Soft Computing

, Volume 22, Issue 5, pp 1687–1710 | Cite as

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

  • Somaiyeh Mahmoud ZadehEmail author
  • David M. W. Powers
  • Karl Sammut
  • Amir Mehdi Yazdani
Methodologies and Application


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.


Autonomous underwater vehicles Autonomy Decision making Motion planning Task assignment Time management Mission management 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest.


  1. 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 1–24Google Scholar
  2. Alvarez A, Caiti A, Onken R (2004) Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J Ocean Eng 29(2):418–429CrossRefGoogle Scholar
  3. Ataei M, Yousefi-Koma A (2015) Three-dimensional optimal path planning for waypoint guidance of an autonomous underwater vehicle. Robot Auton Syst 67:23–32CrossRefGoogle Scholar
  4. 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):619–634Google Scholar
  5. Blidberg DR (2001) The development of autonomous underwater vehicles (AUVs); a brief summary. In: IEEE international conference on robotics and automation (ICRA), vol 6500Google Scholar
  6. 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 3381–3386Google Scholar
  7. Chiang WC, Russell RA (1996) Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann Oper Res 63(1):3–27CrossRefzbMATHGoogle Scholar
  8. Eichhorn M (2015) Optimal routing strategies for autonomous underwater vehicles in time-varying environment. Robot Auton Syst 67:33–43CrossRefGoogle Scholar
  9. 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):511–526CrossRefGoogle Scholar
  10. Gehring H, Homberger J (2001) A parallel two-phase metaheuristic for routing problems with time windows. Asia-Pac J Oper Res 18:35–47zbMATHGoogle Scholar
  11. Higgins AJ (2001) A dynamic tabu search for large-scale generalised assignment problems. Comput Oper Res 28(10):1039–1048MathSciNetCrossRefzbMATHGoogle Scholar
  12. Iori M, Ledesma JR (2015) Exact algorithms for the double vehicle routing problem with multiple stacks. Comput Oper Res 63:83–101MathSciNetCrossRefzbMATHGoogle Scholar
  13. 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 131–140Google Scholar
  14. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: IEEE international conference on neural networks, pp 1942–1948Google Scholar
  15. 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):278–294CrossRefGoogle Scholar
  16. 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):8–12Google Scholar
  17. 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):111–122CrossRefzbMATHGoogle Scholar
  18. 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 262–271Google Scholar
  19. Liu L, Shell DA (2012) Large-scale multi-robot task allocation via dynamic partitioning and distribution. Autonom Robots 33(3):291–307CrossRefGoogle Scholar
  20. Liu Y, Bucknall R (2015) Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment. Ocean Eng 97:126–144CrossRefGoogle Scholar
  21. Lysgaard J, Letchford AN, Eglese RW (2004) A new branch-and-cut algorithm for the capacitated vehicle routing problem. Math Program 100(2):423–445MathSciNetCrossRefzbMATHGoogle Scholar
  22. 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 7–15Google Scholar
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. Martinhon C, Lucena A, Maculan N (2004) Stronger minimum K-tree relaxations for the vehicle routing problem. Eur J Oper Res 158(1):56–71CrossRefzbMATHGoogle Scholar
  29. Movahed MA,Yazdani AM (2011) Application of imperialist competitive algorithm in online PI controller. Second international conference on intelligent systems, modelling and simulation, pp 83–87Google Scholar
  30. 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):898–912CrossRefGoogle Scholar
  31. 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):741–762CrossRefGoogle Scholar
  32. Petres C, Pailhas Y, Evans J, Petillot Y, Lane D (2005) Underwater path planning using fast marching algorithms. Oceans Eur Conf 2:814–819Google Scholar
  33. Petres C, Pailhas Y, Patron P, Petillot Y, Evans J, Lane D (2007) Path planning for autonomous underwater vehicles. IEEE Trans Robot 23(2):331–341CrossRefGoogle Scholar
  34. 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):132–141CrossRefGoogle Scholar
  35. Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Book. ISBN 978-3-540-73189-4Google Scholar
  36. Soltanpoor H, Vafaei JM, Jalali M (2013) Graph-based image segmentation using imperialist competitive algorithm. Advn Comput 3(2):11–21Google Scholar
  37. 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, CanadaGoogle Scholar
  38. 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 91:181–195CrossRefGoogle Scholar
  39. 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 1–19Google Scholar
  40. Zheng C, Li L, Xu F, Sun F, Ding M (2005) Evolutionary route planner for unmanned air vehicles. IEEE Trans Robot 21(4):609–620CrossRefGoogle Scholar
  41. 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 2168–2173Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Computer Science, Engineering and MathematicsFlinders UniversityAdelaideAustralia

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