機械学習による熱駆動人工筋肉の精密位置制御と温度調整
Machine Learning-Enabled Precision Position Control and Thermal Regulation in Advanced Thermal Actuators
ナイロン人工筋肉の位置を外部センサーなしで制御するため、機械学習を用いた定電力開ループ制御器を構築した。物理ベースのノイズ除去データで学習し、ヒステリシスの有無に関わらず多様な熱駆動人工筋肉に適用可能である。
著者: Seyed Mo Mirvakili, Ehsan Haghighat, Douglas Sim
分類: cs.RO, cs.LG
原文アブストラクト
With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for robotics applications. However, the necessity of integrating sensors and controllers poses a limitation to their practical usage. Here we report a constant power open-loop controller based on machine learning. We show that we can control the position of a nylon artificial muscle without external sensors. To this end, we construct a mapping from a desired displacement trajectory to a required power using an ensemble encoder-style feed-forward neural network. The neural controller is carefully trained on a physics-based denoised dataset and can be fine-tuned to accommodate various types of thermal artificial muscles, irrespective of the presence or absence of hysteresis.