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首页> 外文期刊>Journal of aerospace engineering >Nonlinear Multiobjective Time-Dependent TF/TA Trajectory Planning Using a Network Flow-Based Algorithm
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Nonlinear Multiobjective Time-Dependent TF/TA Trajectory Planning Using a Network Flow-Based Algorithm

机译：基于网络流的非线性多目标时间相关TF / TA轨迹规划

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This paper studies the problem of finding the optimum time-dependent trajectory for an unmanned aerial vehicle (UAV) or any aerial robot flying on a low-altitude terrain following/threat avoidance (TF/TA) mission. Using a grid-based discrete scheme, a modified minimum cost network flow (MCNF) algorithm over a large-scale network is proposed. Using the Digital Terrain Elevation Data (DTED) and discrete dynamic equations of motion, the four-dimensional (4D) trajectory (three spatial and one time dimensions) from a source to a destination is obtained exactly through minimization of a cost functional subject to the nonlinear dynamics and mission constraints of the UAV. Several objectives (including the arc length, fuel consumption, flight time, and risk of threat regions) may be assigned to each arc in the network. The algorithm uses scalarization, by which a multiobjective problem can be tackled by repeatedly solving a single-objective subproblem. An attempt is made to reduce the time order of the algorithm using innovative techniques to construct a polynomial-time algorithm. Moreover, owing to the increasing deviation of the inertial navigation system (INS) in terms of time, flying safely and avoding a collision with terrain at low altitudes is a significant problem in the trajectory design of this type of vehicle. An attempt is made to add this constraint to the algorithm to produce a practical and safe trajectory with no evident increase in the complexity and execution time. Numerical results are presented to verify the capability of the proposed approach to generate an admissible trajectory in the minimum possible time compared to previous approaches.
机译：本文研究了为无人飞行器（UAV）或在低空地形跟随/威胁避免（TF / TA）任务上飞行的任何空中机器人找到最佳时间相关轨迹的问题。使用基于网格的离散方案，提出了一种改进的大规模网络上的最小成本网络流（MCNF）算法。使用数字地形高程数据（DTED）和离散的动态运动方程式，可以通过将成本函数主体最小化来精确获得从源到目的地的三维（4D）轨迹（三个空间和一个时间维度）。无人机的非线性动力学和任务约束。可以为网络中的每个弧线分配几个目标（包括弧长，油耗，飞行时间和威胁区域的风险）。该算法使用标量化，通过重复求解单目标子问题可以解决多目标问题。尝试使用创新技术来构建多项式时间算法来减少算法的时间顺序。此外，由于惯性导航系统（INS）在时间上的偏差增加，在这种类型的车辆的轨迹设计中，安全飞行和避免在低海拔地区与地形发生碰撞是一个重大问题。试图将该约束添加到算法中以产生实用且安全的轨迹，而复杂度和执行时间没有明显增加。数值结果表明，与以前的方法相比，该方法能够在最短的时间内生成允许轨迹的能力。

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