Skip to main content

Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents


This paper presents an on-line dynamic path re-planning system for an autonomous underwater vehicle (AUV) to enable it to operate efficiently in a spatiotemporal, cluttered, and uncertain environment. The proposed strategy combines path re-planning with an evolutionary algorithm to adapt and regenerate the trajectory during the course of the mission using continuously updated current profiles from on-board sensors, such as a Horizontal Acoustic Doppler Velocity Logger. A quantum-behaved particle swarm optimization (QPSO) algorithm is used with a cost function which is based on the total time required to travel along the path segments accounting for the effect of space-time variable currents. The proposed path planner is designed to generate an optimal trajectory for an AUV navigating through a spatiotemporal ocean environment in the presence of irregularly shaped terrains as well as obstacles whose position coordinates are uncertain. Simulation results show that using the same on-board computation resources, the proposed path re-planning methodology with reuse of information gained from the previous planning history is able to obtain a more optimized trajectory than one relying on reactive path planning. Subsets of representative Monte Carlo simulations were run to analyse the performance of these dynamic planning systems. The results demonstrate the inherent robustness and superiority of the proposed planner based on path re-planning scheme when compared with the reactive path planning scheme.

This is a preview of subscription content, access via your institution.


  1. 1.

    Smith, R.N., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Robotics. Res 29(12), 1475–1497 (2010). doi:10.1177/0278364910377243

    Article  Google Scholar 

  2. 2.

    Smith, R.N., Pereira, A., Chao, Y., Li, P.P., Caron, D.A., Jones, B.H., Sukhatme, G.S.: Autonomous underwater vehicle trajectory design coupled with predictive ocean models: A case study. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 4770–4777 (2010)

  3. 3.

    Zeng, Z., Lammas, A., Sammut, K., He, F., Tang, Y.: Shell space decomposition based path planning for AUVs operating in a variable environment. Ocean Eng. 91(0), 181–195 (2014). doi:10.1016/j.oceaneng.2014.09.001

  4. 4.

    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 

  5. 5.

    Hollinger, G.A., Pereira, A.A., Sukhatme, G.S.: Learning uncertainty models for reliable operation of Autonomous Underwater Vehicles. In: 2013 IEEE International Conference on Robotics and Automation (ICRA), 6–10 May 2013, pp. 5593–5599

  6. 6.

    Garau, B., Alvarez, A., Oliver, G.: AUV navigation through turbulent ocean environments supported by onboard H-ADCP. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, 15–19 May 2006, pp. 3556–3561

  7. 7.

    Thurnherr, A.M.: A practical assessment of the errors associated with fulldepth LADCP profiles obtained using teledyne RDI workhorse acoustic doppler current profilers. J. Atmos. Oceanic Technol 27(7), 1215– 1227 (2010)

  8. 8.

    Ordonez, C.E., Shearman, R.K., Barth, J.A., Welch, P., Erofeev, A., Kurokawa, Z.: Obtaining absolute water velocity profiles from glider-mounted Acoustic Doppler-Current Profilers. In: Program Book - OCEANS 2012 MTS/IEEE Yeosu: The Living Ocean and Coast - Diversity of Resources and Sustainable Activities (2012)

  9. 9.

    Fong, D.A., Jones, N.L.: Evaluation of AUV-based ADCP measurements. Limnol. Oceanogr. Methods 4(MAR), 58–67 (2006)

    Article  Google Scholar 

  10. 10.

    Firing, E.: GPS attitude determination for shipboard Doppler current profiling. In: Oceans Conference Record (IEEE), pp. 790 (1991)

  11. 11.

    Cowlagi, R.V., Tsiotras, P.: Multiresolution path planning with wavelets: A local replanning approach. In: Proceedings of the American Control Conference, pp. 1220–1225 (2008)

  12. 12.

    Jung, D., Tsiotras, P.: Multiresolution on-line path planning for small unmanned aerial vehicles. In: Proceedings of the American Control Conference, pp. 2744–2749 (2008)

  13. 13.

    Wzorek, M., Doherty, P.: Reconfigurable path planning for an autonomous unmanned aerial vehicle. In: Proceedings - 2006 International Conference on Hybrid Information Technology, ICHIT 2006, pp. 242–249 (2006)

  14. 14.

    Wzorek, M., Kvarnström, J., Doherty, P.: Choosing path replanning strategies for unmanned aircraft systems. In: ICAPS 2010 - Proceedings of the 20th International Conference on Automated Planning and Scheduling, pp. 193–200 (2010)

  15. 15.

    Kuffner Jr, J.J., La Valle, S.M.: RRT-connect: an efficient approach to single-query path planning. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 995–1001 (2000)

  16. 16.

    Zucker, M., Kuffner, J., Branicky, M.: Multipartite RRTs for rapid replanning in dynamic environments. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 1603–1609 (2007)

  17. 17.

    Ferguson, D., Kalra, N., Stentz, A.: Replanning with RRTs. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 1243–1248 (2006)

  18. 18.

    Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    MATH  MathSciNet  Article  Google Scholar 

  19. 19.

    Likhachev, M., Ferguson, D., Gordon, G., Stentz, A., Thrun, S.: Anytime dynamic a*: An anytime, replanning algorithm. In: ICAPS 2005 - Proceedings of the 15th International Conference on Automated Planning and Scheduling, pp. 262–271 (2005)

  20. 20.

    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)

  21. 21.

    Warren, C.W.: Technique for autonomous underwater vehicle route planning. IEEE J. Ocean. Eng. 15(3), 199–204 (1990)

    Article  Google Scholar 

  22. 22.

    Kruger, D., Stolkin, R., Blum, A., Briganti, J.: Estuarine environments. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 4265–4270 (2007)

  23. 23.

    Yang, Y., Wang, S., Wu, Z., Wang, Y.: Motion planning for multi-HUG formation in an environment with obstacles. Ocean Eng. 38(17-18), 2262–2269 (2011)

    Article  Google Scholar 

  24. 24.

    Alvarez, A., Caiti, A., Onken, R.: Evolutionary path planning for autonomous underwater vehicles in a variable ocean. IEEE J. Ocean. Eng 29(2), 418–429 (2004). doi:10.1109/joe.2004.827837

    Article  Google Scholar 

  25. 25.

    Nikolos, I.K., Valavanis, K.P., Tsourveloudis, N.C., Kostaras, A.N.: Evolutionary Algorithm Based Offline/Online Path Planner for UAV Navigation. IEEE Trans. Syst. Man, Cybern. B, Cybern. 33(6), 898–912 (2003). 10.1109/tsmcb.2002.804370

    Article  Google Scholar 

  26. 26.

    Zheng, C., Li, L., Xu, F., Sun, F., Ding, M.: Evolutionary route planner for unmanned air vehicles. IEEE Trans. Robot. 21(4), 609–620 (2005). 10.1109/tro.2005.844684

    Article  Google Scholar 

  27. 27.

    Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013). doi:10.1109/TII.2012.2198665

    Article  Google Scholar 

  28. 28.

    Fu, Y., Ding, M., Zhou, C.: Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for UAV. IEEE Trans. Syst., Man, Cybern. A, Syst., Humans 42(2), 511–526 (2012). doi:10.1109/tsmca.2011.2159586

    Article  Google Scholar 

  29. 29.

    Tam, C., Bucknall, R.: Cooperative path planning algorithm for marine surface vessels. Ocean Eng. 57, 25–33 (2013)

    Article  Google Scholar 

  30. 30.

    Zeng, Z., Sammut, K., He, F., Lammas, A.: Efficient path evaluation for AUVs using adaptive B-spline approximation. In: OCEANS, pp. 2012 MTS/IEEE, Harnessing the Power of the Ocean (2012)

  31. 31.

    Bhattacharya, S., Likhachev, M., Kumar, V.: Topological constraints in search-based robot path planning. Auton. Robot. 33(3), 273–290 (2012)

    Article  Google Scholar 

  32. 32.

    Cui, R., Gao, B., Guo, J.: Pareto-optimal coordination of multiple robots with safety guarantees. Auton. Robot. 32(3), 189–205 (2012)

    Article  Google Scholar 

  33. 33.

    Lau, B., Sprunk, C., Burgard, W.: Kinodynamic motion planning for mobile robots using splines. In: 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 2427–2433 (2009)

  34. 34.

    Cui, R., Ge, S.S., Voon Ee How, B., Sang Choo, Y.: Leader-follower formation control of underactuated autonomous underwater vehicles. Ocean Eng. 37(17–18), 1491–1502 (2010)

    Article  Google Scholar 

  35. 35.

    Li, Z., Yang, C., Ding, N., Bogdan, S., Ge, T.: Robust adaptive motion control for underwater remotely operated vehicles with velocity constraints. Int. J. Control Autom. Syst. 10(2), 421–429 (2012)

    Article  Google Scholar 

  36. 36.

    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)

    Article  Google Scholar 

  37. 37.

    Yang, C., Li, Z., Li, J.: Trajectory planning and optimized adaptive control for a class of wheeled inverted pendulum vehicle models. IEEE Trans. Cybern. 43(1), 24–36 (2013)

    Article  Google Scholar 

  38. 38.

    Zheng, Z., Lammas, A., Sammut, K., Fangpo, H.: Optimal path planning based on annular space decomposition for AUVs operating in a variable environment. In: IEEE Autonomous Underwater Vehicles (AUV) - 2012, Southampton, 24–27 Sept 2012, pp. 1–9

  39. 39.

    Barnsley, M.F., Frame, M.: The influence of Benoît B. Mandelbrot on mathematics. Not. Am. Math. Soc. 59(9), 1208–1221 (2012)

    MATH  MathSciNet  Article  Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Zheng Zeng or Karl Sammut.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zeng, Z., Sammut, K., Lammas, A. et al. Efficient Path Re-planning for AUVs Operating in Spatiotemporal Currents. J Intell Robot Syst 79, 135–153 (2015).

Download citation


  • Dynamic path re-planning
  • Autonomous underwater vehicle
  • Quantum-behaved particle swarm optimization
  • Spatiotemporal current map