Sampling-based algorithms solve the path planning problem by generating random samples in the search-space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization-based procedure that generates new samples to improve the cost-to-come value of vertices in a neighborhood. The application of proposed algorithm adds an exploitative-bias to sampling and results in a faster convergence to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experiments performed fora variety of higher dimensional robotic planning tasks.

中文翻译：

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LES：用于机器人路径规划的本地开发采样

基于采样的算法通过在搜索空间中生成随机样本并逐步增加连接图或树来解决路径规划问题。传统上，这些算法中使用的采样策略偏向于探索以获取有关搜索空间的信息。相比之下，这项工作提出了一种基于优化的过程，该过程可生成新样本以提高邻域中顶点的成本成本。与其他最新的采样技术相比，所提出算法的应用为采样增加了利用偏差，并导致更快地收敛到最优解决方案。使用针对各种更高维度的机器人计划任务执行的基准测试实验证明了这一点。