Multi-strategy improved sparrow search algorithm-based path planning of a unmanned surface vehicle

ZHAN Xiaofei, ZHAO Hong, WANG Ning, LI Wangyang, XIE Yizhe

doi:10.16411/j.cnki.issn1006-7736.2024.01.001

Journal of Dalian Maritime University >

2024 , Vol. 50 >Issue 1: 1 - 10

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.01.001

Multi-strategy improved sparrow search algorithm-based path planning of a unmanned surface vehicle

Expand

(a.Marine Electrical Engineering College;b. Marine Engineering College, Dalian Maritime University, Dalian 116026, China)

Received date: 2023-07-25

Revised date: 2023-08-21

Online published: 2023-09-08

Fold

Abstract

To obtain the navigation path of high-performance unmanned surface vehicle (USV), a multi-strategy improved sparrow search algorithm (MISSA) was proposed.Firstly, a fitness function with a steering angle penalty term was designed; Secondly, the position update strategy was improved by using the golden sine method and parameter self-spiral setting,at the same time information exchange between sparrow individuals was strengthened during the position update process to balance global exploration and local search processes,again,chaotic circular mapping was introduced to improve the quality and diversity of the initial sparrow population.Finally, a local search optimization mechanism was designed to solve the problem of the original sparrow algorithm(SSA) easily falling into local optima and obtain a global path with better fitness. Results show that compared with three excellent algorithms, namely improved A*, improved ant colony algorithm combined with genetics, and original SSA, the MISSA algorithm in this paper performs the best in key performance indicators such as path distance, turning angle, frequency, providing an effective path for autonomous and safe operation of USV.

Key words： unmanned surface vehicle(USV)；global path planning；sparrow search algorithm(SSA)；steering angle penalty；partial search optimization mechanism

Cite this article

ZHAN Xiaofei, ZHAO Hong, WANG Ning, LI Wangyang, XIE Yizhe . Multi-strategy improved sparrow search algorithm-based path planning of a unmanned surface vehicle[J]. Journal of Dalian Maritime University, 2024 , 50(1) : 1 -10 . DOI: 10.16411/j.cnki.issn1006-7736.2024.01.001

References

［1］MA Y, HU M Q, YAN X P. Multi-objective path planning for unmanned surface vehicle with currents effects［J］. ISA Transactions, 2018, 75: 137-156.
［2］XIANG X B, YU C Y, LAPIERRE L, et al. Survey on fuzzy-logic-based guidance and control of marine surface vehicles and underwater vehicles［J］.International Journal of Fuzzy Systems, 2018, 20: 572-586.
［3］何傲, 谢新连, 李猛. 考虑安全性的施工水域路径优化［J］.大连海事大学学报,2019,45(3):114-120.
HE A，XIE X L，LI M. Route optimization in construction waters considering safety［J］. Journal of Dalian Maritime University, 2019,45(3):114-120.(in Chinese)
［4］NI S K, WANG N, QIN Z Y, et al. A distributed coordinated path planning algorithm for maritime autonomous surface ship［J］. Ocean Engineering,2023, 271: 113759.
［5］WANG N, JIN X Z, ER M J. A multilayer path planner for a USV under complex marine environments［J］. Ocean Engineering, 2019, 184:1-10.
［6］CHENG Z H, ZHANG H J, ZHAO Q. The method based on Dijkstra of multi-directional ship’s path planning［C］//2020 Chinese Control and Decision Conference (CCDC). Hefei: IEEE, 2020: 5142-5146.
［7］WANG N, XU H W. Dynamics-constrained global-local hybrid path planning of an autonomous surface vehicle［J］. IEEE Transactions on Vehicular Technology, 2020, 69(7): 6928-6942.
［8］CHEN L, SHAN Y W, TIAN W, et al. A fast and efficient double-tree RRT-like sampling-based planner applying on mobile robotic systems［J］.IEEE/ASMETransactions on Mechatronics, 2018, 23(6): 2568-2578.
［9］WANG J K, MENG M Q H. Optimal path planning using generalized Voronoi graph and multiple potential functions［J］. IEEE Transactions on Industrial Electronics, 2020, 67(12): 10621-10630.
［10］WANG N, ZHANG Y H, AHN C K, et al. Autonomous pilot of unmanned surface vehicles:bridging path planning and tracking［J］. IEEE Transactions on Vehicular Technology, 2021, 71(3): 2358-2374.
［11］毛寿祺, 杨平, 高迪驹, 等. 基于细菌觅食-蚁群算法的无人艇路径规划［J/OL］.控制工程，2022:1-9.doi: 10.14107/j.cnki.kzgc.20210857.
MAO S Q, YANG P, GAO D J, et al. Global path planning for unmanned surface vehicle based on bacterial foraging-improved ant colony hybrid algorithm ［J/OL］. Control Engineering of China，2022: 1-9. doi: 10.14107/j.cnki.kzgc.20210857. (in Chinese)
［12］WANG N, XU H W, LI C Z, et al. Hierarchical path planning of unmanned surface vehicles: a fuzzy artificial potential field approach［J］. International Journal of Fuzzy Systems, 2021, 23: 1797-1808.
［13］XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm［J］. Systems Science & Control Engineering, 2020, 8(1): 22-34.
［14］LIU G Y, SHU C, LIANG Z W, et al. A modified sparrow search algorithm with application in 3d route planning for UAV［J］. Sensors, 2021, 21(4): 1224.
［15］GENG J Q, SUN X M, WANG H H, et al. A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization［J］. Neural Computing and Applications, 2023, 35: 24603-24620.
［16］YUE Y G, CAO L, LU D W, et al. Review and empirical analysis of sparrow search algorithm［J］. Artificial Intelligence Review, 2023，56:10867-10919.
［17］ZHANG Z, HE R, YANG K. A bioinspired path planning approach for mobile robots based on improved sparrow search algorithm［J］. Advances in Manufacturing, 2022, 10: 114-130.
［18］LIANG L H, WEN Y. Rudder roll stabilization with disturbance compensation model predictive control［J］. Journal of Marine Science and Technology, 2018, 24: 249-259.
［19］赵春宇, 姜皓, 徐茂竹，等. 改进A*算法在无人船路径规划中的应用［J］. 浙江工业大学学报, 2022, 50(6):615-620.
ZHAO C Y, JIANG H, XU M Z, et al. Application of improved A* algorithm in unmanned ship path planning［J］. Journal of Zhejiang University of Technology, 2022,50(6):615-620. (in Chinese)
［20］FIDANOVA S, PAPRZYCKI M, ROEVA O. Hybrid GA-ACO algorithm for a model parameters identification problem［C］//2014 Federated Conference on Computer Science and Information Systems.Warsaw：IEEE, 2014: 413-420.
［21］ZHOU J G, CHEN D F. Carbon price forecasting based on improved CEEMDAN and extreme learning machine optimized by sparrow search algorithm［J］. Sustainability, 2021, 13(9): 4896.
［22］GAI J B, ZHONG K Y, DU X J, et al. Detection of gear fault severity based on parameter-optimized deep belief network using sparrow search algorithm［J］. Measurement, 2021, 185:110079.
［23］LIU Z X, ZHANG Y M, YU X, et al. Unmanned surface vehicles: an overview of developments and challenges［J］. Annual Reviews in Control, 2016, 41: 71-93.
［24］OUYANG C T, LIU Y J, ZHU D L. An adaptive chaotic sparrow search optimization algorithm［C］//2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). Nanchang: IEEE, 2021: 76-82.
［25］LI X C, MA X F, XIAO F C, et al. Time-series production forecasting method based on the integration of bidirectional gated recurrent unit (Bi-GRU) network and sparrow search algorithm (SSA)［J］. Journal of Petroleum Science and Engineering， 2022, 208, Part A: 109309.
［26］TANYILDIZI E, DEMIR G. Golden sine algorithm:a novel math-inspired algorithm［J］. Advances in Electrical and Computer Engineering, 2017, 17(2): 71-78.
［27］ZHANG X M, WEN S C. Hybrid whale optimization algorithm with gathering strategies for high-dimensional problems［J］. Expert Systems with Applications, 2021, 179: 115032.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References