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Volume 1 Issue 4
Oct.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(4): 385-396
Rubo Zhang, Pingpeng Tang, Yumin Su, Xueyao Li, Ge Yang and Changting Shi, "An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 385-396, 2014.
Citation: Rubo Zhang, Pingpeng Tang, Yumin Su, Xueyao Li, Ge Yang and Changting Shi, "An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 4, pp. 385-396, 2014.
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An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments

  • 1. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China and College of Electromechanical and Information, Dalian Nationalities University, Dalian 150001, China;

  • 2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China, and also with Wuhan Second Ship Design and Research Institute, Wuhan 430064, China;

  • 3. National Key Laboratory of Science and Technology on Autonomous Underwater Vehicle, Harbin Engineering University, Harbin 150001, China;

  • 4. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China

Funds:

This work was supported by National Natural Science Foundation of China (60975071, 61100005, 60975019).

    Abstract
  • Unmanned surface vehicles (USVs) are important autonomous marine robots that have been studied and gradually applied into practice. However, the autonomous navigation of USVs, especially the issue of obstacle avoidance in complicated marine environment, is still a fundamental problem. After studying the characteristics of the complicated marine environment, we propose a novel adaptive obstacle avoidance algorithm for USVs, based on the Sarsa on-policy reinforcement learning algorithm. The proposed algorithm is composed of local avoidance module and adaptive learning module, which are organized by the "divide and conquer" strategy-based architecture. The course angle compensation strategy is proposed to offset the disturbances from sea wind and currents. In the design of payoff value function of the learning strategy, the course deviation angle and its tendency are introduced into action rewards and penalty policies. The validity of the proposed algorithm is verified by comparative experiments of simulations and sea trials in three sea-state marine environments. The results show that the algorithm can enhance the autonomous navigation capacity of USVs in complicated marine environments.

     

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    • References(31)
  • [1]
    Steimle E T, Hall M L. Unmanned surface vehicles as environmentalmonitoring and assessment tools. In: Proceedings of Oceans 2006.Massachusetts, Boston, USA: IEEE, 2006. 1-5
    [2]
    Bibuli M, Bruzzone G, Caccia M, Indiveri G, Zizzari A A. Linefollowing guidance control: application to the Charlie unmanned surfacevehicle. In: Proceedings of IEEE/RSJ International Conference onIntelligent Robots and Systems, IROS 2008. Nice, France: IEEE, 2008.3641-3646
    [3]
    Aditya S G, Christian S, Shu D, Daniel J S, Craig W. Guidance andcontrol of an unmanned surface vehicle exhibiting sternward motion. In:Proceedings of the Oceans. Hampton Roads, VA: IEEE, 2012. 1-9
    [4]
    Raboin E, Svec P, Nau D, Gupta S K. Model-predictive target defense byteam of unmanned surface vehicles operating in uncertain environments.In: Proceedings of the 2013 IEEE International Conference on Roboticsand Automation (ICRA). Karlsruhe, Germany: IEEE, 2013. 3517-3522
    [5]
    Wang J H, Chen C F, Huang P P, Gu W, Chu J X. Modeling, simulatingand experiment of an autonomous surface vehicle. Energy Procedia,2011, 11(2): 314-318
    [6]
    Motwani A. Survey of unmanned surface vehicles, Technical ReportMIDAS.SMSE.2012.TR.001, Plymouth University, UK, 2012.
    [7]
    Tan Min, Wang Shuo. Research progress on robotics. Acta AutomaticaSinica, 2013, 39(7): 963-972 (in Chinese)
    [8]
    Naeem W, Irwin G W, Yang A. COLREGS-based collision avoidancestrategies for unmanned surface vehicles. Mechatronics, 2012, 22(6):669-678
    [9]
    Szymak P, Praczyk T. Using neural-evolutionary-fuzzy algorithm foranti-collision system of unmanned surface vehicle. In: Proceedings of the17th International Conference on Methods and Models in Automationand Robotics (MMAR). Miedzyzdro, Poland: IEEE, 2012. 286-290
    [10]
    Zhuang Jia-Yuan, Su Yu-Min, Liao Yu-Lei, Sun Han-Bing. Unmannedsurface vehicle local path planning based on marine radar. Journal ofShanghai Jiao Tong University, 2012, 46(9): 1371-1375 (in Chinese)
    [11]
    Yang A L, Niu Q, ZhaoWQ, Li K, Irwin GW. An efficient algorithm forgrid-based robotic path planning based on priority sorting of directionvectors. In: Proceedings of the 2010 International Conference on LifeSystem Modeling and Intelligent Computing. Berlin, Germany: Springer,2010. 456-466
    [12]
    Huntsberger T, Aghazarian H, Howard A, Trotz D C. Stereo vision-basednavigation for autonomous surface vessels. Journal of Field Robotics,2011, 28(1): 3-18
    [13]
    Larson J, Bruch M, Halterman R, Rogers J, Webster R. Advancesin autonomous obstacle avoidance for unmanned surface vehicles. In:Proceedings of the AUVSI Unmanned Systems North America 2007.Washington, DC, USA: AUVSI, 2007. 1-15
    [14]
    Tang P P, Zhang R B, Liu D L, Zou Q J, Shi C T. Research on nearfieldobstacle avoidance for unmanned surface vehicle based on headingwindow. In: Proceedings of the Control and Decision Conference(CCDC). Taiyuan, China: IEEE, 2012. 1262-1267
    [15]
    Krishnamurthy P, Khorrami F, Ng T L. Obstacle avoidance for unmannedsea surface vehicles: a hierarchical approach. In: Proceedings of the17th World Congress, the International Federation of Automatic Control.Seoul, Korea: IFAC, 2008. 6798-6803
    [16]
    Bandyophadyay T, Sarcione L, Hover F S. A simple reactive obstacleavoidance algorithm and its application in Singapore harbor. Field andService Robotics, 2010, 62: 455-465
    [17]
    Kuwata Y, Wolf M T, Zarzhitsky D, Huntsberger T L. Safe maritimeautonomous navigation with COLREGS, using velocity obstacles. IEEEJournal of Oceanic Engineering, 2014, 39(1): 110-119
    [18]
    Campbell S, NaeemW, Irwin GW. A review on improving the autonomyof unmanned surface vehicles through intelligent collision avoidancemanoeuvres. Annual Reviews in Control, 2012, 36(2): 267-283
    [19]
    Thakur A, Svec P, Gupta S K. GPU based generation of state transitionmodels using simulations for unmanned surface vehicle trajectoryplanning. Robotics and Autonomous Systems, 2012, 60(12): 1457-1471
    [20]
    Stelzer R, Prollb T. Autonomous sailboat navigation for short courseracing. Robotics and Autonomous Systems, 2008, 56(7): 604-614
    [21]
    Faltinsen O M. Hydrodynamics of High-Speed Marine Vehicles. Cambridge,UK: Cambridge University Press, 2005.
    [22]
    Xin Bin, Chen Jie, Peng Zhi-Hong. Intelligent optimized control:overview and prospect. Acta Automatica Sinica, 2013, 39(11):1831-1848 (in Chinese)
    [23]
    Yu Z Y, Bao X P, Nonami K. Course keeping control of an autonomousboat using low cost sensors. Journal of System Design and Dynamics,2008, 2(1): 389-400
    [24]
    Wu G X, Sun H B, Zou J, Wan L. The basic motion control strategyfor the water-jet-propelled USV. In: Proceedings of the 2009 IEEEInternational Conference on Mechatronics and Automation. Changchun,China: IEEE, 2009. 611-616
    [25]
    Wu G X, Zou J, Wan L, Qin Z B. Design of the intelligence motioncontrol system for the high-speed USV. In: Proceedings of the 2ndInternational Conference on Intelligent Computation Technology andAutomation. Changsha, China: IEEE, 2009. 50-53
    [26]
    Sutton R S, Barto A G. Reinforcement Learning: An Introduction.Cambridge, MA: MIT Press, 1998.
    [27]
    Singh S P, Jaakkola T, Littman M L, Szepesvari C. Convergence resultsfor single-step on-policy reinforcement-learning algorithms. MachineLearning, 2000, 38(3): 287-308
    [28]
    Watkins C. Learning from Delayed Rewards [Ph. D. dissertation], KingsCollege, University of Cambridge, UK, 1989.
    [29]
    Rummery G A, Niranjan M. On-line Q-learning Using ConnectionistSystems. Cambridge, UK: Department of Engineering, University ofCambridge, 1994.
    [30]
    Feinberg E A, Shwartz A. Handbook of Markov Decision Processes:Methods and Applications. Boston, MA: Kluwer Academic Publishers,2002.
    [31]
    Xu Xin, Shen Dong, Gao Yan-Qing, Wang Kai. Learning controlof dynamical systems based on Markov decision processes: researchfrontiers and outlooks. Acta Automatica Sinica, 2012, 38(5): 673-687(in Chinese)
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