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Tactile surface classification for limbed robots using a pressure sensitive robot skin

机译：使用压敏机器人皮肤的四肢机器人的触觉表面分类

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This paper describes an approach to terrain identification based on pressure images generated through direct surface contact using a robot skin constructed around a high- resolution pressure sensing array. Terrain signatures for classification are formulated from the magnitude frequency responses of the pressure images. The initial experimental results for statically obtained images show that the approach yields classification accuracies >98%. The methodology is extended to accommodate the dynamic pressure images anticipated when a robot is walking or running. Experiments with a one-legged hopping robot yield similar identification accuracies approximate to 99%. In addition, the accuracies are independent with respect to changing robot dynamics (i.e., when using different leg gaits). The paper further shows that the high- resolution capabilities of the sensor enables similarly textured surfaces to be distinguished. A correcting filter is developed to accommodate for failures or faults that inevitably occur within the sensing array with continued use. Experimental results show using the correcting filter can extend the effective operational lifespan of a high- resolution sensing array over 6x in the presence of sensor damage. The results presented suggest this methodology can be extended to autonomous field robots, providing a robot with crucial information about the environment that can be used to aid stable and efficient mobility over rough and varying terrains.
机译：本文介绍了一种基于地形的识别方法，其中基于通过使用高分辨率压力传感阵列周围构造的机器人皮肤通过直接表面接触生成的压力图像。根据压力图像的幅度频率响应来制定用于分类的地形特征。静态获取的图像的初步实验结果表明，该方法的分类精度> 98％。扩展了该方法，以适应机器人行走或跑步时预期的动态压力图像。用单腿跳跃机器人进行的实验得出的相似识别准确率约为99％。另外，关于变化的机器人动力学（即，当使用不同的腿步态时），准确性是独立的。该论文进一步表明，传感器的高分辨率功能可以区分出类似纹理的表面。开发了一种校正滤波器，以适应持续使用在传感阵列内不可避免发生的故障或错误。实验结果表明，在传感器损坏的情况下，使用校正滤波器可以将高分辨率传感阵列的有效工作寿命延长6倍以上。提出的结果表明，该方法可以扩展到自主现场机器人，从而为机器人提供有关环境的关键信息，以帮助在崎and不平的地形上稳定，高效地移动。

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