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Pressure distribution classification and segmentation of human hands in contact with the robot body
The International Journal of Robotics Research ( IF 7.5 ) Pub Date : 2020-03-10 , DOI: 10.1177/0278364920907688
Alessandro Albini 1 , Giorgio Cannata 1
Affiliation  

This article deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile sensors covering its body. This problem is relevant in the domain of physical human–robot interaction for discriminating between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a safe interaction. The underlying assumption used in this article is that voluntary physical interaction tasks involve hand touch over the robot body, and therefore the capability to recognize hand contacts is a key element to discriminate a purposive human touch from other types of interaction. The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements associated to a non-uniform cloud of 3D points (taxels) spread over a non-linear manifold corresponding to the robot body, into tactile images representing the contact pressure distribution in two dimensions. Tactile images can be processed using deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various hand segments: palm and single fingers. Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis concerning the transferability of the system has been performed, considering contacts occurring on a different sensorized robot part.

中文翻译:

人手与机器人身体接触的压力分布分类与分割

这篇文章讨论了一个机器人对人体手部触摸的识别问题,该机器人配备了覆盖其身体的大面积触觉传感器。这个问题与物理人机交互领域相关,用于区分人类和非人类接触并触发和驱动协作任务或机器人运动,或确保安全交互。本文中使用的基本假设是自愿物理交互任务涉及机器人身体上的手部接触,因此识别手部接触的能力是区分有目的的人类接触与其他类型交互的关键因素。所提出的方法基于触觉数据的几何变换,通过与分布在与机器人身体相对应的非线性流形上的非均匀 3D 点(紫杉素)云相关的压力测量,形成代表二维接触压力分布的触觉图像。触觉图像可以使用深度学习算法进行处理,以识别人手并计算各个手部段施加的压力分布:手掌和单指。在覆盖有机器人皮肤的真实机器人上进行的实验结果表明了所提出方法的有效性。此外,为了评估其稳健性,模拟了各种类型的故障。考虑到发生在不同传感机器人部件上的接触,已对系统的可转移性进行了进一步分析。成触觉图像,表示二维接触压力分布。触觉图像可以使用深度学习算法进行处理,以识别人手并计算各个手部段施加的压力分布:手掌和单指。在覆盖有机器人皮肤的真实机器人上进行的实验结果表明了所提出方法的有效性。此外,为了评估其稳健性,模拟了各种类型的故障。考虑到发生在不同传感机器人部件上的接触,已对系统的可转移性进行了进一步分析。成触觉图像,表示二维接触压力分布。触觉图像可以使用深度学习算法进行处理,以识别人手并计算各个手部段施加的压力分布:手掌和单指。在覆盖有机器人皮肤的真实机器人上进行的实验结果表明了所提出方法的有效性。此外,为了评估其稳健性,模拟了各种类型的故障。考虑到发生在不同传感机器人部件上的接触,已对系统的可转移性进行了进一步分析。手掌和单指。在覆盖有机器人皮肤的真实机器人上进行的实验结果表明了所提出方法的有效性。此外,为了评估其稳健性,模拟了各种类型的故障。考虑到发生在不同传感机器人部件上的接触,已对系统的可转移性进行了进一步分析。手掌和单指。在覆盖有机器人皮肤的真实机器人上进行的实验结果表明了所提出方法的有效性。此外,为了评估其稳健性,模拟了各种类型的故障。考虑到发生在不同传感机器人部件上的接触,已对系统的可转移性进行了进一步分析。
更新日期:2020-03-10
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