In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.

中文翻译：

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一种用于快速有效路径规划的非均匀采样方法

在本文中，我们开发了一种非均匀采样方法，用于快速有效地规划自动驾驶汽车的路径。该方法使用一种新颖的非均匀分区方案，将区域划分为无障碍凸单元。分区导致无障碍区域中的大单元格和障碍密集区域中的小单元格。随后，这些单元格的边界用于采样；从而大大减轻了统一采样的负担。与标准的均匀采样器相比，该智能采样器显着 1) 减少了采样空间的大小，同时提供了完整性和最优性保证， 2) 在无障碍区域提供稀疏采样，在障碍丰富区域提供密集采样，以促进更快的探索, 和 3) 消除了由于单元的凸性而对障碍物进行昂贵的碰撞检查的需要。该采样框架被整合到 RRT* 路径规划器中。结果表明，与使用均匀采样器的 RRT* 相比，使用非均匀采样器的 RRT* 具有明显更好的收敛速度和更小的内存占用。