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Article(id=1156264259915993717, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156264148657886112, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2402833, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1713369600000, receivedDateStr=2024-04-18, revisedDate=1733932800000, revisedDateStr=2024-12-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1753604481915, onlineDateStr=2025-07-27, pubDate=1740672000000, pubDateStr=2025-02-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753604481915, onlineIssueDateStr=2025-07-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753604481915, creator=13701087609, updateTime=1753604481915, updator=13701087609, issue=Issue{id=1156264148657886112, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='6', pageStart='2193', pageEnd='2636', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1753604455388, creator=13701087609, updateTime=1753771257443, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1156963767234945803, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156264148657886112, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1156963767234945804, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1156264148657886112, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2419, endPage=2427, ext={EN=ArticleExt(id=1156264260792603256, articleId=1156264259915993717, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Path Planning Algorithm Based on Community Information Transmission Mechanism for AUV, columnId=1156262729162810294, journalTitle=Science Technology and Engineering, columnName=Papers·Automation and Computational Technology, runingTitle=null, highlight=null, articleAbstract=

In order to improve the path planning ability and efficiency of AUV (autonomous underwater vehicle), an AUV path planning algorithm based on the community information transmission mechanism was proposed. Firstly, based on the community information transmission mechanism, the global short and long connection operators were designed to achieve the optimal search of the neighborhood of the planned path points and the probabilistic search outside the neighborhood. Then, the local short and long connection operators were designed, which implements the search for four boundary derived points of the path center point and the connections of feasible paths outside the derived points. Finally, the AUV path planning algorithm flow was completed. Six simulation and two seabed map simulation tests show that, compared with other algorithms, the algorithm has the advantages of strong planning ability, high planning efficiency, and smooth planning path.

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为了提高自主水下机器人(autonomous underwater vehicle,AUV)的路径规划能力和效率,提出了基于社群信息传递机制的AUV路径规划算法。基于社群信息传递机制,首先设计了全局短、长连接算子,实现了已规划路径点邻域内最优点搜索和邻域外概率搜索;然后设计了局部短、长连接算子,实现了路径中心点的四边界衍生点搜索以及衍生点外可行路径连接;最后完成了AUV路径规划算法流程设计。6种模拟和2种海底地图仿真测试表明,与其他算法相比,该算法具有规划能力强、规划效率高以及规划路径光滑的优点。

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江亚峰(1990—),男,汉族,江苏南通人,硕士,讲师。研究方向:移动机器人导航、人工智能、装备控制。E-mail:。

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江亚峰(1990—),男,汉族,江苏南通人,硕士,讲师。研究方向:移动机器人导航、人工智能、装备控制。E-mail:。

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江亚峰(1990—),男,汉族,江苏南通人,硕士,讲师。研究方向:移动机器人导航、人工智能、装备控制。E-mail:。

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Comparison of algorithm testing in six simulated maps

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环境地图 测试算法 最优路径长度/m 平均最优路径长度/m 最小收敛代数 平均收敛代数 代数标准差 单位距离转角/(°)
15×15(Ⅰ) PPABCITM 21.254 21.747 8 17.100 9.245 11.558
PPABIACO 22.728 23.355 13 24.567 12.697 23.759
PPABCHIO 22.728 23.447 12 26.500 10.840 25.739
PPABIPSO 23.314 23.740 10 19.767 9.328 28.953
15×15(Ⅱ) PPABCITM 21.267 21.349 7 16.200 10.015 5.941
PPABIACO 22.728 23.253 13 33.367 15.515 17.820
PPABCHIO 23.314 23.486 9 29.567 14.429 25.093
PPABIPSO 22.728 23.104 11 25.333 12.349 21.779
20×20(Ⅰ) PPABCITM 28.132 28.893 11 22.567 11.691 6.775
PPABIACO 29.799 30.090 15 34.067 15.102 16.611
PPABCHIO 29.799 30.326 12 25.033 15.936 18.121
PPABIPSO 30.385 30.443 20 33.767 13.019 17.772
20×20(Ⅱ) PPABCITM 28.269 28.754 9 19.400 9.115 7.139
PPABIACO 29.799 30.207 16 27.400 12.392 13.591
PPABCHIO 30.385 30.443 19 32.500 16.714 19.253
PPABIPSO 29.799 30.354 15 28.067 14.100 18.121
25×25(Ⅰ) PPABCITM 34.970 35.929 7 18.800 8.771 5.887
PPABIACO 37.456 37.724 13 32.033 18.587 22.827
PPABCHIO 36.870 37.539 15 34.133 20.604 15.867
PPABIPSO 36.870 37.976 17 34.267 14.401 18.308
25×25(Ⅱ) PPABCITM 35.695 36.461 11 20.500 6.761 7.318
PPABIACO 38.870 39.607 17 36.733 19.079 21.996
PPABCHIO 38.042 39.382 15 34.067 18.597 17.744
PPABIPSO 38.627 40.625 22 38.867 18.822 17.475
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6种模拟地图中的算法测试对比

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环境地图 测试算法 最优路径长度/m 平均最优路径长度/m 最小收敛代数 平均收敛代数 代数标准差 单位距离转角/(°)
15×15(Ⅰ) PPABCITM 21.254 21.747 8 17.100 9.245 11.558
PPABIACO 22.728 23.355 13 24.567 12.697 23.759
PPABCHIO 22.728 23.447 12 26.500 10.840 25.739
PPABIPSO 23.314 23.740 10 19.767 9.328 28.953
15×15(Ⅱ) PPABCITM 21.267 21.349 7 16.200 10.015 5.941
PPABIACO 22.728 23.253 13 33.367 15.515 17.820
PPABCHIO 23.314 23.486 9 29.567 14.429 25.093
PPABIPSO 22.728 23.104 11 25.333 12.349 21.779
20×20(Ⅰ) PPABCITM 28.132 28.893 11 22.567 11.691 6.775
PPABIACO 29.799 30.090 15 34.067 15.102 16.611
PPABCHIO 29.799 30.326 12 25.033 15.936 18.121
PPABIPSO 30.385 30.443 20 33.767 13.019 17.772
20×20(Ⅱ) PPABCITM 28.269 28.754 9 19.400 9.115 7.139
PPABIACO 29.799 30.207 16 27.400 12.392 13.591
PPABCHIO 30.385 30.443 19 32.500 16.714 19.253
PPABIPSO 29.799 30.354 15 28.067 14.100 18.121
25×25(Ⅰ) PPABCITM 34.970 35.929 7 18.800 8.771 5.887
PPABIACO 37.456 37.724 13 32.033 18.587 22.827
PPABCHIO 36.870 37.539 15 34.133 20.604 15.867
PPABIPSO 36.870 37.976 17 34.267 14.401 18.308
25×25(Ⅱ) PPABCITM 35.695 36.461 11 20.500 6.761 7.318
PPABIACO 38.870 39.607 17 36.733 19.079 21.996
PPABCHIO 38.042 39.382 15 34.067 18.597 17.744
PPABIPSO 38.627 40.625 22 38.867 18.822 17.475
), ArticleFig(id=1233422561384648916, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156264259915993717, language=EN, label=Table 2, caption=

Comparison of algorithm testing in two types of underwater maps

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环境地图 测试算法 最优路径
长度/m		 平均最优路
径长度/m		 最小收敛
代数		 平均收
敛代数		 代数标准差 单位距离
转角/(°)
海底地图Ⅰ
(智利西部海域)		 PPABCITM 56.619 56.721 9 20.233 12.398 1.712
PPABIACO 61.941 64.817 24 61.667 22.143 6.516
PPABCHIO 60.527 63.363 14 52.867 21.989 6.601
PPABIPSO 61.113 65.002 21 48.967 23.425 8.100
海底地图Ⅱ
(澳大利亚东部海域)		 PPABCITM 85.441 86.463 8 21.967 15.437 2.162
PPABIACO 94.326 97.012 27 70.067 20.417 10.653
PPABCHIO 90.770 93.483 24 52.133 21.529 8.086
PPABIPSO 91.012 95.474 19 40.267 19.645 11.372
), ArticleFig(id=1233422561518866654, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1156264259915993717, language=CN, label=表2, caption=

两种海底地图中的算法测试对比

, figureFileSmall=null, figureFileBig=null, tableContent=
环境地图 测试算法 最优路径
长度/m		 平均最优路
径长度/m		 最小收敛
代数		 平均收
敛代数		 代数标准差 单位距离
转角/(°)
海底地图Ⅰ
(智利西部海域)		 PPABCITM 56.619 56.721 9 20.233 12.398 1.712
PPABIACO 61.941 64.817 24 61.667 22.143 6.516
PPABCHIO 60.527 63.363 14 52.867 21.989 6.601
PPABIPSO 61.113 65.002 21 48.967 23.425 8.100
海底地图Ⅱ
(澳大利亚东部海域)		 PPABCITM 85.441 86.463 8 21.967 15.437 2.162
PPABIACO 94.326 97.012 27 70.067 20.417 10.653
PPABCHIO 90.770 93.483 24 52.133 21.529 8.086
PPABIPSO 91.012 95.474 19 40.267 19.645 11.372
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基于社群信息传递机制的AUV路径规划算法
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江亚峰 1, 2, 3 , 张亮 1, 2 , 袁明新 1, 2, 3 , 王舜 1, 2 , 刘维 4
科学技术与工程 | 论文·自动化技术、计算机技术 2025,25(6): 2419-2427
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科学技术与工程 | 论文·自动化技术、计算机技术 2025, 25(6): 2419-2427
基于社群信息传递机制的AUV路径规划算法
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江亚峰1, 2, 3 , 张亮1, 2, 袁明新1, 2, 3, 王舜1, 2, 刘维4
作者信息
  • 1 江苏科技大学机电与动力工程学院, 张家港 215600
  • 2 张家港江苏科技大学产业技术研究院, 张家港 215600
  • 3 江苏科技大学苏州理工学院, 张家港 215600
  • 4 中科探海(苏州)海洋科技有限责任公司, 苏州 215600

    • 江亚峰(1990—),男,汉族,江苏南通人,硕士,讲师。研究方向:移动机器人导航、人工智能、装备控制。E-mail:。

Path Planning Algorithm Based on Community Information Transmission Mechanism for AUV
Ya-feng JIANG1, 2, 3 , Liang ZHANG1, 2, Ming-xin YUAN1, 2, 3, Shun WANG1, 2, Wei LIU4
Affiliations
  • 1 School of Mechanical & Power Engineering, Jiangsu University of Science and Technology, Zhangjiagang 215600, China
  • 2 Zhangjiagang Industrial Technology Research Institute, Jiangsu University of Science and Technology, Zhangjiagang 215600, China
  • 3 Suzhou Institute of Technology, Jiangsu University of Science and Technology, Zhangjiagang 215600, China
  • 4 T-SEA Marine Technology Co., Ltd., Suzhou 215600, China
出版时间: 2025-02-28 doi: 10.12404/j.issn.1671-1815.2402833
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摘要
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为了提高自主水下机器人(autonomous underwater vehicle,AUV)的路径规划能力和效率,提出了基于社群信息传递机制的AUV路径规划算法。基于社群信息传递机制,首先设计了全局短、长连接算子,实现了已规划路径点邻域内最优点搜索和邻域外概率搜索;然后设计了局部短、长连接算子,实现了路径中心点的四边界衍生点搜索以及衍生点外可行路径连接;最后完成了AUV路径规划算法流程设计。6种模拟和2种海底地图仿真测试表明,与其他算法相比,该算法具有规划能力强、规划效率高以及规划路径光滑的优点。

自主水下机器人  /  社群信息  /  长连接  /  短连接  /  路径规划

In order to improve the path planning ability and efficiency of AUV (autonomous underwater vehicle), an AUV path planning algorithm based on the community information transmission mechanism was proposed. Firstly, based on the community information transmission mechanism, the global short and long connection operators were designed to achieve the optimal search of the neighborhood of the planned path points and the probabilistic search outside the neighborhood. Then, the local short and long connection operators were designed, which implements the search for four boundary derived points of the path center point and the connections of feasible paths outside the derived points. Finally, the AUV path planning algorithm flow was completed. Six simulation and two seabed map simulation tests show that, compared with other algorithms, the algorithm has the advantages of strong planning ability, high planning efficiency, and smooth planning path.

autonomous underwater vehicle  /  community information  /  long connection  /  short connection  /  path planning
江亚峰, 张亮, 袁明新, 王舜, 刘维. 基于社群信息传递机制的AUV路径规划算法. 科学技术与工程, 2025 , 25 (6) : 2419 -2427 . DOI: 10.12404/j.issn.1671-1815.2402833
Ya-feng JIANG, Liang ZHANG, Ming-xin YUAN, Shun WANG, Wei LIU. Path Planning Algorithm Based on Community Information Transmission Mechanism for AUV[J]. Science Technology and Engineering, 2025 , 25 (6) : 2419 -2427 . DOI: 10.12404/j.issn.1671-1815.2402833
正文
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自主水下机器人因其巡航速度快、机动灵活等优势在水下排雷、环境侦查和水中救援等领域得到广泛应用[1-2],而路径规划是自主水下机器人(autonomous underwater vehicle,AUV)自主导航的关键技术之一[3]。传统AUV路径规划主要有A*算法、人工势场法等。任晔等[4]提出了基于多因素改进A*的AUV路径规划算法,通过引入障碍物系数,减少搜索节点等措施来提高规划效率和精度,但搜索节点的减少也会造成优质路径丢失。张强等[5]通过新增滑移区来替换由障碍物造成的斥力场,提出了基于改进人工势场的AUV路径规划方法,但滑移区在复杂地形下存在重叠,易造成路径偏角增大。
传统路径规划通常存在目标不可达、无法获得全局最优等不足,限制了AUV的规划性能。随着仿生智能发展,近年来智能规划算法得到了快速发展。Pan[6]通过精英保留、逆转录等措施改进遗传算法,并实现了AUV多点路径的最优规划,但其在多障碍复杂环境中的适应性还有待检验;Fan等[7]基于差分进化算法开展了动态环境中的AUV路径规划,但全局优化能力有待进一步提高。Huang等[8]提出了基于强化学习机制的AUV粒子群规划算法,但测试环境相对简单,还有待在复杂环境中进一步验证;Li等[9]开展了基于改进蚁群算法的AUV路径规划方法,但局部路径比较曲折,不利于非完整约束AUV的运动控制。相比起传统路径规划,智能规划方法因分布式并行搜索,有助于实现AUV的全局路径规划,但如何提高优化效率和路径平滑度,以及如何避免陷入局部极小是智能规划的研究重点。
鉴于此,现引入社群信息传递机制,设计了AUV路径规划模型,以提高AUV全局规划能力和效率,并采用中心点四边界衍生点优化路径偏角,以提高规划路径的平滑度。
1 社群信息传递路径规划模型
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社群结构是复杂网络的重要特征,社群网络中的有效信息传递机制近年来在疾病传播[10]、污染治理[11]等领域得到了广泛应用。随着对复杂网络结构的深入了解,以多社群联系形式存在的复杂网络另一特性被发现[12],即每个社群结构内的个体围绕核心紧密连接,而各社群间的连接则相对稀疏,如图1所示,社群之间可以通过这种紧密、稀疏交替方式进行信息的快速传播。
由于基于栅格建模的AUV路径规划类似于社群连接,因此为了提高AUV的路径规划性能,文中借鉴社群信息传递机制,通过设计连接算子形成如图2所示的AUV路径规划模型。
由图2可以看出,将AUV的工作空间通过栅格化建模形成社交网络,环境中栅格中心点gi的四边界上的8个邻点定义为衍生点。衍生点周围一定范围内的栅格群构成邻域,邻域内的中心点和衍生点集构成一个社群。基于社群信息传递机制的AUV路径规划,即借鉴社群信息传递中的长连接和短连接机理,从起点S到目标T搜索由若干栅格中心点和衍生点组成的最优无碰路径。
2 环境描述及定义
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2.1 环境地图描述
如图2所示,设AUV环境地图中栅格中心点集合为G={g1, g2…, gi …, gx×y},x和y分别为栅格的最大列标号和行标号。栅格中心点gi的行列标号分别为hi和li,对应衍生点的行列标号分别为h'i和l'i,且与栅格编号gi存在如下关系,即
$l\text{'}{\mathrm{ }}_{i}=\mathrm{m}\mathrm{o}\mathrm{d}({g}_{i}-1,x)+\delta $
$h\text{'}{\mathrm{ }}_{i}=\mathrm{f}\mathrm{i}\mathrm{x}\left(\frac{{g}_{i}-1}{y}\right)+\delta $
式中:δ为调节因子。当衍生点位于当前栅格左侧或下侧边界时δ =-0.5,其余情况δ = 0.5。
2.2 路径点的邻域定义
邻域是AUV进行全局长短连接的关键参数。对于∀m, n∈[1,x×y],且m≠n,栅格中心点gm、gn间邻域长度L(gm, gn)计算公式为
$L({g}_{m},{g}_{n})=\mathrm{m}\mathrm{a}\mathrm{x}(\left|{l}_{n}-{l}_{m}\right|,\left|{h}_{n}-{h}_{m}\right|)$
式(3)中:max(·)为最大值函数;$\left|·\right|$为绝对值函数;(hm, lm)和(hn, ln)分别为中心点gm和gn的行列标号。
定义栅格中心点gm的r邻域中心点集合r(gm)为
$r\left({g}_{m}\right)=\left\{{g}_{n}\right|0<L({g}_{m},{g}_{n})\le r,{g}_{n}\in G\}$
则栅格中心点gm的非r邻域中心点集合$\overline{r\left({\mathrm{g}}_{\mathrm{m}}\right)}$为
$\overline{r\left({\mathrm{g}}_{\mathrm{m}}\right)}=\left\{{g}_{n}\right|L({g}_{m},{g}_{n})>r,{g}_{n}\in G\}$
图3所示为栅格中心点gm邻域r分别为1、2的示例,衍生点的邻域定义同上。
2.3 适应度函数
在AUV路径规划过程中,无论是全局长短连接,还是局部长短连接,评价连接前后路径优劣的适应度函数均建立在路径长度之上。
以某规划路径点的集合为P={p1, p2,…, pN}为例,则该路径适应度函数可以定义为
$F=\frac{\left\|p_{i}-p_{i+1}\right\|}{\sum_{i=1}^{N-1}}$
式(6)中: · 为欧式距离。
3 AUV路径规划主要算子
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3.1 全局短连接算子ψG
设AUV第k代规划路径为P(k)={p1(k), p2(k), …, pN(k) },全局短连接算子ψG主要实现P(k)中路径点在其r邻域内向距目标T最近点的信息传递,目的是实现AUV在局部环境中的高效路径规划。其主要步骤如下。
(1)∀i∈[1, N],求路径点pi(k)的r邻域内无障点集合C(k) ={c1(k), c2(k),…, cM(k)}。
(2)∀j∈[1, M],虚拟顺序连接pi-1(k)、cj(k)、pi+1(k)并判断是否存在有障栅格干涉?若无则求取虚拟连接路径长度适应度F[cj(k)],否则适应度为无穷小。
(3)按照步骤(2),完成C(k)中各点与pi-1(k)、pi+1(k)的虚拟连接及适应度求取,提取适应度最大点c*(k)∈C(k)∪pi(k),即∀j∈M, F[cj(k)]≤F[c*(k)]。
(4)利用c*(k)替换pi(k),并形成新路径P'(k)={ p1(k), p2(k),…, pi-1(k), c*(k), pi+1(k), …, pN(k) }。
算子ψG可描述为:P'(k)←ψG[P(k)]。
3.2 全局随机长连接算子ΓG
全局随机长连接ΓG是在完成全局短连接ψG后,以c*(k)为起点在pi(k)的非r邻域内向距目标T最近点进行概率性的远距离信息传递,目的是帮助AUV跳出局部极小,提高全局规划能力,步骤如下。
(1)求路径点pi(k)非r邻域内R个无障点集合D(k) ={d1(k), d2(k), …, dR(k)}。
(2)∀j∈[1, R],按照概率γ进行c*(k)、dj(k)以及P'(k)中距离dj(k)最近点的虚拟顺序连接,并判断是否存在有障栅格干涉?若无则求取虚拟连接路径长度适应度F[dj(k)],否则适应度为无穷小。
(3)按照步骤(2),完成c*(k)与D(k)中各点,以及P'(k)中距D(k)各点最近点的虚拟连接适应度求取,并提取适应度最大点d*(k)∈D(k),即∀j∈[R],F[dj(k)]≤F[d*(k)]。
(4)利用d*(k)替换c*(k)与P'(k)中距离d*(k)最近点之间的所有路径点,并形成新路径P″(k) ={ p1, p2,…, c*, d*,…, pN }。
算子ΓG可描述为:P″(k)←ΓG[P'(k)/γ]。
3.3 局部短连接算子ψL
局部短连接算子ψL主要是在全局随机长连接ΓG结束后,实现由规划路径点中栅格中心点向其衍生点的信息传递过程,目的是进一步强化AUV局部搜索能力以提高路径光滑性,步骤如下。
(1)从路径P″(k)中提取栅格中心点集E(k)={e1(k), e2(k), …, eP(k)}。
(2) ∀i∈[1, P],求取ei(k)的衍生点集Y(k)={y1(k), y2(k), …, y8(k)}。
(3)选取Y(k)中衍生点yj(k)其中j∈[1,8],虚拟连接yj(k)、路径P″(k)中ei(k)的前后路径点,并判断是否存在有障栅格干涉。若无则求取虚拟连接路径长度适应度F[yj(k)],否则适应度为无穷小。
(4)按照步骤(3)完成Y(k)中所有衍生点的虚拟连接和适应度求取,通过轮盘赌选取出衍生点y*(k)∈Y(k)。
(5)利用y*(k)替换路径P″(k)中ei(k),并形成新路径P‴(k)。
算子ψL可描述为:P‴(k)←ψL[P″(k)]。
3.4 局部长连接算子ΓL
局部长连接算子ΓL是在局部短连接算子ψL完成后,以y*(k)为起点向P‴(k)中后续路径点的信息传递,目的是帮助AUV删除规划路径上冗余连接,进一步提高全局规划能力。设P‴(k)中共Q个路径点,y*(k)为第i个点,即P‴(k)={p1(k), p2(k), …, p(y*)(k), …, pQ(k)},则算子${\Gamma }_{L}$操作步骤如下。
(1)初始化搜索步长t=2。
(2) 虚拟连接P‴(k)中pi(k)与pi+t(k)并判断是否与有障栅格干涉。若有,则结束,否则转步骤(3)。
(3) 删除pi(k)与pi+t(k)之间的点,并形成新路径PIV(k)={ p1(k), p2(k), …, pi(k), pi+t(k),…, pQ(k)}。
(4) 判断t<Q-i?,若是,则t←t+1并转至步骤(2),否则结束。
算子ΓL可描述为:PIV(k)←ΓL[P‴(k)]。
4 AUV路径规划算法流程
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(1)初始化Np、kmax、r、γ等参数,令进化代数k←0。
(2)生成Np条路径组成初始种群Q(0)={P1(0),P2(0),…,PNp(0)}。
(3)全局短连接操作ψG:P'i(k)←ψG[Pi(k)]。
(4)全局随机长连接操作ΓG:P″i(k)←ΓG(P'i(k)/ γ)。
(5)局部短连接操作ψL:P‴i(k)←ψL[P″i(k)]。
(6)局部长连接操作ΓL:${P}_{\mathrm{i}}^{IV}$(k)←ΓL[P‴i(k)],并完成路径更新Pi(k)←${P}_{\mathrm{i}}^{IV}$(k)。
(7)判断是否完成第k代所有路径的全局和局部长短连接?若是则获得种群Q(k+1)并转步骤(8),否则转步骤(3)。
(8)基于适应度计算,筛选出种群Q(k+1)中的最优路径P*(k+1)。若F[P*(k+1)]>F[P*(k)],则保留种群Q(k+1),否则Q(k+1) ←Q(k)。
(9)判断是否达到结束条件:k=kmax?若是则结束并输出最优路径,否则k←k+1并转步骤(3)。
5 仿真与结果分析
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为了验证文中规划算法的有效性,在CPU i5-11400H,主频2.7 GHz,内存16 GB的计算机上进行了模拟地图和海底地图仿真测试,并将结果与改进蚁群规划算法[13](path planning algorithm based on improved ant colony optimization, PPABIACO)、冠状病毒群体免疫规划算法[14](path planning algorithm based on coronavirus herd immunity optimization, PPABCHIO)以及改进粒子群规划算法[15](path planning algorithm based on improved particle swarm optimization, PPABIPSO)进行了性能比较。本文中PPABCITM算法涉及的参数通过算法参数测试得到:Np=30、Kmax=100、r=3、γ=0.2。另外3种算法参数来自相应参考文献。鉴于算法均为概率性搜索,文中对4种规划算法分别进行了30次独立测试。
5.1 模拟地图测试分析
如图4所示,模拟地图根据栅格数量分为3类,每一类又根据地图环境的不同分为两种,共6种环境。由表1可以看出,在6种环境地图中,本文中PPABCITM的最优路径长度和平均路径长度明显短于PPABIACO、PPABCHIO、PPABIPSO的规划长度,前者指标分别平均减少了6.41%、6.52%、6.76%,后者指标分别平均减少了6.09%、6.39%、6.70%,这主要得益于全局短连接的强邻域搜索能力,以及局部长连接时冗余路径删除。此外,PPABCITM的最优和平均路径长度比较接近,但其他3种算法各自相差较大,说明了PPABCITM全局优化能力强且稳定,这主要得益于全局长连接算子能有效帮助算法跳出局部极小,实现全局路径优化。
由最小和平均收敛代数可以看出,PPABCITM值都是最小,相比于其他3种算法,前者分别平均减少了39.413%、32.753%、41.698%,后者分别平均减少了38.383%、35.931%、34.331%,说明了PPABCITM收敛速度快,体现出了高搜索效率,这主要得益于全局短连接的强邻域搜索和全局长连接的全局搜索能力。
由代数标准差可以看出,PPABCITM相比起其他3种算法分别平均减少了38.174%、39.747%、28.087%,说明PPABCITM最稳定,能适应不同环境的路径规划。在单位距离转角指标方面,PPABCITM也明显最小,且相比起其他3种算法分别平均减少了60.940%、63.101%、63.542%,说明由其规划路径总体上最为平滑,这主要得益于局部短连接中衍生点的连接调节了局部路径,以及局部长连接中冗余路径的直连光滑了路径。
如图4所示,相比其他3种算法因受限于需在相邻无障栅格间转移而造成路径多曲折的不足,PPABCITM算法规划的路径明显更加简洁和平滑,从而验证了PPABCITM算法设计的局部短连接和局部长连接算子在降低规划路径长度、提高路径光滑性方面的有效性。
为进一步比较4种算法的搜索性能,文中选择在两个最为复杂的25×25模拟地图环境进行了算法进化曲线比较。如图5所示,从4种算法的进化曲线可以看出,本文提出的PPABCITM算法不仅收敛速度最快,而且平均最优路径长度最短,此外,其余3种算法进化过程中均表现出了一定的振荡特性,而文中算法的搜索稳定性表现优异,从而进一步验证了本文算法的快速性、稳定性和准确性。
5.2 海底地图测试分析
为了进一步验证本文提出的PPABCITM在实际海底地图中路径规划的有效性和优越性,选取两处海底地图进行了相同上述4种规划算法的测试对比。海底地图Ⅰ位于智利西部海域,地形以分散的山丘为主,其海域经纬度为:-73.370 4°W, -73.132 7°E, -42.880 4°S, -42.653 7°N。海底地图Ⅱ位于澳大利亚东部海域,地形以较为集中的礁盘为主,海域经纬度为:146.452 9°W, 146.719 5°E, -17.935 6°S, -17.694 9°N。首先对地图进行膨化处理,并分别在海底地图Ⅰ水下120 m和海底地图Ⅱ水下35 m的定深平面进行算法测试。
表2给出了4种规划算法在两种海底地图中的测试对比,由表2同样可以看出,针对6种测试性能指标,PPABCITM算法同样是4种算法中性能最优的。相比于其他3种规划算法,PPABCITM的最优路径长度分别平均减少了9.006%、6.164%、6.737%,平均最优路径长度分别平均减少了11.682%、8.996%、11.089%,验证了本文算法在障碍范围更大、形态更加丰富的海底地形中仍能规划出最优路径。在最小收敛代数方面,本文算法与其他3种算法相比分别平均减少了66.435%、51.190%、57.519%,平均收敛代数分别平均减少了67.919%、59.796%、52.063%,代数标准差分别平均减少了34.200%、35.957%、34.247%,验证了本文算法面对大尺寸复杂地形仍能保持较高收敛性和稳定性。在单位距离转角指标方面,本文算法与其他3种算法相比分别平均减少了76.716%、73.663%、79.926%,验证了本文算法在复杂海底地形中依旧能规划出更光滑路径。海底地图测试对比进一步验证了PPABCITM的强规划能力、高规划效率等优势在实际地图中仍然有效。
两种海底地图中4种算法的最优规划结果如图6所示,相比起其他3种算法存在的规划路径点偏多、路径段跨度小等不足,PPABCITM算法所规划路径节点少、路径段跨度大,有利于AUV实际导航时的持续平滑航行。图7所示为4种算法在两种海底地图中的最有规划路径的进化曲线比较图,由图7可以看出,本文PPABCITM算法收敛曲线收敛速度最快、振荡最小,且最优路径长度最短,进一步验证了本文算法在实际海底环境中进行路径规划的先进性和有效性。
6 结论
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为了提高复杂环境中AUV的路径规划能力和效率,借鉴社群网络中的有效信息传递机制,提出了一种新的AUV路径规划方法,通过理论分析和仿真测试可以得出以下结论。
(1)基于栅格法进行AUV环境构建,通过路径点邻域以及衍生点集来模拟多社群结构,再借鉴社群信息传递机制设计长、短连接算子,有效实现AUV从起点到目标的路径信息传递,进而实现复杂地形中路径的最优和高效规划。
(2)全局短连接实现了所规划路径点邻域内最优路径搜索,强化了算法的局部规划能力,而全局随机长连接实现了路径点邻域外的搜索,能有效帮助算法跳出局部极小,实现全局最优路径规划。
(3)局部短连接中基于栅格中心点的八衍生点实现了规划路径的局部调整,提高了路径光滑性,而局部长连接实现了规划路径中冗余路径的剔除,在减少路径规划距离同时也提高了路径光滑性。
基金
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  • 工信部高技术船舶项目([2019]360)
  • 张家港市产业链创新产品攻关计划(ZKC2206)
  • 张家港市产学研预研资金(ZKYY2253)
  • 张家港市产学研预研资金(ZKYY2328)
文献
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2025年第25卷第6期
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doi: 10.12404/j.issn.1671-1815.2402833
  • 接收时间：2024-04-18
  • 首发时间：2025-07-27
  • 出版时间：2025-02-28
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  • 收稿日期：2024-04-18
  • 修回日期：2024-12-12
基金
工信部高技术船舶项目([2019]360)
张家港市产业链创新产品攻关计划(ZKC2206)
张家港市产学研预研资金(ZKYY2253)
张家港市产学研预研资金(ZKYY2328)
作者信息
    1 江苏科技大学机电与动力工程学院, 张家港 215600
    2 张家港江苏科技大学产业技术研究院, 张家港 215600
    3 江苏科技大学苏州理工学院, 张家港 215600
    4 中科探海(苏州)海洋科技有限责任公司, 苏州 215600
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https://castjournals.cast.org.cn/joweb/kxjsygc/CN/10.12404/j.issn.1671-1815.2402833
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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