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Open AccessArticle

Estimating Weight of Unknown Objects Using Active Thermography

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Engineering Division, New York University Abu Dhabi, Abu Dhabi 41012, UAE
2
Tandon School of Engineering, New York University, New York, NY 10012, USA
*
Author to whom correspondence should be addressed.
Robotics 2019, 8(4), 92; https://doi.org/10.3390/robotics8040092
Received: 3 September 2019 / Revised: 13 October 2019 / Accepted: 21 October 2019 / Published: 24 October 2019
(This article belongs to the Section Robotics & Automation)
Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them. View Full-Text
Keywords: weight estimation; material characterization; infrared thermography; neural networks weight estimation; material characterization; infrared thermography; neural networks
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MDPI and ACS Style

Aujeszky, T.; Korres, G.; Eid, M.; Khorrami, F. Estimating Weight of Unknown Objects Using Active Thermography. Robotics 2019, 8, 92.

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