Abstract
As a kind of flexible and efficient device that can autonomously complete tasks without human intervention, Unmanned Surface Vehicle (USV) has gained increasing attention in the research field recently. Path planning is an essential hotspot in the study of the USV. Unlike traditional robotic path planning, the path planning of the USV needs to consider the dynamic impact of the water environment as well as the constraints of its own vessel’s kinematics. For the sake of enhancing the practical operability of the navigation, an optimized Genetic Algorithm (GA) based on three-dimensional environment modeling is proposed. By simplifying the 3D coordinate, the algorithm can efficiently deal with the avoidance of dynamic and static obstacles. Population initialization is improved to reduce the calculating load, and the Elitism Strategy is combined to ensure convergence. An innovative Sacrifice Strategy and intraspecific hybrid methods are proposed to further increase the genetic diversity and convergence rate. We also propose a new penalty fitness function. Through simulation results and experiments in a water surface environment, the effectiveness and rationality of this method were verified, providing new ideas for path planning research of the USV.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akshya, J., Priyadarsini, P.L.K.: Graph-based path planning for intelligent UAVs in area coverage applications. J. Intell. Fuzzy Syst. 39(6), 8191–8203 (2020)
De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. University of Michigan (1975)
Deb, K.: An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Eng. 186(2–4), 311–338 (2000)
Hou, Y.Q., Tao, H., Gong, J.B., Liang, X., Zhang, N.: Cooperative trajectory planning for unmanned boat and UAV clusters under multiple constraints. Chin. Ship Res. 16(1), 74–82 (2021)
Lazarowska, A.: Multi-criteria ACO-based algorithm for ship’s trajectory planning. TransNav: Int. J. Marine Navigat. Saf. Sea Transp. 11(1) (2017)
Lazarowska, A.: Research on algorithms for autonomous navigation of ships. WMU J. Marit. Aff. 18(2), 341–358 (2019)
Li, S., Luo, T., Wang, L., Xing, L., Ren, T.: Tourism route optimization based on improved knowledge ant colony algorithm. Compl. Intell. Syst. 8(5), 3973–3988 (2022)
Liu, W., Liu, Y., Bucknall, R.: A robust localization method for unmanned surface vehicle (USV) navigation using fuzzy adaptive Kalman filtering. IEEE Access 7, 46071–46083 (2019)
Medeiros, D.R.d.S., Fernandes, M.A.C.: Distributed genetic algorithms for low-power, low-cost and small-sized memory devices. Electronics 9(11), 1891 (2020)
Ni, S., Liu, Z., Cai, Y., Wang, X.: Modelling of ship’s trajectory planning in collision situations by hybrid genetic algorithm. Polish Maritime Res. 25(3 (99)), 14–25 (2018)
Nunia, V., Poonia, R.C.: A review and comparative study on surface vehicle path planning algorithm. In: Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence, pp. 106–109 (2021)
Ouyang, Y., Wang, Z., Huang, X., Yang, L.: Unmanned boat formation path planning technique based on improved RRT algorithm. Chin. Ship Res. 15(3), 18–24 (2020)
Sang, H., You, Y., Sun, X., Zhou, Y., Liu, F.: The hybrid path planning algorithm based on improved a* and artificial potential field for unmanned surface vehicle formations. Ocean Eng. 223, 108709 (2021)
Singh, Y., Sharma, S., Sutton, R., Hatton, D., Khan, A.: A constrained a* approach towards optimal path planning for an unmanned surface vehicle in a maritime environment containing dynamic obstacles and ocean currents. Ocean Eng. 169, 187–201 (2018)
Song, R., Liu, Y., Bucknall, R.: Smoothed a* algorithm for practical unmanned surface vehicle path planning. Appl. Ocean Res. 83, 9–20 (2019)
Wang, N., Hongwei, X., Li, C., Yin, J.: Hierarchical path planning of unmanned surface vehicles: a fuzzy artificial potential field approach. Int. J. Fuzzy Syst. 23, 1797–1808 (2021)
Zhenyu, W., Guang, H., Feng, L., Jiping, W., Liu, S.: Collision avoidance for mobile robots based on artificial potential field and obstacle envelope modelling. Assem. Autom. 36(3), 318–332 (2016)
Xin, J., Zhong, J., Yang, F., Cui, Y., Sheng, J.: An improved genetic algorithm for path-planning of unmanned surface vehicle. Sensors 19(11), 2640 (2019)
Xu, X., Cai, P., Ahmed, Z., Yellapu, V.S., Zhang, W.: Path planning and dynamic collision avoidance algorithm under colregs via deep reinforcement learning. Neurocomputing 468, 181–197 (2022)
Zhao, F., Zhao, J., Yan, S., Feng, J.: Dynamic conditional networks for few-shot learning. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11219, pp. 20–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01267-0_2
Zhao, J., Li, J., Zhao, F., Yan, S., Feng, J.: Marginalized CNN: learning deep invariant representations (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, L., Ye, X., Xie, P., Liu, X. (2024). An Autonomous Recovery Guidance System for USV Based on Optimized Genetic Algorithm. In: Fang, L., Pei, J., Zhai, G., Wang, R. (eds) Artificial Intelligence. CICAI 2023. Lecture Notes in Computer Science(), vol 14474. Springer, Singapore. https://doi.org/10.1007/978-981-99-9119-8_24
Download citation
DOI: https://doi.org/10.1007/978-981-99-9119-8_24
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9118-1
Online ISBN: 978-981-99-9119-8
eBook Packages: Computer ScienceComputer Science (R0)