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Obstacle Avoidance Approaches for Autonomous Navigation of Unmanned Surface Vehicles

Published online by Cambridge University Press:  10 October 2017

Riccardo Polvara Open the ORCID record for Riccardo Polvara [Opens in a new window] ,
Sanjay Sharma ,
Jian Wan ,
Andrew Manning  and
Robert Sutton
Riccardo Polvara*
Affiliation:
(Autonomous Marine Systems Research Group, Faculty of Science and Engineering, University of Plymouth, Plymouth, Devon, PL4 8AA, UK)
Sanjay Sharma
Affiliation:
(Autonomous Marine Systems Research Group, Faculty of Science and Engineering, University of Plymouth, Plymouth, Devon, PL4 8AA, UK)
Jian Wan
Affiliation:
(Autonomous Marine Systems Research Group, Faculty of Science and Engineering, University of Plymouth, Plymouth, Devon, PL4 8AA, UK)
Andrew Manning
Affiliation:
(Autonomous Marine Systems Research Group, Faculty of Science and Engineering, University of Plymouth, Plymouth, Devon, PL4 8AA, UK) (HR Wallingford, Howbery Park, Wallingford, Oxfordshire, OX10 8BA, UK) (Geography & Geology, School of Environmental Sciences, Faculty of Science & Engineering, University of Hull, Hull, HU6 7RX, UK)
Robert Sutton
Affiliation:
(Autonomous Marine Systems Research Group, Faculty of Science and Engineering, University of Plymouth, Plymouth, Devon, PL4 8AA, UK)
*
(E-mail: [email protected])
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Abstract

The adoption of a robust collision avoidance module is required to realise fully autonomous Unmanned Surface Vehicles (USVs). In this work, collision detection and path planning methods for USVs are presented. Attention is focused on the difference between local and global path planners, describing the most common techniques derived from classical graph search theory. In addition, a dedicated section is reserved for intelligent methods, such as artificial neural networks and evolutionary algorithms. Born as optimisation methods, they can learn a close-to-optimal solution without requiring large computation effort under certain constraints. Finally, the deficiencies of the existing methods are highlighted and discussed. It has been concluded that almost all the existing method do not address sea or weather conditions, or do not involve the dynamics of the vessel while defining the path. Therefore, this research area is still far from being considered fully explored.

Keywords

Unmanned Surface VehicleCollision AvoidancePath Planning
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 241 - 256
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Achanta, R., Hemami, S., Estrada, F. and Susstrunk, S. (2009). Frequency-tuned Salient Region Detection. IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2009, 1597–1604.Google Scholar
Allen, C. H. (2012). The Seabots are Coming Here: Should they be Treated as ‘Vessels’? The Journal of Navigation, 65, 749–752.CrossRefGoogle Scholar
Almeida, C., Franco, T., Ferreira, H., Martins, A., Santos, R., Almeida, J. M. and Silva, E. (2009). Radar Based Collision Detection Developments on USV ROAZ II. Oceans 09, Bremen, Germany, 1–6.Google Scholar
Alves, J., Oliveira, P., Oliveira, R., Pascoal, A., Rufino, M., Sebastiao, L. and Silvestre, C. (2006). Vehicle and Mission Control of the DELFIM Autonomous Surface Craft. 14th Mediterranean Conference on Control and Automation, 1–6.Google Scholar
Azzabi, T., Amor, S. B., and Nejim, S. (2014). Obstacle Detection for Unmanned Surface Vehicle. International Conference on Electrical Sciences and Technologies in Maghreb 2013, 5, 1–7.CrossRefGoogle Scholar
Bandyophadyay, T., Sarcione, L. and Hover, F. S. (2010). A simple reactive obstacle avoidance algorithm and its application in Singapore Harbor. In Field and Service Robotics (455–465). Springer Berlin Heidelberg.Google Scholar
Barnich, O. and Van Droogenbroeck, M. (2011). ViBe: A universal background subtraction algorithm for video sequences. IEEE Transactions on Image Processing, 20, 1709–1724.Google Scholar
Belcher, P. (2002). A sociological interpretation of the COLREGS. The Journal of Navigation, 55, 213–224.Google Scholar
Bertram, V. (2008). Unmanned Surface Vehicles: A Survey. Skibsteknisk Selskab, Copenhagen, Denmark, 1–14.Google Scholar
Bibuli, M., Bruzzone, G., Caccia, M. and Lapierre, L. (2009). Path-following algorithms and experiments for an unmanned surface vehicle. Journal of Field Robotics 26, 669–688.CrossRefGoogle Scholar
Blaich, M., Koehler, S., Schuster, M., Reuter, J. and Tietz, T. (2015). Mission Integrated Collision Avoidance for USVs using Laser Ranger. Oceans 2015, Genova, Italy, 1–5.Google Scholar
Borghgraef, A., Barnich, O., Lapierre, F., Van Droogenbroeck, M., Philips, W. and Acheroy, M. (2010). An Evaluation of Pixel-Based Methods for the Detection of Floating Objects on the Sea Surface. EURASIP Journal on Advances in Signal Processing, 1–11 2010(1), 978451.Google Scholar
Bouguet, J.-y. (2000). Pyramidal Implementation of the Lucas Kanade Feature Tracker. Intel Corporation, Microprocessor Research Labs.Google Scholar
Campbell, S., Naeem, W. and Irwin, G. W. (2012). A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres. Annual Reviews in Control, 36(2), 267–283.Google Scholar
Casalino, G., Turetta, A. and Simetti, E. (2009). A Three-layered Architecture for Real Time Path Planning and Obstacle Avoidance for Surveillance USVs Operating in Harbour Fields. Oceans 2009, Bremen, Germany, 1–8.Google Scholar
Chang, K.-Y., Jan, G. E., and Parberry, I. (2003). A Method for Searching Optimal Routes with Collision Avoidance on Raster Charts. The Journal of Navigation, 56, 371–384.Google Scholar
Chen, J., Pan, W., Guo, T., Huang, C. and Wu, H. (2013). An Obstacle Avoidance Algorithm Designed for USV Based on Single Beam Sonar and Fuzzy Control. IEEE International Conference on Robotics and Biomimetics (ROBIO), 2446–2451.CrossRefGoogle Scholar
COLREGS. (1972). International Regulations for Prevention of Collisions at Sea, 1972. International Maritime Organization (IMO).Google Scholar
Dijkstra, E. W. (1959). A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, 1, 269–271.CrossRefGoogle Scholar
Ettinger, S., Nechyba, M., Ifju, P. and Waszak, M. (2003). Towards Flight Autonomy: Vision-based Horizon Detection for Micro Air Vehicles. Proceedings of the Florida Conference in Recent Advances in Robotics, 7, 617–640.Google Scholar
Fahimi, F. (2008). Autonomous robots: modeling, path planning, and control, 107, Springer Science & Business Media.Google Scholar
Fischler, M. A. and Bolles, R. C. (1981). Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Communications of the ACM, 24, 381–395.CrossRefGoogle Scholar
Gal, O. and Zeitouni, E. (2013). Tracking objects using PHD filter for USV autonomous capabilities. In Robotic sailing 2012 (3–12). Springer Berlin Heidelberg.Google Scholar
Glick, T. F., and Kohn, D. (1996). On evolution: the development of the theory of natural selection, Hackett Publishing.Google Scholar
Halterman, R. and Bruch, M. (2010). Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection. Proc. SPIE 7692, Unmanned Systems Technology XII, 76920D.Google Scholar
Harris, C. and Stephens, M.. (1988). A Combined Corner and Edge Detector. Proceedings of the Alvey Vision Conference, 147–151.Google Scholar
Hart, P., Nilsson, N. and Raphael, B. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4, 100–107.Google Scholar
Hasegawa, K. (2009). Advanced marine traffic automation and management system for congested waterways and coastal areas, Proceedings of 8th ship control system symposium, The Hague, Netherlands, 234–258.Google Scholar
Hasegawa, K., and Kouzuki, A. (1987). Automatic collision avoidance system for ships using fuzzy control. Journal of the Kansai Society of Naval Architects, 205.Google Scholar
Hough, P. (1962). Method and Means for Recognizing Complex Patterns. U.S. Patent 3.069.654, December 18.Google Scholar
Hwang, C.-N. (2002). The Integrated Design of Fuzzy Collision-Avoidance and H-∞ Autopilots on Ships. The Journal of Navigation, 55, 117–136.Google Scholar
Ito, M., Zhnng, F. and Yoshida, N. (1999). Collision avoidance control of ship with genetic algorithm. Proceedings of the 1999 IEEE International Conference on Control Applications, 2, 1791–1796.Google Scholar
Jodoin, P. M., Konrad, J. and Saligrama, V. (2008). Modeling background activity for behavior subtraction. ICDSC 2008. Second ACM/IEEE International Conference on Distributed Smart Cameras, 1–10.CrossRefGoogle Scholar
Kao, S.-L., Lee, K.-T., Chang, K.-Y. and Ko, M.-D. (2007). A Fuzzy Logic Method for Collision Avoidance in Vessel Traffic Service. The Journal of Navigation, 60, 17–31.Google Scholar
Kemp, J. (2002). Collision Regulations - Discussion. The Journal of Navigation, 55, 145–155.Google Scholar
Kemp, J., Bechley, M., Cockcroft, N., Jurdzinski, M. and Thirslund, S. (2012). Collision Avoidance at Sea in the Mid-20th Century. The Journal of Navigation, 65, 191–205.Google Scholar
Khatib, O. (1985). Real-time Obstacle Avoidance for Manipulators and Mobile Robots. IEEE International Conference on Robotics and Automation, 2, 500–505.Google Scholar
Kuwata, Y., Wolf, M. T., Zarzhitsky, D. and Huntsberger, T. L. (2014). Safe Maritime Autonomous Navigation with COLREGS, Using Velocity Obstacles. IEEE Journal of Oceanic Engineering, 39, 110–119.Google Scholar
Larson, J., Bruch, M., Ebken, J., Warfare, N., Diego, S. and Diego, S. (2007a). Autonomous Navigation and Obstacle avoidance for unmanned surface vehicles. Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series, 17–20.Google Scholar
Larson, J., Bruch, M., Halterman, R., Rogers, J. and Webster, R. (2007b). Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles. Techniques, 1–15.Google Scholar
Lazarowska, A. (2015). Ship's Trajectory Planning for Collision Avoidance at Sea Based on Ant Colony Optimisation. The Journal of Navigation, 68, 291–307.Google Scholar
Lebbad, A., and Nataraj, S. (2015). A Bayesian algorithm for vision based navigation of autonomous surface vehicles. IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM), Siem Reap, Cambodia, 59–64.Google Scholar
Lee, C. Y. (1961). An Algorithm for Path Connections and Its Applications. IRE Transactions on Electronic Computers, 3, 346–365.Google Scholar
Lee, S.-M., Kwon, K.-Y. and Joh, J. (2004). A fuzzy logic for autonomous navigation of marine vehicles satisfying COLREG guidelines. International Journal of Control Automation and Systems, 2, 171–181.Google Scholar
Lee, T., Kim, H., Chung, H., Bang, Y. and Myung, H.. (2015) Energy efficient path planning for a marine surface vehicle considering heading angle, Ocean Engineering, 107(1) , 118–131.CrossRefGoogle Scholar
Lee, Y.-I. and Kim, Y.-G. (2004). Intelligent Data Engineering and Automated Learning – IDEAL, 5th International Conference, Exeter, UK.Google Scholar
Leng, J., Liu, J. and Xu, H. (2013). Online Path Planning based on MILP for Unmanned Surface Vehicles. Oceans 2013, San Diego, USA, 1–7.Google Scholar
Liu, Y.-H., Du, X.-M. and Yang, S.-H. (2006). Advances in Machine Learning and Cybernetics: 4th International Conference, ICMLC 2005, Guangzhou, China, August 18-21, 2005, Revised Selected Papers. Chap. The Design of a Fuzzy-Neural Network for Ship Collision Avoidance, edited by D. S. Yeung, Z.-Q. Liu, X.-Z. Wang, and H. Yan, 804–812. Berlin, Heidelberg: Springer Berlin Heidelberg.Google Scholar
Liu, Z., Zhang, Y., Yu, X. and Yuan, C. (2016). Unmanned surface vehicles: An overview of developments and challenges. Annual Reviews in Control, 41, 71–93.CrossRefGoogle Scholar
Maini, R., and Aggarwal, H. (2009). Study and Comparison of Various Image Edge Detection Techniques. International Journal of Image Processing, 147002, 1–12.Google Scholar
Naeem, W., and Sutton, R. (2009). An Intelligent Integrated Navigation and Control Solution for an Unmanned Surface Craft. IET Irish Signals and Systems Conference, Dublin, Ireland, 1–6.Google Scholar
Niu, H., Lu, Y., Savvaris, A. and Tsourdos, A. (2016). Efficient Path Planning Algorithms for Unmanned Surface Vehicle. IFAC-PapersOnLine, 49(23), 121–126.Google Scholar
Park, K. T. and Jeong, J. (2012). Object detection in infrared image with sea clutter. 13th International Symposium on Advanced Intelligent Systems (ISIS) and 2012 Joint 6th International Conference on Soft Computing and Intelligent System (SCIS), 2143–2146.Google Scholar
Perera, L. P., Carvalho, J. P. and Guedes Soares, C. (2012). Intelligent ocean navigation and Fuzzy-Bayesian decision/action formulation. IEEE Journal of Oceanic Engineering, 37, 204–219.Google Scholar
Perera, L. P., Ferrari, V., Santos, F. P., Hinostroza, M. A. and Guedes Soares, C. (2015). Experimental Evaluations on Ship Autonomous Navigation and Collision Avoidance by Intelligent Guidance. IEEE Journal of Oceanic Engineering, 40, 374–387.Google Scholar
Schuster, M., Blaich, M. and Reuter, J. (2014). Collision Avoidance for Vessels using a Low-Cost Radar Sensor. 19th The International Federation of Automatic Control, Cape Town, South Africa, 2009, 9673–9678.Google Scholar
Simetti, E., Torelli, S., Casalino, G., and Turetta, A. (2014). Experimental Results on Obstacle Avoidance for High Speed Unmanned Surface Vehicles. Oceans 2014-St. John's , Canada, 1–6.Google Scholar
Simmons, R. and Henriksen, L. (1996). Obstacle Avoidance and Safeguarding for a Lunar Rover. AIAA Forum on Advanced Developments in Space Robotics.Google Scholar
Smierzchalski, R. (1999). Evolutionary trajectory planning of ships in navigation traffic areas. Journal of Marine Science and Technology, 4, 1–6.Google Scholar
Smierzchalski, R., and Michalewicz, Z. (2000). Modelling of ship trajectory in collision situations by an evolutionary algorithm. IEEE Transactions on Evolutionary Computation. Citeseer.Google Scholar
Statheros, T., Howells, G., and McDonald-Maier, K. (2008). Autonomous Ship Collision Avoidance Navigation Concepts, Technologies and Techniques. The Journal of Navigation, 61, 129–142.Google Scholar
Sutton, R. S. and Barto, A. G. (1998). Introduction to Reinforcement Learning. 1st. Cambridge, MA, USA: MIT Press.Google Scholar
Szlapczynski, R. (2006). A New Method of Ship Routing on Raster Grids, with Turn Penalties and Collision Avoidance. The Journal of Navigation, 59, 27–42.CrossRefGoogle Scholar
Szlapczynski, R. (2013). Evolutionary Sets of Safe Ship Trajectories Within Traffic Separation Schemes. The Journal of Navigation, 66, 65–81.CrossRefGoogle Scholar
Szlapczynski, R. (2015). Evolutionary Planning of Safe Ship Tracks in Restricted Visibility. The Journal of Navigation, 68, 39–51.Google Scholar
Tam, C., Bucknall, R. and Greig, A. (2009). Review of Collision Avoidance and Path Planning Methods for Ships in Close Range Encounters. The Journal of Navigation, 62, 455–476.Google Scholar
Tan, A., Wee, W. C. and Tan, T. J. (2010). Criteria and Rule Based Obstacle Avoidance for USVs. 2010 International WaterSide Security Conference, Carrara, Italy, 1–6.Google Scholar
Tang, P., Zhang, R., Liu, D., Zou, Q. and Shi, C. (2012). Research on Near-field Obstacle Avoidance for Unmanned Surface Vehicle Based on Heading Window. Proceedings of the 24th Chinese Control and Decision Conference, Taiyuan, China, 1262–1267.Google Scholar
Tsou, M.-C. (2010). Integration of a Geographic Information System and Evolutionary Computation for Automatic Routing in Coastal Navigation. The Journal of Navigation, 63, 323–341.Google Scholar
Wang, H., Wei, Z., Ow, C. S, Ho, K. T, Feng, B. and Huang, J. (2012). Improvement in Real-time Obstacle Detection System for USV. 12th International Conference on Control Automation Robotics Vision (ICARCV), Guangzhou, China, 1317–1322.Google Scholar
Wang, H., Wei, Z., Wang, S., Ow, C. S., Ho, K. T. and Feng, B. (2011). A Vision-based Obstacle Detection System for Unmanned Surface Vehicle. IEEE Conference on Robotics, Automation and Mechatronics, Qingdao, China, 364–369.Google Scholar
Wilson, P. A., Harris, C. J. and Hong, X. (2003). A Line of Sight Counteraction Navigation Algorithm for Ship Encounter Collision Avoidance. The Journal of Navigation, 56, 111–121.Google Scholar
Xie, S., Wu, P., Peng, Y., Luo, J, Qu, D., Li, Q. and Gu, J. (2014). The Obstacle Avoidance Planning of USV Based on Improved Artificial Potential Field. IEEE International Conference on Information and Automation (ICIA), Hailar, China, 746–751.Google Scholar
Zeng, X. (2003). Evolution of the safe path for ship navigation. Applied Artificial Intelligence, 17, 87–104.Google Scholar
Zhang, R., Tang, P., Su, Y., Li, X., Yang, G. and Shi, C. (2014). An Adaptive Obstacle Avoidance Algorithm for Unmanned Surface Vehicle in Complicated Marine Environments. IEEE/CAA Journal of Automatica Sinica, 1, 385–396.Google Scholar