论文标题
领带：机器人运动计划的时间信息探索
TIE: Time-Informed Exploration For Robot Motion Planning
论文作者
论文摘要
任何时间基于抽样的方法是解决基诺动态运动计划问题的有吸引力的技术。这些算法可以很好地扩展到更高的维度，并可以有效处理状态和控制约束。但是，需要一种智能探索策略来加速其收敛并避免冗余计算。使用可达性分析中的想法，这项工作定义了“时间信息的集合”，该集合在找到初始解决方案之后，将搜索时间优化的kino动态计划搜索。这样的时间信息集合（TIS）包括所有可以改善当前最佳解决方案的轨迹，因此该集合之外的探索是多余的。基准测试实验表明，基于TIS的探索策略可以加速基于抽样的基诺动态运动计划者的收敛性。
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.
