发表机构
The University of Electro-Communications; Chugai Technos Corporation; ATR Computational Neuroscience Laboratories（东京电机大学; 中外技术株式会社; ATR计算神经科学实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对恶劣环境中机器人无传感器远程操作难题，提出利用充水柔性管传输动力与信息的方法，通过建模实现同步动力传输和位置估计，经实验验证稳定位置控制，还有现场参数识别法，推动恶劣环境下机器人可靠远程控制。
AI 中文摘要
机器人传感器和电子设备在恶劣环境中容易出现故障。水液压驱动配合细长管可实现无执行器侧传感器的远程操作，管的弹性还能降低关节阻抗。但因低阻抗和有限的摄像头可见性，在不同负载条件下将关节（或末端执行器）精确到目标位置具有挑战性。本研究提出一种新方法，利用充水柔性管在无执行器侧传感器的情况下分别向执行器传输驱动功率和执行器侧信息。通过基于压力波动对传输过程中的体积损失建模并考虑少量空气混入，可通过长达50米的管子实现同步动力传输和位置估计。实验验证了无传感器水液压缸在不同负载下的稳定位置控制，还提出了一种现场参数识别方法，无需执行器侧传感器即可考虑管子和空气夹带的变化。这些成果促进了恶劣环境中机器人的可靠远程控制。
英文摘要
Robot sensors and electronic equipment are prone to failure in harsh environments. With water hydraulic drive, thin and long tubes enable remote operation without actuator-side sensors. Furthermore, the elasticity of the tubes reduces the impedance of the joints (actuators), benefiting robot tasks involving unexpected contact with the environment or vibrations. However, owing to the low impedance and limited camera visibility, accurately positioning the joint (or end effector) to the target location under varying load conditions is challenging. This study proposes a novel method that employs water-filled flexible tubes to enable the transmission of driving power and actuator-side information to and from the actuator, respectively, without actuator-side sensors. By modeling volumetric loss during transmission based on pressure fluctuations and incorporating minor air entrapment, simultaneous power transmission and position estimation is achieved through a tube up to 50 m. Thus, it becomes possible to use a feedback control framework that was previously difficult to implement in sensorless systems. Experimental validation confirms stable position control of a sensorless water hydraulic cylinder under varying loads. Furthermore, a field parameter-identification method accounts for tube and air entrainment variability without requiring actuator-side sensors. These contributions promote reliable remote control of robots in harsh environments.
Comments11 pages, 11 figures, and 7 tables. Yuki Nakamura and Shuto Yoshimura contributed equally to this work
