论文标题
使用迭代$δ$空间的动态无人机轨迹的两步计划
Two-step Planning of Dynamic UAV Trajectories using Iterative $δ$-Spaces
论文作者
论文摘要
无人机轨迹计划通常是在两步方法中进行的，其中低维路径被完善至动态轨迹。但是，所得的轨迹仅在局部最佳。另一方面，高维状态空间中的直接计划会产生全球最佳解决方案，但耗时，因此对于时间约束的应用程序不可行。为了解决这个问题，我们提出了$δ$ - 空格，这是一种修剪的高维状态空间表示轨迹改进。它不仅包含一个较低维路径周围的区域，还包括多个近乎最佳路径的结合。因此，它不太容易发生局部最小值。此外，我们使用增加尺寸的$δ$空格提出了任何时间算法。我们将我们的方法与基于最新的搜索轨迹计划方法进行比较，并在2D和3D环境中对其进行评估，以生成二阶和三阶UAV轨迹。
UAV trajectory planning is often done in a two-step approach, where a low-dimensional path is refined to a dynamic trajectory. The resulting trajectories are only locally optimal, however. On the other hand, direct planning in higher-dimensional state spaces generates globally optimal solutions but is time-consuming and thus infeasible for time-constrained applications. To address this issue, we propose $δ$-Spaces, a pruned high-dimensional state space representation for trajectory refinement. It does not only contain the area around a single lower-dimensional path but consists of the union of multiple near-optimal paths. Thus, it is less prone to local minima. Furthermore, we propose an anytime algorithm using $δ$-Spaces of increasing sizes. We compare our method against state-of-the-art search-based trajectory planning methods and evaluate it in 2D and 3D environments to generate second-order and third-order UAV trajectories.
