论文标题
基于双向抽样的搜索，没有两个点边界值解决方案
Bidirectional Sampling Based Search Without Two Point Boundary Value Solution
论文作者
论文摘要
与单向同行相比，双向运动计划方法平均会减少计划时间。在单晶可行的运动计划中，使用双向搜索找到连续的运动计划需要向前和反向搜索树之间的边缘连接。这样的树树连接需要解决两点边界值问题（BVP）。但是，对于许多系统，两点BVP解决方案可能难以计算。我们提出了一种新颖的双向搜索策略，该策略不需要解决两点BVP。反向树的成本信息没有直接连接前向树木，而是用作前进搜索的指导启发式信息。这使得向前搜索可以快速收敛到可行的解决方案，而无需求解两点BVP。我们提出了两种使用此策略的新算法（GBRRT和GABRRT），并使用多个动态系统和现实世界硬件实验运行多个软件模拟，以表明我们的算法在快速找到初始可行的解决方案方面比现有的先进方法执行了PAR或更好。
Bidirectional motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an edge connection between the forward and reverse search trees. Such a tree-tree connection requires solving a two-point Boundary Value Problem (BVP). However, a two-point BVP solution can be difficult or impossible to calculate for many systems. We present a novel bidirectional search strategy that does not require solving the two-point BVP. Instead of connecting the forward and reverse trees directly, the reverse tree's cost information is used as a guiding heuristic for the forward search. This enables the forward search to quickly converge to a feasible solution without solving the two-point BVP. We propose two new algorithms (GBRRT and GABRRT) that use this strategy and run multiple software simulations using multiple dynamical systems and real-world hardware experiments to show that our algorithms perform on-par or better than existing state-of-the-art methods in quickly finding an initial feasible solution.
