In recent years, learning-based approaches have revolutionized motion planning. The data generation process for these methods involves caching a large number of high quality paths for different queries (start, goal pairs) in various environments. Conventionally, a uniform random strategy is used for sampling these queries. However, this leads to inclusion of "trivial paths" in the dataset (e.g.,, straight line paths in case of length-optimal planning), which can be solved efficiently if the planner has access to a steering function. This work proposes a "non-trivial" query sampling procedure to add more complex paths in the dataset. Numerical experiments show that a higher success rate can be attained for neural planners trained on such a non-trivial dataset.
翻译:近年来,以学习为基础的方法使运动规划发生了革命性的变化。这些方法的数据收集过程涉及为不同环境中的不同查询(启动、目标对)累积大量高质量的路径。从公约角度来说,在对这些查询进行抽样时采用了统一的随机战略。然而,这导致将“三轨路径”纳入数据集(例如,长程规划情况下的直线路径),如果规划者能够使用一个指导功能,这些方法的生成过程就能够有效解决。这项工作提出了“非三轨”查询取样程序,以便在数据集中添加更复杂的路径。数字实验表明,在这种非三轨数据集方面受过培训的神经规划者可以达到更高的成功率。</s>