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A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment

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Abstract

Expansion of today’s underwater scenarios and missions necessitates the requisition for robust decision making of the autonomous underwater vehicle (AUV); hence, design an efficient decision-making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a global route planner (GRP) and a local path planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively guided to the target of interest in the context of an uncertain underwater environment. The architecture involves three main modules: The GRP module at the top level deals with the task priority assignment, mission time management, and determination of a feasible route between start and destination point in a large-scale environment. The LPP module at the lower level deals with safety considerations and generates collision-free optimal trajectory between each specific pair of waypoints listed in obtained global route. Re-planning module tends to promote robustness and reactive ability of the AUV with respect to the environmental changes. The experimental results for different simulated missions demonstrate the inherent robustness and drastic efficiency of the proposed scheme in enhancement of the vehicles autonomy in terms of mission productivity, mission time management, and vehicle safety.

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References

  • Al-Hasan S, Vachtsevanos G (2002) Intelligent route planning for fast autonomous vehicles operating in a large natural terrain. In: Elsevier Science B.V., robotics and autonomous systems, vol 40, pp 1–24

  • 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

    Article  Google Scholar 

  • Ataei M, Yousefi-Koma A (2015) Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle. Robot Auton Syst 67:23–32

    Article  Google Scholar 

  • Besada-Portas E, DeLaTorre L, DeLaCruz JM, DeAndrés-Toro B (2010) Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans Robot 26(4):619–634

  • Blidberg DR (2001) The development of autonomous underwater vehicles (AUVs); a brief summary. In: IEEE international conference on robotics and automation (ICRA), vol 6500

  • Carsten J, Ferguson D, Stentz A (2006) 3D field D*: improved path planning and replanning in three dimensions. In: IEEE international conference on intelligent robots and systems (IROS ’06), pp 3381–3386

  • Chiang WC, Russell RA (1996) Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann Oper Res 63(1):3–27

    Article  MATH  Google Scholar 

  • Eichhorn M (2015) Optimal routing strategies for autonomous underwater vehicles in time-varying environment. Robot Auton Syst 67:33–43

    Article  Google Scholar 

  • Fu Y, Ding M, Zhou C (2012) Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans Syst Man Cybern - A: Syst Hum 42(2):511–526

    Article  Google Scholar 

  • Gehring H, Homberger J (2001) A parallel two-phase metaheuristic for routing problems with time windows. Asia-Pac J Oper Res 18:35–47

    MATH  Google Scholar 

  • Higgins AJ (2001) A dynamic tabu search for large-scale generalised assignment problems. Comput Oper Res 28(10):1039–1048

    Article  MathSciNet  MATH  Google Scholar 

  • Iori M, Ledesma JR (2015) Exact algorithms for the double vehicle routing problem with multiple stacks. Comput Oper Res 63:83–101

    Article  MathSciNet  MATH  Google Scholar 

  • Karimanzira D, Jacobi M, Pfuetzenreuter T, Rauschenbach T, Eichhorn M, Taubert R, Ament C (2014) First testing of an AUV mission planning and guidance system for water quality monitoring and fish behavior observation in net cage fish farming. In: Elsevier, Information Processing in Agriculture, pp 131–140

  • Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948

  • Kladis GP, Economou JT, Knowles K, Lauber J, Guerra TM (2011) Energy conservation based fuzzy tracking for unmanned aerial vehicle missions under a priori known wind information. Eng Appl Artif Intell 24(2):278–294

    Article  Google Scholar 

  • Kumar R, Kumar M (2010) Exploring genetic algorithm for shortest path optimization in data networks. In: Glob J Comput Sci Technol (GJCST 2010) 10(11):8–12

  • Kwok KS, Driessen BJ, Phillips CA, Tovey CA (2002) Analyzing the multiple-target-multiple-agent scenario using optimal assignment algorithms. J Intell Robot Syst 35(1):111–122

    Article  MATH  Google Scholar 

  • Likhachev M, Ferguson D, Gordon G, Stentz A, Thrun S (2005) Anytime dynamic A*: an anytime, replanning algorithm. In: 5th international conference on automated planning and scheduling (ICAPS 2005), pp 262–271

  • Liu L, Shell DA (2012) Large-scale multi-robot task allocation via dynamic partitioning and distribution. Autonom Robots 33(3):291–307

    Article  Google Scholar 

  • Liu Y, Bucknall R (2015) Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment. Ocean Eng 97:126–144

    Article  Google Scholar 

  • Lysgaard J, Letchford AN, Eglese RW (2004) A new branch-and-cut algorithm for the capacitated vehicle routing problem. Math Program 100(2):423–445

    Article  MathSciNet  MATH  Google Scholar 

  • Mahmoud Zadeh S, Powers D, Sammut K, Lammas A, Yazdani AM (2015) Optimal route planning with prioritized task scheduling for AUV missions. In: IEEE international symposium on robotics and intelligent sensors, pp 7–15

  • Mahmoud Zadeh S, Powers D, Yazdani AM (2016) A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment. arXiv preprint arXiv:1604.02524

  • Mahmoud Zadeh S, Powers D, Sammut K, Yazdani AM (2016) Differential evolution for efficient AUV path planning in time variant uncertain underwater environment. arXiv preprint arXiv:1604.02523

  • Mahmoud Zadeh S, Yazdani A, Sammut K, Powers DMW (2016) AUV rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms. Robotics (cs.RO). arXiv:1604.07002

  • Mahmoud Zadeh S, Powers D, Sammut K, Yazdani AM (2016) Toward efficient task assignment and motion planning for large scale underwater mission. Robotics (cs.RO). arXiv:1604.04854

  • Mahmoud Zadeh S, Powers D, Sammut K, Yazdani AM (2016) Biogeography-based combinatorial strategy for efficient AUV motion planning and task-time management. Robotics (cs.RO). arXiv:1604.04851

  • Martinhon C, Lucena A, Maculan N (2004) Stronger minimum K-tree relaxations for the vehicle routing problem. Eur J Oper Res 158(1):56–71

    Article  MATH  Google Scholar 

  • Movahed MA,Yazdani AM (2011) Application of imperialist competitive algorithm in online PI controller. Second international conference on intelligent systems, modelling and simulation, pp 83–87

  • Nikolos IK, Valavanis KP, Tsourveloudis NC, Kostaras AN (2003) Evolutionary algorithm based offline/online path planner for UAV navigation. IEEE Trans Syst Man Cybern B: Cybern 33(6):898–912

    Article  Google Scholar 

  • Pereira AA, Binney J, Hollinger GA, Sukhatme GS (2013) Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. J Field Robot 30(5):741–762

    Article  Google Scholar 

  • Petres C, Pailhas Y, Evans J, Petillot Y, Lane D (2005) Underwater path planning using fast marching algorithms. Oceans Eur Conf 2:814–819

    Google Scholar 

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

    Article  Google Scholar 

  • Roberge V, Tarbouchi M, Labonte G (2013) Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans Ind Inf 9(1):132–141

    Article  Google Scholar 

  • Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Book. ISBN 978-3-540-73189-4

  • Soltanpoor H, Vafaei JM, Jalali M (2013) Graph-based image segmentation using imperialist competitive algorithm. Advn Comput 3(2):11–21

    Google Scholar 

  • Wang H, Zhao J, Bian X, Shi X (2005) An improved path planner based on adaptive genetic algorithm for autonomous underwater vehicle. In: IEEE international conference on mechatronics & automation, Niagara Falls, Canada

  • Zeng Z, Lammas A, Sammut K, He F, Tang Y (2014) Shell space decomposition based path planning for AUVs operating in a variable environment. J Ocean Eng 91:181–195

    Article  Google Scholar 

  • Zeng Z, Sammut K, Lammas A, He F, Tang Y (2014) Efficient path re-planning for AUVs operating in spatiotemporal currents. J Intell Robot Syst. Springer, pp 1–19

  • Zheng C, Li L, Xu F, Sun F, Ding M (2005) Evolutionary route planner for unmanned air vehicles. IEEE Trans Robot 21(4):609–620

    Article  Google Scholar 

  • Zhu A, Yang S (2010) An improved SOM-based approach to dynamic task assignment of multi-robots. In: 8th World congress on intelligent control and automation (WCICA), no. 5554341, pp 2168–2173

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Acknowledgements

Somaiyeh M. Zadeh and Amirmehdi Yazdani are funded by Flinders International Postgraduate Research Scholarship (FIPRS) program, Flinders University of South Australia. This research is also supported through a FIPRS scheme from Flinders University.

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Authors and Affiliations

  1. School of Computer Science, Engineering and Mathematics, Flinders University, Adelaide, SA, Australia

    Somaiyeh Mahmoud Zadeh, David M. W. Powers, Karl Sammut & Amir Mehdi Yazdani

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  1. Somaiyeh Mahmoud Zadeh
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  2. David M. W. Powers
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  3. Karl Sammut
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  4. Amir Mehdi Yazdani
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Correspondence to Somaiyeh Mahmoud Zadeh.

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Communicated by V. Loia.

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Mahmoud Zadeh, S., Powers, D.M.W., Sammut, K. et al. A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment. Soft Comput 22, 1687–1710 (2018). https://doi.org/10.1007/s00500-016-2433-2

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  • DOI: https://doi.org/10.1007/s00500-016-2433-2

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