[1]张宏瀚,王亚博,李娟,等.近海复杂环境下UUV动态路径规划方法研究[J].智能系统学报,2024,19(1):114-121.[doi:10.11992/tis.202302028]
ZHANG Honghan,WANG Yabo,LI Juan,et al.Dynamic path-planning method of UUV in an offshore complex environment[J].CAAI Transactions on Intelligent Systems,2024,19(1):114-121.[doi:10.11992/tis.202302028]
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ZHANG Honghan,WANG Yabo,LI Juan,et al.Dynamic path-planning method of UUV in an offshore complex environment[J].CAAI Transactions on Intelligent Systems,2024,19(1):114-121.[doi:10.11992/tis.202302028]
近海复杂环境下UUV动态路径规划方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第1期
页码:
114-121
栏目:
学术论文—机器人
出版日期:
2024-01-05
- Title:
- Dynamic path-planning method of UUV in an offshore complex environment
- Keywords:
- unmanned underwater vehicle; dynamic path-planning; rapidly exploring random tree; dynamic window approach; adaptive; underwater environment; local path-planning; obstacle avoidance
- 分类号:
- TP273
- 文献标志码:
- 2023-07-21
- 摘要:
- 为解决近海环境下水下无人航行器（unmanned underwater vehicle，UUV）的动态路径规划问题，本文提出一种结合全局和局部动态路径规划的算法。首先，本文提出一种基于自适应目标引导的快速拓展随机树算法，以增加随机树生长的方向性，并通过转向和重选策略减少无效拓展加快算法的收敛速度。接着，获得全局路径之后使用自适应子节点选取策略获取动态窗口法的子目标点，将复杂的全局动态任务规划分解为多个简单的动态路劲规划，从而防止动态窗口法陷入局部极小值。最后，通过UUV出港任务仿真实验验证了算法的有效性和实用性。
- Abstract:
- This paper proposes an algorithm that combines global and local dynamic path-planning methods to solve the dynamic path-planning problem for unmanned underwater vehicles (UUVs) in near-shore environments. First, a fast-expanding random tree algorithm based on adaptive target guidance is proposed to increase the directionality of random tree growth. The algorithm then achieves rapid convergence using turning and reselection strategies to reduce ineffective expansion. Further, an adaptive sub-node selection strategy is used after obtaining the global path to acquire sub-target points for the dynamic window approach. This approach decomposes the complex global dynamic task planning into multiple simple dynamic path-planning tasks, preventing the dynamic window approach from falling into local minima. Finally, the effectiveness and practicality of the algorithm are verified using simulation experiments of UUV departure tasks.
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- 相似文献/References:
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[1]成怡,肖宏图.融合改进A*算法和Morphin算法的移动机器人动态路径规划[J].智能系统学报,2020,15(3):546.[doi:10.11992/tis.201812023]
CHENG Yi,XIAO Hongtu.Mobile-robot dynamic path planning based on improved A* and Morphin algorithms[J].CAAI Transactions on Intelligent Systems,2020,15():546.[doi:10.11992/tis.201812023]
[2]韩月,陈鹏云,沈鹏.基于改进粒子滤波的AUV海底地形辅助定位方法[J].智能系统学报,2020,15(3):553.[doi:10.11992/tis.201903027]
HAN Yue,CHEN Pengyun,SHEN Peng.Seabed terrain-aided positioning method based on improved particle filtering for AUVs[J].CAAI Transactions on Intelligent Systems,2020,15():553.[doi:10.11992/tis.201903027]
备注/Memo
收稿日期:2023-02-28。
基金项目:国家自然科学基金项目（51909044）；黑龙江省自然科学基金项目（E2018023）.
作者简介:张宏瀚，副教授，主要研究方向为海上无人系统总体优化技术、无人潜航器智能控制。获国家科技进步二等奖1项，第一发明人授权发明专利8项，发表学术论文30余篇。E-mail：zhanghonghan2008@163.com;王亚博，硕士研究生，主要研究方向为无人潜航器智能控制。E-mail：wangyabo@hrbeu.edu.cn;李娟，教授，博士，主要研究方向为水下机器人、船舶智能控制和多智能体系统。获科技进步奖 1项。获授权国家发明专利7项。出版学术著作2部，发表学术论文30余篇。E-mail：lijuan041@hrbeu.edu.cn
通讯作者:李娟. E-mail：lijuan041@hrbeu.edu.cn
更新日期/Last Update:
1900-01-01