Motion planning in uncertain environments with vision-like sensors
Motion planning in uncertain environments with vision-like sensors
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DOI：
10.1016/j.automatica.2007.04.022
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
S. Chakravorty;J. Junkins
中科院分区：
文献类型：
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作者：
S. Chakravorty;J. Junkins
在这项工作中，我们提出了一种在不确定环境中使用类似视觉的传感器进行智能路径规划的方法，即允许非局部感知环境的传感器。例子包括探索未知地形的移动机器人或在混乱的城市环境中导航的微型无人机。证明了在一定的假设条件下，不确定环境下的路径规划问题可以被描述为一个不确定的马尔可夫决策过程的自适应最优控制，其特征是已知的控制相关系统和未知的控制无关的环境。然后，路径规划的策略简化为基于环境的当前估计来计算控制策略，在自适应控制文献中也称为“确定性-等价”原则。我们的方法允许将类似视觉的传感器包括在问题公式中，正如经验证据所表明的那样，这加速了规划算法的收敛。此外，我们还证明了本文提出的路径规划和估计问题具有特殊的结构，可以利用这种结构来显著降低相关算法的计算负担。我们将这种方法应用于移动漫游车在完全未知的地形中的路径规划问题。
In this work we present a methodology for intelligent path planning in an uncertain environment using vision-like sensors, i.e., sensors that allow the sensing of the environment non-locally. Examples would include a mobile robot exploring an unknown terrain or a micro-UAV navigating in a cluttered urban environment. We show that the problem of path planning in an uncertain environment, under certain assumptions, can be posed as the adaptive optimal control of an uncertain Markov decision process, characterized by a known, control-dependent system, and an unknown, control-independent environment. The strategy for path planning then reduces to computing the control policy based on the current estimate of the environment, also known as the “certainty-equivalence” principle in the adaptive control literature. Our methodology allows the inclusion of vision-like sensors into the problem formulation, which, as empirical evidence suggests, accelerates the convergence of the planning algorithms. Further we show that the path planning and estimation problems, as formulated in this paper, possess special structure which can be exploited to significantly reduce the computational burden of the associated algorithms. We apply this methodology to the problem of path planning of a mobile rover in a completely unknown terrain.