论文标题
BIAIT*：对称双向最佳路径计划和自适应启发式
BiAIT*: Symmetrical Bidirectional Optimal Path Planning with Adaptive Heuristic
论文作者
论文摘要
适应性的树木（AIT*）是一种算法，它使用特定问题的启发式方法来避免不必要的搜索，从而大大提高了其性能，尤其是当碰撞检查昂贵时。但是，AIT*中的启发式估计会消耗大量计算资源，其不对称的双向搜索策略无法完全利用双向方法的潜力。在本文中，我们提出了一个称为biait*的AIT*的扩展。与AIT*不同，Biait*使用对称的双向搜索进行启发式和空间搜索。提出的方法允许Biait*比AIT*更快地找到初始解决方案，并在发生碰撞时使用较少的计算更新启发式。我们通过模拟和实验评估了BIAIT*的性能，结果表明，Biait*可以比最先进的方法更快地找到溶液。我们还分析了BIAIT*和AIT*之间不同性能的原因。此外，我们讨论了两个简单但有效的修改，以充分利用适应性启发式方法的潜力。
Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially when collision checking is expensive. However, the heuristic estimation in AIT* consumes lots of computational resources, and its asymmetric bidirectional searching strategy cannot fully exploit the potential of the bidirectional method. In this article, we propose an extension of AIT* called BiAIT*. Unlike AIT*, BiAIT* uses symmetrical bidirectional search for both the heuristic and space searching. The proposed method allows BiAIT* to find the initial solution faster than AIT*, and update the heuristic with less computation when a collision occurs. We evaluated the performance of BiAIT* through simulations and experiments, and the results show that BiAIT* can find the solution faster than state-of-the-art methods. We also analyze the reasons for the different performances between BiAIT* and AIT*. Furthermore, we discuss two simple but effective modifications to fully exploit the potential of the adaptively heuristic method.
