Journal of Intelligent & Robotic Systems

, Volume 94, Issue 1, pp 265–282 | Cite as

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

  • Somaiyeh MahmoudZadehEmail author
  • David M. W. Powers
  • Karl Sammut
  • Amir Mehdi Yazdani
  • Adham Atyabi


This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle’s task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle’s battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a path-planer that acts in a smaller scale to provide vehicle’s safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions.


Autonomous underwater vehicle Path planning Autonomous mission Task allocation Mission timing Mission management 



Task index


Priority of task i


Risk percentage associated with task i


Absolute time required for completion of task i


Vertices of the network that corresponds to waypoints


Edges of the network


Number of waypoints in the network


Number of edges in the network


Position of arbitrary waypoint i in 3-D space


An arbitrary edge that connects \(p^{i}_{x,y,z}\) to \(p^{j}_{x,y,z}\)


The weight assigned to eij


Distance between position of \(p^{i}_{x,y,z}\) and \(p^{j}_{x,y,z}\)


Time required for traversing edge eij




Obstacle’s position


Obstacle’s radius


Obstacle’s uncertainty rate


The current velocity vector


X component of the current vector


Y component of the current vector


Two dimensional x-y space


The center of the vortex in the current map

The radius of the vortex in the current map


The strength of the vortex in the current map


Symbol of the three dimensional terrain


The AUV state on NED frame {n}

[X, Y, Z]

Vehicles North, x, East, y, Depth, z, position along the path ℘


The Euler angle of roll


The Euler angle of pitch


The Euler angle of yaw


Vehicle’s water referenced velocity in the body frame {b}


The surge component of the velocity υ


The sway component of the velocity υ


The heave component of the velocity υ

The potential trajectory generated by the local path planner


Control point along the path ℘


Number of control points along an arbitrary path ℘


Length of the candidate path ℘


The local path flight time


The expected time for passing an edge


computational time for generating a local path


An arbitrary route including sequences of tasks and waypoints


The route traveled time


The total available time for the mission


Computation time for checking re-routing criterion and its process


The cost of local path generated by path planner


The cost of tasks completion


The total cost of route including C and C


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  1. 1.
    Iwakami, H., Ura, T., Asakawa, K., Hujii, T., Nose, Y., Kojima, J., Shirasaki, Y., Asia, T., Uchida, S., Higashi, N., Hukuchi, T.: Approaching whales by autonomous underwater vehicle. Mar. Technol. Soc. J. 36(1), 80–87 (2002)CrossRefGoogle Scholar
  2. 2.
    Marthiniussen, R., Vestgard, K., Klepaker, R., Storkersen, N.: HUGIN-AUV concept and operational experiences to date. In: Oceans’04 MTS/IEEE Techno-Ocean ’04 (IEEE Cat.No.04CH37600), pp. 2 (2004)Google Scholar
  3. 3.
    An, E., Dhanak, M., Shay, L.K., Smith, S., Leer, J.V.: Coastal oceanography using a small AUV. J. Atmos. Ocean. Technol. 18, 215–234 (2001)CrossRefGoogle Scholar
  4. 4.
    Djapic, V., Nad, D.: Using Collaborative Autonomous Vehicles in Mine Countermeasures. Oceans’10 IEEE, Sydney (2010)CrossRefGoogle Scholar
  5. 5.
    Carsten, J., Ferguson, D., Stentz, A.: 3D field D*: improved path planning and replanning in three dimensions. In: IEEE International Conference on Intelligent Robots and Systems, pp. 3381–3386 (2006)Google Scholar
  6. 6.
    Garau, B., Bonet, M., Alvarez, A., Ruiz, S., Pascual, A.: 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 (2009)Google Scholar
  7. 7.
    Koay, T.B., Chitre, M.: Energy-efficient path planning for fully propelled AUVs in congested coastal waters. Oceans 2013 MTS/IEEE Bergen: The Challenges of the Northern Dimension (2013)Google Scholar
  8. 8.
    Petres, C., Pailhas, Y., Evans, J., Petillot, Y., Lane, D.: Underwater path planning using fast marching algorithms. Oceans Eur. Conf. Brest. France 2, 814–819 (2005)Google Scholar
  9. 9.
    Petres, C., Pailhas, Y., Patron, P., Petillot, Y., Evans, J., Lane, D.: Path planning for autonomous underwater vehicles. IEEE Trans. Robot. 23(2), 331–341 (2007)CrossRefGoogle Scholar
  10. 10.
    Cui, R., Li, Y., Yan, W.: Mutual information-based multi-AUV path planning for scalar field sampling using multidimensional RRT*. IEEE Trans. Syst. Man Cybern. Syst. 46 (5), 993–1004 (2016). CrossRefGoogle Scholar
  11. 11.
    Yazdani, A.M., Sammut, K., Yakimenko, O.A., Lammas, A., MahmoudZadeh, S., Tang, Y.: IDVD-based trajectory generator for autonomous underwater docking operations. Robot. Auton. Syst. 92, 12–29 (2017)CrossRefGoogle Scholar
  12. 12.
    Kwok, K.S., Driessen, B.J., Phillips, C.A., Tovey, C.A.: Analyzing the multiple-target-multiple-agent scenario using optimal assignment algorithms. J. Intell. Robot. Syst. 35(1), 111–122 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Higgins, A.J.: A dynamic tabu search for large-scale generalised assignment problems. Comput. Oper. Res. 28(8), 1039–1048 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Liu, L., Shell, D.A.: Large-scale multi-robot task allocation via dynamic partitioning and distribution. Auton. Robot. 33(3), 291–307 (2012)CrossRefGoogle Scholar
  15. 15.
    Chiang, W.C., Russell, R.A.: Simulated annealing metaheuristics for the vehicle routing problem with time windows. Ann. Oper. Res. J. 63(1), 3–27 (1996)CrossRefzbMATHGoogle Scholar
  16. 16.
    Lysgaard, J., Letchford, A.N., Eglese, R.W.: A new branch-and-cut algorithm for the capacitated vehicle routing problem. Math. Programm. 100(2), 423–445 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    MahmoudZadeh, S., Powers, D., Sammut, K.: An autonomous dynamic motion-planning architecture for efficient AUV mission time management in realistic sever ocean environment. J. Robot. Auton. Syst. 87, 81–103 (2017). CrossRefGoogle Scholar
  18. 18.
    Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inf. 9(1), 132–141 (2013). CrossRefGoogle Scholar
  19. 19.
    Besada-Portas, E., DeLaTorre, L., DeLaCruz, J.M., DeAndrés-Toro, B.: Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans. Robot. 26(3), 619–634 (2010)CrossRefGoogle Scholar
  20. 20.
    MahmoudZadeh, S., Yazdani, A., Sammut, K., Powers, D.M.W.: AUV rendezvous online path planning in a highly cluttered undersea environment using evolutionary algorithms. In: Proceeding in Journal of Applied Soft Computing (ASOC). Online Dec (2017).
  21. 21.
    Zeng, Z., Sammut, K., Lammas, A., He, F., Tang, Y.: Shell space decomposition based path planning for AUVs operating in a variable environment. Ocean Eng. 91, 181–195 (2014)CrossRefGoogle Scholar
  22. 22.
    Ataei, M., Yousefi-Koma, A.: Three-Dimensional Optimal Path Planning for Waypoint Guidance of an Autonomous Underwater Vehicle. Robotics and Autonomous Systems (2015)Google Scholar
  23. 23.
    MahmoudZadeh, S., Powers, D., Sammut, K., Yazdani, A.M.: Differential Evolution for Efficient AUV Path Planning in Time Variant Uncertain Underwater Environment. Robotics (cs.RO). arXiv:1604.02523 (2016)
  24. 24.
    MahmoudZadeh, S., Powers, D., Sammut, K., Lammas, A., Yazdani, A.M.: Optimal route planning with prioritized task scheduling for AUV missions. In: IEEE International Symposium on Robotics and Intelligent Sensors, pp. 7–15 (2015)Google Scholar
  25. 25.
    MahmoudZadeh, S., Powers, D., Yazdani, A.M.: A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment. In: IEEE Congress on Evolutionary Computation (CEC). Vancouver, Canada. July. 678-684 CoRR arXiv:1604.02524 (2016)
  26. 26.
    MahmoudZadeh, S., Powers, D., Sammut, K., Yazdani, A.M.: Toward efficient task assignment and motion planning for large scale underwater mission. Int. J. Adv. Robot. Syst. (SAGE) 13, 1–13 (2016). CrossRefGoogle Scholar
  27. 27.
    MahmoudZadeh, S., Powers, D., Sammut, K., Yazdani, A.M.: Biogeography-based combinatorial strategy for efficient AUV motion planning and task-time management. J. Mar. Sci. Appl. 15(3), 463–477 (2016). Google Scholar
  28. 28.
    MahmoudZadeh, S., Powers, D., Sammut, K., Yazdani, A.M.: A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment. J. Soft Comput. 21(4), 1–24 (2016). Google Scholar
  29. 29.
    MahmoudZadeh, S., Powers, D., Atyabi, A.: UUV’s hierarchical DE-based motion planning in a semi dynamic underwater wireless sensor network. In: Proceeding of IEEE Transaction on Cybernetics (2018)Google Scholar
  30. 30.
    MahmoudZadeh, S., Powers Atyabi, A.D., Sammut, K., Yazdani, A.: A hierarchal planning framework for AUV mission management in a spatio-temporal varying ocean. In: Computers & Electrical Engineering. Online 5 Jan (2018).
  31. 31.
    Garau, B., Alvarez, A., Oliver, G.: AUV navigation through turbulent ocean environments supported by onboard H-ADCP. In: IEEE International Conference on Robotics and Automation. Orlando (2006)Google Scholar
  32. 32.
    Fossen, T.I.: Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics Trondheim, Norway (2002)Google Scholar
  33. 33.
    Yang, X.S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) 5th Symposium on Stochastic Algorithms, Foundations and Applications, vol. 5792, pp. 169–178. Lecture Notes in Computer Science (2009)Google Scholar
  34. 34.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, UK (2010)Google Scholar
  35. 35.
    Yang, X.S., He, X.: Firefly algorithm: Recent advances and applications. Int. J. Swarm Intell. 1(1), 36–50 (2013)CrossRefGoogle Scholar
  36. 36.
    Li, Z., Yang, C., Ding, N., Bogdan, S., Ge, T.: Robust adaptive motion control for underwater remotely operated vehicles with velocity constraints. Intern. J. Control. Autom. Syst. 10(2), 421–429 (2012)CrossRefGoogle Scholar
  37. 37.
    Price, K., Storn, R.: Differential evolution – A simple evolution strategy for fast optimization. Dr. Dobb’s J. 22(3), 18–24,78 (1997)zbMATHGoogle Scholar
  38. 38.
    Li, Z., Yang, C., Su, C.Y., Ye, W.: Adaptive fuzzy-based motion generation and control of mobile under-actuated manipulators. Eng. Appl. Artif. Intell. 30, 86–95 (2014)CrossRefGoogle Scholar

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© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Science and EngineeringFlinders UniversityAdelaideAustralia
  2. 2.Center for Maritime Engineering, Control and ImagingFlinders UniversityAdelaideAustralia
  3. 3.Seattle Children’s Research InstituteUniversity of WashingtonWashingtonUSA

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