Multi-vessel Local Path Planning and Collision Avoidance for USV Based on an Improved DWA

Abstract

Safe collision avoidance for unmanned surface vehicles (USVs) in complex environments is challenging, as it must meet operational and motion characteristics while complying with COLREGs. To address these challenges, a novel boundary-enclosed map representation is proposed, which enhances path planning and collision avoidance efficiency. Based on this map, the multi-vessel COLREGs-compliant dynamic window approach (MCCDWA) algorithm is developed, with its objective function and velocity space formulated within the dynamic window approach (DWA) framework. For local path planning, the algorithm generates trajectories that satisfy the USV’s maneuvering constraints and adhere to COLREGs by utilizing the Nomoto first-order model and the designed velocity space. For collision-avoidance decision-making, a collision risk index and the time to closest point of approach are incorporated to determine avoidance timing and evaluate path safety. Simulation experiments and case studies using historical automatic identification system (AIS) data show that the MCCDWA algorithm meets USV operational requirements and real-world navigation scenarios. The algorithm also achieves a high collision-avoidance success rate while maintaining COLREGs compliance, demonstrating its applicability in practical maritime environments.

Keywords

Boundary-enclosed map / COLREGs / Unmanned surface vehicles / Multi-vessel collision avoidance / MCCDWA algorithm

References

[1] Abebe M, Noh Y, Seo C, Kim D, Lee I. Developing a ship collision risk index estimation model based on dempster-shafer theory. Applied Ocean Research, 2021, 113: 102735

[2] Chen H, Lin Z, Chen Z, Jian J, Liu C. Adaptive DWA algorithm with decision tree classifier for dynamic planning in USV navigation. Ocean Engineering, 2025, 321: 120328

[3] Cho Y, Han J, Kim J, Lee P, Park SB. Experimental validation of a velocity obstacle based collision avoidance algorithm for unmanned surface vehicles. IFAC-PapersOnLine, 2019, 52(21): 329-334

[4] Cui G, Song J, Han Y, Wu H, Zhou F, Wang Y (2025) Chaos-based inertia weight particle swarm optimization for path planning of unmanned surface vehicles. Journal of Marine Science and Application. https://doi.org/10.1007/s11804-025-00713-5

[5] Er MJ, Ma C, Liu T, Gong H. Intelligent motion control of unmanned surface vehicles: a critical review. Ocean Engineering, 2023, 280: 114562

[6] Fiorini P, Shiller Z. Motion planning in dynamic environments using velocity obstacles. The International Journal of Robotics Research, 1998, 17(7): 760-772

[7] Fox D, Burgard W, Thrun S. The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 1997, 4: 23-33

[8] Gang L, Wang Y, Sun Y, Zhou L, Zhang M. Estimation of vessel collision risk index based on support vector machine. Advances in Mechanical Engineering, 2016, 8(11): 1687814016671250

[9] Gao D, Zhou P, Shi W, Wang T, Wang Y. A dynamic obstacle avoidance method for unmanned surface vehicle under the international regulations for preventing collisions at sea. Journal of Marine Science and Engineering, 2022, 10(7): 901

[10] Guan W, Wang K. Autonomous collision avoidance of unmanned surface vehicles based on improved a-star and dynamic window approach algorithms. IEEE Intelligent Transportation Systems Magazine, 2023, 15(3): 36-50

[11] Hadi B, Khosravi A, Sarhadi P. Deep reinforcement learning for adaptive path planning and control of an autonomous underwater vehicle. Applied Ocean Research, 2022, 129: 103326

[12] Han S, Wang L, Wang Y. A potential field-based trajectory planning and tracking approach for automatic berthing and COLREGs-compliant collision avoidance. Ocean Engineering, 2022, 266: 112877

[13] Han S, Wang L, Wang Y, He H. A dynamically hybrid path planning for unmanned surface vehicles based on non-uniform Theta* and improved dynamic windows approach. Ocean Engineering, 2022, 257: 111655

[14] Hart P, Nilsson N, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 1968, 4(2): 100-107

[15] Hu Y, Zhang A, Tian W, Zhang J, Hou Z. Multi-ship collision avoidance decision-making based on collision risk index. Journal of Marine Science and Engineering, 2020, 8(9): 640

[16] Huang Y, Chen L, van Gelder PHAJM. Generalized velocity obstacle algorithm for preventing ship collisions at sea. Ocean Engineering, 2019, 173: 142-156

[17] International Maritime Organization. Convention on the international regulations for preventing collisions at sea, 1972 (COLREGs), 1972, London, IMO Publications

[18] Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 1986, 5: 90-98

[19] Kim H, Kim SH, Jeon M, Kim J, Song S, Paik KJ. A study on path optimization method of an unmanned surface vehicle under environmental loads using genetic algorithm. Ocean Engineering, 2017, 142: 616-624

[20] Kim HG, Yun SJ, Choi YH, Ryu JK, Suh JH. Collision avoidance algorithm based on COLREGs for unmanned surface vehicle. Journal of Marine Science and Engineering, 2021, 9(8): 863

[21] Li C, Yao L, Mi C. Fusion Algorithm based on improved A* and DWA for USV path planning. Journal of Marine Science and Application, 2025, 24: 224-237

[22] Li S, Li C. Path planning for unmanned surface vehicles in dynamic environments based on artificial potential field and global guided reinforcement learning. Journal of Marine Science and Application, 2025, 25: 575-586

[23] Li Y, Zhang H. Collision avoidance decision method for unmanned surface vehicle based on an improved velocity obstacle algorithm. Journal of Marine Science and Engineering, 2022, 10(8): 1047

[24] Liang C, Zhang X, Watanabe Y, Deng Y. Autonomous collision avoidance of unmanned surface vehicles based on improved a star and minimum course alteration algorithms. Applied Ocean Research, 2021, 113: 102755

[25] Liang Z, Yang Y, Huang Z, Zhou S. USV path planning algorithm based on 20D-A* and IDWA algorithms under COLREGs. Ocean Engineering, 2025, 331: 121389

[26] Liao Y, Tang X, Chen C, Ren Z, Pang S, Zhang G. Path planning of oil spill recovery system with double usvs based on artificial potential field method. Journal of Marine Science and Application, 2025, 24: 606-618

[27] Lu Y, Huo X, Arslan O, Tsiotras P. Incremental multi-scale search algorithm for dynamic path planning with low worst-case complexity. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41(6): 1556-1570

[28] Lyridis DV. An improved ant colony optimization algorithm for unmanned surface vehicle local path planning with multi-modality constraints. Ocean Engineering, 2021, 241: 109890

[29] Lyu H, Liu W, Guo S, Tan G, Fu C, Sun X, Zhao Y, Zhang L, Yin Y. Autonomous collision avoidance method for MASSs based on precise potential field modelling and COLREGs constraints in complex sailing environments. Ocean Engineering, 2024, 292: 116530

[30] MahmoudZadeh S, Abbasi A, Yazdani A, Wang H, Liu Y. Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment. Ocean Engineering, 2022, 254: 111328

[31] Nguyen M, Zhang S, Wang X. A novel method for risk assessment and simulation of collision avoidance for vessels based on AIS. Algorithms, 2018, 11(12): 204

[32] Peeters G, Kotzé M, Afzal MR, Catoor T, Van Baelen S, Geenen P, Vanierschot M, Boonen R, Slaets P. An unmanned inland cargo vessel: design, build, and experiments. Ocean Engineering, 2020, 201: 107056

[33] Qin L, Fang Y, Lu Z, Gao S. Improving A* algorithm for path planning of unmanned surface vessels in maritime rescue. Ships and Offshore Structures, 20251-9

[34] Ramkumar Sudha SK, Mishra D, Hameed IA. A coverage path planning approach for environmental monitoring using an unmanned surface vehicle. Ocean Engineering, 2024, 310: 118645

[35] Song R, Liu Y, Bucknall R. A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment. Ocean Engineering, 2017, 129: 301-317

[36] Sonntag V, Perrusquia A, Tsourdos A, Guo W. A COLREGs compliance reinforcement learning approach for USV manoeuvring in track-following and collision avoidance problems. Ocean Engineering, 2025, 316: 119907

[37] Teitgen R, Monsuez B, Kukla R, Pasquier R, Foinet G. Dynamic trajectory planning for ships in dense environment using collision grid with deep reinforcement learning. Ocean Engineering, 2023, 281: 114807

[38] Tian Z, Deng S, Wu Z, Zhou C (2025) Reinforcement learning applications in unmanned underwater vehicles: a review. Journal of Marine Science and Application. https://doi.org/10.1007/s11804-025-00723-3

[39] Wang N, Jin X, Er MJ. A multilayer path planner for a USV under complex marine environments. Ocean Engineering, 2019, 184: 1-10

[40] 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

[41] Woo J, Yu C, Kim N. Deep reinforcement learning-based controller for path following of an unmanned surface vehicle. Ocean Engineering, 2019, 183: 155-166

[42] Xu D, Yang J, Zhou X, Xu H. Hybrid path planning method for USV using bidirectional A* and improved DWA considering the manoeuvrability and COLREGs. Ocean Engineering, 2024, 298: 117210

[43] Yang S, Yue T, Niu W, Gao W, Yang M, Sun T, Wang C. A study on path planning of long-voyage underwater glider in complex ocean environment. Ships and Offshore Structures, 20251-15

[44] Yao Y, Liang X, Li M, Yu K, Chen Z, Ni C, Teng Y. Path planning method based on D* lite algorithm for unmanned surface vehicles in complex environments. China Ocean Engineering, 2021, 35: 372-383

[45] Yu J, Yang M, Zhao Z, Wang X, Bai Y, Wu J, Xu J. Path planning of unmanned surface vessel in an unknown environment based on improved D*Lite algorithm. Ocean Engineering, 2022, 266: 112873

[46] Yuan X, Tong C, He G, Wang H. Unmanned vessel collision avoidance algorithm by dynamic window approach based on COLREGs considering the effects of the wind and wave. Journal of Marine Science and Engineering, 2023, 11: 1831

[47] Zafar Mohd N, Mohanta JC. Methodology for path planning and optimization of mobile robots: a review. Procedia Computer Science, 2018, 133: 141-152

[48] Zhang J, Ling H, Tang Z, Song W, Lu A. Path planning of USV in confined waters based on improved A* and DWA fusion algorithm. Ocean Engineering, 2025, 322: 120475

[49] Zhang M, Montewka J, Manderbacka T, Kujala P, Hirdaris S. A big data analytics method for the evaluation of ship-ship collision risk reflecting hydrometeorological conditions. Reliability Engineering & System Safety, 2021, 213: 107674

[50] Zhu Z, Wu P, Liu Y, Wei Y, Yin Y. A novel route-plan-guided artificial potential field method for ship collision avoidance: modeling, integration and test. Ocean Engineering, 2023, 288: 116088

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