论文标题
分段线性运动计划在静态，移动或变形障碍物中
Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles
论文作者
论文摘要
我们提出了一种新的方法，可以通过静态，移动甚至变形障碍物刺穿的复杂环境计划最短的分段线性运动。使用矩优化方法，我们制定了半芬矿程序的层次结构，该程序产生的下限越来越精炼，单调地收敛到最佳路径长度。 对于计算障碍性，我们的全球力矩优化方法激发了迭代运动计划者，该计划者的表现优于基于竞争的采样和非线性优化基线。我们的方法本地处理连续的时间限制，而无需时间离散，并且与基于流行的采样方法相比，有可能通过维度更好地扩展。
We propose a novel method for planning shortest length piecewise-linear motions through complex environments punctured with static, moving, or even morphing obstacles. Using a moment optimization approach, we formulate a hierarchy of semidefinite programs that yield increasingly refined lower bounds converging monotonically to the optimal path length. For computational tractability, our global moment optimization approach motivates an iterative motion planner that outperforms competing sampling-based and nonlinear optimization baselines. Our method natively handles continuous time constraints without any need for time discretization, and has the potential to scale better with dimensions compared to popular sampling-based methods.
