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
文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting col...文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting collisions at sea, COLREGs)和良好船艺的要求.使用速度障碍法判断船舶的碰撞危险度并将多船会遇避让的过程中避让的安全性、经济性以及船舶领域侵入程度作为建立避碰决策的目标函数.算法测试的结果中,WSSA与原始樽海鞘群算法(SSA)以及经典粒子群算法(partide swam optimization, PSO)相比较,WSSA算法在收敛的精度和速度方面都明显优于SSA和PSO算法.结果表明:WSSA在寻找最优碰撞路线的过程中迭代的次数更少,精度更高.展开更多
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
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。
文摘文中针对多船会遇避碰决策中过渡依赖单一寻优决策的问题采用了加入自适应权重的樽海鞘群优化算法(weight salp swarm algorithm, WSSA),在算法中融入国际海上避碰规则(convention on the international regulations for presenting collisions at sea, COLREGs)和良好船艺的要求.使用速度障碍法判断船舶的碰撞危险度并将多船会遇避让的过程中避让的安全性、经济性以及船舶领域侵入程度作为建立避碰决策的目标函数.算法测试的结果中,WSSA与原始樽海鞘群算法(SSA)以及经典粒子群算法(partide swam optimization, PSO)相比较,WSSA算法在收敛的精度和速度方面都明显优于SSA和PSO算法.结果表明:WSSA在寻找最优碰撞路线的过程中迭代的次数更少,精度更高.
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.
文摘针对传统速度障碍(velocity obstacles,VO)法进行局部路径规划时避碰路径长、避碰时间长以及忽略紧迫局面会遇中避让责任的约束等问题,提出一种适用于水面无人艇(unmanned surface vehicle,USV)的改进VO法。将四元船舶领域和碰撞威胁距离引入VO避碰时机的计算中,确定了避碰阈值;将最晚施舵距离作为碰撞危险局面过渡到紧迫局面的临界值,构建了紧迫局面模型;设计了USV避碰模型,并通过国际海上避碰规则(International Regulations for Collision Avoidance at Sea,GOLREGs)进行约束。比较分析了对遇、右交叉、左交叉和追越4种会遇的避碰仿真。仿真结果表明:改进的VO法平均路径缩短39.88%、平均时间减少44.26%,改进后的局部规划能够在保证安全避碰的条件下满足GOLREGs第8条和第13~17条的避让要求。
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。
