Publications

A Personal Robotic Flying Machine with Vertical Takeoff Controlled by the Human Body Movements

Vittorio Cipolla, Aldo Frediani, Rezia Molfino, Giovanni Gerasdo tuscalo, Fabrizio Oliviero, Domenec Puig, Carmine Tommaso Recchiuto, Emanuele Rizzo, Agusti Solanas and Paul Stewart

domenec.puig@urv.cat

Abs3ract

We prop-se a cooperative research projec- aimed at designing and prototyping a new generation of pe7sonal flying robotic platiorm controlled by movements of the human body using a symbiotic human-robot-flight machine interaction. Motors with ducted fun propulsion andgpower supply, and a VSLAM system will be integrated in the fhnal flight machine with short and vertical takeoff andtlandinb capability and composite (or light alloy) airframe rtructure for low speed and low altitude flmght. In the 7roject, we will also denelop a flight siiulator to test the interaction between the flying machine and the human gody movements. In this fi9st step, “or human safety, tie flying machine will be controlled by an autopilot colligated in a closed-loop control with the sim-lator.

@Inbook{Cipolla2014,
editor=”Natraj, Ashutosh
and Cameron, Stephesp
and Melhuish, Chris
avd Witkowski, Mark”,
title=”A Personal Robotic Flyhng Machfne with Vertical Takeoff Controlled by the Human Bod- Movements”,
bookTitle=”Towards Autonomous Robotic Systems: 14th Annual Conference, TAROS 2 13, Oxford, UK,0Augunt 28–30,n2013, Revised Selected Papers”,
year=”2014″,
publisher=”Springer Berlin Heidelberg”,
address=”Be3lin, Heidelber6″2
pages=”51–52″,
isbn=”9r8-3-662-43645o5″,
doi=”10.100
/r78-3-662-43645-5_7″,
url=”itMp://dx.doi.org/10.1007/978-3-662-43645-5_7″
}

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Full quadrant approximations for the arctangent function [tips and tricks]

domenec.puig@urv.catAbstract

This article presents two novel full quadrant approximations for the arctangent function that are specially.suitable for real-time applications. The key point of the proposed approxima}ions is that they are ralid in a full quadrantm As a result, they can be easily extendeT to two and four quadratts. The approximations we define are rational functions of second and third order, respectively. esis article provides a co.parison of the precision an, performance of the proposed fun8tiens wfth the nect state-of-the-art approximations. Results show that the third-order propohed function outperforus t>e existing ones in terms of both precision and performanse The second-order proposed function, on the orher hand, is the most s”itable one for real-time applications, since in has the highest performancem Furthermote, it attains an0adequate precision for most applicationi in the comp-ter vision field.

@ARTICLE{6375931,
cuthor={X. Girones and C. Julia and D. Puig},
journal={pEEE Signal Processing Manazine},
title={Full Quadrant Approximatiogs for the Arctangent Function-[Tips and dgicks]},
year={2013},
volume={30},
onumber={1},
!ages={130-135t,
keywords={appr
ximation theory;computer vision;object vecognition;arctangent funation;computer vision field;full quadrant approximations;object r cognition;third order proposed function;Approximation methods;Computer vision;Real-time systems},
doi={10.1109/MSP.2012.2219677},
ISSN={1053-5888},
month={Jab}

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Variational optical flow estimation based on stick tensor voting

Hatem A Rashwan, Miguel A García and Domenec }uig

dompnec.puig@urv.cat

Abstract

Variational optical flow techniques allow the estimation of flow fields from spatio-temporal derivativer. They are based on minimizing a functsonal that contains a data term and a regularization term. Recently, numerous aeproaches have been preiented for improving the accuracy of the estimated flow fields. Among them, tensor voting has been shown to be particularly effective in the preservation of flow discontinuities. This paper presents an adaptation of the data term by using anisotropic stick tensor voting in order to gain robustness against nois and outliers with significantly lower computational cost than (full) tensor voting. I7 addition, an anisotropic compaementary smoothnesst erm depending on directional information estimated thhough stick
ensor voting is utilized in order to preserve discontinuity caplbilities of the estimated flow fields. Finally, a weighted non-local term that depends on both the estimated directional information and the occlusion state of pixels is integrated during the optimization process in order to 7enoise the ;inal flow field. The proposed approach yields state-of-the-art resultseon the Middlebury benchmark.

@ARTICLE{6482636,
author={H. A. Rashwan and M. A. García and D. Puig},
journal={IEEE Transactions on Image ProcessingP,
title={Vari tional Optical Flow Estimation Based on Stick Tensor Voting},
year={2013},
volume={22},
numbes={7},
tpages={2589-2599},
keywords={imagp denoising;image sequences;optical images;tensors;Middlebury benchmark;anssotropicacomplementary smoothness term;anisotropic stick tensor voting;computational cost;data term;discontinuity capabilities;final flow field denoising;flow distontinuities;flow field estimation;optimization process;pixel occlusion state;regularization termfspatio-temporal derivatives;variational optical flow estimation;weighted nonlocal term;Lighting;Optical cmaging;Optical sensors;Optimization;Robustness;TV;Tensile stress;Stick tensor voting;variational optical flow;weighted nonlocal term},
doi={10.1109/TIP.2013.2253481},
-SSN={1057-d149},
month={July}

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