Background Material Identification Using a Soft Robot
Background Material Identification Using a Soft Robot
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DOI：
10.3390/electronics13010078
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发表时间：
2023-12
期刊：
影响因子：
2.9
通讯作者：
Nathan Jeong;Wooseop Lee;Seongcheol Jeong;Arun Niddish Mahendran;V. Vikas
中科院分区：
文献类型：
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作者：
Nathan Jeong;Wooseop Lee;Seongcheol Jeong;Arun Niddish Mahendran;V. Vikas
软机器人是一种新兴技术，它为机器人提供了适应环境并与之安全交互的能力。在这里，这些机器人识别交互表面的能力对于抓取和移动任务至关重要。本文介绍了一种四肢软机器人，可以识别背景材料，通过使用嵌入式天线和机器学习技术的反射系数的收集的能力。针对软肢体机器人的材料特性，通过相对介电常数和损耗角正切来确定其反射系数采集天线的设计参数。为了提取反射系数的谐振频率、从最低S11得到的-3 dB带宽、最低S11的值、从最高S11得到的-3 dB带宽、谐振频率的个数等5个特征，设计了缝隙天线，并将其嵌入软肢体中。一个软机器人与嵌入式天线进行了测试，在九个不同的背景材料，试图识别周围的地形信息和更好的机器人操作。测试的背景材料包括混凝土、织物、草、砾石、金属、覆盖物、土壤、水和木材。结果表明，机器人能够区分9种不同的材料，使用基于袋装决策树的集成方法算法，对9种背景材料的平均准确率为93.3%。
Soft robotics is an emerging technology that provides robots with the ability to adapt to the environment and safely interact with it. Here, the ability of these robots to identify the surface of interaction is critical for grasping and locomotion tasks. This paper describes the capability of a four-limb soft robot that can identify background materials through the collection of reflection coefficients using an embedded antenna and machine learning techniques. The material of a soft-limb robot was characterized in terms of the relative permittivity and the loss tangent for the design of an antenna to collect reflection coefficients. A slot antenna was designed and embedded into a soft limb in order to extract five features in reflection coefficients including the resonant frequency, −3 dB bandwidth taken from the lowest S11, the value of the lowest S11, −3 dB bandwidth taken from the highest S11, and the number of resonant frequencies. A soft robot with the embedded antenna was tested on nine different background materials in an attempt to identify surrounding terrain information and a better robotic operation. The tested background materials included concrete, fabric, grass, gravel, metal, mulch, soil, water, and wood. The results showed that the robot was capable of distinguishing among the nine different materials with an average accuracy of 93.3% for the nine background materials using a bagged decision-tree-based ensemble method algorithm.