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Stochastic optimal control navigation with the avoidance of unsafe configurations

机译:随机最佳控制导航,避免不安全配置

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Long-time planning horizons are required to safely navigate one vehicle in the presence of another, possibly non-cooperative vehicle. They give rise to computational issues preventing the real-time implementation of safe navigation algorithms. In this paper, we consider two nonholonomic vehicles, of which one (blue) has the goal to enter the ???tail??? of the other (red). Neither the goal nor the navigation strategy of the red vehicle is known by the blue vehicle. To anticipate this uncertainty, the blue vehicle uses infinite horizon stochastic optimal control. Using the stochastic optimal control and backward Kolmogorov equation, the blue is navigated to avoid unsafe configurations from which the red can enter the ???tail??? of the blue and gain advantage over it. Our results are illustrated by numerical simulations and the feasibility of the control for the real-time implementation is tested with small-scale robot experiments.
机译:需要长期规划视野,以安全地在另一个可能的非合作车辆存在下安全地在一辆车辆中航行。它们导致计算问题,防止了安全导航算法的实时实施。在本文中,我们考虑两个非完整的车辆,其中一个(蓝色)的目标进入???尾巴???另一个(红色)。蓝色车辆也不知道红色车辆的目标和导航策略都没有。为了预见这种不确定性,蓝色车辆使用无限的地平线随机最佳控制。使用随机最优控制和后退kolmogorov方程,蓝色导航,避免红色可以从中进入的不安全配置???蓝色和增益优势。我们的结果是通过数值模拟来说明,并使用小型机器人实验测试了实时实现的控制的可行性。

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