Abstract
This paper proposes a framework to investigate the influence of physical interactions to sensory information, during robotic palpation. We embed a capacitive tactile sensor on a robotic arm to probe a soft phantom and detect and classify hard inclusions within it. A combination of PCA and K-Means clustering is used to: first, reduce the dimensionality of the spatiotemporal data obtained through the probing of each area in the phantom; second categorize the re-encoded data into a given number of categories. Results show that appropriate probing interactions can be useful in compensating for the quality of the data, or lack thereof. Finally, we test the proposed framework on a palpation scenario where a Support Vector Machine classifier is trained to discriminate amongst different types of hard inclusions. We show the proposed framework is capable of predicting the best-performing motion strategy, as well as the relative classification performance of the SVM classifier, solely based on unsupervised cluster analysis methods.
Highlights
In the last decades, substantial efforts have been made in enhancing the sensing capabilities of robots by providing them with a sense of touch (Dahiya et al 2010; Drimus et al 2014)
We propose a framework to explore the way active physical interactions with a soft body affect the structure of haptic spatio-temporal information
The properties of the phantom organ designed to test the ability of the robotic agent to be detect hard inclusions by their depth and size, as shown to be important in previous systems (Herzig et al 2018)
Summary
Substantial efforts have been made in enhancing the sensing capabilities of robots by providing them with a sense of touch (Dahiya et al 2010; Drimus et al 2014). Haptic sensing differs from other modalities, such as vision, in virtue of its tight coupling with, and need of, physical interactions. Haptic sensing requires direct physical contacts with sensing targets, inducing spatio-temporal force patterns on the contact surface, which may or may not be the consequence of motor behaviors of the robots. B Luca Scimeca more, force patterns are significantly related to the shape and mechanical properties of sensing surfaces (e.g. stiffness) and the target objects (Scimeca et al 2018; Iida and Nurzaman 2016)
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