多边形机器人的渐近最优多查询路径规划
Asymptotically-Optimal Multi-Query Path Planning for a Polygonal Robot
AI总结：
针对多边形机器人的多查询路径规划，提出旋转堆叠可见性图（RVG）算法，通过堆叠不同方向的简化可见性图实现同时平移与旋转，该算法分辨率完备且渐近最优，实验证明其在计算时间和解最优性上显著优于现有方法。
摘要：
最短路径路线图，也称为简化可见性图，为在二维环境中计算最优路径提供了一种高效的多查询方法。结合闵可夫斯基和计算，最短路径路线图可以为二维中的平移机器人计算最优路径。在本研究中，我们探索了一个直观的想法：为多边形完整约束机器人堆叠一组不同方向上的简化可见性图，以支持近最优路径的快速计算，允许同时进行二维平移和旋转。所得到的算法，即旋转堆叠可见性图（RVG），被证明是分辨率完备且渐近最优的。大量计算实验表明，RVG在计算时间和解的最优性方面显著优于最先进的单查询和基于采样的多查询方法。
英文摘要：
Shortest-path roadmaps, also known as reduced visibility graphs, provides a highly efficient multi-query method for computing optimal paths in two-dimensional environments. Combined with Minkowski sum computations, shortest-path roadmaps can compute optimal paths for a translating robot in 2D. In this study, we explore the intuitive idea of stacking up a set of reduced visibility graphs at different orientations for a polygonal holonomic robot to support the fast computation of near-optimal paths, allowing simultaneous 2D translation and rotation. The resulting algorithm, rotation-stacked visibility graph (RVG), is shown to be resolution-complete and asymptotically optimal. Extensive computational experiments show RVG significantly outperforms state-of-the-art single- and multi-query sampling-based methods on both computation time and solution optimality fronts.
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