García-Samartín Jorge Francisco, Molina-Gómez Raúl, Barrientos Antonio
Centro de Automática y Robótica (UPM-CSIC), Universidad Politécnica de Madrid-Consejo Superior de Investigaciones Científicas, José Gutiérrez Abascal 2, 28006 Madrid, Spain.
Biomimetics (Basel). 2024 Feb 21;9(3):127. doi: 10.3390/biomimetics9030127.
Soft robotics faces challenges in attaining control methods that ensure precision from hard-to-model actuators and sensors. This study focuses on closed-chain control of a segment of PAUL, a modular pneumatic soft arm, using elastomeric-based resistive sensors with negative piezoresistive behaviour irrespective of ambient temperature. PAUL's performance relies on bladder inflation and deflation times. The control approach employs two neural networks: the first translates position references into valve inflation times, and the second acts as a state observer to estimate bladder inflation times using sensor data. Following training, the system achieves position errors of 4.59 mm, surpassing the results of other soft robots presented in the literature. The study also explores system modularity by assessing performance under external loads from non-actuated segments.
软体机器人在实现能确保来自难以建模的致动器和传感器的精度的控制方法方面面临挑战。本研究聚焦于对模块化气动软体手臂PAUL的一个节段进行闭环控制，该节段使用具有负压阻行为且不受环境温度影响的基于弹性体的电阻式传感器。PAUL的性能依赖于气囊的充气和放气时间。该控制方法采用两个神经网络：第一个将位置参考值转换为阀门充气时间，第二个作为状态观测器，利用传感器数据估计气囊充气时间。经过训练后，该系统实现了4.59毫米的位置误差，超过了文献中介绍的其他软体机器人的结果。该研究还通过评估在非致动节段的外部负载下的性能来探索系统的模块化。
