洋流环境下基于DVFH+的AUV避障控制

朱仲本; 张嘉豪; 薛祎凡; 秦洪德

doi:10.11993/j.issn.2096-3920.2024-0077

洋流环境下基于DVFH+的AUV避障控制

doi: 10.11993/j.issn.2096-3920.2024-0077

哈尔滨工程大学青岛创新发展基地, 山东青岛, 266000

基金项目: 国家自然科学基金杰出青年基金(52025111); 山东省自然科学基金青年基金(ZR202211130097).

详细信息

作者简介:
朱仲本(1988-), 男, 博士, 副教授, 主要研究方向为水下定位导航与智能控制

中图分类号: TJ630.33; U675.9
计量
- 文章访问数: 44
- HTML全文浏览量: 22
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2024-05-09
- 修回日期: 2024-07-14
- 录用日期: 2024-07-18
- 网络出版日期: 2025-01-24

Obstacle Avoidance Control of AUV Based on DVFH+ in Ocean Current Environment

Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, China

摘要

摘要: 针对向量场直方图法(VFH)的改进算法VFH+忽视自主水下航行器(AUV)动力学性能和洋流环境的影响, 且对阈值设置敏感的问题, 文中提出了一种基于动力学的VFH+(DVFH+)。通过AUV的动力学参数来限制期望艏向的输出, 解决了原算法期望输出跳变的问题, 从而改善了AUV的跟踪性能; 考虑真实洋流环境下的漂角补偿, 优化了避障算法, 提高了其鲁棒性和适应性; 根据障碍物信息自适应调节阈值大小, 从而计算得到符合AUV周围环境特征的规划指令, 保证航行的高效性和安全性。采用REMUS 100 AUV模型进行仿真实验, 结果表明, 文中所提出的DVFH+能给出更加光滑可行的避障路线, 适用于复杂环境下的AUV避障, 且有效避免了原算法因阈值设置不合理导致的路径绕远及规划失败等情况。
- 自主水下航行器 /
- 避障控制 /
- 向量场直方图 /
- 洋流环境 /
- 动力学性能
Abstract: Aiming at the problem that the improved vector field histogram (VFH) algorithm VFH+ ignores dynamic performance of autonomous undersea vehicle (AUV) and the impact of ocean current environment, and is sensitive to threshold setting, dynamic-based VFH+ (DVFH+) is proposed in this paper. The dynamic parameters of AUV are used to limit the output of the expected heading, which solves the problem of the expected output hopping of the original algorithm and improves the tracking performance of AUV; considering the drift angle compensation in the real ocean current environment, the obstacle avoidance algorithm is optimized to improve its robustness and adaptability; according to the obstacle information, the threshold value can be adjusted adaptively, and the planning instructions can be calculated according to the environmental characteristics around AUV, so as to ensure the efficiency and safety of navigation. The REMUS 100 AUV model is used for simulation and the results show that DVFH+ proposed in this paper can provide a smoother and more feasible obstacle avoidance route, which is suitable for obstacle avoidance of AUV in complex environment, and effectively avoids the path detouring and planning failure caused by the unreasonable threshold setting of the original algorithm.
- autonomous undersea vehicle /
- obstacle avoidance control /
- vector filed histogram /
- ocean current environment /
- dynamic performance

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图 1 坐标系及漂角示意图

Figure 1. Schematic diagram of coordinate system and drift angle

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图 2 环境极坐标图示意

Figure 2. Schematic diagram of environmental polar coordinates

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图 3 单元格的放大角度${{{{\boldsymbol{\gamma}} _{{\boldsymbol{ij}}}}}}$示意图

Figure 3. Schematic diagram of cell magnification angle ${{{{\boldsymbol{\gamma}} _{{\boldsymbol{ij}}}}}}$

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图 4 掩模示意图

Figure 4. Mask diagram

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图 5 候选方向选取示意图

Figure 5. Schematic diagram of candidate direction selection

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图 6 期望航向角的映射说明

Figure 6. Description of the mapping of the desired heading angle

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图 7 DVFH+流程图

Figure 7. DVFH+ flow chart

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图 8 REMUS 100 AUV

Figure 8. REMUS 100 AUV

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图 9 低阈值VFH+和DVFH+对比

Figure 9. Comparison of low threshold VFH+ and DVFH+

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图 10 高阈值VFH+和DVFH+对比

Figure 10. Comparison of high threshold VFH+ and DVFH+

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图 11 洋流环境下VFH+和DVFH+对比

Figure 11. Comparison of VFH+ and DVFH+ in ocean current environment

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图 12 基于低阈值VFH+和DVFH+的AUV艏向跟踪曲线

Figure 12. Based on low threshold VFH+ and DVFH+ AUV heading tracking curve

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图 13 基于高阈值VFH+和DVFH+的AUV艏向跟踪曲线

Figure 13. Based on high threshold VFH+ and DVFH+ AUV heading tracking curve

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图 14 洋流环境下基于中阈值VFH+和DVFH+的AUV艏向跟踪曲线

Figure 14. Based on medium threshold VFH+ and DVFH+ in ocean current environment

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表 1 传统VFH+的算法步骤

Table 1. Algorithm steps of traditional VFH+

步骤	具体描述
1	生成地图网格C。以二维网格表示大地坐标系下的环境, 每个障碍物单元格的确定性值用${c_{ij}}$表示
2	生成活动窗口${C_{\rm{a}}}$。${C_{\rm{a}}}$中的每个障碍物单元格都被表示为一个大小为${m_{ij}}$、方向为${\beta _{ij}}$的向量, ${m_{ij}}$如式(2)所示, ${\beta _{ij}}$是从AUV中心到单元格的方向
3	生成主极坐标直方图${H_{{\mathrm{p}}}}$。将AUV周围的环境以分辨率α分成数个扇区(如图2), 每个扇区的障碍物强度为${C_{\rm{a}}}$中在该扇区内的所有障碍物向量大小${m_{ij}}$的总和。基于AUV的安全半径${r_{\rm{s}}}$和障碍物单元格距离${d_{ij}}$, 定义单元格的放大角度${\gamma _{ij}}$(如图3), 因此单个障碍物单元格可影响到多个扇区的障碍物强度计算
4	生成掩模极坐标直方图${H_{\rm{m}}}$。设置合适的阈值T, 障碍物强度高于阈值的扇区被判定为闭锁扇区, 否则为自由扇区。根据AUV艏向和最小回转半径${r_{\rm{t}}}$, 再额外确定一些闭锁扇区, 见图4
5	生成期望艏向。若${H_{\rm{m}}}$中全部的扇区皆为自由扇区, 则期望艏向为目标方向, 否则, 连续的自由扇区被称为山谷, 根据山谷所包含的扇区个数将其分为宽谷和窄谷, 然后将每个山谷中的候选方向添加到列表中。选取候选方向的具体方法参见图5。计算所有候选方向的代价函数值并选择代价最小的方向来确定期望转向, 代价函数如式(3)所示

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表 2 REMUS 100 AUV模型参数

Table 2. REMUS 100 AUV model parameters

参数	数值
质量m	31.9 kg
长度l	1.6 m
最大线速度${u_{\max }}$	2.5 m/s
最大角速度${r_{\max }}$	0.1 rad/s
最大角加速度${\dot r_{\max }}$	0.4 rad/s²

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表 3 算法参数

Table 3. Algorithm parameters

参数	数值	参数	数值
活动窗口半径${r_{\rm{a}}}$	50 m	控制律参数ε	1
AUV安全半径${r_{{\mathrm{s}}}}$	1.5 m	控制律参数p	100
确定性值${c_{ij}}$	15	控制律参数κ	200
参数a	6251	权重${\omega _1}$	0.8
参数b	2.5	权重${\omega _2}$	0.2
环境分辨率α	5°	阈值下界${T_{\min }}$	300
宽窄谷区分度s	18	阈值上界${T_{\max }}$	1 000 000
代价函数系数${\mu _1}$	0.6	低阈值${T_{\rm{l}}}$	200 000
代价函数系数${\mu _2}$	0.2	中阈值${T_{\rm{m}}}$	500 000
代价函数系数${\mu _{_3}}$	0.2	高阈值${T_{h}}$	800 000

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