An Improved Meta-Heuristic Approach for Unmanned Surface Vehicle Path Planning

Abstract

Efficient path planning for unmanned surface vehicles (USVs) is critical for autonomous maritime operations in complex environments. However, existing methods often struggle to balance path optimality with computational efficiency under multi-threat constraints. To tackle the challenging path planning problem for USVs in multi-threat maritime environments, this paper presents an adaptive inertia weight and triple learning factors-based competition of tribes and cooperation of members (AIWL-CTCM) algorithm. To enhance the efficiency of static path planning for USVs in complex maritime environments, the proposed AIWL-CTCM algorithm is designed to simultaneously achieving path optimality and navigation safety, thereby balancing multiple key performance criteria including path length, smoothness, collision avoidance, and convergence speed. The algorithm incorporates three key innovations. First, an adaptive inertia weight strategy that iteratively adjusts weights to improve convergence efficiency and solution quality. Second, a sine-acceleration-based optimization of the learning factors, alleviating the limitations imposed by static individual and tribal learning parameters. Third, a dynamic competition factor that varies with fitness values to accelerate the discovery of optimal solutions. Simulation results confirm that the AIWL-CTCM algorithm significantly surpasses the conventional CTCM and other benchmark optimization algorithms in terms of path planning accuracy, convergence speed, and stability.

Keywords

Unmanned surface vehicles / Path planning / Adaptive optimization / AIWL-CTCM / Competition and cooperation

References

[1] Alshammrei S, Boubaker S, Kolsi L. Improved Dijkstra algorithm for mobile robot path planning and obstacle avoidance. Comput. Mater. Contin, 2022, 72(3): 5939-5954

[2] Bai Y, Wang X, Yang Y, Yu J, Zhao Z, Jin X. Autonomous decision-making of unmanned surface vehicle operation with multi-source sensing. IEEE 2020 Chinese Automation Congress (CAC), 2020: 2570-2575

[3] Cao J, Li Y, Zhao S, Bi X. Genetic-algorithm-based global path planning for AUV. IEEE 2016 9th International Symposium on Computational Intelligence and Design (ISCID), 2016: 79-82 Vol. 2

[4] Cao L. Improved genetic algorithm for fast path planning of USV. SPIE MIPPR 2015: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 2015: 526-531 Vol. 9815

[5] Chen D. Application of improved genetic algorithms in path planning. Journal of Internet Technology, 2024, 25(7): 1091-1099

[6] Chen Z, Li S, Khan AT, Mirjalili S. Competition of tribes and cooperation of members algorithm: An evolutionary computation approach for model free optimization. Expert Systems with Applications, 2025, 265: 125908

[7] Hashali SD, Yang S, Xiang X. Route planning algorithms for unmanned surface vehicles (USVs): a comprehensive analysis. Journal of Marine Science and Engineering, 2024, 12(3): 382

[8] He Z, Liu C, Chu X, Negenborn RR, Wu Q. Dynamic anti-collision A-star algorithm for multi-ship encounter situations. Applied Ocean Research, 2022, 118: 102995

[9] He H, Rosales JV, Van Zwijnsvoorde T, Lataire E, Delefortrie G. COLREGs-compliant model predictive collision avoidance for autonomous ships in restricted environments. Ocean Engineering, 2025, 338: 121966

[10] IMO. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs), 1972, London, International Maritime Organization

[11] Katsouras G, Dimitriou E, Karavoltsos S, Samios S, Sakellari A, Mentzafou A, Scoullos M. Use of unmanned surface vehicles (USVs) in water chemistry studies. Sensors, 2024, 24(9): 2809

[12] Li B, Ni K, Zhou F, Li Y, Huang W, Jiang H, Liu F. Research on robot path planning based on fused Dijkstra and TEB algorithms. Journal of Mechanical Science and Technology, 2025, 39(8): 4651-4660

[13] Lyu H, Yin Y. COLREGS-constrained real-time path planning for autonomous ships using modified artificial potential fields. The Journal of Navigation, 2019, 72(3): 588-608

[14] Ma Y, Hu M, Yan X. Multi-objective path planning for unmanned surface vehicle with currents effects. ISA transactions, 2018, 75: 137-156

[15] Makar A. Determination of the minimum safe distance between a USV and a hydro-engineering structure in a restricted water region sounding. Energies, 2022, 15(7): 2441

[16] Mao S, Yang P, Gao D, Bao C, Wang Z. A motion planning method for unmanned surface vehicle based on improved RRT algorithm. Journal of Marine Science and Engineering, 2023, 11(4): 687

[17] Moline MA, Blackwell SM, von Alt C, Allen B, Austin T, Case J, Forrester N, Goldsborough R, Purcell M, Stokey R. Remote environmental monitoring units: An autonomous vehicle for characterizing coastal environments. Journal of Atmospheric and Oceanic Technology, 2005, 22(11): 1797-1808

[18] Qi J, Yang H, Sun H. MOD-RRT*: A sampling-based algorithm for robot path planning in dynamic environment. IEEE Transactions on Industrial Electronics, 2020, 68(8): 7244-7251

[19] Qiao Y, Yin J, Wang W, Duarte F, Yang J, Ratti C. Survey of deep learning for autonomous surface vehicles in marine environments. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(4): 3678-3701

[20] Sun B, Niu N. Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm. Ocean Engineering, 2024, 311: 118944

[21] Tang G, Tang C, Claramunt C, Hu X, Zhou P. Geometric A-star algorithm: An improved A-star algorithm for AGV path planning in a port environment. IEEE access, 2021, 9: 59196-59210

[22] Taylor J, Howe JA, Thorburn J, Fox CJ, McGonigle C, Rybanska P. An assessment of demersal elasmobranch occurrence and associated habitats using an autonomous underwater vehicle (AUV). Marine Ecology, 2025, 46(1): e70000

[23] Vahid S, Dideban A. Optimal path planning for unmanned surface vehicle using new modified local search ant colony optimization. Journal of Marine Science and Technology, 2022, 27(4): 1207-1219

[24] Wang Y, Li J, Zhao S, Su P, Fu H, Niu H. Hybrid path planning for USV with kinematic constraints and COLREGS based on improved APF-RRT and DWA. Ocean Engineering, 2025, 318: 120128

[25] Wang Y, Wang B, Li Z, Xu C. A novel particle swarm optimization based on hybrid-learning model. Math. Biosci. Eng, 2023, 20: 7056-7087

[26] Wang Z, Xiang X, Xiong X, Yang S. Kalman filter-based acoustic guidance docking system for autonomous underwater vehicle. International Conference on Intelligent Robotics and Applications, 2024, Singapore, Springer Nature: 68-79

[27] Wu Y, Wang T, Liu S. A review of path planning methods for marine autonomous surface vehicles. Journal of Marine Science and Engineering, 2024, 12(5): 833

[28] Xia G, Han Z, Zhao B, Liu C, Wang X. Global path planning for unmanned surface vehicle based on improved quantum ant colony algorithm. Mathematical Problems in Engineering, 2019, 2019(1): 2902170

[29] Yu Z, Si Z, Li X, Wang D, Song H. A novel hybrid particle swarm optimization algorithm for path planning of UAVs. IEEE Internet of Things Journal, 2022, 9(22): 22547-22558

[30] Yuan P, Li Y, Cui J, Li Y, Zhang J, Zhao Y. Optimal path planning and navigation platform development for unmanned surface vehicles in complex inland waterways using a potential ant-colony algorithm. Ocean Engineering, 2025, 334: 121599

[31] Zhang H, Tao Y, Zhu W. Global path planning of unmanned surface vehicle based on improved A-Star algorithm. Sensors, 2023, 23(14): 6647

[32] Zhang J, Cui Y, Li G, Ren J. Dynamic path planning algorithm for unmanned surface vehicle under island – reef environment. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(5): 7252-7268

[33] Zhang X, Lin Q. Three-learning strategy particle swarm algorithm for global optimization problems. Information Sciences, 2022, 593: 289-313

[34] Zheng H, Liu C. An overview of unmanned surface vehicles: methods, practices, and applications. Control Engineering Practice, 2025, 164: 106479

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.