Collision- and Freezing-Free Navigation in Dynamic Environments Using Learning to Search
Collision- and Freezing-Free Navigation in Dynamic Environments Using Learning to Search
复制标题
使用学习搜索在动态环境中实现无碰撞和无冻结导航
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
C. Wang
中科院分区：
文献类型：
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作者：
Chung;C. Wang
虽然无碰撞导航可以使用现有的基于规则的方法，它变得更有吸引力，使用从演示（LfD）的方法，以减轻繁琐的规则设计和参数调整过程的负担。此外，在冷冻机器人问题中，一旦环境超过一定程度的复杂性，可能没有足够的空间让机器人使用这些规划或导航方法进行导航，即使对移动实体有完美的预测。在本文中，有人认为，在动态环境中的无碰撞导航是学习从示威与适当的功能集，而不使用的路径规划。通过实验证明，利用所得到的策略解决冷冻机器人问题是可行的.仿真结果表明，经过改进的学习搜索（LEARCH）方法能够在动态环境中实现无碰撞和无冻结导航。
While collision-free navigation could be done using existing rule-based approaches, it becomes more attractive to use learning from demonstration (LfD) approaches to ease the burden of tedious rule designing and parameter tuning procedures. In addition, in the freezing robot problem, once the environment surpasses a certain level of complexity, there may be no sufficient space for a robot to navigate using these planning or navigation approaches even with perfect predictions of moving entities. In this paper, it is argued that collision-free navigation in dynamic environments is learnable from demonstrations with proper feature sets without the use of a path planner. It is feasible to solve the freezing robot problem using the policies learned from demonstration. The simulation results demonstrate that the Learning to Search (LEARCH) approach with the proposed modification is capable of achieving collision- and freezing-free navigation in dynamic environments.