DIT*通过方向滤波加速路径规划，比现有方法更快收敛。
Direction Informed Trees (DIT*): Optimal Path Planning via Direction Filter and Direction Cost Heuristic
- 用方向相似性筛选最近邻，动态估计每条边的方向成本
- 在R^4到R^16空间中比同类算法更快收敛，实测有效
- 适合需要快速单次查询路径规划的机器人应用
最优路径规划需从起点到目标找到一系列可行状态以优化目标。主流算法如EIT*采用代价启发式引导搜索，但高效与准确常难以兼顾。本文提出方向感知树（DIT*），一种基于采样的规划器，聚焦于每条边的搜索方向优化，实现探索过程中的目标偏向。将边视为广义向量，引入相似性指数构建方向滤波器，用于选择最近邻并估计方向代价。该代价启发式用于边评估，使探索能高效共享方向信息。DIT*在测试问题中（R^4至R^16）收敛速度优于现有单次查询采样规划器，并已在多种真实场景中验证其有效性。实验视频见：https://youtu.be/2SX6QT2NOek
原文摘要 · Abstract (English)
Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally efficient, but achieving both can be challenging due to their conflicting nature. This paper proposes Direction Informed Trees (DIT*), a sampling-based planner that focuses on optimizing the search direction for each edge, resulting in goal bias during exploration. We define edges as generalized vectors and integrate similarity indexes to establish a directional filter that selects the nearest neighbors and estimates direction costs. The estimated direction cost heuristics are utilized in edge evaluation. This strategy allows the exploration to share directional information efficiently. DIT* convergence faster than existing single-query, sampling-based planners on tested problems in R^4 to R^16 and has been demonstrated in real-world environments with various planning tasks. A video showcasing our experimental results is available at: https://youtu.be/2SX6QT2NOek
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