Uncertainty Measured Markov Decision Process in Dynamic Environments
Uncertainty Measured Markov Decision Process in Dynamic Environments
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DOI：
10.1109/icra40945.2020.9197064
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发表时间：
2020-05
期刊：
影响因子：
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通讯作者：
Souravik Dutta;Banafsheh Rekabdar;Chinwe Ekenna
中科院分区：
文献类型：
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作者：
Souravik Dutta;Banafsheh Rekabdar;Chinwe Ekenna
在存在视觉遮挡和运动目标的情况下，成功的机器人路径规划是具有挑战性的。解决这一问题的经典方法除了部分可观测的马尔可夫决策过程外，还使用了视觉和感知算法来辅助路径规划以进行追逐躲避和机器人跟踪。提出了一种预测路径规划过程，该过程测量并利用了机器人运动规划过程中存在的不确定性。我们结合马尔可夫决策过程(MDP)开发了一种主观逻辑的变体，并提供了与生成的可行轨迹相关的信任、不信任和不确定性的度量。然后，我们对MDP进行建模，以从可能的选择列表中确定最佳路径规划方法。结果表明，与相关工作相比，基于目标与跟踪机器人之间的最近距离和简化的追踪者轨迹，具有较高的准确率。
Successful robot path planning is challenging in the presence of visual occlusions and moving targets. Classical methods to solve this problem have used visioning and perception algorithms in addition to partially observable markov decision processes to aid in path planning for pursuitevasion and robot tracking.We present a predictive path planning process that measures and utilizes the uncertainty present during robot motion planning. We develop a variant of subjective logic in combination with the Markov decision process (MDP) and provide a measure for belief, disbelief, and uncertainty in relation to feasible trajectories being generated. We then model the MDP to identify the best path planning method from a list of possible choices. Our results show a high percentage accuracy based on the closest acquired proximity between a target and a tracking robot and a simplified pursuer trajectory in comparison with related work.