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.

翻译：自适应信息树（AIT*）是一种利用问题特定启发式信息避免无效搜索的算法，尤其在碰撞检测计算成本较高时能显著提升性能。然而，AIT*中的启发式估计消耗大量计算资源，且其非对称双向搜索策略无法充分发挥双向方法的潜力。本文提出AIT*的扩展算法BiAIT*。与AIT*不同，BiAIT*在启发式搜索与空间搜索环节均采用对称双向搜索机制。该方法使BiAIT*能比AIT*更快地发现初始解，并在发生碰撞时以更低计算代价更新启发式信息。通过仿真与实验评估BiAIT*性能，结果表明BiAIT*能比最先进方法更快地找到可行解。我们同时分析了BiAIT*与AIT*性能差异的原因，并进一步讨论了两种简单有效的改进方案，以充分挖掘自适应启发式方法的潜力。