FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling
FAD learning: Separate learning for three accelerations -learning for dynamics of boat through motor babbling
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DOI：
10.1109/icra.2016.7487779
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发表时间：
2016-05
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影响因子：
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通讯作者：
Akio Numakura;Shigenobu Kato;Kazuyuki Sato;Takeya Tomizawa;T. Miyoshi;T. Akashi;Chyon Hae Kim
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文献类型：
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作者：
Akio Numakura;Shigenobu Kato;Kazuyuki Sato;Takeya Tomizawa;T. Miyoshi;T. Akashi;Chyon Hae Kim
本文对一艘小船进行了建模和测量。在一些捕捞任务中，为了有效地捕获贝类或鱼类，锚地是不适用的。目前，许多渔民在执行捕捞任务的同时，也在手动稳定船只。提出了一种基于FAD学习的船舶建模方法。在该方法中，利用我们开发的两个动力学学习树(DLTS)的在线学习来学习船的自由动力学加速度和执行器加速度。为了测量水下机器人的位置、速度和加速度，我们开发了一种水下摄像机的图像处理方法。在实验中，一艘船在水池中进行了马达的嗡嗡声。该船的动力学数据由DLTS学习。通过对DLTS预测的速度进行验证，验证了模型的有效性。
This paper addresses the modeling and measurement of a small boat. In some fishing tasks, anchorage is not applicable in order to capture shellfishes or fishes efficiently. Currently, many fishermen are manually stabilizing boats simultaneously with the capturing task. We propose a boat modeling method named FAD learning. In this method, the free dynamics acceleration and actuator acceleration of a boat are learned using the online learning of two dynamics learning trees (DLTs), which are developed by us. In order to measure the position, velocity, and acceleration, we developed an image processing method with an underwater camera. In the experiment, the motor babbling of a boat was performed on a water pool. The dynamical data from the boat was learned by DLTs. The effectiveness of the modeling was confirmed through the validation of the velocity that was predicted by DLTs.