Fuzzy dual-window DWA algorithm for USV in dense obstacle conditions
-
摘要:
目的 旨在研究密集障碍物海况下无人艇的自主避障问题。 方法 为此，提出一种基于模糊推理的改进型双窗口动态窗口法（DWA）避障算法，即在常规速度窗口的基础上设计基于艇载传感器的感知窗口，构成双窗口模型以进一步优化约束速度空间，然后根据障碍物的分布，动态调整评价函数权值。 结果 仿真实验结果显示，相比原始DWA算法，改进算法在未知密集障碍物海况下规划的路径更加合理、光滑，可避免无人艇从密集障碍物群外绕行，同时确保了避障航行安全性，迭代次数和运行时间可缩短20%以上。 结论 研究成果对无人艇自主避障技术发展具有较为明显的科学价值和实际意义。 Abstract:Objectives The problem of autonomous obstacle avoidance of unmanned surface vehicles (USV) under dense obstacle sea conditions is studied. Methods An improved dual-window dynamic window approach (DWA) obstacle avoidance algorithm based on a fuzzy control strategy is proposed; that is, a sensing window based on an onboard sensor is designed on the basis of the conventional speed window, and a dual-window model composed of the speed window and sensing window is further optimized. The speed space is constrained and the weight of the evaluation function is dynamically adjusted based on the fuzzy control strategy in accordance with the obstacle distribution state and distance between the USV and obstacles. Results The simulation experimental results show that, compared with the original DWA algorithm, the path planned by the improved algorithm under unknown dense obstacle sea conditions is smoother and more reasonable, which not only solves the problem of USVs detouring outside dense obstacle groups, but also improves the safety of obstacle avoidance navigation and reduces the number of iterations and running time by more than 20%. Conclusions The results of this study have certain reference value for research on the autonomous obstacle avoidance technology of USVs. -
图 2 基于原始DWA算法的无人艇航行避障轨迹
Figure 2. Obstacle avoidance trajectory of USV based on original DWA algorithm
图 4 基于原始DWA与本文双窗口DWA算法的对比仿真实验
Figure 4. Comparison experiment based on original DWA and improved dual-window DWA algorithm
图 7 双窗口DWA算法下
$ \alpha =2，\beta =1，\gamma =15 $ 时的仿真实验结果Figure 7. Simulation experiment results when
$ \alpha =2，\beta =1，\gamma =15 $ under the dual-window DWA algorithm
图 8 双窗口DWA算法下
$ \alpha =2，\beta =15，\gamma =15 $ 时的仿真实验结果Figure 8. Simulation experiment results when
$ \alpha =2，\beta =15，\gamma =15 $ under the dual-window DWA algorithm
图 9 基于FDDWA算法的无人艇避障仿真实验结果 (
$ \alpha =2，\beta =\beta_d，\gamma =\gamma_d $ )Figure 9. Simulation experiment results of obstacle avoidance for USV based on FDDWA algorithm (
$ \alpha =2，\beta =\beta_d，\gamma =\gamma_d $ )
图 10 不同权值参数下的无人艇避障轨迹对比
Figure 10. Comparison of USV's obstacle avoidance trajectory under different weight parameters
表 1 实验结果数据
Table 1. Experimental result data
参数 轨迹长度/m 运行时间/s 安全距离/m α=2，β=1，γ=15 155.90 12.259 0.45 α=2，β=15，γ=15 158.40 13.835 2.41 
下载: 导出CSV
表 2 模糊逻辑控制输入输出子集
Table 2. Fuzzy logic control input and output subset
变量 模糊子集 输入 $D$ 小（S）、中（M）、大（H） $I$ 小（S）、大（H） 输出 $\gamma $ 小（S）、中（M）、大（H） $\beta $ 小（S）、中（M）、大（H）
下载: 导出CSV
表 3 输出量
$\gamma $ 的模糊规则Table 3. Fuzzy rules of γ
$\gamma $ I S H $D{\rm{ = }}S$ H S $D{\rm{ = }}M$ H S $D{\rm{ = }}H$ H H
下载: 导出CSV
表 4 输出量
$\;\beta $ 的模糊规则Table 4. Fuzzy rules of β
$\beta $ I S H $D{\rm{ = }}S$ S H $D{\rm{ = }}M$ S M $D{\rm{ = }}H$ S M
下载: 导出CSV
表 5 无人艇速度限制
Table 5. Speed limit of USV
参数 数值 最小前进速度$ {u_{\min }}/ $(m·s−1) 0 最大前进速度${u_{\max }}/$(m·s−1) 1.5 前进加速度$\dot u/$(m·s−2) 0.5 最小艏摇角速度${r_{\min }}/$((°)·s−1) 0 最大艏摇角速度${r_{\max }}/$((°)·s−1) 30（−30） 艏摇角加速度$\dot r/$((°)·s−1) 5
下载: 导出CSV
表 6 算法参数设置
Table 6. Parameter settings of algorithm
参数 数值 最小安全距离${D_{\min }}/m$ 2.0 障碍物外包圆直径$R/\rm m$ 1.5 采样时间间隔${d_{\rm t}}/s$ 1.0 前进速度离散间隔$ {d_u}/ $(m·s−1) 0.1 艏摇角速度离散间隔${d_r}/$((°)·s−1) 1.0 预测轨迹采样间隔数$T/\rm s$ 7.0
下载: 导出CSV
表 7 密集度函数参数设置
Table 7. Parameter setting of density function
参数 数值 $\delta $ 0.30 $\varepsilon $ 0.25 $\mu $ 0.50 $a$ 1/50 $b$ 1/2
下载: 导出CSV
表 8 无人艇及感知窗口初始状态设置
Table 8. Initial state setting of USV and sensing window
参数 数值 起始坐标 （0，0） 目标坐标 （90，90） 感知窗口角度/(°) 100 感知窗口半径/m 13 起始艏向$\vartheta $/(°) π/4 起始前进速度$u/$(m·s−1) 0 起始艏摇角速度$r/$((°)·s−1) 0
下载: 导出CSV
表 9 实验数据
Table 9. Experimental data
权值参数 安全距离/m 迭代次数/次 轨迹长度/m 运行时间/s α=2，β=15，γ=1 3.56 109 127.30 14.445 α=2，β=1，γ=15 3.34 86 125.10 10.768 α=2，β=βd，γ=γd 4.09 85 124.10 10.071
下载: 导出CSV
-
[1] 张洋洋, 瞿栋, 柯俊, 等. 基于速度障碍法和动态窗口法的无人水面艇动态避障[J]. 上海大学学报(自然科学版), 2017, 23(1): 1–16.ZHANG Y Y, QU D, KE J, et al. Dynamic obstacle avoidance for USV based on velocity obstacle and dynamic window method[J]. Journal of Shanghai University (Natural Science Edition), 2017, 23(1): 1–16 (in Chinese). [2] KHATIB O. Real-time obstacle avoidance for manipulators and mobile robots[J]. The International Journal of Robotics Research, 1986, 5(1): 90–98. doi: 10.1177/027836498600500106 [3] 卢恩超, 张邓斓, 宁雅男, 等. 改进人工势场法的机器人路径规划[J]. 西北大学学报(自然科学版), 2012, 42(5): 735–738.LU E C, ZHANG D L, NING Y N, et al. Path planning for robot based on improved artificial potential field method[J]. Journal of Northwest University (Natural Science Edition), 2012, 42(5): 735–738 (in Chinese). [4] BORENSTEIN J, KOREN Y. The vector field histogram-fast obstacle avoidance for mobile robots[J]. IEEE Transactions on Robotics and Automation, 1991, 7(3): 278–288. doi: 10.1109/70.88137 [5] 魏新勇, 黄烨笙, 洪晓斌. 基于VFH*的水面无人艇局部避障方法[J]. 中国测试, 2018, 44(12): 39–45. doi: 10.11857/j.issn.1674-5124.2018.12.007WEI X Y, HUANG Y S, HONG X B. Local obstacle avoidance method for unmanned surface vehicle based on VFH* algorithm[J]. China Measurement & Testing Technology, 2018, 44(12): 39–45 (in Chinese). doi: 10.11857/j.issn.1674-5124.2018.12.007 [6] 许心德, 关胜晓. 未知环境下基于VFH*的机器人避障[J]. 计算机仿真, 2010, 27(3): 156–160. doi: 10.3969/j.issn.1006-9348.2010.03.039XU X D, GUAN S X. Obstacle avoidance for robots based on VFH* method in uncertain environment[J]. Computer Simulation, 2010, 27(3): 156–160 (in Chinese). doi: 10.3969/j.issn.1006-9348.2010.03.039 [7] SIMMONS R. The curvature-velocity method for local obstacle avoidance[C]//Proceedings of IEEE International Conference on Robotics and Automation. Minneapolis, MN, USA: IEEE, 1996: 3375–3382. [8] FOX D, BURGARD W, THRUN S, et al. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23–33. [9] 程传奇, 郝向阳, 李建胜, 等. 融合改进A*算法和动态窗口法的全局动态路径规划[J]. 西安交通大学学报, 2017, 51(11): 137–143.CHENG C Q, HAO X Y, LI J S. Global dynamic path planning based on fusion of improved A* algorithm and dynamic window approach[J]. Journal of Xi'an Jiaotong University, 2017, 51(11): 137–143 (in Chinese). [10] TANG P P, ZHANG R B, LIU D L, et al. Research on near-field obstacle avoidance for unmanned surface vehicle based on heading window[C]//2012 24th Chinese Control and Decision Conference (CCDC). Taiyuan, China: IEEE, 2012: 1262–1267. [11] ZHANG R B, TANG P P, SU Y M, et al. An adaptive obstacle avoidance algorithm for unmanned surface vehicle in complicated marine environments[J]. IEEE/CAA Journal of Automatica Sinica, 2014, 1(4): 385–396. doi: 10.1109/JAS.2014.7004666 [12] 田永永, 李梁华. 基于速度方向判定的动态窗口法[J]. 农业装备与车辆工程, 2018, 56(8): 39–42. doi: 10.3969/j.issn.1673-3142.2018.08.010TIAN Y Y, LI L H. Dynamic window approach based on judgment of speed direction[J]. Agricultural Equipment & Vehicle Engineering, 2018, 56(8): 39–42 (in Chinese). doi: 10.3969/j.issn.1673-3142.2018.08.010 [13] 王永雄, 田永永, 李璇, 等. 穿越稠密障碍物的自适应动态窗口法[J]. 控制与决策, 2019, 34(5): 927–936.WANG Y X, TIAN Y Y, LI X, et al. Self-adaptive dynamic window approach in dense obstacles[J]. Control and Decision, 2019, 34(5): 927–936 (in Chinese). -
点击查看大图
计量
- 文章访问数: 404
- HTML全文浏览量: 125
- PDF下载量: 62
- 被引次数: 0
