论文标题
高斯信念树的偶然造成渐近运动计划的限制
Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning
论文作者
论文摘要
在本文中，我们通过概率保证解决了基于采样的运动计划和测量不确定性的问题。我们将基于树木的基于树木的传统运动计划概括为确定性系统，并提出信念-USHAMCAL {a} $，该框架将任何基于动力学树的计划者扩展到线性（或可线化）系统的信念空间。我们为信仰空间介绍了适当的抽样技术和距离指标，以保留基础计划者的概率完整性和渐近最优性能。我们证明了我们在模拟方面有效和渐近地找到安全低成本路径的方法，可用于载体和非自主系统。
In this paper, we address the problem of sampling-based motion planning under motion and measurement uncertainty with probabilistic guarantees. We generalize traditional sampling-based tree-based motion planning algorithms for deterministic systems and propose belief-$\mathcal{A}$, a framework that extends any kinodynamical tree-based planner to the belief space for linear (or linearizable) systems. We introduce appropriate sampling techniques and distance metrics for the belief space that preserve the probabilistic completeness and asymptotic optimality properties of the underlying planner. We demonstrate the efficacy of our approach for finding safe low-cost paths efficiently and asymptotically optimally in simulation, for both holonomic and non-holonomic systems.
