High level task planning with inference for the TIAGo robot

The need to combine task planning and motion planning in robotics is well understood. The task planner generates a plan to solve the problem while the motion planner executes the actions of the problem. The previous framework is applied in many state machines that solve complex problems. But in this project we want to present an interface that communicates the task planner layer and the motion planner layer, and updates the geometric information of the environment to inform the task planner. This framework allows to solve complex tasks with basic information of the goal, and replan whenever the motion could not be executed. All the information of the problems is modelled as logical predicates. The objective of this project is to generate a generic model of the environment, with a set of feasible motions of the robot, and use this interface to solve many different planning problems involving those actions, by just giving simple goals. The result is to make the robot more autonomous and allow that any user could use it by giving simple orders. Moreover this project presents the different frameworks and algorithms used to simulate those actions in the robot such as: Sequential Quadratic Programming optimization, Rapidly Random Exploring Tree (RRT) or SBPL global planning. It also shows an introduction to PDDL language used to model the problem and the actions, and the Fast-Froward (FF) solver that is the responsible to translate the problem as a graph and solve it. Finally we test it on different experiments in simulation, by using the TIAGo platform of PAL robotics. The results are promising and allow to dream in service robots solving complex tasks simply computing and modelling basic actions

Polyacrylates Derived from Biobased Ethyl Lactate Solvent via SET-LRP

The precise synthesis of polymers derived from alkyl lactate ester acrylates is reported for the first time. Kinetic experiments were conducted to demonstrate that Cu(0) wire-catalyzed single electron transfer-living radical polymerization (SET-LRP) in alcohols at 25 °C provides a green methodology for the LRP of this forgotten class of biobased monomers. The acrylic derivative of ethyl lactate (EL) solvent and homologous structures with methyl and n-butyl ester were polymerized with excellent control over molecular weight, molecular weight distribution, and chain-end functionality. Kinetics plots in conventional alcohols such as ethanol and methanol were first order in the monomer, with molecular weight increasing linearly with conversion. However, aqueous EL mixtures were found to be more suitable than pure EL to mediate the SET-LRP process. The near-quantitative monomer conversion and high bromine chain-end functionality, demonstrated by matrix-assisted laser desorption ionization time-of-flight analysis, further allowed the preparation of innovative biobased block copolymers containing rubbery poly(ethyl lactate acrylate) poly(ELA) sequences. For instance, the poly(ELA)-b-poly(glycerol acrylate) block copolymer self-assembled in water to form stable micelles with chiral lactic acid-derived block-forming micellar core as confirmed by the pyrene-probe-based fluorescence technique. Dynamic light scattering and transmission electron microscopy measurements revealed the nanosize spherical morphology for these biobased aggregates

Famílies botàniques de plantes medicinals

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia, Assignatura: Botànica Farmacèutica, Curs: 2013-2014, Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són els recull de 175 treballs d’una família botànica d’interès medicinal realitzats de manera individual. Els treballs han estat realitzat per la totalitat dels estudiants dels grups M-2 i M-3 de l’assignatura Botànica Farmacèutica durant els mesos d’abril i maig del curs 2013-14. Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pel professor de l’assignatura i revisats i finalment co-avaluats entre els propis estudiants. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica

High level task planning with inference for the TIAGo robot

The need to combine task planning and motion planning in robotics is well understood. The task planner generates a plan to solve the problem while the motion planner executes the actions of the problem. The previous framework is applied in many state machines that solve complex problems. But in this project we want to present an interface that communicates the task planner layer and the motion planner layer, and updates the geometric information of the environment to inform the task planner. This framework allows to solve complex tasks with basic information of the goal, and replan whenever the motion could not be executed. All the information of the problems is modelled as logical predicates. The objective of this project is to generate a generic model of the environment, with a set of feasible motions of the robot, and use this interface to solve many different planning problems involving those actions, by just giving simple goals. The result is to make the robot more autonomous and allow that any user could use it by giving simple orders. Moreover this project presents the different frameworks and algorithms used to simulate those actions in the robot such as: Sequential Quadratic Programming optimization, Rapidly Random Exploring Tree (RRT) or SBPL global planning. It also shows an introduction to PDDL language used to model the problem and the actions, and the Fast-Froward (FF) solver that is the responsible to translate the problem as a graph and solve it. Finally we test it on different experiments in simulation, by using the TIAGo platform of PAL robotics. The results are promising and allow to dream in service robots solving complex tasks simply computing and modelling basic actions

Photoinduced upgrading of lactic acid-based solvents to block copolymer surfactants

We report a new strategy toward the development of block copolymer surfactants from chemicals of the lactic acid family. A particularly unique aspect of this work is the use of green solvents as biobased platform chemicals to generate well-defined and nanostructure-forming materials. Herein, efficient functionalization of ethyl lactate (EL) and N,N-dimethyl lactamide (DML) solvents with acrylate groups generated monomers that could be polymerized by the photoinduced copper-catalyzed living radical polymerization process to yield polymeric materials with different water solubilities. These lactic acid-derived monomers were used as a major component in well-defined diblock copolymers composed of poly(EL acrylate) and poly(DML acrylate) segments as hydrophobic and hydrophilic building blocks, respectively. The resulting amphiphilic copolymers could self-assemble in aqueous solution to form nanoparticles with different morphologies (e.g., large-compound micelles and vesicles). Subsequently, the formed amphiphilic polymers were employed as efficient stabilizers in the emulsion polymerization of methyl methacrylate and styrene, offering a facile method for the synthesis of well-defined and stable polymer latexes in the range of 100-200 nm, demonstrating the practical significance of these biobased polymers in nanomaterial synthesis