Sampling-based motion planning algorithms such as RRT* are well-known for their ability to quickly find an initial solution and then converge to the optimal solution asymptotically. However, the convergence rate can be slow for highdimensional planning problems, particularly for dynamical systems where the sampling space is not just the configuration space but the full state space. In this paper, we introduce the idea of using a partial-final-state-free (PFF) optimal controller in kinodynamic RRT* [1] to reduce the dimensionality of the sampling space. Instead of sampling the full state space, the proposed accelerated kinodynamic RRT*, called Kino-RRT*, only samples part of the state space, while the rest of the states are selected by the PFF optimal controller. We also propose a delayed and intermittent update of the optimal arrival time of all the edges in the RRT* tree to decrease the computation complexity of the algorithm. We tested the proposed algorithm using 4-D and 10-D state-space linear systems and showed that Kino-RRT* converges much faster than the kinodynamic RRT* algorithm.

翻译：以抽样为基础的运动规划算法,如RRT* 众所周知,它们有能力迅速找到初步解决办法,然后逐渐接近最佳解决办法。然而,对于高维规划问题,特别是取样空间不仅仅是配置空间,而且是整个状态空间的动态系统而言,趋同率可能缓慢。在本文中,我们提出了在运动动力RRT* * [1] 中使用局部-最终无状态最佳控制器以减少取样空间的维度,而不是对全状态空间进行取样,而拟议的加速动力RRT*,称为Kino-RRT*,只是州空间的一部分样本,而其余各州则由PFF最佳控制器选定。我们还提议对RRT* 树所有边缘的最佳到达时间进行延迟和间歇性更新,以降低算法的复杂性。我们用4-D 和 10D 状态-空间线性系统测试了拟议的算法,并表明Kino-RRT* 的结合速度比动态动态RRT* 算法要快得多。