Obstacle avoidance algorithm for ships in complex waters based on dynamic window approach
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摘要:
目的 提出一种改进的动态窗口法，以解决近海水域智能船舶在面对夹击及动静混合会遇时无法有效避让的船舶避障问题。 方法 为得到在近海水域航行的船舶约束条件，针对近海水域对船舶避障的影响因素进行分析，同时提出近海水域船舶航行最低避障要求；然后对动态窗口法（DWA）的目标函数进行优化改进，并将其与船舶和障碍物的距离相关联，以提升船舶在航行图中的安全性，同时将目标函数中的航向权值引入船舶会遇态势判断，以使目标船舶可以有效判断船舶的避障责任；最后，通过仿真模拟验证改进算法的有效性。 结果 仿真结果表明，所提的改进算法在分别遭遇夹击以及复杂会遇的情况下，能够清晰地判断船舶的避障责任，降低航行过程中的速度变化陡峭度，且所规划的船舶航行路径可以有效提升船舶运行安全性。 结论 所提避障算法可为解决近海水域智能船舶遭遇复杂会遇情景的避碰失败问题提供参考。 Abstract:Objectives An improved dynamic window method is proposed to solve the obstacle avoidance problem of intelligent ships in offshore waters, which cannot be effectively avoided when facing pinch and mixed dynamic-static encounters. Methods In order to obtain the constraints of ships navigating in offshore waters, the factors affecting ship obstacle avoidance in offshore waters are analyzed, and the minimum obstacle avoidance requirements for ship navigation in offshore waters are proposed. The objective function of the Dynamic Window Approach (DWA) is then optimized, improved and correlated with the distance between the ship and obstacles to enhance the safety of the ship in the navigational chart, while the heading weights in the objective function are introduced into the judgment of the ship's encounter posture so as to enable the target ship to effectively judge its obstacle-avoidance responsibility. Finally, the effectiveness of the improved algorithm is verified through simulation. Results The simulation results show that the proposed improved algorithm can clearly judge the ship's obstacle-avoidance responsibility and reduce the steepness of speed change in the sailing process, and the planned ship sailing path can effectively improve the safety of ship operation in case of encountering pincer attacks and complex encounters respectively. [Conlusions] The proposed obstacle avoidance algorithm can provide useful references for solving the collision avoidance failure problem of intelligent ships encountering complex encounter scenarios in offshore waters. -
图 2 采用动态窗口法时遭遇夹击会遇场景避碰失败示意图
Figure 2. Schematic diagram of encounter scenario avoidance failure when using the DWA
图 7 仿真运行过程中的遭遇距离及对应时间对比图
Figure 7. Comparison of distance and corresponding time during the simulation run
图 8 仿真运行时速度及运行时间对比图
Figure 8. Speed and running time comparison chart during simulation run
表 1 初始参数设定
Table 1. Initial parameter setting
参数 数值 参数 数值 vmin /(m·s−1) 0 $ {\omega }_{\mathrm{m}\mathrm{a}\mathrm{x}} $/rad $ {\text{π}}/6 $ vmax /(m·s−1) 5 $ \dot{\omega } $/(rad·s−1) $ {\text{π}}/36 $ $ \dot{v} $/(m·s−2) 2 T $ 0.2 $ $ {\omega }_{\mathrm{m}\mathrm{i}\mathrm{n}} $/rad $ -{\text{π}}/6 $ Dx $ 0.2 $ 
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表 2 3种算法在仿真运行时的步长及耗时对比
Table 2. Comparison of step size and time of three algorithms
算法 步长/步 运行耗时/s Ⅰ 397 34.7 Ⅱ 345 30.4 Ⅲ 292 22.6
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表 3 应用3种算法的仿真航行速度陡峭程度对比
Table 3. Comparison of the speed steepness of the three algorithms
算法 陡峭程度$ {\boldsymbol{\theta }}_{1} \left(\tan \left( \dfrac{{\Delta\boldsymbol{u}}_{1}}{{\Delta\boldsymbol{t}}_{1}} \right)\right)$ 陡峭程度$ {\boldsymbol{\theta }}_{2} \left(\tan \left( \dfrac{\Delta {\boldsymbol{u}}_{2}}{\Delta {\boldsymbol{t}}_{2}} \right)\right)$ Ⅰ 0.003 75 0.001 94 Ⅱ 0.001 85 0.000 92 Ⅲ 0.001 97 0.000 58
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[1] SEUWOU P, BANISSI E, UBAKANMA G, et al. Actor-network theory as a framework to analyse technology acceptance model's external variables: the case of autonomous vehicles[C]//11th International Conference on Global Security, Safety and Sustainability. London: Springer, 2017: 305−320. [2] ZHENG J, MENG F, LI Y. Design and experimental testing of a free-running ship motion control platform[J]. IEEE Access, 2018, 6: 4690–4696. doi: 10.1109/ACCESS.2017.2778180 [3] LUMELSKY V J, STEPANOV A A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape[J]. Algorithmica, 1987, 2(1): 403–430. [4] YAO Y L, LIANG X F, LI M Z, et al. Path planning method based on D* lite algorithm for unmanned surface vehicles in complex environments[J]. China Ocean Engineering, 2021, 35(3): 372–383. doi: 10.1007/s13344-021-0034-z [5] WANG L P, ZHANG Z, ZHU Q D, et al. Ship route planning based on double-cycling genetic algorithm considering ship maneuverability constraint[J]. IEEE Access, 2020, 8: 190746–190759. doi: 10.1109/ACCESS.2020.3031739 [6] MOUSAZADEH H, JAFARBIGLU H, ABDOLMALEKI H, et al. Developing a navigation, guidance and obstacle avoidance algorithm for an unmanned surface vehicle (USV) by algorithms fusion[J]. Ocean Engineering, 2018, 159: 56–65. doi: 10.1016/j.oceaneng.2018.04.018 [7] 宁君, 李学健, 李伟, 等. 基于改进人工势场法的船舶自动避碰研究[J]. 舰船科学技术, 2021, 43(23): 59–64, 154. doi: 10.3404/j.issn.1672-7649.2021.12.011NING J, LI X J, LI W, et al. Research on ship automatic collision avoidance based on improved artificial potential field method[J]. Ship Science and Technology, 2021, 43(23): 59–64, 154 (in Chinese). doi: 10.3404/j.issn.1672-7649.2021.12.011 [8] FOX D, BURGARD W, THRUN S. The dynamic window approach to collision avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23–33. [9] 詹京吴, 黄宜庆. 融合安全A*算法与动态窗口法的机器人路径规划[J]. 计算机工程, 2022, 48(9): 105–112, 120. doi: 10.19678/j.issn.1000-3428.0062264ZHAN J W, HUANG Y Q. Path planning of robot combing safety A* algorithm and dynamic window approach[J]. Computer Engineering, 2022, 48(9): 105–112, 120 (in Chinese). doi: 10.19678/j.issn.1000-3428.0062264 [10] 李文刚, 汪流江, 方德翔, 等. 联合A*与动态窗口法的路径规划算法[J]. 系统工程与电子技术, 2021, 43(12): 3694–3702. doi: 10.12305/j.issn.1001-506X.2021.12.33LI W G, WANG L J, FANG D X, et al. Path planning algorithm combining A* with DWA[J]. Systems Engineering and Electronics, 2021, 43(12): 3694–3702 (in Chinese). doi: 10.12305/j.issn.1001-506X.2021.12.33 -
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