This paper introduces an enhancement to traditional sampling-based planners, resulting in efficiency increases for high-dimensional holonomic systems such as hyper-redundant manipulators, snake-like robots, and humanoids. Despite the performance advantages of modern sampling-based motion planners, solving high dimensional planning problems in near real-time remains a considerable challenge. The proposed enhancement to popular sampling-based planning algorithms is aimed at circumventing the exponential dependence on dimensionality, by progressively exploring lower dimensional volumes of the configuration space. Extensive experiments comparing the enhanced and traditional version of RRT, RRT-Connect, and Bidirectional T-RRT on both a planar hyper-redundant manipulator and the Baxter humanoid robot show significant acceleration, up to two orders of magnitude, on computing a solution. We also explore important implementation issues in the sampling process and discuss the limitations of this method.

本文介绍了对基于采样的传统规划器的增强，从而提高了高维完整系统的效率，例如超冗余机械手，类蛇机器人和类人动物。尽管基于采样的现代运动计划器具有性能优势，但近乎实时地解决高维计划问题仍然是一个巨大的挑战。对流行的基于采样的计划算法的拟议增强旨在通过逐步探索配置空间的较低维度体积来规避对维度的指数依赖性。在平面超冗余操纵器和Baxter人形机器人上比较了增强版和传统版RRT，RRT-Connect和双向T-RRT的大量实验表明，该加速器具有显着的加速度，计算解决方案时，最多两个数量级。我们还将探讨采样过程中的重要实施问题，并讨论此方法的局限性。