论文标题
随机机器人导航中连续时间端到端风险的上限
Upper Bounds for Continuous-Time End-to-End Risks in Stochastic Robot Navigation
论文作者
论文摘要
我们提出了一种分析方法，以估计具有线性控制的ITO动力学的自动源代理的运动计划的连续时间碰撞概率。由于现实世界中固有的不确定性，计划算法产生的运动计划在现实中不能完全由自主代理完美地执行。估计端到端风险对于表征轨迹安全并计划风险最佳轨迹至关重要。在本文中，我们使用布朗运动的特性以及Boole和Boole和Hunter的不平等，从而导致了上限，从而导致了随机机器人导航的连续时间风险。使用地面机器人导航示例，我们从数值上证明我们的方法要比幼稚的蒙特卡洛采样方法快得多，并且所提出的边界的性能优于离散时间风险界限。
We present an analytical method to estimate the continuous-time collision probability of motion plans for autonomous agents with linear controlled Ito dynamics. Motion plans generated by planning algorithms cannot be perfectly executed by autonomous agents in reality due to the inherent uncertainties in the real world. Estimating end-to-end risk is crucial to characterize the safety of trajectories and plan risk optimal trajectories. In this paper, we derive upper bounds for the continuous-time risk in stochastic robot navigation using the properties of Brownian motion as well as Boole and Hunter's inequalities from probability theory. Using a ground robot navigation example, we numerically demonstrate that our method is considerably faster than the naive Monte Carlo sampling method and the proposed bounds perform better than the discrete-time risk bounds.
