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Research on Real-Time Obstacle Avoidance Planning for an Unmanned Surface Vessel based on the Grid Cell Mechanism

Published online by Cambridge University Press:  03 July 2020

Yun Li Open the ORCID record for Yun Li [Opens in a new window]  and
Jian Zheng
Yun Li
Affiliation:
(Merchant Marine College, Shanghai Maritime University, 201306Shanghai, China)
Jian Zheng*
Affiliation:
(Transport and Communications College, Shanghai Maritime University, 201306Shanghai, China)
*
(E-mail: [email protected])
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Abstract

Obstacle avoidance navigation for an unmanned surface vessel is a research focus for ship autonomy in which the real-time requirement in practical application is very serious, and always necessitates a complicated structure model to guarantee real-time performance. This paper proposes the grid cell activation model to reduce the complexity of modelling and to simplify an obstacle avoidance algorithm. Combined with the goal-oriented probability model to design a dynamic positive-loss-rate expectation evaluation function, it produces the proper strategy for obstacle avoidance. Case studies on multi-obstacle layouts and special circumstances are conducted and presented. The results indicate that the grid cell obstacle avoidance algorithm can effectively implement obstacle avoidance planning and ensure real-time requirements. A comparison with the potential field algorithm is performed, which shows good results and verifies the feasibility of the algorithm.

Keywords

Real-Time Obstacle AvoidanceGrid CellGoal-OrientedUnmanned Surface Vessel
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 6 , November 2020 , pp. 1358 - 1371
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Copyright
Copyright © The Royal Institute of Navigation 2020

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References

REFERENCES

Antonelo, E. A., Schrauwen, B. and Campenhout, J. V. (2007). Generative modeling of autonomous robots and their environments using reservoir computing. Neural Processing Letters, 26, 233–249.10.1007/s11063-007-9054-9CrossRefGoogle Scholar
Chen, P., Huang, Y., Mou, J. and van Gelder, P. H. A. J. M. (2018). Ship collision candidate detection method: A velocity obstacle approach. Ocean Engineering, 170, 186–198.10.1016/j.oceaneng.2018.10.023CrossRefGoogle Scholar
David, A. and Pierre, L. (2006). Hippocampal neuroanatomy. Oxford: Oxford University Press, 37–115.Google Scholar
González, D., Pérez, J., Milanés, V. and Nashashibi, F. (2016). A review of motion planning techniques for automated vehicles. IEEE Transactions on intelligent transportation systems, 17(4), 1135–1145.10.1109/TITS.2015.2498841CrossRefGoogle Scholar
Hong, J. P. and Park, K. (2011). A new mobile robot navigation using a turning point searching algorithm with the consideration of obstacle avoidance. The International Journal of Advanced Manufacturing Technology, 52, 763–775.10.1007/s00170-010-2749-5CrossRefGoogle Scholar
Kozynchenkoa, A. I. and Kozynchenko, S. A. (2018). Applying the dynamic predictive guidance to ship collision avoidance: Crossing case study simulation. Ocean Engineering, 164, 640–649.10.1016/j.oceaneng.2018.07.012CrossRefGoogle Scholar
Lee, K., Parka, C. and Eun, Y. (2018). Real-time collision avoidance maneuvers for spacecraft proximity operations via discrete-time Hamilton–Jacobi theory. Aerospace Science and Technology, 77, 688–695.10.1016/j.ast.2018.04.010CrossRefGoogle Scholar
Naeem, W., Irwin, G. W. and Yang, A. (2012). COLREGs-based collision avoidance strategies for unmanned surface vehicles. Mechatronics, 22, 669–678.10.1016/j.mechatronics.2011.09.012CrossRefGoogle Scholar
O'Keefe, J. and Nadel, L. (1978). The hippocampus as a cognitive map. American Journal of Psychology, 168(3), 863.Google Scholar
Pu, H. Y., Feng, D. and Li, X. M., et al. (2017). Maritime autonomous obstacle avoidance in a dynamic environment based on collision cone of ellipse. Chinese Journal of Scientific Instrument, 38(7), 1756–1762.Google Scholar
Qu, H., Xing, K. and Alexander, T. (2013). An improved genetic algorithm with coevolutionary strategy for global path planning of multiple mobile robots. Neurocomputing, 120, 509–517.10.1016/j.neucom.2013.04.020CrossRefGoogle Scholar
Raja, P. and Pugazhenthi, S. (2012). Optimal path planning of mobile robots: A review. International Journal of the Physical Sciences, 7(9), 1314–1320.10.5897/IJPS11.1745CrossRefGoogle Scholar
Shen, H., Hashimoto, H., Matsuda, A., Taniguchi, Y., Terada, D. and Guo, C. (2019). Automatic collision avoidance of multiple ships based on deep Q-learning. Applied Ocean Research, 86, 268–288.10.1016/j.apor.2019.02.020CrossRefGoogle Scholar
Shi, B. H., Su, Y. X. and Zhang, H. J. (2018). Obstacles modeling method in cluttered environments using satellite images and its application to path planning for USV. International Journal of Naval Architecture and Ocean Engineering, 3, 1–9.Google Scholar
Solstad, T., Moser, E. I. and Einevoll, G. T. (2006). From grid cells to place cells: A mathematical model. Hippocampus, 16(12), 1026–1031.10.1002/hipo.20244CrossRefGoogle ScholarPubMed
Song, A. L., Su, B. Y., Dong, C. Z., Shen, D. W., Xiang, E. Z. and Mao, F. P. (2018). A two-level dynamic obstacle avoidance algorithm for unmanned surface vehicles. Ocean Engineering, 170, 351–360.10.1016/j.oceaneng.2018.10.008CrossRefGoogle Scholar
Sun, Q. G. (2017). Path planning for ship navigation based on support vector machine. Liaoning, China: Dalian Maritime University.Google Scholar
Sun, Y. D. (2018). A Research on Intelligent Planning for Ship Navigation Path. Liaoning, China: Dalian Maritime University.Google Scholar
van Strien, N. M., Cappaert, N. L. and Witter, M. P. (2009). The anatomy of memory: An interactive overview of the parahippocampal-hippocampal network. Nature Reviews Neuroscience, 10(4), 272–282.10.1038/nrn2614CrossRefGoogle ScholarPubMed
Wang, C. B., Zhang, X. Y. and Zhang, J. W., et al. (2018). Method for intelligent obstacle avoidance decision-making of unmanned vessel in unknown waters. Chinese Journal of Ship Research, 13(6), 72–77.Google Scholar
Wang, Y. Y., Yao, P. and Dou, Y. M. (2019). Monitoring trajectory optimisation for unmanned surface vessel in sailboat race. International Journal for Light and Electron Optics, 176, 394–400.10.1016/j.ijleo.2018.09.104CrossRefGoogle Scholar
Wei, H. C. and Loo, C. K. (2018). Topological Gaussian ARAM for biologically inspired topological map building. Neural Computing and Applications, 29, 1055–1072.Google Scholar
Witter, M. P. and Moser, E. I. (2006). Spatial representation and the architecture of the entorhinal cortex. Trends in Neurosciences, 29(12), 671–678.10.1016/j.tins.2006.10.003CrossRefGoogle ScholarPubMed
Wu, B., Xiong, Y. and Wen, Y. Q. (2014). Automatic collision avoidance algorithm for unmanned surface vessel based on velocity obstacles. Journal of Dalian Maritime University, 40(2), 13–16.Google Scholar
Yao, P., Wang, H. L. and Ji, H. X. (2017). Gaussian mixture model and receding horizon control for multiple UAV search in complex environment. Nonlinear Dynamics, 88, 903–919.10.1007/s11071-016-3284-1CrossRefGoogle Scholar
Yu, N. G., Fang, L., Luo, Z. W., Yuan, Y., Jiang, X. and Cai, J. (2016). From grid cells to place cells: A gauss distribution activation function model. Journal of biomedical engineering, 33(6), 1158–1167.Google ScholarPubMed
Yu, N. G., Yuan, Y. H. and Li, T., et al. (2018). A cognitive map building algorithm by means of cognitive mechanism of hippocampus. Acta Automatica Sinica, 44(1), 52–73.Google Scholar
Zhang, Y. K. (2008). Research on Path Planning for Unmanned Surface vehicle. Heilongjiang, China: Harbin Engineering University.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(4), 385–396.Google Scholar
Zhang, G. Q., Deng, Y. J., Zhang, W. D. and Huang, C. (2018). Novel DVS guidance and path-following control for underactuated ships in presence of multiple static and moving obstacles. Ocean Engineering, 170, 100–110.10.1016/j.oceaneng.2018.10.009CrossRefGoogle Scholar
Zhao, Y. X., Li, W. and Shi, P. (2016). A real-time collision avoidance learning system for Unmanned Surface Vessels. Neurocomputing, 182, 255–266.10.1016/j.neucom.2015.12.028CrossRefGoogle Scholar
Zhou, Y. and Wu, D. W. (2017). Location estimation model based on the transformation from grid cells to place cells. Journal of Electronics and Information Technology, 39(9), 2272–2276.Google Scholar
Zhu, D. Q. and Zhong, Y. M. (2010). Survey on technology of mobile robot path planning. Control and Decision, 25(7), 961–967.Google Scholar
Zhu, L. H., Cheng, X. H. and Yuan, F. G. (2016). A 3D collision avoidance strategy for UAV with physical constraints. Measurement, 77, 40–49.10.1016/j.measurement.2015.09.006CrossRefGoogle Scholar