面向移动机器人路径规划的优化型Informed RRT算法
Optimised Informed RRTs for Mobile Robot Path Planning
AI总结：
针对基础RRT路径规划次优、RRT*计算成本高的问题，本文提出优化型信息引导RRT算法族，结合信息引导采样与多种路径优化器加速收敛，实验证明基于基础RRT的该算法在不同规划时长下均优于基于RRT*的同类算法。
摘要：
基于基础快速探索随机树（Rapidly-exploring Random Trees，RRT）的路径规划器速度快、效率高，因此适用于实时机器人路径规划，但几乎必然是次优的。相比之下，最优RRT（RRT*）能够收敛到最优解，但在实际应用中计算成本可能很高。近期研究聚焦于提升RRT*的收敛速度，最成功的策略包括 informed sampling（信息引导采样）、路径优化以及二者的结合。然而，信息引导采样及其与路径优化的结合尚未应用于基础RRT。此外，虽然有多种路径优化器可用于加速收敛，但缺乏对其效果的对比研究。本文研究了利用信息引导采样和路径优化来加速基于基础RRT和RRT*的规划器，由此得到一系列被称为optimised informed RRTs（优化型信息引导RRT）的算法。我们应用了不同的路径优化器并对比了它们的效果。研究目标是确定：对速度快但几乎必然次优的基础RRT路径规划器，应用信息引导采样和路径优化后，能否达到与基于RRT*的规划器相当甚至更优的性能。分析表明，无论是在规划时间有限还是规划时间更充足的情况下，基于RRT的优化型信息引导RRT都比基于RRT*的同类算法表现更优。
英文摘要：
Path planners based on basic rapidly-exploring random trees (RRTs) are quick and efficient, and thus favourable for real-time robot path planning, but are almost-surely suboptimal. In contrast, the optimal RRT (RRT*) converges to the optimal solution, but may be expensive in practice. Recent work has focused on accelerating the RRT*'s convergence rate. The most successful strategies are informed sampling, path optimisation, and a combination thereof. However, informed sampling and its combination with path optimisation have not been applied to the basic RRT. Moreover, while a number of path optimisers can be used to accelerate the convergence rate, a comparison of their effectiveness is lacking. This paper investigates the use of informed sampling and path optimisation to accelerate planners based on both the basic RRT and the RRT*, resulting in a family of algorithms known as optimised informed RRTs. We apply different path optimisers and compare their effectiveness. The goal is to ascertain if applying informed sampling and path optimisation can help the quick, though almost-surely suboptimal, path planners based on the basic RRT attain comparable or better performance than RRT*-based planners. Analyses show that RRT-based optimised informed RRTs do attain better performance than their RRT*-based counterparts, both when planning time is limited and when there is more planning time.
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