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Citation:
　S. MahmoudZadeh,D. M. W Powers,A. M. Yazdani,et al.Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm[J].Journal of Marine Science and Application,2018,(4):585-591.[doi:10.1007/s11804-018-0034-4]
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Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm

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Title:
Efficient AUV Path Planning in Time-Variant Underwater Environment Using Differential Evolution Algorithm
Author(s):
S. MahmoudZadeh1;  D. M. W Powers1;  A. M. Yazdani1;  K. Sammut2;  A. Atyabi3
Affilations:
Author(s):
S. MahmoudZadeh1;  D. M. W Powers1;  A. M. Yazdani1;  K. Sammut2;  A. Atyabi3
1 School of Computer Science, Engineering and Mathematics, Flinders University Tonsley, Adelaide, SA 5042, Australia;
2 Centre for Maritime Engineering, Control and Imaging, Flinders University, Adelaide, SA 5042, Australia;
3 Seattle Children’s Research Institute, University of Washington, Washington, WA 98195, USA
Keywords:
Path planning; Differential evolution; Autonomous underwater vehicles; Evolutionary algorithms; Obstacle avoidance
分类号:
-
DOI:
10.1007/s11804-018-0034-4
Abstract:
Robust and efficient AUV path planning is a key element for persistence AUV maneuvering in variable underwater environments. To develop such a path planning system, in this study, differential evolution (DE) algorithm is employed. The performance of the DE-based planner in generating time-efficient paths to direct the AUV from its initial conditions to the target of interest is investigated within a complexed 3D underwater environment incorporated with turbulent current vector fields, coastal area, islands, and static/dynamic obstacles. The results of simulations indicate the inherent efficiency of the DE-based path planner as it is capable of extracting feasible areas of a real map to determine the allowed spaces for the vehicle deployment while coping undesired current disturbances, exploiting desirable currents, and avoiding collision boundaries in directing the vehicle to its destination. The results are implementable for a realistic scenario and on-board real AUVas the DE planner satisfies all vehicular and environmental constraints while minimizing the travel time/distance, in a computationally efficient manner.

References:

Alvarez A, Caiti A, Onken R (2004) Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Ocean Eng 29(2):418-429. https://doi.org/10.1109/JOE.2004.827837
Asl AN, Menhaj MB, Sajedin A (2014) Control of leader follower formation and path planning of mobile robots using a sexual reproduction optimization (aro). JASC 14(PartC):563-576. https://doi.org/10.1016/j.asoc.2013.07.030
Carroll KP, McClaran SR, Nelson EL, Barnett DM, Friesen DK, William G (1992) AUV path planning:an A* approach to path planning with consideration of variable vehicle speeds and multiple, overlapping, time-dependent exclusion zones. Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology. 79-84. https://doi.org/10.1109/AUV.1992.225191
Fernandez-Perdomo E, Cabrera-Gamez J, Hernandez-Sosa D, IsernGonzalez J, Dominguez-Brito AC, Redondo A, Coca J, Ramos AG, Fanjul EAl, Garcia M, 2010. Path planning for gliders using regional ocean models:application of Pinzon path planner with the ESEOAT model and the RU27 trans-Atlantic flight data. IEEE Oceans, Sydney, Australia 1-10. https://doi.org/10.1109/OCEANSSYD.2010.5603684
Garau B, Alvarez A, Oliver G, 2006. AUV navigation through turbulent ocean environments supported by onboard H-ADCP. IEEE International Conference on Robotics and Automation (ICRA), Orlando, Florida, May. https://doi.org/10.1109/ROBOT.2006.1642245
Garau B, Bonet M, Alvarez A, Ruiz S, Pascual A (2009) Path planning for autonomous underwater vehicles in realistic oceanic current fields:application to gliders in the Western Mediterranean sea. J Marit Res 6(2):5-21
Kruger D, Stolkin R, Blum A, Briganti J (2007) Optimal AUV path planning for extended missions in complex, fast flowing estuarine environments. IEEE International Conference on Robotics and Automation, Rom, Italy 4265-4270. https://doi.org/10.1109/ROBOT.2007.364135
Koay TB, 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.1-9. https://doi.org/10.1109/OCEANS-Bergen.2013.6608168
Lolla T, Ueckermann MP, Yigit K, Haley Jr PJ, Lermusiaux PFJ (2012) Path planning in time dependent flow fields using level set methods. IEEE International Conference on Robotics and Automation(ICRA). 166-173. https://doi.org/10.1109/ICRA.2012.6225364
MZadeh S, Powers DMW, Sammut K, Lammas A, Yazdani A (2015) Optimal route planning with prioritized task scheduling for AUV missions. IEEE International Symposium on Robotics and Intelligent Sensors 7-15. https://doi.org/10.1109/IRIS.2015.7451578
MZadeh S, Powers DMW, Yazdani A (2016) A novel efficient taskassign route planning method for AUV guidance in a dynamic cluttered environment. IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada, pp.678-684, 2016
Pereira AA, Binney J, Jones BH, Ragan M, Sukhatme GS (2011) Toward risk aware mission planning for autonomous underwater vehicles. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, CA, USA. https://doi.org/10.1109/IROS.2011.6095157
Price K, Storn R (1997) Differential evolution-a simple evolution strategy for fast optimization. Dr Dobb’s J 22(4):18-24
Soulignac M (2011) Feasible and optimal path planning in strong current fields. IEEE Trans Robot 27(1):89-98. https://doi.org/10.1109/TRO.2010.2085790
Soulignac M, Taillibert P, Rueher M (2008) Adapting the wavefront expansion in presence of strong currents. IEEE International Conference on Robotics and Automation (ICRA) 1352-1358. https://doi.org/10.1109/ROBOT.2008.4543391
Thompson DR, Chien S, Chao Y, Li P, Cahill B, Levin J, Schofield O, Balasuriya A, Petillo S, Arrott M, Meisinger M (2010) Spatiotemporal path planning in strong, dynamic, uncertain currents. IEEE International Conference on Robotics and Automation(ICRA). 4778-4783. https://doi.org/10.1109/ROBOT.2010.5509249
Tam C, Bucknall R, Greig A (2009) Review of collision avoidance and path planning methods for ships in close range encounters. J Navig 62:455-476. https://doi.org/10.1017/S0373463308005134
Witt J, Dunbabin M (2008) Go with the flow:optimal AUV path planning in coastal environments. Australasian Conference on Robotics and Automation (ACRA)
Yilmaz NK, Evangelinos C, Lermusiaux PFJ, Patrikalakis NM (2008) Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE J Ocean Eng 33(4):522-537. https://doi.org/10.1109/JOE.2008.2002105

Memo

Memo:
Received date:2016-12-9;Accepted date:2018-5-5。
Corresponding author:S. MahmoudZadeh,somaiyeh.mahmoudzadeh@flinders.edu.au
Last Update: 2019-03-05