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Bayesian Local Sampling-based Planning
arXiv - CS - Artificial Intelligence Pub Date : 2019-09-08 , DOI: arxiv-1909.03452
Tin Lai, Philippe Morere, Fabio Ramos, Gilad Francis

Sampling-based planning is the predominant paradigm for motion planning in robotics. Most sampling-based planners use a global random sampling scheme to guarantee probabilistic completeness. However, most schemes are often inefficient as the samples drawn from the global proposal distribution, and do not exploit relevant local structures. Local sampling-based motion planners, on the other hand, take sequential decisions of random walks to samples valid trajectories in configuration space. However, current approaches do not adapt their strategies according to the success and failures of past samples. In this work, we introduce a local sampling-based motion planner with a Bayesian learning scheme for modelling an adaptive sampling proposal distribution. The proposal distribution is sequentially updated based on previous samples, consequently shaping it according to local obstacles and constraints in the configuration space. Thus, through learning from past observed outcomes, we maximise the likelihood of sampling in regions that have a higher probability to form trajectories within narrow passages. We provide the formulation of a sample-efficient distribution, along with theoretical foundation of sequentially updating this distribution. We demonstrate experimentally that by using a Bayesian proposal distribution, a solution is found faster, requiring fewer samples, and without any noticeable performance overhead.

中文翻译:

基于贝叶斯局部抽样的规划

基于采样的规划是机器人运动规划的主要范式。大多数基于抽样的规划器使用全局随机抽样方案来保证概率完整性。然而,大多数方案通常效率低下,因为样本来自全局提案分布,并且没有利用相关的局部结构。另一方面,基于局部采样的运动规划器采用随机游走的顺序决策来采样配置空间中的有效轨迹。然而,当前的方法并没有根据过去样本的成功和失败来调整他们的策略。在这项工作中,我们引入了一个基于局部采样的运动规划器,它具有贝叶斯学习方案,用于对自适应采样建议分布进行建模。提议分布根据之前的样本依次更新,因此,根据配置空间中的局部障碍和约束对其进行塑造。因此,通过从过去观察到的结果中学习,我们最大限度地提高了在狭窄通道内形成轨迹的可能性更高的区域的采样可能性。我们提供了样本有效分布的公式,以及顺序更新此分布的理论基础。我们通过实验证明,通过使用贝叶斯提议分布,可以更快地找到解决方案,需要更少的样本,并且没有任何明显的性能开销。我们提供了样本有效分布的公式,以及顺序更新此分布的理论基础。我们通过实验证明,通过使用贝叶斯提议分布,可以更快地找到解决方案,需要更少的样本,并且没有任何明显的性能开销。我们提供了样本有效分布的公式,以及顺序更新此分布的理论基础。我们通过实验证明,通过使用贝叶斯提议分布,可以更快地找到解决方案,需要更少的样本,并且没有任何明显的性能开销。
更新日期:2020-01-22
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