This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-gu...This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.展开更多
随着全球贸易总量的增加,港口运营效率的重要性日益凸显。准确预测海运集装箱船舶的预计到达时间(Estimated Time of Arrival,ETA)可有效提升物流效率。然而,在实践中,大多数海运存在转运和中途停留;同时航行路线也面临各种因素的影响,...随着全球贸易总量的增加,港口运营效率的重要性日益凸显。准确预测海运集装箱船舶的预计到达时间(Estimated Time of Arrival,ETA)可有效提升物流效率。然而,在实践中,大多数海运存在转运和中途停留;同时航行路线也面临各种因素的影响,这使得船舶ETA充满着不确定性与复杂性。误差较大的船舶ETA不仅阻碍了码头其他利益相关者的有效规划和执行,也导致抵达码头的货物类型和数量存在巨大的波动,增加了多式联运风险。基于机器学习方法,结合AIS历史数据和其他已知因素建立人工神经网络模型,对于长距离海运中集装箱船舶的运行轨迹和ETA进行预测。结果表明,所建立的模型能够有效预测船舶的运行轨迹和ETA。展开更多
文摘This paper considers the problem of generating a flight trajectory for a single fixed-wing unmanned combat aerial vehicle （UCAV） performing an air-to-surface multi-target attack （A/SMTA） mission using satellite-guided bombs. First, this problem is formulated as a variant of the traveling salesman problem （TSP）, called the dynamic-constrained TSP with neighborhoods （DCT- SPN）. Then, a hierarchical hybrid approach, which partitions the planning algorithm into a roadmap planning layer and an optimal control layer, is proposed to solve the DCTSPN. In the roadmap planning layer, a novel algorithm based on an updatable proba- bilistic roadmap （PRM） is presented, which operates by randomly sampling a finite set of vehicle states from continuous state space in order to reduce the complicated trajectory planning problem to planning on a finite directed graph. In the optimal control layer, a collision-free state-to-state trajectory planner based on the Gauss pseudospectral method is developed, which can generate both dynamically feasible and optimal flight trajectories. The entire process of solving a DCTSPN consists of two phases. First, in the offline preprocessing phase, the algorithm constructs a PRM, and then converts the original problem into a standard asymmet- ric TSP （ATSP）. Second, in the online querying phase, the costs of directed edges in PRM are updated first, and a fast heuristic searching algorithm is then used to solve the ATSP. Numerical experiments indicate that the algorithm proposed in this paper can generate both feasible and near-optimal solutions quickly for online purposes.
文摘随着全球贸易总量的增加,港口运营效率的重要性日益凸显。准确预测海运集装箱船舶的预计到达时间(Estimated Time of Arrival,ETA)可有效提升物流效率。然而,在实践中,大多数海运存在转运和中途停留;同时航行路线也面临各种因素的影响,这使得船舶ETA充满着不确定性与复杂性。误差较大的船舶ETA不仅阻碍了码头其他利益相关者的有效规划和执行,也导致抵达码头的货物类型和数量存在巨大的波动,增加了多式联运风险。基于机器学习方法,结合AIS历史数据和其他已知因素建立人工神经网络模型,对于长距离海运中集装箱船舶的运行轨迹和ETA进行预测。结果表明,所建立的模型能够有效预测船舶的运行轨迹和ETA。
