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Global path planning for unmanned surface vehicles based on improved ant colony optimization and waypoint refinement
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Xiaori Gao1, *, Pengfei Xu1, Xinbo Liu2, Shujia Yan3, Lidong Wang4
Navigation of China | 2026, 49(2) : 104 - 111
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Navigation of China | 2026, 49(2): 104-111
Intelligent Shipping
Global path planning for unmanned surface vehicles based on improved ant colony optimization and waypoint refinement
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Xiaori Gao1, *, Pengfei Xu1, Xinbo Liu2, Shujia Yan3, Lidong Wang4
Affiliations
  • 1.Navigation College, Dalian Maritime University, Dalian 116026, China
  • 2.Shanghai Marine Equipment Research Institute, Shanghai 200031, China
  • 3.College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
  • 4.School of Sciences, Dalian Maritime University, Dalian 116026, China
Published: 2026-04-25 doi: 10.3969/j.issn.1000-4653.2026.02.012
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To overcome the deficiencies of traditional ant colony optimization (ACO) in pheromone updating, local optima convergence, and path planning safety, this study proposes a global path planning algorithm based on improved ACO and turning-point refinement. The heuristic function is improved using the reciprocal of the Euclidean distance between current path nodes and the destination, along with balancing parameters for iteration number, search quality, and efficiency, thereby enhancing global and local search capabilities while avoiding local optima. An adaptive pheromone evaporation coefficient is designed by utilizing characteristics of cosine function to dynamically adjust the convergence of the proposed ant colony optimization in its early and late stages. Considering the complexity of maritime environments and practical navigation requirements, a grid-based navigation environment is constructed. An obstacle-adjacent node detection method and fixed-point approximation algorithm is proposed for turning point refinement to improve navigation safety and ensure optimized paths better conform to maritime practice. Simulation experiments demonstrate that, compared with traditional ACO and other improved algorithms, the proposed algorithm shortens the average path length by approximately 39% and reduces the average iteration number by 79%, significantly improving solution quality and convergence efficiency. It effectively alleviates issues of insufficient search directionality and susceptibility to local optima. These results verify the reliability of the proposed approach for global path planning of unmanned surface vehicles and its high efficiency in redundant waypoint optimization, thereby providing effective decision support in practical applications.

intelligent shipping  /  path planning  /  ant colony algorithm  /  unmanned surface vessels  /  waypoints refinement
Xiaori Gao, Pengfei Xu, Xinbo Liu, Shujia Yan, Lidong Wang. Global path planning for unmanned surface vehicles based on improved ant colony optimization and waypoint refinement[J]. Navigation of China, 2026 , 49 (2) : 104 -111 . DOI: 10.3969/j.issn.1000-4653.2026.02.012
Year 2026 volume 49 Issue 2
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doi: 10.3969/j.issn.1000-4653.2026.02.012
  • Receive Date:2025-02-25
  • Online Date:2026-05-19
  • Published:2026-04-25
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  • Received:2025-02-25
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Affiliations
    1.Navigation College, Dalian Maritime University, Dalian 116026, China
    2.Shanghai Marine Equipment Research Institute, Shanghai 200031, China
    3.College of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
    4.School of Sciences, Dalian Maritime University, Dalian 116026, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
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种数
Number of
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Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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