Baby Blue : tradução do seriado Breaking Bad em livro impresso

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Instituto de Artes, Departamento de Desenho Industrial, 2017. Relatório dos projetos de diplomação em Design, nas habilitações de Programação Visual e Projeto de Produto.O presente documento descreve o projeto de graduacao no qual o objetivo e produzir um livro-objeto baseado no seriado Breaking Bad (2008 – 2013) de Vince Gilligan sob uma perspectiva do Design Grafico e de Produto. A partir da analise do seriado considerando suas particularidades narrativas e linguisticas, criou-se sua traducao para o formato de um livro impresso que tenha sua fisicalidade incorporada como linguagem. Neste relatorio serao apresentados questionamentos acerca dos meios envolvidos e do livro como objeto fisico. Alem disso, e apresentada tambem uma analise do seriado Breaking Bad, sua linguagem cinematografica, e um conjunto de referencias que resultam no projeto do livro-objeto exposto ao final do relatorio

Nonlinear model predictive control for aerial manipulation

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.Peer ReviewedPostprint (author's final draft

Plant-Inspired Soft Bistable Structures Based on Hygroscopic Electrospun Nanofibers

The tissue composition and microstructures of plants have dynamic morphologies that change according to their environments. Recently, multifunctional responsive materials and smart structures also took inspiration from these plants' features. Dionaea muscipula leaves provide a remarkable example of an optimized structure that, owing to the synergistic integration of bistability, material, and geometrical properties, permits to overcome the performance limits of purely diffusive processes. In this paper, a hygroscopic bistable structure (HBS) inspired by the Venus flytrap leaves is presented, obtained by bonding prestretched poly(dimethylsiloxane) (PDMS) layers prior to depositing electrospun polyethylene oxide (PEO) nanofibers. A hygroresponsive bilayer (HBL) is also obtained by electrospinning of PEO on an unstretched PDMS layer. The hygroscopic material (Young's modulus and hygroscopic expansion) is mechanically characterized so as to predict the response time of a bending HBL in response to a step humidity variation. The HBS response time (≈1 s) is sensibly lower than the one of purely diffusive HBL (≈10 s) thanks to bistability. An illustrative implementation is also presented, exploiting an HBS to trigger the curvature of a PDMS optical focusing system. The developed plant-inspired soft bistable structure can also be used for sensing (e.g., humidity), energy harvesting, as well as advanced soft robotics applications

Shape estimation based on Kalman filtering: Towards fully soft proprioception

An innovative methodology to realize a sensing system able to estimate the shape of a soft robot arm without hampering 'softness' is presented. The system is based on a low-cost plastic optical fiber (POF) used as curvature sensor and on a simplified steady-state model, both integrated in an Adaptive Extended Kalman Filter (AEKF). Sensory feedback was obtained through accelerometers and it was used as quantitative benchmark for the AEKF. The AEKF estimation turned out to be more accurate (RMS error < 5°) than the model prediction alone and the soft sensor alone, thus supporting the proposed fully soft proprioception strategy

Nonlinear model predictive control for aerial manipulation

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.Peer Reviewe