine vehicles in accordance with the rules of the road. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’06), Orlando, FL, May 2006, pp 3581–3587

  4. Bertram V (2008) Unmanned surface vehicles—a Survey. In: Proceedings of skibsteknisk selskab meeting, Copenhagen, Denmark, March 2008

  5. Bibuli M, Bruzzone G, Caccia M, Indiveri G, Zizzari AA (2008) Line following guidance control: application to the Charlie unmanned surface vehicle. In: Proceedings of the IEEE international conference on intelligent robots and systems (IROS’08), Nice, France, September 2008, pp 3641–3646

  6. Caccia M, Bibuli M, Bono R, Bruzzone G (2008) Basic navigation, guidance and control of an unmanned surface vehicle. Auton Robots 25(4): 349–365

    Article  Google Scholar 

  7. Clauss GF, Kauffeldt A, Otten N (2009) AGaPaS—autonomous Galileo-supported rescue vessel for persons overboard. In: Proceedings of international conference on ocean, offshore and arctic engineering (OMAE’09), Honolulu, Hawaii, May 2009

  8. Comaniciu D, Meer P, Senior Member (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Google Scholar 

  9. Curcio J, Leonard J, Patrikalakis N (2005) SCOUT—a low cost autonomous surface platform for research in cooperative autonomy. In: Proceedings of the MTS/IEEE oceans conference, Washington D.C., September 2005, pp 725–729

  10. Doerffer J (1992) Oil spill response in marine environment. Pergamon Press, New York

    Google Scholar 

  11. Dunbabin M, Lang B, Wood B (2008) Vision-based docking using an autonomous surface vehicle. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’08), Pasadena, CA, USA, May 2008, pp 26–32

  12. El-Fakdi A, Carreras M (2008) Policy gradient based Reinforcement Learning for real autonomous underwater cable tracking. In: Proceedings of the international conference on intelligent robots and systems (IROS’08), Nice, France, September 2008, pp 3635–3640

  13. Fahimi F (2007) Sliding-mode formation control for underactuated surface vessels. IEEE Trans Robot 23(3): 617–622

    Article  Google Scholar 

  14. Fang J, Wong K-FV (2001) Optimization of an oil boom arrangement. In: Proceedings of Biennial international conference on oil spills. Tampa, FL, March 2001

  15. Fang J, Wong K-FV (2006) An advanced VOF algorithm for oil boom design. Int J Model Simul 26(1): 36–44

    Google Scholar 

  16. Farrel JA, Barth M (1998) The global positioning system and inertial navigation. McGraw-Hill, New York

    Google Scholar 

  17. Fingas MF, Charles J (2000) The basics of oil spill cleanup, 2 edn. CRC Press, Boca Raton

    Book  Google Scholar 

  18. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6): 381–395

    Article  MathSciNet  Google Scholar 

  19. Gaskett C, Wettergreen D, Zelinsky A (1999) Reinforcement Learning applied to the control of an Autonomous Underwater Vehicle. In: Proceedings of the Australian conference on robotics and automation (AUCRA99), Brisbane, Australia, March 1999, pp 125–131

  20. Giesbrecht JL, Goi HK, Barfoot TD, Francis BA (2009) A vision-based robotic follower vehicle. Proc Int Soc Opt Photonics (SPIE) 7332: 733210-1–733210-12

    Google Scholar 

  21. Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice using MATLAB. Wiley, New York

    Google Scholar 

  22. Grewal MS, Henderson VD, Miyasako RS (1991) Application of Kalman filtering to the calibration and alignment of inertial navigation systems. IEEE Trans Autom Control 36(1): 4–13

    Article  MathSciNet  Google Scholar 

  23. Grier P (2010) Containment boom effort comes up short in BP oil spill. The Christian Science Monitor, Boston

  24. Hutchinson S, Hage GD, Corke PI (1996) A tutorial on visual servo control. IEEE Trans Robot Autom 12(5): 651–670

    Article  Google Scholar 

  25. Joshi N, Kang SB, Zitnick CL, Szeliski R (2010) Image deblurring using inertial measurement sensors. ACM Trans Graph 29(4): 1–9

    Article  Google Scholar 

  26. Kakalis NMP, Ventikos Y (2008) Robotic swarm concept for efficient oil spill confrontation. J Hazard Mater 154(1–3): 880–887

    Article  Google Scholar 

  27. Kim M, Chong NY, Yu W (2009) Robust DOA estimation and target docking for mobile robots. Intell Serv Robot 2(1): 41–51

    Article  Google Scholar 

  28. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2): 91–110

    Article  Google Scholar 

  29. Martinez-Marin T, Duckett T (2005) Fast Reinforcement Learning for vision-guided mobile robots. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’05), Barcelona, Spain, April 2005, pp 4170–4175

  30. Martins A, Almeida JM, Ferreira H, Silva H, Dias N, Dias A, Almeida C, Silva EP (2007) Autonomous surface vehicle docking manoeuvre with visual information. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’07), Roma, Italy, April 2007, pp 4994–4999

  31. Murarka A, Kuhlmann G, Gulati S, Sridharan M, Flesher C, Stone WC (2009) Vision-based frozen surface egress: a docking algorithm for the ENDURANCE AUV. In: Proceedings of the international symposium on unmanned untethered submersible technology (UUST), New Hampshire, USA, August 2009

  32. Murphy RR, Steimle E, Hall M, Lindemuth M, Trejo D, Hurlebaus S, Medina-Cetina Z, Slocum D (2011) Robot-assisted bridge inspection. J Intelligent Robot Syst 64(1): 77–95

    Article  Google Scholar 

  33. NOAA (2010) Deepwater horizon MC252 gulf incident oil budget. http://www.noaanews.noaa.gov/stories2010/PDFs/DeepwaterHorizonOilBudget20100801.pdf. Accessed 2 Aug 2010

  34. Ondřej C, Jiri M (2005) Matching with PROSAC—progressive sample consensus. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, San Diego,USA, July 2005, pp 220–226

  35. Park J-Y, Jun B-H, Lee P-M, Oh J (2009) Experiments on vision guided docking of an autonomous underwater vehicle using one camera. Ocean Eng 36(1): 48–61

    Article  Google Scholar 

  36. Parker LE (2008) Multiple mobile robot systems. In: Siciliano B, Khatib O (eds) Handbook of robotics, chapter 40. Springer, Berlin

    Google Scholar 

  37. Pereira A, Das J, Sukhatme GS (2008) An experimental study of station keeping on an underactuated ASV. In: Proceedings of the IEEE international conference on intelligent robots and systems (IROS’08), Nice, France, September 2008, pp 3164–3171

  38. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. In: Proceedings of the European Conference on Computer Vision (ECCV’06), Graz,Austria, May 2006, pp 430–443

  39. Sullivan G (2010) Miles of oil containment boom sit in warehouse, waiting for BP or U.S. to Use. Pajamas Media, June, 8 2010

  40. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge

    Google Scholar 

  41. Wang J, Gu W, Zhu J (2009) Design of an autonomous surface vehicle used for marine environment monitoring. In: Proceedings of the international conference on advanced computer control, Los Alamitos, CA, USA, 2009, pp 405–0409

  42. Welch G, Bishop G (2006) An introduction to the Kalman filter. Technical Report TR 95-041, University of North Carolina at Chapel Hill, July 2006

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Correspondence to Young-Ho Kim.

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Kim, YH., Lee, SW., Yang, H.S. et al. Toward autonomous robotic containment booms: visual servoing for robust inter-vehicle docking of surface vehicles. Intel Serv Robotics 5, 1–18 (2012). https://doi.org/10.1007/s11370-011-0100-0

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