研究了单架无人作战飞机(UCAV)攻击多个地面目标的三维轨迹规划问题.首先,将问题形式化为一类特殊的旅行商问题(TSP),即带动力学约束的邻域访问TSP问题(DCTSPN).其次,针对规划空间维度过高、搜索代价过大的问题,提出了一种基于概率路标图(PRM)的方法.该方法借鉴了基于采样的运动规划方法的思想,并结合多种组合优化技术,将原本连续状态空间中的轨迹规划问题转化为离散拓扑图上的路由问题.求解过程分为离线预处理和在线查询两个阶段.离线阶段采用Halton拟随机采样算法及Noon-Bean转换方法,将原问题转化为经典的非对称旅行商问题(ATSP)；在线阶段根据战场态势的实时变化,快速更新路标图,然后采用LKH算法在线求解问题的近似最优解.为了保证生成的飞行轨迹满足平台的运动学/动力学约束,算法基于Gauss伪谱法构建了局部轨迹规划器.最后,以攻击时间最短为优化指标对算法进行了仿真实验.结果表明,本文提出的方法能够以较高的精度和在线收敛速度生成真实可行的、较优的多目标攻击轨迹.%A trajectory planning problem for a single unmanned combat aerial vehicle ( UCAV) performing an air-to-surface multi-target attack mission was studied. First, this problem was mathematically formulated as a variant of the traveling salesman problem ( TSP) , called the dynamic-constrained TSP with Neighborhoods (DCTSPN). A novel planning algorithm based on an updatable probabilistic road map ( PRM) was then presented to solve the DCTSPN. This algorithm blends the sampling-based motion planning ideas with combinatorial optimization, and can reduce the complicated trajectory planning problem in high-dimensional continuous state space to a routing problem on a finite discrete graph, while maintaining completeness guarantees (in a probabilistic sense). The entire planning procedure was divided into two phases: 1) In online preprocessing phase, the original problem was converted into a standard ATSP ( Asymmetric TSP) by Halton quasirandom number generator and the Noon-Bean transformation algorithm; 2) In online querying phase, a fast heuristic searching algorithm was used to solve the ATSP. To generate dynamically feasible flight trajectories, a trajectory planning algorithm based on the Gauss pseudospectral method ( GPM) was developed. Numerical experiments indicate that the algorithm adopted can generate both feasible and near-optimal attack trajectories quickly for online purposes.
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