[1]李娟,张子浩,张宏瀚.复杂环境下DWA与RRT算法融合的AUV局部路径规划[J].智能系统学报,2024,19(4):961-973.[doi:10.11992/tis.202301009]
LI Juan,ZHANG Zihao,ZHANG Honghan.Local path planning for AUV with fusion of DWA and RRT algorithms in a complex environment[J].CAAI Transactions on Intelligent Systems,2024,19(4):961-973.[doi:10.11992/tis.202301009]
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LI Juan,ZHANG Zihao,ZHANG Honghan.Local path planning for AUV with fusion of DWA and RRT algorithms in a complex environment[J].CAAI Transactions on Intelligent Systems,2024,19(4):961-973.[doi:10.11992/tis.202301009]
复杂环境下DWA与RRT算法融合的AUV局部路径规划
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第4期
页码:
961-973
栏目:
学术论文—智能系统
出版日期:
2024-07-05
- Title:
- Local path planning for AUV with fusion of DWA and RRT algorithms in a complex environment
- Keywords:
- autonomous underwater vehicles; path planning; dynamic window; rapid-exploration random tree; speed space; evaluation function; underwater environment; dynamic obstacle avoidance
- 分类号:
- TP242
- 摘要:
- 针对复杂水下环境下的自主水下航行器（autonomous underwater vehicle，AUV）局部路径规划问题，传统动态窗口法(dynamic window approach，DWA）存在复杂障碍物中陷入局部停滞，动态避障性能不佳等问题，本文提出了一种基于DWA与快速随机搜索树（rapid-exploration random tree，RRT)算法融合的路径规划算法。改进的DWA算法速度空间根据整个动态窗口的周期生成，重设了评价函数并结合AUV任务环境引入洋流能耗评价函数；改进的RRT算法在局部已知空间内规划导引点，帮助DWA脱离局部停滞状态并实现更安全的动态避障。将2种算法融合，实现了AUV在复杂水下环境中的局部路径规划。仿真表明，该融合算法能够降低AUV在洋流中的能耗代价，解决了DWA在复杂障碍物中陷入局部停滞的问题，能够安全有效地躲避动态避障物。
- Abstract:
- For the local path planning problem of autonomous underwater vehicle (AUV) in a complex underwater environment, traditional dynamic window approach(DWA) has the problems of getting into local stagnation in complex obstacles and poor dynamic obstacle avoidance performance, etc. In this paper, we propose a path planning algorithm based on the fusion of DWA and Rapid-exploration random tree(RRT) algorithms. The improved DWA algorithm generates the velocity space based on the whole dynamic window period, resets the evaluation function and introduces the evaluation function of ocean current energy consumption in an AUV mission environment; the improved RRT algorithm plans the guide points in a local known space, which helps DWA to get out of the local stagnation and achieve a safer dynamic obstacle avoidance. The two algorithms are fused to achieve local path planning for AUV in a complex underwater environment. Simulations show that the fusion algorithm can reduce the energy cost of AUV in ocean currents, solve the problem of DWA getting into local stagnation in complex obstacles, and can avoid dynamic obstacles safely and effectively.
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备注/Memo
收稿日期:2023-01-10。
基金项目:国家自然科学基金面上项目(5217110503)；山东省自然科学基金面上项目(ZR202103070036)；水下机器人重点实验室基金项目(JCKYS2021SXJQR-09).
作者简介: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技术。获国防科技进步特等奖 1项、一等奖2项、二等奖1项。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：lijuan041hrbeu.edu.cn;张子浩, 硕士研究生，主要研究方向为水下机器人的智能规划。 E-mail：594602580@qq.com;张宏瀚，副教授，博士，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、国防科技进步特等奖1项、一等奖2项。授权国家发明专利8项。发表学术论文30余篇。 E-mail：zhanghonghan2008@163.com
通讯作者:张宏瀚. E-mail：zhanghonghan2008@163.com
更新日期/Last Update:
1900-01-01