• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

基于改进的碰撞风险函数多尺度A*算法的船舶路径规划

Ship path planning based on improved multi-scale A* algorithm of collision risk function.

作者信息

Song Chunyu, Guo Teer, Sui Jianghua

机构信息

Navigation and Ship Engineering College, Dalian Ocean University, 116023, Dalian, China.

出版信息

Sci Rep. 2024 Dec 6;14(1):30418. doi: 10.1038/s41598-024-80712-8.

DOI:10.1038/s41598-024-80712-8
PMID:39639072
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11621347/
Abstract

To improve the safety of ship navigation in complex sea areas and reduce planning time while achieving optimal path planning. The paper proposes an improved A* algorithm that incorporates ship collision risk assessment. The paper utilizes multi-scale raster maps to divide the sea chart in the context of complex sea areas, and combines the Line-of-sight (LOS) algorithm to solve the zigzag paths that may appear in this planning context. Moreover, in order to improve the efficiency of optimal path planning in the context of complex sea areas while ensuring path safety, the paper proposes a collision risk function to optimize the determination of the cost of A* algorithm nodes, thereby enhancing the heuristic function of the A* algorithm. The improved A* algorithm can consider both path length and collision risk to plan the optimal path and to enhance the overall quality of the planning results. To verify the advantages of the improved algorithm proposed in the paper, the Zhoushan Islands sea area with complex environment is selected as the planning background for simulation study. The results show that the improved algorithm with the introduction of the collision risk function reduces the path planning time, the number of expanded nodes, and the path length by 30%, 11%, and 5.8%, respectively, compared with the original algorithm, which can effectively reduce the computational burden of the algorithm. This study provides a relatively complete and scientific route planning strategy for the study of the safe navigation of smart ships in complex sea areas.

摘要

为提高复杂海域船舶航行安全,减少规划时间并实现最优路径规划。本文提出一种结合船舶碰撞风险评估的改进A算法。利用多尺度栅格地图在复杂海域背景下划分海图,并结合视线(LOS)算法解决该规划背景下可能出现的曲折路径。此外,为在确保路径安全的同时提高复杂海域背景下最优路径规划的效率,本文提出一种碰撞风险函数来优化A算法节点代价的确定,从而增强A算法的启发式函数。改进的A算法能够兼顾路径长度和碰撞风险来规划最优路径,提高规划结果的整体质量。为验证本文提出的改进算法的优势,选取环境复杂的舟山群岛海域作为规划背景进行仿真研究。结果表明,引入碰撞风险函数后的改进算法与原算法相比,路径规划时间、扩展节点数和路径长度分别减少了30%、11%和5.8%,能有效降低算法的计算负担。本研究为复杂海域智能船舶安全航行研究提供了一种相对完整且科学的航线规划策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/2e994be24645/41598_2024_80712_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/7397fc46468b/41598_2024_80712_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/bce7282b54d4/41598_2024_80712_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/3e7e2a8f6a09/41598_2024_80712_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/2f8dec27ee3c/41598_2024_80712_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/d72fa4d0e5e7/41598_2024_80712_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/16d00ef8bc46/41598_2024_80712_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/5ad8d904eb12/41598_2024_80712_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/8ed6997ad7ae/41598_2024_80712_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/d3d4af65989a/41598_2024_80712_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/2e994be24645/41598_2024_80712_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/7397fc46468b/41598_2024_80712_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/bce7282b54d4/41598_2024_80712_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/3e7e2a8f6a09/41598_2024_80712_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/2f8dec27ee3c/41598_2024_80712_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/d72fa4d0e5e7/41598_2024_80712_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/16d00ef8bc46/41598_2024_80712_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/5ad8d904eb12/41598_2024_80712_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/8ed6997ad7ae/41598_2024_80712_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/d3d4af65989a/41598_2024_80712_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/13a1/11621347/2e994be24645/41598_2024_80712_Fig10_HTML.jpg

相似文献

1
Ship path planning based on improved multi-scale A* algorithm of collision risk function.基于改进的碰撞风险函数多尺度A*算法的船舶路径规划
Sci Rep. 2024 Dec 6;14(1):30418. doi: 10.1038/s41598-024-80712-8.
2
A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.一种用于未知障碍物环境下的无人巡航船的混合多目标路径规划算法。
Sensors (Basel). 2022 Mar 22;22(7):2429. doi: 10.3390/s22072429.
3
Autonomous ship navigation with an enhanced safety collision avoidance technique.采用增强型安全避碰技术的自主船舶导航。
ISA Trans. 2024 Jan;144:271-281. doi: 10.1016/j.isatra.2023.10.019. Epub 2023 Oct 18.
4
An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning.基于深度强化学习的无人船自主路径规划模型。
Sensors (Basel). 2020 Jan 11;20(2):426. doi: 10.3390/s20020426.
5
The Algorithm of Determining an Anti-Collision Manoeuvre Trajectory Based on the Interpolation of Ship's State Vector.基于船舶状态向量插值的避碰操纵轨迹确定算法
Sensors (Basel). 2021 Aug 6;21(16):5332. doi: 10.3390/s21165332.
6
Beam Search Algorithm for Ship Anti-Collision Trajectory Planning.束搜索算法在船舶避碰航迹规划中的应用。
Sensors (Basel). 2019 Dec 4;19(24):5338. doi: 10.3390/s19245338.
7
Multiobjective path optimization of an indoor AGV based on an improved ACO-DWA.基于改进蚁群算法-动态窗口法的室内自动导引车多目标路径优化
Math Biosci Eng. 2022 Aug 26;19(12):12532-12557. doi: 10.3934/mbe.2022585.
8
Improved Exponential and Cost-Weighted Hybrid Algorithm for Mobile Robot Path Planning.用于移动机器人路径规划的改进指数与成本加权混合算法
Sensors (Basel). 2025 Apr 19;25(8):2579. doi: 10.3390/s25082579.
9
Unmanned Surface Vehicle Collision Avoidance Path Planning in Restricted Waters Using Multi-Objective Optimisation Complying with COLREGs.基于多目标优化并符合《国际海上避碰规则》的受限水域无人水面艇避碰路径规划
Sensors (Basel). 2022 Aug 3;22(15):5796. doi: 10.3390/s22155796.
10
Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm.基于改进蚁群算法的果园无人机喷雾多目标航点规划算法设计与验证
Front Plant Sci. 2023 Feb 2;14:1101828. doi: 10.3389/fpls.2023.1101828. eCollection 2023.

引用本文的文献

1
Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments.用于动态工业环境中自动导引车路径规划的生物粒子群优化与强化学习算法
Sci Rep. 2025 Jan 2;15(1):463. doi: 10.1038/s41598-024-84821-2.

本文引用的文献

1
A new method for unmanned aerial vehicle path planning in complex environments.一种在复杂环境中进行无人机路径规划的新方法。
Sci Rep. 2024 Apr 22;14(1):9257. doi: 10.1038/s41598-024-60051-4.
2
UAV path planning based on third-party risk modeling.基于第三方风险建模的无人机路径规划
Sci Rep. 2023 Dec 14;13(1):22259. doi: 10.1038/s41598-023-49396-4.
3
Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot.用于多目标移动机器人路径规划的改进A*算法与贪心算法相结合的方法
Sci Rep. 2022 Aug 2;12(1):13273. doi: 10.1038/s41598-022-17684-0.
4
Deep Reinforcement Learning for Indoor Mobile Robot Path Planning.深度强化学习在室内移动机器人路径规划中的应用。
Sensors (Basel). 2020 Sep 25;20(19):5493. doi: 10.3390/s20195493.
5
An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning.基于深度强化学习的无人船自主路径规划模型。
Sensors (Basel). 2020 Jan 11;20(2):426. doi: 10.3390/s20020426.
6
A novel DVS guidance principle and robust adaptive path-following control for underactuated ships using low frequency gain-learning.一种基于低频增益学习的欠驱动船舶新型 DVS 制导原理和鲁棒自适应路径跟踪控制。
ISA Trans. 2015 May;56:75-85. doi: 10.1016/j.isatra.2014.12.002. Epub 2015 Jan 8.