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引用次数: 9
摘要
通过启发式代价(heuristic cost)[1] -[3]指导节点选择,实时构建a -search guided tree (AGT),实现快速的动力学规划。为了提高计算效率,本文提出了AGT的两种变体。一种改进的AGT (i-AGT)通过对控制动作进行优先级排序来影响节点的扩展,类似于对节点进行优先级排序。双向AGT (BAGT)以节点选择为重点,引入了从目标出发的第二棵树,以提供第一棵树的更好的启发式成本。BAGT的有效性取决于第二棵树对目标附近的障碍物信息进行编码。实例研究表明,i-AGT持续降低了树的复杂度,提高了计算效率;BAGT在很大程度上起作用,但并非总是如此,特别是在简单的情况下没有观察到任何好处。
Improved A-search guided tree construction for kinodynamic planning
With node selection being directed by a heuristic cost [1]–[3], A-search guided tree (AGT) is constructed on-the-fly and enables fast kinodynamic planning. This work presents two variants of AGT to improve computation efficiency. An improved AGT (i-AGT) biases node expansion through prioritizing control actions, an analogy of prioritizing nodes. Focusing on node selection, a bi-directional AGT (BAGT) introduces a second tree originated from the goal in order to offer a better heuristic cost of the first tree. Effectiveness of BAGT pivots on the fact that the second tree encodes obstacles information near the goal. Case study demonstrates that i-AGT consistently reduces the complexity of the tree and improves computation efficiency; and BAGT works largely but not always, particularly with no benefit observed for simple cases.