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A parallel genetic approach to path-planning with upstream-current avoidance for multi-AUG deployment

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Abstract

Autonomous underwater gliders are robotic underwater vehicles that do not require operator input. They are frequently deployed in large-scale, long-term ocean-sampling missions that take advantage of their buoyancy-driven engines and very low power consumption. A limitation of the buoyancy-driven engine design is that the glider must travel at slow speeds and is unable to confront strong upstream currents. Optimal path-planning to minimize the upstream-current effect and distance traversed is helpful to guide the glider navigating in the ocean. In a glider path-planning problem, reachability signifies that a minimal upstream-current effect along a path is achieved, while efficiency refers to optimizing path distance, to make it as short as possible. Reachability and efficiency are sometimes inconsistent goals in glider path-planning, and obtaining an optimal solution between both aspects of path-planning constitutes a multi-objective optimization problem. In order to discover an optimal path for glider safety, a parallel genetic approach to the glider path-planning is developed here. We present a novel scheme of upstream-current avoidance to solve the critical path-planning problem in deployment of multiple gliders. The benefits of the proposed approach are used to produce characteristic curves that express how improved cruising capability benefits path diversity. The reachability of an important scheme of upstream-current avoidance is validated by a maximum likelihood function. Moreover, a new crossover operator is proposed to encourage individuals of offspring in solution diversity. Numeric results demonstrate that the proposed path-planning approach determines an optimal path that reduces the upstream-current effect, and also shortens the cruising distance in multi-glider path-planning solutions.

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References

  • Ahmed F, Deb K (2013) Multi-objective optimal path planning using elitist non-dominated sorting genetic algorithms. Soft Comput 17:1283–1299

    Article  Google Scholar 

  • Al-Mayyahi A, Wang W, Birch P (2014) Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation. Robotics 3:349–370

    Article  Google Scholar 

  • Alvarez A, Caiti A, Onken R (2004) Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Ocean Eng 29:418–429

    Article  Google Scholar 

  • Banzhaf W (1990) The molecular traveling salesman. Biol Cybern 64:7–14

    Article  Google Scholar 

  • Brito M, Smeed D, Griffiths G (2014) Underwater glider reliability and implications for survey design. J Atmos Ocean Technol 31:2858–2870

    Article  Google Scholar 

  • Castillo O, Trujillo L, Melin P (2007) Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots. Soft Comput 11:269–279

    Article  Google Scholar 

  • Fernandez-Perdomo E, Cabrera-Gamez J, Hernandez-Sosa D, Isern-Gonzalez J, Dominguez-Brito AC, Prieto-Maranon V (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), pp 2350–2355

  • Fogel DB (1993) Applying evolutionary programming to selected traveling salesman problems. Cybern Syst 24:27–36

    Article  MathSciNet  Google Scholar 

  • Garau B, Alvarez A, Oliver G (2005) Path planning of autonomous underwater vehicles in current fields with complex spatial variability: an A* approach. In: Proceedings of the 2005 IEEE international conference on robotics and automation, pp 194–198

  • Garcia MAP, Montiel O, Castillo O, Sepúlveda R, Melin P (2009) Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation. Appl Soft Comput 9(3):1102–1110

    Article  Google Scholar 

  • Goldberg DE, Robert Lingle J (1985) Alleles, loci, and the traveling salesman problem. In: Proceedings of the 1st international conference on genetic algorithms

  • Holland JH (1992) Adaptation in natural and artificial systems. MIT Press, Cambridge

    Book  Google Scholar 

  • Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  • Kalarani G, Ranihemamalini R (2014) A survey of the various path planning techniques used in the navigation of autonomous mobile robot. Indian J Appl Res 4:442–444

    Google Scholar 

  • Lamini C, Benhlima S, Elbekri A (2018) Genetic algorithm based approach for autonomous mobile robot path planning. Proc Comput Sci 127:180–189

    Article  Google Scholar 

  • Montiel O, Orozco-Rosas U, Sepúlveda R (2015) Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles. Expert Syst Appl 42(12):5177–5191

    Article  Google Scholar 

  • Montiel-Ross O, Sepúlveda R, Castillo O, Melin P (2013) Ant colony test center for planning autonomous mobile robot navigation. Comput Appl Eng Educ 21(2):214–229

    Article  Google Scholar 

  • 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 10:1119–1126

    Article  Google Scholar 

  • Navidi W (ed) (2010) Commonly used distributions. In: Statistics for engineers and scientists, vol 3. McGraw-Hill, New York, pp 262–263

  • Nazarahari M, Khanmirza E, Doostie S (2019) Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Syst Appl 115:106–120

    Article  Google Scholar 

  • Nonaka M, Xie S-P (2003) Covariations of sea surface temperature and wind over the Kuroshio and its extension: evidence for ocean-to-atmosphere feedback. J Clim 16:1404–1413

    Article  Google Scholar 

  • Orozco-Rosas U, Montiel O, Sepúlveda R (2018) Parallel bacterial potential field algorithm for path planning in mobile robots: a GPU implementation. Fuzzy Log Augment Neural Optim Algorithms Theor Asp Real Appl 749:207–222

    Google Scholar 

  • Page BR, Ziaeefard S, Pinar AJ, Mahmoudian N (2017) Highly maneuverable low-cost underwater glider: design and development. IEEE Robot Autom Lett 2(1):344–349

    Article  Google Scholar 

  • Patle BK, Parhi DRK, Jagadeesh A, Kashyap SK (2018) Matrix-binary codes based genetic algorithm for path planning of mobile robot. Comput Electr Eng 67:708–728

    Article  Google Scholar 

  • Petres C, Pailhas Y, Patron P, Petillot Y, Evans J, Lane D (2007) Path planning for autonomous underwater vehicles. IEEE Trans Rob 23(2):331–341

    Article  Google Scholar 

  • Shifei L, Yanhui W, Yanbin G (2012) 3D path planning for AUV using fuzzy logic. In: 2012 International conference on computer science and information processing (CSIP), pp 599–603

  • 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, 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, pp 1–6

  • Shih C-C, Horng M-F, Pan T-S, Pan J-S, Chen C-Y (2016) A genetic-based effective approach to path-planning of autonomous underwater glider with upstream-current avoidance in variable oceans. Soft Comput 21:1–18

    Google Scholar 

  • Soulignac M (2011) Feasible and optimal path planning in strong current fields. IEEE Trans Robot 27:89–98

    Article  Google Scholar 

  • Warren CW (1989) Global path planning using artificial potential fields. Int Conf Robot Autom 1:316–321

    Google Scholar 

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Acknowledgements

This research has been financially supported in part by the MOST ROC (Taiwan) under Grants “MOST 107-2221-E-492-028.” The financial support is gratefully appreciated.

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Authors and Affiliations

  1. Taiwan Ocean Research Institute, National Applied Research Laboratories, Kaohsiung, Taiwan

    Chien-Chou Shih

  2. Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan

    Mong-Fong Horng

  3. Department of Industrial Technology Education, National Kaohsiung Normal University, Kaohsiung, Taiwan

    Chun-Yu Chen

Authors
  1. Chien-Chou Shih
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  2. Mong-Fong Horng
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  3. Chun-Yu Chen
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Correspondence to Chun-Yu Chen.

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Shih, CC., Horng, MF. & Chen, CY. A parallel genetic approach to path-planning with upstream-current avoidance for multi-AUG deployment. Soft Comput 24, 8427–8441 (2020). https://doi.org/10.1007/s00500-019-04409-1

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