基于RRT星和APF的欠驱动水下航行器混合路径规划算法。

IF 3.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Reports Pub Date : 2025-08-05 DOI:10.1038/s41598-025-11500-1

Boyu Zhang, Yishan Su, Shanlin Sun, Wei Luo, Qing Huang

{"title":"基于RRT星和APF的欠驱动水下航行器混合路径规划算法。","authors":"Boyu Zhang, Yishan Su, Shanlin Sun, Wei Luo, Qing Huang","doi":"10.1038/s41598-025-11500-1","DOIUrl":null,"url":null,"abstract":"To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility. Experimental and simulation results demonstrate that the proposed algorithm reduces path planning time by 50.0-73.6%, decreases the number of nodes by 71.0-77.8%, and lowers the number of iterations by 61.0-85.0%, while achieving a 100% success rate in complex environments. The final path length is reduced to 1766.1 m, and the maximum turning angle is limited to 11.35°, fully satisfying the motion constraints of AUV.","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"28493"},"PeriodicalIF":3.9000,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12325776/pdf/","citationCount":"0","resultStr":"{\"title\":\"Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.\",\"authors\":\"Boyu Zhang, Yishan Su, Shanlin Sun, Wei Luo, Qing Huang\",\"doi\":\"10.1038/s41598-025-11500-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility. Experimental and simulation results demonstrate that the proposed algorithm reduces path planning time by 50.0-73.6%, decreases the number of nodes by 71.0-77.8%, and lowers the number of iterations by 61.0-85.0%, while achieving a 100% success rate in complex environments. The final path length is reduced to 1766.1 m, and the maximum turning angle is limited to 11.35°, fully satisfying the motion constraints of AUV.\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"28493\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-08-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12325776/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-11500-1\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-11500-1","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

摘要

针对自主水下航行器（AUV）的运动约束和实时性要求，提出了一种新的路径规划算法：定向锥与目标偏置动态人工势场RRT* (DCGB-DAPF-RRT*)。该算法集成了四个关键技术：定向锥采样、目标偏置采样、自适应步长和动态人工势场，以提高采样效率和路径质量。此外，采用冗余节点剪枝和三次非均匀b样条插值来提高轨迹的平滑性和保证运动的可行性。实验和仿真结果表明，该算法路径规划时间缩短50.0% ~ 73.6%，节点数减少71.0 ~ 77.8%，迭代次数减少61.0 ~ 85.0%，在复杂环境下成功率达到100%。最终路径长度减少到1766.1 m，最大转弯角度限制在11.35°，完全满足AUV的运动约束。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.

查看原文本刊更多论文

Hybrid path planning algorithm for underactuated AUV based on RRT star and APF.

To address the kinematic constraints and real-time requirements of autonomous underwater vehicle (AUV), this paper proposes a novel path planning algorithm: Directional Cone and Goal-Biased Dynamic Artificial Potential Field RRT* (DCGB-DAPF-RRT*). The algorithm integrates four key techniques-Directional Cone sampling, Goal-Biased sampling, Adaptive Step Length, and a Dynamic Artificial Potential Field-to improve sampling efficiency and path quality over conventional RRT-based methods. In addition, redundant node pruning and cubic non-uniform B-spline interpolation are employed to enhance trajectory smoothness and ensure kinematic feasibility. Experimental and simulation results demonstrate that the proposed algorithm reduces path planning time by 50.0-73.6%, decreases the number of nodes by 71.0-77.8%, and lowers the number of iterations by 61.0-85.0%, while achieving a 100% success rate in complex environments. The final path length is reduced to 1766.1 m, and the maximum turning angle is limited to 11.35°, fully satisfying the motion constraints of AUV.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Scientific Reports Natural Science Disciplines-

CiteScore

7.50

自引率

4.30%

发文量

19567

审稿时长

3.9 months

期刊介绍： We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.