Skip to main content

Advertisement

SpringerLink
Log in

AUV Path Planning Based on Differential Evolution with Environment Prediction

  • Regular Paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Energy-efficient path planning is essential for the autonomous underwater vehicle (AUV)-based ocean exploration. Existing static environment-based AUV path planners do not work well in dynamic ocean environments. A novel onboard sensing system-based AUV path planning strategy is proposed, and it is suitable for a regional dynamic environment to improve the energy utilization efficiency of an AUV working in a small-scale and dynamic mission area. Firstly, unlike the existing methods, the onboard sensing system including horizontal acoustic doppler current profile and detecting sonar is used to obtain environmental information, and the probabilistic multiple hypothesis tracker and Kalman filter are employed to carry out multi-step prediction of the environment. After that, the differential evolution algorithm is introduced as the optimizer, and a novel prediction-based path evaluator is designed to evaluate the fitness of possible paths. Besides, a novel prediction-based online re-planning strategy is designed, which is beneficial to reduce the impact of forecast error and the planning is thus closed-loop. Finally, multiple simulation experiments are designed to verify the effectiveness and superiority of the path planner, and the results show that the proposed planning strategy can reduce the AUV energy consumption by at least 4.6% compared with static environment-based planners.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Code Availability

The MATLAB codes used and analyzed during the study are available from the first author on reasonable request.

References

  1. Li, D., Wang, P., Du, L.: Path planning technologies for autonomous underwater vehicles-a review. IEEE Access 7, 9745–9768 (2019)

    Article  Google Scholar 

  2. Perera, L.P., Carvalho, J., Soares, C.G.: Intelligent ocean navigation and fuzzy-Bayesian decision/action formulation. IEEE J. Ocean. Eng. 37(2), 204–219 (2012)

    Article  Google Scholar 

  3. Soulignac, M.: Feasible and optimal path planning in strong current fields. IEEE Trans. Robot. 27(1), 89–98 (2010)

    Article  Google Scholar 

  4. Zhang, H.H., Gong, L., Chen, T., Wang, L., Zhang, X.: Global path planning methods of UUV in coastal environment. In: Proc. IEEE International Conference on Mechatronics and Automation (ICMA), pp 1018–1023, Harbin, China (2016)

  5. Yu, H., Wang, Y.: Multi-objective AUV path planning in large complex battlefield environments. In: Proc. International Symposium on Computational Intelligence and Design (ISCID), vol. 2, pp 345–348, Hangzhou, China (2014)

  6. Lolla, T., Ueckermann, M.P., Yiğit, K., Haley, P.J., Lermusiaux, P.F.: Path planning in time dependent flow fields using level set methods. In: Proc. IEEE International Conference on Robotics and Automation (ICRA), pp 166–173, Saint Paul, MN, USA (2012)

  7. Ammar, A., Bennaceur, H., Châari, I., Koubâa, A., Alajlan, M.: Relaxed dijkstra and A* with linear complexity for robot path planning problems in large-scale grid environments. Soft Comput. 20 (10), 4149–4171 (2016)

    Article  Google Scholar 

  8. Hernández, J.D., Istenič, K., Gracias, N., Palomeras, N., Campos, R., Vidal, E., Garcia, R., Carreras, M.: Autonomous underwater navigation and optical mapping in unknown natural environments. Sensors 16(8), 1174 (2016)

    Article  Google Scholar 

  9. Janson, L., Ichter, B., Pavone, M.: Deterministic sampling-based motion planning: Optimality, complexity, and performance. Int. J. Robot. Res. 37(1), 46–61 (2018)

    Article  Google Scholar 

  10. Xiong, C., Lu, D., Zeng, Z., Lian, L., Yu, C.: Path planning of multiple unmanned marine vehicles for adaptive ocean sampling using elite group-based evolutionary algorithms. J. Intell. Robot. Syst. pp. 1–15 (2020)

  11. Wang, X., Yao, X., Zhang, L.: Path planning under constraints and path following control of autonomous underwater vehicle with dynamical uncertainties and wave disturbances. J. Intell. Robot. Syst. pp 1–18 (2020)

  12. Zeng, Z., Zhou, H., Lian, L.: Exploiting ocean energy for improved AUV persistent presence: path planning based on spatiotemporal current forecasts. J. Mar. Sci. Technol. 25(1), 26–47 (2020)

    Article  Google Scholar 

  13. Perez-Carabaza, S., Besada-Portas, E., Lopez-Orozco, J.A., Jesus, M.: Ant colony optimization for multi-UAV minimum time search in uncertain domains. Appl. Soft Comput. 62, 789–806 (2018)

    Article  Google Scholar 

  14. MahmoudZadeh, S., Powers, D.M., Yazdani, A.M.: A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment. In: Proc. IEEE Congress on Evolutionary Computation (CEC), pp 678–684, Vancouver, BC, Canada (2016)

  15. Mahmoudzadeh, S., Powers, D.M.W., Atyabi, A.: UUV’s hierarchical DE-based motion planning in a semi dynamic underwater wireless sensor network. IEEE Trans. Cybern. 49(8), 2992–3005 (2019)

    Article  Google Scholar 

  16. Tsai, C.C., Huang, H.C., Chan, C.K.: Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation. IEEE Trans. Ind. Electron. 58(10), 4813–4821 (2011)

    Article  Google Scholar 

  17. 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)

    Article  Google Scholar 

  18. Isern-González, J., Hernández-Sosa, D., Fernández-Perdomo, E., Cabrera-Gámez, J., Domínguez-Brito, A.C., Prieto-Marañón, V.: Path planning for underwater gliders using iterative optimization. In: Proc. IEEE International Conference on Robotics and Automation (ICRA), pp 1538–1543, Shanghai, China (2011)

  19. Kim, K., Ura, T.: Towards a new strategy for AUV navigation in sea currents: A quasi-optimal approach. In: Proc. IEEE Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies (SSC), pp 1–10, Tokyo, Japan (2011)

  20. Barron, C.N., Kara, A.B., Martin, P.J., Rhodes, R.C., Smedstad, L.F.: Formulation, implementation and examination of vertical coordinate choices in the Global Navy Coastal Ocean Model (NCOM). Ocean Model. 11(3-4), 347–375 (2006)

    Article  Google Scholar 

  21. Garau, B., Alvarez, A., Oliver, G.: AUV navigation through turbulent ocean environments supported by onboard H-ADCP. In: Proc. IEEE International Conference on Robotics and Automation (ICRA), pp 3556–3561, Orlando, FL, USA (2006)

  22. Lamb, H.: Hydrodynamics. Cambridge University Press, Cambridge (1993)

    MATH  Google Scholar 

  23. Karmozdi, A., Hashemi, M., Salarieh, H., Alasty, A.: INS-DVL navigation improvement using rotational motion dynamic model of AUV. IEEE Sensors J. 20(23), 14329–14336 (2020)

    Article  Google Scholar 

  24. Lammas, A.K., Sammut, K., He, F.: A 6 DoF navigation algorithm for autonomous underwater vehicles. In: Proc. OCEANS 2007-Europe, pp 1–6, Aberdeen, UK (2007)

  25. Pereira, A.A., Binney, J., Hollinger, G.A., Sukhatme, G.S.: Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. J.Field Robot. 30(5), 741–762 (2013)

    Article  Google Scholar 

  26. Gonzalez, J.P., Stentz, A.: Planning with uncertainty in position an optimal and efficient planner. In: Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 2435–2442, Edmonton, Alta., Canada (2005)

  27. Pak, J.M., Kim, P.S., You, S.H., Lee, S.S., Song, M.K.: Extended least square unbiased FIR filter for target tracking using the constant velocity motion model. Int. J. Control Autom Syst. 15(2), 947–951 (2017)

    Article  Google Scholar 

  28. Chen, H.Y., Liu, M.Q., Zhang, S.L.: Energy-efficient localization and target tracking via underwater mobile sensor networks. Front Inform Technol Electron Eng 19(8), 999–1012 (2018)

    Article  Google Scholar 

  29. Zeng, Z., Sammut, K., Lian, L., He, F., Lammas, A., Tang, Y.: A comparison of optimization techniques for AUV path planning in environments with ocean currents. Robot. Auton. Syst. 82, 61–72 (2016)

    Article  Google Scholar 

  30. Das, S., Mullick, S.S., Suganthan, P.N.: Recent advances in differential evolution–an updated survey. Swarm Evol Comput 27, 1–30 (2016)

    Article  Google Scholar 

  31. Prautzsch, H., Boehm, W., Paluszny, M.: Bézier and B-spline Techniques. Springer Science & Business Media, Berlin (2013)

    MATH  Google Scholar 

  32. Lo, K.W., Ferguson, B.G.: Automatic detection and tracking of a small surface watercraft in shallow water using a high-frequency active sonar. IEEE Trans. Aerosp. Electron. Syst. 40(4), 1377–1388 (2004)

    Article  Google Scholar 

  33. Bar-Shalom, Y., Li, X.R., Kirubarajan, T.: Estimation with Applications to Tracking and Navigation: Theory Algorithms and Software. John Wiley & Sons, Germany (2004)

    Google Scholar 

  34. Gauvrit, H., Le Cadre, J.P., Jauffret, C.: A formulation of multitarget tracking as an incomplete data problem. IEEE Trans. Aerosp. Electron. Syst. 33(4), 1242–1257 (1997)

    Article  Google Scholar 

  35. Hu, Z., Leung, H., Blanchette, M.: Statistical performance analysis of track initiation techniques. IEEE Trans. Signal Process. 45(2), 445–456 (1997)

    Article  Google Scholar 

  36. Han, C., Zhu, H., Duan, Z.: Multi-source Information Fusion. Tsinghua University Press, Beijing (2010)

    Google Scholar 

  37. Zeng, Z., Sammut, K., Lammas, A., He, F., Tang, Y.: Efficient path re-planning for AUVs operating in spatiotemporal currents. Intell. Robot. Syst. 79(1), 135–153 (2015)

    Article  Google Scholar 

  38. Li, J.J., Zhang, R.B., Yu, Y.: Research on route obstacle avoidance task planning based on differential evolution algorithm for auv. In: International Conference in Swarm Intelligence. Springer International Publishing, Cham (2014)

  39. Wang, J., Shi, W., Xu, L., Zhou, L., Niu, Q., Liu, J.: Design of optical-acoustic hybrid underwater wireless sensor network. J. Netw. Comput. Appl. 92, 59–67 (2017)

    Article  Google Scholar 

Download references

Funding

This work was supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grants U1709203, U1809212 and U1909206, the Zhejiang Provincial Natural Science Foundation of China under grant LZ19F030002, the Key Research and Development Program of Zhejiang Province under Grant 2019C03109, and the NSFC under Grant 62088102.

Author information

Authors and Affiliations

  1. The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, 310027, China

    Jiaxin Zhang & Meiqin Liu

  2. College of Electrical Engineering, Zhejiang University, Hangzhou, 310027, China

    Jiaxin Zhang, Senlin Zhang & Ronghao Zheng

  3. The Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049, China

    Meiqin Liu

Authors
  1. Jiaxin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meiqin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Senlin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ronghao Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J. Zhang and M. Liu contributed to the design and implementation of the research, the analysis of the results and the writing of the first draft of the manuscript. S. Zhang and R. Zheng commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Meiqin Liu.

Ethics declarations

Competing interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, J., Liu, M., Zhang, S. et al. AUV Path Planning Based on Differential Evolution with Environment Prediction. J Intell Robot Syst 104, 23 (2022). https://doi.org/10.1007/s10846-021-01533-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-021-01533-9

Keywords

Search

Navigation