SR-RRT:选择性的基于收缩的RRT计划器

Junghwan Lee, OSung Kwon, Liangjun Zhang, Sung-eui Yoon
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引用次数: 42

摘要

我们提出了一种新颖的基于收缩的计划,选择性收缩的RRT,用于有效地处理具有不同特征的各种环境。首先,我们提出了一个桥接线测试,可以识别狭窄通道周围的区域,然后在这些区域选择性地执行基于优化的收缩操作。我们还提出了一种非碰撞线测试,即桥线测试的双重算子,作为一种剔除方法,以避免在大开放的自由空间附近生成样本,从而在狭窄的通道周围生成更多的样本。这两个测试的计算开销很小,并与基于收缩的RRT集成在一起。为了证明我们的方法的好处,我们用不同的基准测试了我们的方法,这些基准测试有不同数量的狭窄通道。我们的方法分别比基本RRT和基于优化的缩回RRT的性能提高了21倍和3.5倍。此外,我们的方法在所有具有或不具有狭窄通道的测试基准中始终如一地提高了其他测试方法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SR-RRT: Selective retraction-based RRT planner
We present a novel retraction-based planner, selective retraction-based RRT, for efficiently handling a wide variety of environments that have different characteristics. We first present a bridge line-test that can identify regions around narrow passages, and then perform an optimization-based retraction operation selectively only at those regions. We also propose a non-colliding line-test, a dual operator to the bridge line-test, as a culling method to avoid generating samples near wide-open free spaces and thus to generate more samples around narrow passages. These two tests are performed with a small computational overhead and are integrated with a retraction-based RRT. In order to demonstrate benefits of our method, we have tested our method with different benchmarks that have varying amounts of narrow passages. Our method achieves up to 21 times and 3.5 times performance improvements over a basic RRT and an optimization-based retraction RRT, respectively. Furthermore, our method consistently improves the performances of other tested methods across all the tested benchmarks that have or do not have narrow passages.
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