Efficient and reliable generation of global path plans are necessary for safe execution and deployment of autonomous systems. In order to generate planning graphs which adequately resolve the topology of a given environment, many sampling-based motion planners resort to coarse, heuristically-driven strategies which often fail to generalize to new and varied surroundings. Further, many of these approaches are not designed to contend with partial-observability. We posit that such uncertainty in environment geometry can, in fact, help drive the sampling process in generating feasible, and probabilistically-safe planning graphs. We propose a method for Probabilistic Roadmaps which relies on particle-based Variational Inference to efficiently cover the posterior distribution over feasible regions in configuration space. Our approach, Stein Variational Probabilistic Roadmap (SV-PRM), results in sample-efficient generation of planning-graphs and large improvements over traditional sampling approaches. We demonstrate the approach on a variety of challenging planning problems, including real-world probabilistic occupancy maps and high-dof manipulation problems common in robotics.
翻译:为了安全地执行和部署自主系统,必须制定高效和可靠的全球路径计划。为了产生能够充分解决特定环境的地形学的规划图,许多抽样运动规划者采用粗略、超自然的战略,往往无法概括到新的和不同的环境。此外,许多这些方法的设计不是为了应付局部可观察性。我们认为,环境几何中的这种不确定性事实上可以帮助推动取样过程,从而产生可行的、概率安全的规划图。我们提出了一个概率性路线图的方法,它依靠粒子变异的推论,以有效覆盖空间配置可行区域的后方分布。我们的方法,Stephen Varicity Probablicity 路线图(SV-PRM),其结果是抽样高效地生成规划图,并大大改进传统的采样方法。我们展示了各种具有挑战性的规划问题的方法,包括真实世界概率占用图和机器人中常见的高操纵问题。