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通过嵌入6D应变传感器提升软体机器人本体感觉。

Advancing Soft Robot Proprioception Through 6D Strain Sensors Embedding.

作者信息

Feliu-Talegon Daniel, Abdullahi Adamu Yusuf, Mathew Anup Teejo, Alkayas Abdulaziz Y, Renda Federico

机构信息

Department of Mechanical and Nuclear Engineering, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates.

出版信息

Soft Robot. 2025 Aug;12(4):465-476. doi: 10.1089/soro.2024.0017. Epub 2025 Jan 21.

Abstract

Soft robots and bioinspired systems have revolutionized robot design by incorporating flexibility and deformable materials inspired by nature's ingenious designs. Similar to many robotic applications, sensing and perception are paramount to enable soft robots to adeptly navigate the unpredictable real world, ensuring safe interactions with both humans and the environment. Despite recent progress, soft robot sensorization still faces significant challenges due to the virtual infinite degrees of freedom of the system and the need for efficient computational models capable of estimating valuable information from sensor data. In this article, we present a new model-based proprioceptive system for slender soft robots based on strain sensing and a strain-based modeling approach called Geometric Variable-Strain (GVS). We develop a flexible 2-Plate 6D strain sensor (Flex-2P6D) capable of measuring the 6 dimensions (6D) strain at specific points of the soft robot with an accuracy higher than 95%. Coupled with the GVS approach, the proposed methodology is able to directly measure the configuration variables and reconstruct complex robot shapes with very high accuracy, even in very challenging conditions. The sensors are embedded inside the soft body, which makes them also suitable for underwater operation and physical interaction with the environment. Something that we also demonstrate experimentally. We believe that our approach has the potential to be applied across a wide variety of applications, including observation and exploration missions, as well as human-robot interaction, where the states of the system are required for implementing precise closed-loop control and estimation methods.

摘要

软机器人和仿生系统通过融入受自然巧妙设计启发的柔性和可变形材料,彻底改变了机器人设计。与许多机器人应用类似,传感和感知对于使软机器人能够在不可预测的现实世界中灵活导航、确保与人类和环境的安全交互至关重要。尽管最近取得了进展,但由于系统几乎具有无限的自由度,以及需要能够从传感器数据中估计有价值信息的高效计算模型,软机器人的传感器化仍然面临重大挑战。在本文中,我们提出了一种基于应变传感和一种名为几何可变应变(GVS)的基于应变的建模方法的新型基于模型的细长软机器人本体感知系统。我们开发了一种灵活的双板六维应变传感器(Flex-2P6D),能够在软机器人的特定点测量六维应变,精度高于95%。结合GVS方法,所提出的方法能够直接测量配置变量并以非常高的精度重建复杂的机器人形状,即使在极具挑战性的条件下也是如此。传感器嵌入在软体内,这使得它们也适用于水下操作以及与环境的物理交互。我们也通过实验证明了这一点。我们相信,我们的方法有潜力应用于广泛的各种应用中,包括观测和探索任务,以及人机交互,在这些应用中,实现精确的闭环控制和估计方法需要系统的状态信息。

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