Ship collision prevention has always been a hot topic of research for navigation safety.Recently,autonomous ships have gained much attention as a means of solving collision problems by machine control with a collision...Ship collision prevention has always been a hot topic of research for navigation safety.Recently,autonomous ships have gained much attention as a means of solving collision problems by machine control with a collision-avoidance algorithm.An important question is how to determine optimal path planning for autonomous ships.This paper proposes a path-planning method of collision avoidance for multi-ship encounters that is easy to realize for autonomous ships.The ship course-control system uses fuzzy adaptive proportion-integral-derivative(PID)control to achieve real-time control of the system.The automatic course-altering process of the ship is predicted by combining the ship-motion model and PID controller.According to the COLREGs,ships should take different actions in different encounter situations.Therefore,a scene-identification model is established to identify these situations.To avoid all the TSs,the applicable course-altering range of the OS is obtained by using the improved velocity obstacle model.The optimal path of collision avoidance can be determined from an applicable course-altering range combined with a scene-identification model.Then,the path planning of collision avoidance is realized in the multi-ship environment,and the simulation results show a good effect.The method conforms to navigation practice and provides an effective method for the study of collision avoidance.展开更多
为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of a...为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。展开更多
为了解决船舶部分场景中仅靠变向避让效果差的问题,研究了多物标环境下符合避碰规则的船舶可变速自动避碰决策方法。基于船舶会遇四阶段理论和船舶领域模型量化船舶碰撞危险度,通过可变速MMG模型和模糊自适应PID航向控制方法推演船舶定...为了解决船舶部分场景中仅靠变向避让效果差的问题,研究了多物标环境下符合避碰规则的船舶可变速自动避碰决策方法。基于船舶会遇四阶段理论和船舶领域模型量化船舶碰撞危险度,通过可变速MMG模型和模糊自适应PID航向控制方法推演船舶定、变速改向操纵过程。在此基础上,改进了基于操纵过程推演和速度障碍理论的动态可行操纵区间求解算法。以实船为仿真目标,进行了不同操纵方案下的对比实验和多物标场景下的仿真实验。将程序运行步长设置为1 s,结果表明:(1)预设他船位置(4 n mile,4 n mile),航向270,航速12 kn,本船位置(0 n mile,0 n mile),航向000,航速12 kn的交叉相遇态势下,变向变速避让和仅变向避让采取操纵行动的最晚时间点分别为711 s和643 s;(2)在物标较远的多物标环境下,本船O保向保速至663 s,与目标船T_(A),T_(C),T_(D)构成碰撞危险,采取目标航向、转速区间为[48~°,61~°]、[75 r/min,85 r/min]中任意组合可让清所有物标。展开更多
基金supported by the Natural Science Foundation of China(grant no.52071249)the National Key Research and Development Program(grant no.2019YFB1600603).
文摘Ship collision prevention has always been a hot topic of research for navigation safety.Recently,autonomous ships have gained much attention as a means of solving collision problems by machine control with a collision-avoidance algorithm.An important question is how to determine optimal path planning for autonomous ships.This paper proposes a path-planning method of collision avoidance for multi-ship encounters that is easy to realize for autonomous ships.The ship course-control system uses fuzzy adaptive proportion-integral-derivative(PID)control to achieve real-time control of the system.The automatic course-altering process of the ship is predicted by combining the ship-motion model and PID controller.According to the COLREGs,ships should take different actions in different encounter situations.Therefore,a scene-identification model is established to identify these situations.To avoid all the TSs,the applicable course-altering range of the OS is obtained by using the improved velocity obstacle model.The optimal path of collision avoidance can be determined from an applicable course-altering range combined with a scene-identification model.Then,the path planning of collision avoidance is realized in the multi-ship environment,and the simulation results show a good effect.The method conforms to navigation practice and provides an effective method for the study of collision avoidance.
文摘为研究避碰规则、无人水面艇(unmanned surface vessel,USV)运动学特点和海上交通复杂度等因素约束下的USV自主避碰技术,在分析初始动态窗口法的基础上,考虑《国际海上避碰规则》(International Regulations for Preventing Collisions at Sea,COLREGs)关于避碰行动时机、避让幅度、复航时机等方面的要求,建立融合避碰规则的动态窗口模型,设计融合避碰规则的动态窗口法。通过对比仿真实验验证该方法的可行性和有效性,具有一定的现实意义。
文摘为解决大数据下船舶会遇识别算法效率不高且存在误判等问题,提出一种融合国际海上避碰规则(International Regulations for Preventing Collisions at Sea,COLREGs)的带噪声的基于密度的空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法,建立船舶会遇识别模型。在DBSCAN算法对邻域内的船舶数量进行统计时,计算船舶间的最近会遇距离(distance to closest point of approach,DCPA)和最近会遇时间(time to closest point of approach,TCPA),初步筛选邻域内的噪声点;基于模糊综合评价模型计算船舶会遇风险,对邻域内的船舶进行二次筛选,实现船舶会遇态势的提取。结果表明:改进后的DBSCAN算法过滤掉传统DBSCAN算法识别到的非会遇局面,并且在同一会遇局面下的船舶数量均保持在4艘以内;输出的会遇船舶风险演变趋势对实际水域内高风险船舶的监控适用性较好,能有效辅助船舶避碰。所提识别模型对保障航行安全和提高海事监管效率具有重要意义。
文摘为了解决船舶部分场景中仅靠变向避让效果差的问题,研究了多物标环境下符合避碰规则的船舶可变速自动避碰决策方法。基于船舶会遇四阶段理论和船舶领域模型量化船舶碰撞危险度,通过可变速MMG模型和模糊自适应PID航向控制方法推演船舶定、变速改向操纵过程。在此基础上,改进了基于操纵过程推演和速度障碍理论的动态可行操纵区间求解算法。以实船为仿真目标,进行了不同操纵方案下的对比实验和多物标场景下的仿真实验。将程序运行步长设置为1 s,结果表明:(1)预设他船位置(4 n mile,4 n mile),航向270,航速12 kn,本船位置(0 n mile,0 n mile),航向000,航速12 kn的交叉相遇态势下,变向变速避让和仅变向避让采取操纵行动的最晚时间点分别为711 s和643 s;(2)在物标较远的多物标环境下,本船O保向保速至663 s,与目标船T_(A),T_(C),T_(D)构成碰撞危险,采取目标航向、转速区间为[48~°,61~°]、[75 r/min,85 r/min]中任意组合可让清所有物标。
