Hostname: page-component-78c5997874-94fs2 Total loading time: 0 Render date: 2024-11-13T04:20:22.620Z Has data issue: false hasContentIssue false
  • English
  • Français

Path planning method for unmanned underwater vehicles eliminating effect of currents based on artificial potential field

Published online by Cambridge University Press:  14 April 2021

Danjie Zhu  and
Simon X. Yang
Danjie Zhu*
Affiliation:
Laboratory of Advanced Robotics and Intelligent System (ARIS), School of Engineering, University of Guelph, Guelph, ON, CanadaN1G2W1
Simon X. Yang
Affiliation:
Laboratory of Advanced Robotics and Intelligent System (ARIS), School of Engineering, University of Guelph, Guelph, ON, CanadaN1G2W1
*
*Corresponding author. E-mail: danjie@uoguelph.ca
Get access Rights & Permissions [Opens in a new window]

Abstract

To eliminate the effect of ocean currents for optimal path planning for unmanned underwater vehicles (UUVs) in the underwater environment, an intelligent algorithm is designed and proposed in this paper. The algorithm consists of two parts: an artificial potential field-based algorithm that derives the shortest path and avoids collision accidents; and an adjusting function that eliminates the effect of ocean currents. The planning results of the intelligent algorithm are presented in detail, and compared with the conventional algorithm that does not consider the effect of currents. The effectiveness of the optimised path planning method given in this paper is proved.

Keywords

artificial potential fieldcurrents effectoptimal path planningunmanned underwater vehicles
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 5 , September 2021 , pp. 955 - 967
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aghababa, M. (2012). 3D path planning for underwater vehicles using five evolutionary optimization algorithms avoiding static and energetic obstacles. Application of Ocean Resources, 38, 48–62.CrossRefGoogle Scholar
Cao, X., Sun, C.-Y. and Chen, M.-Z. (2019). Path planning for autonomous underwater vehicle in time-varying current. IEEE Transactions on Intelligent Transportation and System, 13(8), 1265–1271.CrossRefGoogle Scholar
Dijkstra, E. (1959). Communication with an automatic computer. Ph.D. thesis, University of Amsterdam, The Netherlands.Google Scholar
Emami, M. and Taban, M. R. (2018). A low complexity integrated navigation system for underwater vehicles. Journal of Navigation, 71(5), 1161–1177.CrossRefGoogle Scholar
Gafurov, S. and Klochkov, E. (2015). Autonomous unmanned underwater vehicles development tendencies. Procedia Engineering, 106, 141–148.CrossRefGoogle Scholar
Hart, P. E., Nilsson, N. J. and Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on System, Science and Cybernetics, SSC-4(2), 100–107.CrossRefGoogle Scholar
Hovem, L. A., Torstad, E., Bjerager, P., Bjornsen, T. and Danielsen, H. K. (2014). Managing Risk in Deepwater Frontiers - Key Learnings from Five Continents. Moscow, Russia: World Petroleum Congress.Google Scholar
Hu, C., Wang, R., Yan, F. and Chen, N. (2015). Should the desired heading in path following of autonomous vehicles be the tangent direction of the desired path? IEEE Transactions on. Intelligent Transportation and System, 16(6), 3084–3094.CrossRefGoogle Scholar
Hu, Z., Zhu, D., Cui, C. and Sun, B. (2019). Trajectory tracking and re-planning with model predictive control of autonomous underwater vehicles. Journal of Navigation, 72, 321–341.CrossRefGoogle Scholar
Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. International Journal of Robotics Research, 5(1), 90–98.CrossRefGoogle Scholar
Li, D., Wang, P. and Du, L. (2019a). Path planning technologies for autonomous underwater vehicles - a review. IEEE Access, 7, 9745–9768.CrossRefGoogle Scholar
Li, B., Zhao, R., Xu, G., Wang, G., Su, Z., and Chen, Z. (2019b) Three-Dimensional Path Planning for an Under-Actuated Autonomous Underwater Vehicle. Proceedings of the 29th International Ocean and Polar Engineering Conference, Honolulu, Hawaii, USA, June 2019, 1, 1518–1524.Google Scholar
Lin, Z., Yue, M., Wu, X. and Tian, H. (2019). An improved artificial potential field method for path planning of mobile robot with subgoal adaptive selection. In Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., and Zhou D. (eds), Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science, vol 11740. Cham, Switzerland: Springer, pp. 211–220.CrossRefGoogle Scholar
Lin, X., Wang, Z.-Q. and Chen, X.-Y. (2020). Path Planning with Improved Artificial Potential Field Method Based on Decision Tree. International Conference on Integrated Navigation Systems (ICINS).Google Scholar
Panda, M., Das, B., Subudhi, B. and Pati, B. (2020). A comprehensive review of path planning algorithms for autonomous underwater vehicles. International Journal of Autonomous Computation, 17(3), 321–352.CrossRefGoogle Scholar
Ralli, E. and Hirzinger, G. (1994). Fast path planning for robot manipulators using numerical potential fields in the configuration space. IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, 3, 1922–1929.Google Scholar
Singh, Y., Sharma, S., Sutton, R., Hatton, D. and Khan, A. (2018). A constrained a* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Engineering, 169, 187–201.CrossRefGoogle Scholar
Song, J., Zhao, M., Liu, Y., Liu, H. and Guo, X. (2019). Multi-Rotor UAVs Path Planning Method Based on Improved Artificial Potential Field Method. Chinese Control Conference (CCC), 8242–8247.CrossRefGoogle Scholar
Wang, X., Yao, X. and Zhang, L. (2020). Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances. Journal of Intelligent and Robotic Systems: Theory and Applications, 99(3–4), 891–908.CrossRefGoogle Scholar
Yu, H. and Su, T. (2001). A destination driven navigator with dynamic obstacle motion prediction. IEEE International Conference on Robotics and Automation (ICRA), 3, 2692–2697.Google Scholar
Yu, W., Kin Huat, L. and Chen, L. (2020). Cooperative path planning for heterogeneous unmanned vehicles in a search-and-track mission aiming at an underwater target. IEEE Transactions on Vehicular Technology, 69(6), 6782–6787.Google Scholar
Zhou, Z., Wang, J., Zhu, Z., Yang, D. and Wu, J. (2018). Tangent navigated robot path planning strategy using particle swarm optimized artificial potential field. Optik, 158, 639–651.CrossRefGoogle Scholar
Zhu, D., Liu, Y. and Sun, B. (2018). Task assignment and path planning of a multi-AUV system based on a Glasius bio-inspired self-organizing map algorithm. Journal of Navigation, 71, 482–496.CrossRefGoogle Scholar