A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperati...A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving(MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm(GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm(GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm(ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.展开更多
针对风力助航船舶航线优化中存在的风能利用效率量化不足、油耗预测精度受限以及多目标协同优化机制缺失等问题,提出1种融合动态风帆控制与混合驱动预测的多目标航线优化方法。通过建立基于流体力学特性的动态风帆控制策略模型,实现风...针对风力助航船舶航线优化中存在的风能利用效率量化不足、油耗预测精度受限以及多目标协同优化机制缺失等问题,提出1种融合动态风帆控制与混合驱动预测的多目标航线优化方法。通过建立基于流体力学特性的动态风帆控制策略模型,实现风帆辅助推力的空间矢量解析,该模型突破传统静态攻角设定的局限性,可即时动态调整帆角参数,使风能转化效率处于较高水平。为解决传统物理模型环境适应性差与数据驱动方法物理可解释性弱的双重局限,构建物理约束下的人工神经网络分层融合架构,通过船舶运动学方程构建特征空间基底,采用注意力机制引导的人工神经网络进行残差学习。该方法在保留能耗物理机理的同时,实现数据特征与流体力学方程的双向耦合,经北大西洋航线的验证表明,其油耗预测平均绝对百分比误差(mean absolute percentage error,MAPE)较纯物理模型降低21.9%,较纯数据驱动方法的可解释性也大大提升。在此基础上,建立包含时间成本和燃油消耗的多目标优化模型,设计基于非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-Ⅱ)和逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)的协同优化算法,其非劣解集收敛速度较标准算法得以提升。以“新伊敦”轮为对象的实证研究表明:优化后的航线在北大西洋典型航次中,风帆有效工作效率提升,相较于传统推荐航线,优化航线的单航次航行时间缩短5%左右,油耗成本和固定成本分别降低9.1%和4.95%,总成本降低超过7.2%,有效的提高了风力助航船的经济效益并较少了对环境的污染。展开更多
为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,...为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,跟踪并记录船舶航迹点,计算船舶的速度和航向并推算船位。提出了一种基于视频船舶航迹点的密度聚类识别航道两侧航标的方法,实现航道自适应可视化。基于船位推算识别并预警航行状态异常的船舶。实验结果表明:航标、船舶的检测正确率分别达84.8%、90.3%,相较单一相机检测模型,正确率分别提高了32.1%、5.5%;能够自适应可视化航道并识别、预警航行异常船舶。展开更多
基金the National Science and Technology Support Program(No.2015BAG20B05)
文摘A new method for a cooperative multi-task allocation problem(CMTAP) is proposed in this paper,taking into account the multi-ship, multi-target, multi-task and multi-constraint characteristics in a multi-ship cooperative driving(MCD) system. On the basis of the general CMTAP model, an MCD task assignment model is established. Furthermore, a genetic ant colony hybrid algorithm(GACHA) is proposed for this model using constraints, including timing constraints, multi-ship collaboration constraints and ship capacity constraints. This algorithm uses a genetic algorithm(GA) based on a task sequence, while the crossover and mutation operators are based on similar tasks. In order to reduce the dependence of the GA on the initial population, an ant colony algorithm(ACA) is used to produce the initial population. In order to meet the environmental constraints of ship navigation, the results of the task allocation and path planning are combined to generate an MCD task planning scheme. The results of a simulated experiment using simulated data show that the proposed method can make the assignment more optimized on the basis of satisfying the task assignment constraints and the ship navigation environment constraints. Moreover, the experimental results using real data also indicate that the proposed method can find the optimal solution rapidly, and thus improve the task allocation efficiency.
文摘针对风力助航船舶航线优化中存在的风能利用效率量化不足、油耗预测精度受限以及多目标协同优化机制缺失等问题,提出1种融合动态风帆控制与混合驱动预测的多目标航线优化方法。通过建立基于流体力学特性的动态风帆控制策略模型,实现风帆辅助推力的空间矢量解析,该模型突破传统静态攻角设定的局限性,可即时动态调整帆角参数,使风能转化效率处于较高水平。为解决传统物理模型环境适应性差与数据驱动方法物理可解释性弱的双重局限,构建物理约束下的人工神经网络分层融合架构,通过船舶运动学方程构建特征空间基底,采用注意力机制引导的人工神经网络进行残差学习。该方法在保留能耗物理机理的同时,实现数据特征与流体力学方程的双向耦合,经北大西洋航线的验证表明,其油耗预测平均绝对百分比误差(mean absolute percentage error,MAPE)较纯物理模型降低21.9%,较纯数据驱动方法的可解释性也大大提升。在此基础上,建立包含时间成本和燃油消耗的多目标优化模型,设计基于非支配排序遗传算法(non-dominated sorting genetic algorithm,NSGA-Ⅱ)和逼近理想解排序法(technique for order preference by similarity to ideal solution,TOPSIS)的协同优化算法,其非劣解集收敛速度较标准算法得以提升。以“新伊敦”轮为对象的实证研究表明:优化后的航线在北大西洋典型航次中,风帆有效工作效率提升,相较于传统推荐航线,优化航线的单航次航行时间缩短5%左右,油耗成本和固定成本分别降低9.1%和4.95%,总成本降低超过7.2%,有效的提高了风力助航船的经济效益并较少了对环境的污染。
文摘为减少因船舶偏离航道而造成的搁浅、碰撞航标或桥墩等水上交通事故,提出了一种基于多目相机自动识别航道的桥区航行异常船舶预警方法。基于YOLOv5(You Only Look Once version 5)目标检测算法,联动变、定焦相机识别并定位航标和船舶,跟踪并记录船舶航迹点,计算船舶的速度和航向并推算船位。提出了一种基于视频船舶航迹点的密度聚类识别航道两侧航标的方法,实现航道自适应可视化。基于船位推算识别并预警航行状态异常的船舶。实验结果表明:航标、船舶的检测正确率分别达84.8%、90.3%,相较单一相机检测模型,正确率分别提高了32.1%、5.5%;能够自适应可视化航道并识别、预警航行异常船舶。
文摘星载合成孔径雷达(Synthetic Aperture Radar,SAR)在轨处理技术对提升遥感应用效率至关重要,能解决传统模式下星地数传带宽瓶颈和数据处理延迟问题,在军事侦察、灾害应急监测等领域意义重大。然而,现有在轨处理硬件系统在功能覆盖和处理复杂任务能力方面存在不足,大多针对单个处理环节或简单任务进行研究,无法满足复杂星载任务需求。本文设计了一种大规模星载SAR处理系统,以应对这些挑战。该系统支持数据预处理、SAR成像、舰船检测、重聚焦、目标识别、几何矫正等多种操作,具备广域舰船检测和广域舰船识别两种复杂功能。通过提出基于多节点协同重构的星载SAR多任务兼容方法,详细设计了系统硬件方案,包括交互主控板、检测板、成像板、识别板等硬件板卡。交互主控板负责数据输入、预处理和分发;检测板实现目标检测、重聚焦等功能;成像板利用专用片上系统(System on Chip,SoC)芯片进行高速SAR成像;识别板完成虚警剔除和目标识别。同时,针对广域舰船检测和识别功能,分别设计了相应的数据流和流水线。在广域舰船检测中,数据经预处理后分发至各板卡,成像后进行目标检测、虚警剔除、动目标定位和几何矫正;广域舰船识别在检测基础上增加重聚焦环节,以实现精准识别。实验采用模拟信号源仿真星载SAR回波数据,对系统功能和性能进行验证。结果显示,SAR成像功能的分辨率、峰值旁瓣比和积分旁瓣比均满足要求,目标检测发现率达93.7%,虚警密度为2.96个/万平方公里。在处理时效性方面,广域舰船检测和识别任务分别达到1∶2和1∶2.5准实时处理水平。综上,该系统处理质量良好,成像速度出色,为星载SAR大规模在轨处理技术发展提供了借鉴。
