Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments
Local episode-based learning of multi-objective behavior coordination for a mobile robot in dynamic environments
复制标题
动态环境中移动机器人多目标行为协调的基于局部事件的学习
DOI：
10.1109/fuzz.2003.1209380
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
N. Kubota
中科院分区：
文献类型：
--
作者：
Y. Nojima;F. Kojima;N. Kubota
本文研究的是移动机器人多目标行为协调的局部学习方法。多目标行为协调起着整合基本行为模块输出的作用。为每个由模糊规则、产生式规则等表示的行为模块分配行为权重。通过更新这些行为权重，移动机器人可以采取多目标定位动作。然而，协调规则是针对静态环境设计的，移动机器人必须在有移动障碍物的动态环境中学习或更新协调规则。因此，我们提出了一种基于局部情节的学习，这是一种利用短期记忆中先前感知和动作之间关系的自我参照的学习方法。
This paper is concerned with a local learning method of a multi-objective behavior coordination for a mobile robot. The multiobjective behavior coordination plays a role in integrating outputs of basic behavioral modules. A behavioral weight is assigned to each behavioral module represented by fuzzy rules, production rules, and so on. By updating these behavioral weights, the mobile robot can take a multi-objective situated action. However, the coordination rule is designed suitably static environments and the mobile robot must learn or update coordination rule in dynamic environments with moving obstacles. Therefore, we propose a local episode-based learning which is a learning method using self-reference of the relationship between previous perception and action in short-term memory.