arXiv subjects

Jorge Dias

Publications and source records attributed to Jorge Dias.

39 records · Page 3Linked to original sources

arXiv2018

SocialRobot: Towards a Personalized Elderly Care Mobile Robot

SocialRobot is a collaborative European project, which focuses on providing a practical and interactive solution to improve the quality of life of elderly people. Having this in mind, a state of the art robotic mobile platform has been integrated with virtual social care technology to meet the elderly individual needs and requirements, following a human centered approach. In this short paper, we make an overview of SocialRobot, the developed architecture and the human-robot interactive scenarios being prepared and tested in the framework of the project for dissemination and exploitation purposes.

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arXiv2017

Discrete Cosserat Approach for Multi-Section Soft Robots Dynamics

In spite of recent progress, soft robotics still suffers from a lack of unified modeling framework. Nowadays, the most adopted model for the design and control of soft robots is the piece-wise constant curvature model, with its consolidated benefits and drawbacks. In this work, an alternative model for multisection soft robots dynamics is presented based on a discrete Cosserat approach, which, not only takes into account shear and torsional deformations, essentials to cope with out-of-plane external loads, but also inherits the geometrical and mechanical properties of the continuous Cosserat model, making it the natural soft robotics counterpart of the traditional rigid robotics dynamics model. The soundness of the model is demonstrated through extensive simulation and experimental results for both plane and out-of-plane motions.

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arXiv2014

Touch attention Bayesian models for robotic active haptic exploration of heterogeneous surfaces

This work contributes to the development of active haptic exploration strategies of surfaces using robotic hands in environments with an unknown structure. The architecture of the proposed approach consists two main Bayesian models, implementing the touch attention mechanisms of the system. The model pi_per perceives and discriminates different categories of materials (haptic stimulus) integrating compliance and texture features extracted from haptic sensory data. The model pi_tar actively infers the next region of the workspace that should be explored by the robotic system, integrating the task information, the permanently updated saliency and uncertainty maps extracted from the perceived haptic stimulus map, as well as, inhibition-of-return mechanisms. The experimental results demonstrate that the Bayesian model pi_per can be used to discriminate 10 different classes of materials with an average recognition rate higher than 90% . The generalization capability of the proposed models was demonstrated experimentally. The ATLAS robot, in the simulation, was able to perform the following of a discontinuity between two regions made of different materials with a divergence smaller than 1cm (30 trials). The tests were performed in scenarios with 3 different configurations of the discontinuity. The Bayesian models have demonstrated the capability to manage the uncertainty about the structure of the surfaces and sensory noise to make correct motor decisions from haptic percepts.

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