提出MIT*，通过预估可行集加速机器人路径规划初始收敛。
Estimated Informed Anytime Search for Sampling-Based Planning via Adaptive Sampler
- 基于先验可行解成本构建预估可行集，提前聚焦搜索区域。
- 自适应采样器动态调整策略，提升高维空间探索效率。
- 适合高维、狭小空间的实时机器人路径规划任务。
机器人路径规划常涉及连续值、高维度问题。现有启发式采样方法如Informed RRT*利用可行集和任意时间策略逐步优化路径。然而，当未找到解时，这些方法需重新采样并遍历整个配置空间，耗时且计算开销大。本文提出多可行树（MIT*），在首次解出现前即基于先验可接受解成本构建预估可行集，从而加快初始收敛速度。MIT*还引入自适应采样器，根据探索过程动态调整采样策略，并采用与路径长度相关的稀疏碰撞检测机制，引导懒惰反向搜索。实验表明，在R^4至R^16的问题中，MIT*显著优于现有单查询采样方法，且在真实机器人操作任务中成功应用。视频演示见：https://youtu.be/30RsBIdexTU。
原文摘要 · Abstract (English)
Path planning in robotics often involves solving continuously valued, high-dimensional problems. Popular informed approaches include graph-based searches, such as A*, and sampling-based methods, such as Informed RRT*, which utilize informed set and anytime strategies to expedite path optimization incrementally. Informed sampling-based planners define informed sets as subsets of the problem domain based on the current best solution cost. However, when no solution is found, these planners re-sample and explore the entire configuration space, which is time-consuming and computationally expensive. This article introduces Multi-Informed Trees (MIT*), a novel planner that constructs estimated informed sets based on prior admissible solution costs before finding the initial solution, thereby accelerating the initial convergence rate. Moreover, MIT* employs an adaptive sampler that dynamically adjusts the sampling strategy based on the exploration process. Furthermore, MIT* utilizes length-related adaptive sparse collision checks to guide lazy reverse search. These features enhance path cost efficiency and computation times while ensuring high success rates in confined scenarios. Through a series of simulations and real-world experiments, it is confirmed that MIT* outperforms existing single-query, sampling-based planners for problems in R^4 to R^16 and has been successfully applied to real-world robot manipulation tasks. A video showcasing our experimental results is available at: https://youtu.be/30RsBIdexTU
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