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首页> 外文期刊>Systems Engineering and Electronics, Journal of >Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack
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Hybrid hierarchical trajectory planning for a fixed-wing UCAV performing air-to-surface multi-target attack

机译：固定翼UCAV执行空对地多目标攻击的混合层次弹道规划

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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 (DCTSPN). 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 probabilistic 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 asymmetric 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.
机译：本文考虑了为使用卫星制导炸弹执行空地多目标攻击（A / SMTA）任务的单个固定翼无人战斗机（UCAV）生成飞行轨迹的问题。首先，将此问题表述为旅行商问题（TSP）的一种变体，称为带邻域的动态约束TSP（DCTSPN）。然后，提出了一种层次混合方法，将规划算法分为路线图规划层和最优控制层，以解决DCTSPN问题。在路线图规划层中，提出了一种基于可更新概率路线图（PRM）的新算法，该算法通过从连续状态空间中随机采样有限的一组车辆状态进行操作，从而将复杂的轨迹规划问题减少到有限的规划中有向图。在最优控制层中，开发了一种基于高斯伪谱方法的无碰撞状态到状态轨迹规划器，该规划器可以生成动态可行和最优飞行轨迹。解决DCTSPN的整个过程包括两个阶段。首先，在离线预处理阶段，该算法构造一个PRM，然后将原始问题转换为标准的非对称TSP（ATSP）。其次，在在线查询阶段，首先更新PRM中有向边的成本，然后使用快速启发式搜索算法求解ATSP。数值实验表明，本文提出的算法可以快速生成在线可行和接近最优的解。

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