Cap a un model de distribució urbana de mercaderies (DUM) més sostenible a Barcelona: anàlisi dels instruments de planificació i propostes de millora

Treballs Finals del Màster d’Energies Renovables i Sostenibilitat Energètica, Facultat de Física, Universitat de Barcelona, Curs: 2017-2018, Tutora: Maria Teresa Valdrí FortunyLa distribució urbana de mercaderies (DUM) és una activitat vital per a les ciutats, però també està vinculada a importants problemàtiques socioambientals. Els Plans de Mobilitat Urbana Sostenible (PMUS) són l’instrument jurídic més utilitzat arreu d’Europa per a definir les prioritats en matèria de mobilitat i DUM de cada municipi. Actualment, Barcelona està en procés de redactar el que serà ja el seu tercer PMUS, en aquest cas pel període 2019-2024. En el marc dels treballs previs a la redacció d’aquest nou pla, s’han analitzat els instruments de planificació d’àmbit europeu, estatal i autonòmic que són d’aplicació a la ciutat comtal. No obstant, per la seva rellevància i caràcter vinculant, s’ha posat el focus en aquells instruments contemplats en la Llei catalana 9/2003, de 13 de juny, de la mobilitat. Les superilles, un dels punts destacats de l’actual PMUS de Barcelona, generen grans oportunitats a nivell de sostenibilitat, però també grans reptes que cal abordar conjuntament des de l’àmbit acadèmic, l’Administració pública, el sector privat i la ciutadania. En aquest treball es plantegen tres propostes per assolir un model de DUM més sostenible i eficient. Totes les opcions plantejades es fonamenten en una distribució nocturna fins a plataformes logístiques dins de la ciutat (de diferents característiques segons la proposta) i una distribució en última milla amb mitjans de baixes emissions. Tanmateix, també s’han identificat obstacles que poden dificultar la implantació de models més sostenibles com ara: la manca d’adaptació dels comerciants i els operadors logístics a noves formes de DUM; les dificultats per vehicular de forma eficient una xarxa de plataformes logístiques dins de la ciutat; l’alt cost de la inversió; entre d’altres. En definitiva, l’objectiu últim d’aquest TFM és aportar noves propostes a un debat de ciutat que necessàriament ha de portar a la implantació d’un model de DUM més sostenibl

Garment manipulation dataset for robot learning by demonstration through a virtual reality framework

.Being able to teach complex capabilities, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled using learning from demonstration datasets. The few garment folding datasets available nowadays to the robotics research community are either gathered from human demonstrations or generated through simulation. The former have the huge problem of perceiving human action and transferring it to the dynamic control of the robot, while the latter requires coding human motion into the simulator in open loop, resulting in far-from-realistic movements. In this article, we present a reduced but very accurate dataset of human cloth folding demonstrations. The dataset is collected through a novel virtual reality (VR) framework we propose, based on Unity’s 3D platform and the use of a HTC Vive Pro system. The framework is capable of simulating very realistic garments while allowing users to interact with them, in real time, through handheld controllers. By doing so, and thanks to the immersive experience, our framework gets rid of the gap between the human and robot perception-action loop, while simplifying data capture and resulting in more realistic sampleThis work was developed in the context of the project CLOTHILDE (”CLOTH manIpulation Learning from DEmonstrations”) which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 741930) and is also supported by the BURG project PCI2019-103447 funded by MCIN/ AEI /10.13039/501100011033 and by the ”European Union”.Peer ReviewedPostprint (published version

Carbon monoxide reduces neuropathic pain and spinal microglial activation by inhibiting nitric oxide synthesis in mice

Background: Carbon monoxide (CO) synthesized by heme oxygenase 1 (HO-1) exerts antinociceptive effects during inflammation but its role during neuropathic pain remains unknown. Our objective is to investigate the exact contribution of CO derived from HO-1 in the modulation of neuropathic pain and the mechanisms implicated. Methodology/Principal Findings: We evaluated the antiallodynic and antihyperalgesic effects of CO following sciatic nerve injury in wild type (WT) or inducible nitric oxide synthase knockout (NOS2-KO) mice using two carbon monoxide-releasing molecules (CORM-2 and CORM-3) and an HO-1 inducer (cobalt protoporphyrin IX, CoPP) daily administered from days 10 to 20 after injury. The effects of CORM-2 and CoPP on the expression of HO-1, heme oxygenase 2 (HO-2), neuronal nitric oxide synthase (NOS1) and NOS2 as well as a microglial marker (CD11b/c) were also assessed at day 20 after surgery in WT and NOS2-KO mice. In WT mice, the main neuropathic pain symptoms induced by nerve injury were significantly reduced in a time-dependent manner by treatment with CO-RMs or CoPP. Both CORM-2 and CoPP treatments increased HO-1 expression in WT mice, but only CoPP stimulated HO-1 in NOS2-KO animals. The increased expression of HO-2 induced by nerve injury in WT, but not in NOS2-KO mice, remains unaltered by CORM-2 or CoPP treatments. In contrast, the over-expression of CD11b/c, NOS1 and NOS2 induced by nerve injury in WT, but not in NOS2-KO mice, were significantly decreased by both CORM-2 and CoPP treatments. These data indicate that CO alleviates neuropathic pain through the reduction of spinal microglial activation and NOS1/NOS2 over-expression. Conclusions/Significance: This study reports that an interaction between the CO and nitric oxide (NO) systems is taking place following sciatic nerve injury and reveals that increasing the exogenous (CO-RMs) or endogenous (CoPP) production of CO may represent a novel strategy for the treatment of neuropathic pain

A virtual reality framework for fast dataset creation applied to cloth manipulation with automatic semantic labelling

© 2023 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.Teaching complex manipulation skills, such as folding garments, to a bi-manual robot is a very challenging task, which is often tackled through learning from demonstration. The few datasets of garment-folding demonstrations available nowadays to the robotics research community have been either gathered from human demonstrations or generated through simulation. The former have the great difficulty of perceiving both cloth state and human action as well as transferring them to the dynamic control of the robot, while the latter require coding human motion into the simulator in open loop, i.e., without incorporating the visual feedback naturally used by people, resulting in far-from-realistic movements. In this article, we present an accurate dataset of human cloth folding demonstrations. The dataset is collected through our novel virtual reality (VR) framework, based on Unity’s 3D platform and the use of an HTC Vive Pro system. The framework is capable of simulating realistic garments while allowing users to interact with them in real time through handheld controllers. By doing so, and thanks to the immersive experience, our framework permits exploiting human visual feedback in the demonstrations while at the same time getting rid of the difficulties of capturing the state of cloth, thus simplifying data acquisition and resulting in more realistic demonstrations. We create and make public a dataset of cloth manipulation sequences, whose cloth states are semantically labeled in an automatic way by using a novel low-dimensional cloth representation that yields a very good separation between different cloth configurations.The research leading to these results receives funding from the European Research Council (ERC) from the European Union Horizon 2020 Programme under grant agreement no. 741930 (CLOTHILDE: CLOTH manIpulation Learning from DEmonstrations) and project SoftEnable (HORIZONCL4-2021-DIGITAL-EMERGING-01-101070600). Authors also received funding from project CHLOE-GRAPH (PID2020-118649RB-I00) funded by MCIN/ AEI /10.13039/501100011033 and COHERENT (PCI2020-120718-2) funded by MCIN/ AEI /10.13039/501100011033 and cofunded by the ”European Union NextGenerationEU/PRTR”.Peer ReviewedPostprint (author's final draft