Path planning for an autonomous underwater vehicle in a cluttered underwater environment based on the heat method
By:
References
- Chen, J., Zhu, H., Zhang, L. and Sun, Y. (2018). Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization, Ocean Engineering 156: 217–223.
- Cheng, C., Zhu, D., Bing, S., Chu, Z. and Sheng, Z. (2015). Path planning for autonomous underwater vehicle based on artificial potential field and velocity synthesis, 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering, Halifax, Canada, pp. 717–721.
- Cohen, L.D. and Kimmel, R. (1997). Global minimum for active contour models: A minimal path approach, International Journal of Computer Vision 24(1): 57–78.
- Crane, K.,Weischedel, C. andWardetzky, M. (2012). Geodesics in heat, Computer Science 12(9): 1–10.
- Crane, K.,Weischedel, C. andWardetzky, M. (2013). Geodesics in heat: A new approach to computing distance based on heat flow, ACM Transactions on Graphics 32(5): 152.
- Crane, K., Weischedel, C. and Wardetzky, M. (2017). The heat method for distance computation, Communications of the ACM 60(11): 90–99.
- Hedjar, R. and Bounkhel, M. (2019). An automatic collision avoidance algorithm for multiple marine surface vehicles, International Journal of Applied Mathematics and Computer Science 29(4): 759–768, DOI: 10.2478/amcs-2019-0056.
- Klaučo, M., Blažek, S. and Kvasnica, M. (2016). An optimal path planning problem for heterogeneous multi-vehicle systems, International Journal of Applied Mathematics and Computer Science 26(2): 297–308, DOI: 10.1515/amcs-2016-0021.
- Koay, T.-B. and Chitre, M. (2013). Energy-efficient path planning for fully propelled AUVs in congested coastal waters, OCEANS 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension, Bergen, Norway, pp. 1–9.
- Lolla, T., Ueckermann, M., Yi, K., Haley Jr., K. and Lermusiaux, P. (2012). Path planning in time dependent flow fields using level set methods, IEEE International Conference on Robotics and Automation, Saint Paul, USA, pp. 166–173.
- MahmoudZadeh, S., Yazdani, A., Sammut, K. and Powers, D. (2017). Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms, Applied Soft Computing 70(9): 929–945.
- Makdah, A.A.R.A., Daher, N., Asmar, D. and Shammas, E. (2019). Three-dimensional trajectory tracking of a hybrid autonomous underwater vehicle in the presence of underwater current, Ocean Engineering 185: 115–132.
- Mirebeau, J.-M. (2014). Anisotropic fast-marching on Cartesian grids using lattice basis reduction, SIAM Journal on Numerical Analysis 52(4): 1573–1599.
- Mirebeau, J.-M. (2018). Fast-marching methods for curvature penalized shortest paths, Journal of Mathematical Imaging and Vision 60: 784–815.
- Niu, H., Ji, Z., Savvaris, A. and Tsourdos, A. (2020). Energy efficient path planning for unmanned surface vehicle in spatially-temporally variant environment, Ocean Engineering 196: 106766.
- Niu, H., Lu, Y., Savvaris, A. and Tsourdos, A. (2018). An energy-efficient path planning algorithm for unmanned surface vehicles, Ocean Engineering 161: 308–321.
- Pêtrès, C., Pailhas, Y., Patrón, P., Petillot, Y. and David, L. (2007). Path planning for autonomous underwater vehicles, IEEE Transactions on Robotics 23(2): 331–341.
- Peyré, G., Péchaud, M., Keriven, R. and Cohen, L. (2010). Geodesic methods in computer vision and graphics, Foundations and Trends in Computer Graphics and Vision 5(3–4): 197–397.
- Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A. (2018). A constrained A* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents, Ocean Engineering 169: 187–201.
- Song, R., Liu, W., Liu, Y. and Bucknall, R. (2015). A two-layered fast marching path planning algorithm for an unmanned surface vehicle operating in a dynamic environment, OCEANS 2015, Genova, Italy, pp. 1–8.
- Song, R., Liu, Y. and Bucknall, R. (2017). A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment, Ocean Engineering 129: 301–317.
- Soulignac, M. (2011). Feasible and optimal path planning in strong current fields, IEEE Transactions on Robotics 27(1): 89–98.
- Soulignac, M., Taillibert, P. and Rueher, M. (2008). Adapting the wavefront expansion in presence of strong currents, IEEE International Conference on Robotics and Automation, Pasadena, USA, pp. 1352–1358.
- Witt, J. and Dunbabin, M. (2008). Go with the flow: Optimal AUV path planning in coastal environments, Proceedings of the 2008 Australasian Conference on Robotics and Automation, Sydney, Australia, pp. 1–9.
- Wu, Y. (2019). Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission, Ocean Engineering 182: 162–173.
- Yang, F., Chai, L., Chen, D. and Cohen, L. (2018). Geodesic via asymmetric heat diffusion based on Finsler metric, Asian Conference on Computer Vision, Perth, Australia, pp. 371–386.
- Yang, F. and Cohen, L.D. (2016). Geodesic distance and curves through isotropic and anisotropic heat equations on images and surfaces, Journal of Mathematical Imaging and Vision 55(2): 210–228.
- Zeng, Z., Lian, L., Sammut, K., He, F., Tang, Y. and Lammas, A. (2015). A survey on path planning for persistent autonomy of autonomous underwater vehicles, Ocean Engineering 110: 303–313.
Language: English
Page range: 289 - 301
Submitted on: Jul 14, 2020
Accepted on: Dec 3, 2020
Published on: Jul 8, 2021
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Related subjects:
© 2021 Kaiyue Sun, Xiangyang Liu, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.