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在自主群机器人控制器设计中使用符号挖掘

Toward Using Symbolic Discovery in Designing Controllers of Autonomous Swarm Robots
课程网址: http://videolectures.net/ecmlpkdd09_suzuki_tusddcasr/  
主讲教师: Einoshin Suzuki
开课单位: 九州大学
开课时间: 2009-10-20
课程语种: 英语
中文简介:
在本文中,我们提出了一种方法,该方法使用符号数据挖掘方法来迭代设计测试分析周期,以设计自主群机器人的控制器。即使设计者无法获得车载信号,该方法也是适用的,这是常见的机器人类型。作为第一步,我们解决了一个特定的任务,其中两个群体机器人试图在致命碰撞之前在方形区域中尽可能多地访问细胞。使用传统技术的快速分析,依赖于人类检查,揭示了有趣的要点,包括群体机器人之间的所需类型的相互作用以及控制器的可能改进。我们考虑数据挖掘方法的可能用法,包括有效的轨迹发现方法,有效的小部分发现方法和鲁棒的部分分类器发现方法。
课程简介: In this paper, we propose an approach which iterates a designtest- analysis cycle using symbolic data mining methods for designing controllers of autonomous swarm robots. The approach is applicable even if the onboard signal is unavailable to the designer, which is common for such kinds of robots. As the first step, we tackle a specific task in which two swarm robots try to visit as many cells as possible in a square field before a fatal collision. Quick analysis using conventional techniques, relying also on human inspection revealed interesting essentials including a desirable type of interaction between the swarm robots and possible refinements of the controllers. We consider possible usages of data mining methods including an efficient trajectory discovery method, an effective minority subset discovery method, and a robust partial classifier discovery method.
关 键 词: 符号数据挖掘; 机器人; 轨迹发现方法
课程来源: 视频讲座网
最后编审: 2019-03-27:lxf
阅读次数: 37