Anytime sampling-based methods are an attractive technique for solving kino-dynamic motion planning problems. These algorithms scale well to higher dimensions and can efficiently handle state and control constraints. However, an intelligent exploration strategy is required to accelerate their convergence and avoid redundant computations. Using ideas from reachability analysis, this work defines a "Time-Informed Set", that focuses the search for time-optimal kino-dynamic planning after an initial solution is found. Such a Time-Informed Set (TIS) includes all trajectories that can potentially improve the current best solution and hence exploration outside this set is redundant. Benchmarking experiments show that an exploration strategy based on the TIS can accelerate the convergence of sampling-based kino-dynamic motion planners.

翻译：任何时间取样方法都是解决动态运动规划问题的有吸引力的技术。这些算法规模要高一些,能够有效地处理状态和控制限制。然而,需要聪明的勘探战略来加速其趋同和避免重复计算。这项工作利用可达性分析的构想,定义了“时间化集集 ”, 其重点是在找到初步解决办法后寻找时间-最佳的动态动态规划。这种时间化集集包括所有可能改进目前最佳解决办法的轨迹,因此,在这套方法之外进行勘探是多余的。基准化实验表明,基于TIS的勘探战略可以加速基于取样的动态运动规划者的趋同。