Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction
Probabilistic Autonomous Robot Navigation in Dynamic Environments with Human Motion Prediction
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DOI：
10.1007/s12369-009-0037-z
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发表时间：
2010-03-01
影响因子：
4.7
通讯作者：
Trahanias, Panos E.
中科院分区：
文献类型：
--
作者：
Foka, Amalia F.;Trahanias, Panos E.
研究了动态拥挤环境下自主机器人的导航问题。提出了预测导航范式，其中概率规划与障碍物回避以及对人类和/或其他障碍物的未来运动预测相结合。预测导航是通过使用分层部分可观测马尔可夫决策过程(POMDP)以全局方式执行的，该过程可以在每个时间步长在线求解并提供机器人执行的实际动作。利用一种新的方法在预测导航模型内执行避障，这是通过利用未来的运动预测来决定到目标位置的不被其他移动对象移动阻挡的路径，并通过使机器人能够增加或降低其移动速度或通过执行迂回操作来实现的。机器人能够在所采用的统一导航模型内确定在每种情况下哪种避障行为是最优的。
This paper considers the problem of autonomous robot navigation in dynamic and congested environments. The predictive navigation paradigm is proposed where probabilistic planning is integrated with obstacle avoidance along with future motion prediction of humans and/or other obstacles. Predictive navigation is performed in a global manner with the use of a hierarchical Partially Observable Markov Decision Process (POMDP) that can be solved online at each time step and provides the actual actions the robot performs. Obstacle avoidance is performed within the predictive navigation model with a novel approach by deciding paths to the goal position that are not obstructed by other moving objects movement with the use of future motion prediction and by enabling the robot to increase or decrease its speed of movement or by performing detours. The robot is able to decide which obstacle avoidance behavior is optimal in each case within the unified navigation model employed.