论文标题
G $ \ MATHBF {^2} $ VD PLANNER：具有基于网格的广义Voronoi图的有效运动计划
G$ \mathbf{^2} $VD Planner: Efficient Motion Planning With Grid-based Generalized Voronoi Diagrams
论文作者
论文摘要
在本文中，新提出的用于移动机器人的有效运动计划方法（G $ \ Mathbf {^2} $ VD）是一种有效的运动计划方法（G $ \ Mathbf {^2} $ VD）。与现有方法不同，这项工作的新颖性是双重的：1）提出了一种新的基于州晶格的路径搜索方法，其中搜索空间减少到了新的Voronoi走廊，以进一步提高搜索效率； 2）提出了一种有效的基于二次编程的路径平滑方法，其中认为障碍物的间隙被认为是为了改善硬约束路径平滑方法的路径间隙。我们在各种具有挑战性的模拟场景和室外环境中验证方法的效率和平滑度。结果表明，在路径搜索阶段，计算效率提高了17.1％，并且使用建议的方法平滑的路径平滑速度比先进的基于基于稀疏的结构的路径平滑方法快6.6倍，并且比流行的定时弹性频带计划者快53.3倍。可以在https://youtu.be/imxgthgvp58上找到一个在我们校园内显示户外导航的视频。
In this paper, an efficient motion planning approach with grid-based generalized Voronoi diagrams (G$ \mathbf{^2} $VD) is newly proposed for mobile robots. Different from existing approaches, the novelty of this work is twofold: 1) a new state lattice-based path searching approach is proposed, in which the search space is reduced to a novel Voronoi corridor to further improve the search efficiency; 2) an efficient quadratic programming-based path smoothing approach is presented, wherein the clearance to obstacles is considered to improve the path clearance of hard-constrained path smoothing approaches. We validate the efficiency and smoothness of our approach in various challenging simulation scenarios and outdoor environments. It is shown that the computational efficiency is improved by 17.1% in the path searching stage, and path smoothing with the proposed approach is 6.6 times faster than an advanced sparse-banded structure-based path smoothing approach and 53.3 times faster than the popular timed-elastic-band planner. A video showing outdoor navigation on our campus is available at https://youtu.be/iMXGthgvp58.
