论文标题
适应性的树木（AIT*）：通过适应性启发式驱动器快速渐近的最佳路径计划
Adaptively Informed Trees (AIT*): Fast Asymptotically Optimal Path Planning through Adaptive Heuristics
论文作者
论文摘要
知情的基于抽样的计划算法利用问题知识以获得更好的搜索性能。这些知识通常表示为解决方案成本的启发式估计，并用于订购搜索。这项知情搜索的实际改进取决于启发式的准确性。 选择适当的启发式是困难的。适用于整个问题域的启发式方法通常易于定义和廉价地评估，但对于特定的问题实例可能不利。特定于问题实例的启发式方法通常很难定义或评估昂贵，但可以使搜索本身变得微不足道。 本文介绍了基于BIT*的基于渐近的最佳采样器的适应性知识树（AIT*）。 AIT*通过使用不对称的双向搜索同时估算并利用特定问题的启发式方法来适应每个问题实例。这使其可以快速找到初始解决方案并趋向最佳。 AIT*可以像RRT连接一样快地解决测试问题，同时也倾向于最佳。
Informed sampling-based planning algorithms exploit problem knowledge for better search performance. This knowledge is often expressed as heuristic estimates of solution cost and used to order the search. The practical improvement of this informed search depends on the accuracy of the heuristic. Selecting an appropriate heuristic is difficult. Heuristics applicable to an entire problem domain are often simple to define and inexpensive to evaluate but may not be beneficial for a specific problem instance. Heuristics specific to a problem instance are often difficult to define or expensive to evaluate but can make the search itself trivial. This paper presents Adaptively Informed Trees (AIT*), an almost-surely asymptotically optimal sampling-based planner based on BIT*. AIT* adapts its search to each problem instance by using an asymmetric bidirectional search to simultaneously estimate and exploit a problem-specific heuristic. This allows it to quickly find initial solutions and converge towards the optimum. AIT* solves the tested problems as fast as RRT-Connect while also converging towards the optimum.
