Enhancing physical human-robot interaction requires the improvement in the tactile perception of physical touch. Robot skin sensors exhibiting piezoresistive behavior can be used in conjunction with collaborative robots. In past work, fabrication of these tactile arrays was done using cleanroom techniques such as spin coating, photolithography, sputtering, wet and dry etching onto flexible polymers. In this paper, we present an addictive, non-cleanroom improved process of depositing PEDOT: PSS, which is the organic polymer responsible for the piezoresistive phenomenon of the robot skin sensor arrays. This publication details the patterning of the robot skin sensor structures and the adaptation of the inkjet printing technology to the fabrication process. This increases the possibility of scaling the production output while reducing the cleanroom fabrication cost and time from an approximately five-hour PEDOT: PSS deposition process to five minutes. Furthermore, the testing of these skin sensor arrays is carried out on a testing station equipped with a force plunger and an integrated circuit designed to provide perception feedback on various force load profiles controlled in an automated process. The results show uniform deposition of the PEDOT: PSS, consistent resistance measurement, and appropriate tactile response across an array of 16 sensors.
ShadowSense: Detecting Human Touch in a Social Robot Using Shadow Image Classification
This paper proposes and evaluates the use of image classification for detailed, full-body human-robot tactile interaction. A camera positioned below a translucent robot skin captures shadows generated from human touch and infers social gestures from the captured images. This approach enables rich tactile interaction with robots without the need for the sensor arrays used in traditional social robot tactile skins. It also supports the use of touch interaction with non-rigid robots, achieves high-resolution sensing for robots with different sizes and shape of surfaces, and removes the requirement of direct contact with the robot. We demonstrate the idea with an inflatable robot and a standing-alone testing device, an algorithm for recognizing touch gestures from shadows that uses Densely Connected Convolutional Networks, and an algorithm for tracking positions of touch and hovering shadows. Our experiments show that the system can distinguish between six touch gestures under three lighting conditions with 87.5 - 96.0% accuracy, depending on the lighting, and can accurately track touch positions as well as infer motion activities in realistic interaction conditions. Additional applications for this method include interactive screens on inflatable robots and privacy-maintaining robots for the home.
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- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
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- 1 to 24
- Sponsoring Org:
- National Science Foundation
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