Estimating Objects’ Weight in Precision Grips Using Skin-Like Sensors

Azevedo, Francisco; Carmona, J.A. Medina; Paulino, Tiago; Moreno, Plínio

doi:10.1007/978-3-319-70836-2_57

Cited by 1 publication

(2 citation statements)

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“…Inspired by how humans estimate the weight of unknown objects by unsupported holding, a fundamentally different approach involves a robotic arm performing a precision grip of the object to estimate its weight [14]. The robotic arm is equipped with tactile sensors for slip detection and to measure contact forces during object manipulation (holding).…”

Section: Related Workmentioning

confidence: 99%

“…In a subsequent work [19], the weight of the object is estimated from currents flowing in motor servos where results showed successful estimation of the weight of the object with a 22% average error. A method is also presented in [14] for estimating the mass of an object using a precision grip on a humanoid robot. A recent research work presented a method to estimate the weight of an object during a precision grip made by a humanoid robot where tactile sensors on the fingertips provide 3D force information during a movement of grasping and lifting a cup filled with different masses, with static and dynamic friction taken into consideration [20].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Weight of Unknown Objects Using Active Thermography

Aujeszky¹,

Κορρές²,

Eid³

et al. 2019

Robotics

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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.

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Section: Related Workmentioning

confidence: 99%