Sampling-based motion planning algorithms have been continuously developed for more than two decades. Apart from mobile robots, they are also widely used in manipulator motion planning. Hence, these methods play a key role in collaborative and shared workspaces. Despite numerous improvements, their performance can highly vary depending on the chosen parameter setting. The optimal parameters depend on numerous factors such as the start state, the goal state and the complexity of the environment. Practitioners usually choose these values using their experience and tedious trial and error experiments. To address this problem, recent works combine hyperparameter optimization methods with motion planning. They show that tuning the planner's parameters can lead to shorter planning times and lower costs. It is not clear, however, how well such approaches generalize to a diverse set of planning problems that include narrow passages as well as barely cluttered environments. In this work, we analyze optimized planner settings for a large set of diverse planning problems. We then provide insights into the connection between the characteristics of the planning problem and the optimal parameters. As a result, we provide a list of recommended parameters for various use-cases. Our experiments are based on a novel motion planning benchmark for manipulators which we provide at https://mytuc.org/rybj.

翻译：基于采样的运动规划算法已持续发展超过二十年。除移动机器人外，这些算法也广泛用于机械臂运动规划，因此在协作与共享工作空间中发挥着关键作用。尽管已有众多改进，但其性能仍高度依赖于所选参数设置。最优参数取决于起始状态、目标状态及环境复杂度等多重因素。从业者通常依靠经验与繁琐的试错实验来确定这些参数。为解决该问题，近期研究将超参数优化方法与运动规划相结合，表明调整规划器参数可缩短规划时间并降低规划代价。然而，这类方法对包含狭窄通道及稀疏障碍环境在内的多样化规划问题的泛化能力尚不明确。本研究针对大规模多样化规划问题分析了优化后的规划器参数配置，进而揭示了规划问题特征与最优参数之间的关联，最终为不同应用场景提供推荐参数列表。所有实验基于我们发布的新型机械臂运动规划基准集（https://mytuc.org/rybj）。