IBBT：面向不确定性下运动规划的知情批量信念树
IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty
AI总结：
本文提出IBBT算法，将不确定性运动规划分解为确定性采样建图与知情启发式信念树搜索，实现可收敛、可复用的任意时间增量规划，并在多环境中验证其更快且能找到非平凡规划。
摘要：
在本工作中，我们提出了用于运动和感知不确定性下运动规划的知情批量信念树（Informed Batch Belief Trees，IBBT）算法。原始随机运动规划问题被分解为一个确定性运动规划问题和一个图搜索问题。我们使用基于采样的方法（如PRM或RRG）求解确定性规划问题，以构建标称轨迹图。随后，基于该标称轨迹图计算针对原始问题的知情剩余代价启发式。最后，我们利用所提出的启发式在图上进行搜索，从而生长一棵信念树。IBBT在批量状态采样、标称轨迹图构建、启发式计算以及图上搜索之间交替进行，以寻找信念空间运动规划。IBBT是一种任意时间、增量式算法。随着越来越多批量的样本被加入图中，该算法找到的运动规划会收敛到最优规划。IBBT通过在连续迭代之间复用结果而保持高效。信念树搜索是由知情启发式引导的有序搜索。我们在不同的规划环境中测试了IBBT。数值研究证实，IBBT能够找到非平凡的运动规划，并且与以往类似方法相比速度更快。
英文摘要：
In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties. The original stochastic motion planning problem is divided into a deterministic motion planning problem and a graph search problem. We solve the deterministic planning problem using sampling-based methods such as PRM or RRG to construct a graph of nominal trajectories. Then, an informed cost-to-go heuristic for the original problem is computed based on the nominal trajectory graph. Finally, we grow a belief tree by searching over the graph using the proposed heuristic. IBBT interleaves between batch state sampling, nominal trajectory graph construction, heuristic computing, and search over the graph to find belief space motion plans. IBBT is an anytime, incremental algorithm. With an increasing number of batches of samples added to the graph, the algorithm finds motion plans that converge to the optimal one. IBBT is efficient by reusing results between sequential iterations. The belief tree searching is an ordered search guided by an informed heuristic. We test IBBT in different planning environments. Our numerical investigation confirms that IBBT finds non-trivial motion plans and is faster compared with previous similar methods.
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