Mechanoreception, the ability of robots to detect mechanical stimuli from the internal and external environments, contributes significantly to improving safety and task performance during the operation of robots in unstructured environments. Various approaches have been proposed to endow robot systems with mechanoreception. In the case of soft robots, the state-of-the-art mechanosensory solutions typically embedded dedicated deformable sensors into the soft body, giving rise to fabrication complexity and signal sophistication. In this study, we propose a novel mechanoreception scheme to enable pneumatic-driven soft robots to perceive proprioceptive movements as well as external contacts. Both internal and external mechanical parameters can be decoded from intuitive cues of body deformation and pneumatic pressure signals. In contrast to most existing solutions employing dedicated deformable sensors, the proposed approach only utilizes pressure feedback, which is typically available from the pneumatic pressure sensors incorporated in the control loop of most pneumatic soft robots. The concept was implemented and validated on a proprietary robotic gripper with a linear soft pneumatic actuator, demonstrating the capability in simultaneous detection of actuator position and external contact forceAfter the proposed approach, the gripper can achieve both active and passive mechanosensation, with demonstrated experiments in grasping force estimation, contact loss detection, object stiffness identification, and contour measurements. This approach offers an alternative route to achieving excellent internal/environmental awareness without requiring dedicated sensing modalities.

中文翻译：

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通过直观的身体线索进行软机器人的机械感受。


机械感受是机器人检测来自内部和外部环境的机械刺激的能力，对于提高机器人在非结构化环境中操作期间的安全性和任务性能做出了重大贡献。人们已经提出了各种方法来赋予机器人系统机械感受能力。就软体机器人而言，最先进的机械感觉解决方案通常将专用的可变形传感器嵌入到软体中，从而增加了制造的复杂性和信号的复杂性。在这项研究中，我们提出了一种新颖的机械感受方案，使气动驱动的软机器人能够感知本体感觉运动以及外部接触。内部和外部机械参数都可以根据身体变形和气动压力信号的直观线索进行解码。与大多数采用专用可变形传感器的现有解决方案相比，所提出的方法仅利用压力反馈，这通常可从大多数气动软机器人控制回路中并入的气动压力传感器获得。该概念在带有线性软气动执行器的专有机器人抓手上进行了实施和验证，展示了同时检测执行器位置和外部接触力的能力。采用所提出的方法后，抓手可以实现主动和被动机械感觉，并通过抓取实验进行了验证力估计、接触损失检测、物体刚度识别和轮廓测量。这种方法提供了一种无需专用传感方式即可实现出色的内部/环境意识的替代途径。