A genetic-based effective approach to path-planning of autonomous underwater glider with upstream-current avoidance in variable oceans

• Aghababa MP (2012) 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Appl Ocean Res. doi:10.1016/j.apor.2012.06.002

• Ahmed ZH (2010) Genetic algorithm for the traveling salesman problem using sequential constructive crossover operator. Int J Biom Bioinform 3(6):96–105

  Google Scholar 

• Ahmed F, Deb K (2013) Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput. doi:10.1007/s00500-012-0964-8

  Google Scholar 

• Albayrak M, Allahverdi N (2011) Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms. Expert Syst Appl. doi:10.1016/j.eswa.2010.07.006

• Alcázar V, Veloso M, Borrajo D (2011) Adapting a rapidly-exploring random tree for automated planning. In: Proceedings of the fourth international symposium on combinatorial Ssearch (SoCS-2011), pp 1–9

• Alvarez A, Caiti A, Onken R (2004) Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Oceanic Eng. doi:10.1109/JOE.2004.827837

• Barkley RA (1970) The Kuroshio current. Sci J 6:54–60

• Bhadoria A, Singh RK (2014) Optimized angular a star algorithm for global path search based on neighbor node evaluation. Int J Intell Syst Appl (IJISA). doi:10.5815/ijisa

• Castillo O, Trujillo L, Melin P (2007) Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots. Soft Comput. doi:10.1007/s00500-006-0068-4

• Clifton M, Paul G, Kwok N, Dikai L, Wang DL (2008) Evaluating performance of multiple RRTs. In: Mechtronic and embedded systems and spplication, pp 564–569. doi:10.1109/MESA.2008.4735749

• Donald B, Xavier P, Canny J, Reif J (1993) Kinodynamic motion planning. J ACM. doi:10.1145/174147.174150

• Fernandez-Perdomo E et al (2010) Path planning for gliders using regional ocean models: application of Pinzón path planner with the ESEOAT model and the RU27 trans-Atlantic flight data. In: Proceedings of IEEE OCEANS, pp 1–10. doi:10.1109/OCEANSSYD.2010.5603684

• Fernandez-Perdomo E, Cabrera-Gamez J, Hernandez-Sosa D, Isern-Gonzalez J, Dominguez-Brito AC, Prieto-Maranon V, Ramos AG (2011) Adaptive bearing sampling for a constant-time surfacing A* path planning algorithm for gliders. In: Proceedings of IEEE international conference on robotics and automation (ICRA). doi:10.1109/ICRA.2011.5980137

• Groba C, Sartal A, Vázquez XH (2015) Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction: An application to fish aggregating devices. Comput Oper. doi:10.1016/j.cor.2014.10.012

  MATH  Google Scholar 

• Hart PE, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. IEEE Trans Syst Sci Cybern 4:100–107. doi:10.1109/TSSC.1968.300136

  Article  Google Scholar 

• Holland JH (1984) Genetic algorithms and adaptation. In: Adaptive control of Ill-defined systems. Springer US, Boston, pp 317–333. doi:10.1007/978-1-4684-8941-5_21

• Hong TP, Huang KY, Lin WY (2002) Applying genetic algorithms to game search trees. Soft Comput. doi:10.1007/s005000100154

  MATH  Google Scholar 

• Larrañaga P, Kuijpers CMH, Murga RH, Inza I, Dizdarevic S (1999) Genetic algorithms for the travelling salesman problem: a review of representations and operators. Artif Intell. doi:10.1023/a:1006529012972

• LaValle SM, Kuffner JJ Jr (2000) Rapidly-exploring random trees: progress and prospects. In: Algorithmic and computational robotics: new directions: the fourth workshop on the algorithmic foundations of robotics, pp 293–308

• LaValle SM (2006) Motion planning. In: Planning algorithms, 1st edn. Cambridge University Press, Cambridge, pp 79–80

• Leonard NE, Paley DA, Lekien F, Sepulchre R, Fratantoni DM, Davis RE (2007) Collective motion, sensor networks, and ocean sampling. Proc IEEE 95:48–74. doi:10.1109/JPROC.2006.887295

  Article  Google Scholar 

• Lovie P (2005) Coefficient of variation. In: Encyclopedia of statistics in behavioral science. John Wiley & Sons, Ltd. doi:10.1002/0470013192.bsa107

• Majumdar J, Bhunia AK (2011) Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times. J Comput Appl Math. doi:10.1016/j.cam.2010.12.027

• Mensah V, Jan S, Chiou M-D, Kuo TH, Lien R-C (2014) Evolution of the Kuroshio Tropical Water from the Luzon Strait to the east of Taiwan. Deep-Sea Res. doi:10.1016/j.dsr.2014.01.005

• Moura A, Rijo R, Silva P, Crespo S (2010) A multi-objective genetic algorithm applied to autonomous underwater vehicles for sewage outfall plume dispersion observations. Appl Soft Comput. doi:10.1016/j.asoc.2010.05.009

• Nagata Y, Soler D (2012) A new genetic algorithm for the asymmetric traveling salesman problem. Expert Syst Appl. doi:10.1016/j.eswa.2012.02.029

  Google Scholar 

• NIMA (2002) Calculations and conversions. In: The American practical navigator: bowditch, 2nd edn. Paradise Cay Publications, National Imagery and Mapping Agency, pp 331–332

• Pêtrès C, Pailhas Y, Patron P, Petillot Y, Evans J, Lane D (2007) Path planning for autonomous underwater vehicles. IEEE T Robot. doi:10.1109/tro.2007.895057

• Rudnick DL, Davis RE, Eriksen CC, Fratantoni DM, Perry MJ (2004) Underwater gliders for ocean research. Mar Technol Soc J 38(2):73–84

  Article  Google Scholar 

• Schrijver A (2005) On the history of combinatorial optimization. In: Handbooks in operations research and management science, vol 12. Elsevier, Amsterdam, pp 1–68. doi:10.1016/S0927-0507(05)12001-5

• Sherman J, Davis R, Owens WB, Valdes J (2001) The autonomous underwater glider “Spray”. IEEE J Oceanic Eng 26(4):437–446. doi:10.1109/48.972076

  Article  Google Scholar 

• Shih C-C, Horng M-F, Pan J-S (2012) 3-D adaptive bearing sampling for AUG route planning in extensible ocean model. In: Proceedings of 14th conference on undersea technology (CUST 2012), Kaohsiung, Taiwan (R.O.C), pp 71–84

• Shih C-C, Yang Y, Horng M-F, Pan T-S, Pan J-S (2014) An effective approach to genetic path planning for autonomous underwater glider in a variable ocean. In: Proceedings of international forum on systems and mechatronics (IFSM2014), Tainan, Taiwan (R.O.C), pp 1–6

• Skiena SS (2008) Graph problems: polynomial-time. In: The algorithm design manual, 2nd edn. Springer-Verlag, London, pp 495–496. doi:10.1007/978-1-84800-070-4

• Soulignac M (2011) Feasible and optimal path planning in strong current fields. IEEE T Robot. doi:10.1109/tro.2010.2085790

• Spong MW, Vidyasagar M (2008) Velocity kinematics-the manipulator Jacoobian. In: Robot dynamics and control. Wiley India Pvt. Limited, pp 99–100

• Wang Y (2014) The hybrid genetic algorithm with two local optimization strategies for traveling salesman problem. Comput Ind Eng. doi:10.1016/j.cie.2014.01.015

  Google Scholar 

• Webb DC, Simonetti PJ, Jones CP (2001) SLOCUM: an underwater glider propelled by environmental energy. IEEE J Oceanic Eng 26(4):447–452. doi:10.1109/48.972077

  Article  Google Scholar 

• Weisstein EW (2002) CRC concise encyclopedia of mathematics, 2nd edn. Chapman and Hall/CRC, pp 1990–1991

• Young JH, Wan KC (2013) RRT-based path planning with kinematic constraints of AUV in underwater structured environment. In: Proceedings of 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI 2013). doi:10.1109/urai.2013.6677328

• Yu X, Gen M (2010) Simple evolutionary algorithms. In: Introduction to evolutionary algorithms, 1st edn. Springer-Verlag, London, pp 15–24. doi:10.1007/978-1-84996-129-5

• Zhang Z, Zhao Z (2014) A multiple mobile robots path planning algorithm based on A-star and Dijkstra algorithm. Int J Smart Home 8(3):75–86

  Article  MathSciNet  Google Scholar 

Download references