Learning weighted linguistic rules to control an autonomous robot
Learning weighted linguistic rules to control an autonomous robot
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DOI：
10.1002/int.v24:3
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发表时间：
2009-03
影响因子：
3
通讯作者：
M. Mucientes;R. Alcalá;J. Alcalá-Fdez;J. Casillas
中科院分区：
文献类型：
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作者：
M. Mucientes;R. Alcalá;J. Alcalá-Fdez;J. Casillas
提出了一种在移动的机器人中学习行为的方法。它包括一种技术，自动生成的输入输出数据加上遗传模糊系统，获得合作加权规则。我们的方法比其他方法的优点是，设计者必须选择只有几个参数的值，得到的控制器是一般的（控制器的质量不依赖于环境），和学习过程发生在模拟，但控制器的工作也对真实的机器人具有良好的性能。该方法已被用来学习的墙壁以下的行为，并得到的控制器进行了测试，使用Nomad 200机器人在模拟和真实的环境。© 2009威利期刊公司.
A methodology for learning behaviors in mobile robotics has been developed. It consists of a technique to automatically generate input–output data plus a genetic fuzzy system that obtains cooperative weighted rules. The advantages of our methodology over other approaches are that the designer has to choose the values of only a few parameters, the obtained controllers are general (the quality of the controller does not depend on the environment), and the learning process takes place in simulation, but the controllers work also on the real robot with good performance. The methodology has been used to learn the wall-following behavior, and the obtained controller has been tested using a Nomad 200 robot in both simulated and real environments. © 2009 Wiley Periodicals, Inc.