Abstract
The cultivar Loureiro of Vitis vinifera is one of the most economically important, recommended in almost the totality of the Regiao Demarcada dos Vinhos Verdes. In vineyards, the grape productivity of this cultivar is normal while in others it is extremely low. The aim of this work was to study the morphology and germination of Vitis vinifera cv. Loureiro pollen with high and low productivity. The pollen grain was examined under light, transmission and scanning electron microscopy. Typically V. vinifera pollen present three furrows but in the cultivar Loureiro we found tricolporated and acolporated (without furrows or pores) pollen grains. Both pollen types present generative and vegetative cells with the usual aspect and a dense cytoplasm rich in organelles. In the acolporated pollen a continuous exine layer and an irregular intine layer were observed. Differences were found in the starch accumulation, since only in tricolporated pollen abundant plastids filled with numerous starch granules were observed. To determine the causes of the low productivity of this cultivar we tested pollen viability by the fluorochromatic reaction and pollen germinability by in vitro assays. We observed that the acolporated pollen grain is viable, but no germination was recorded.

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Localization is essential to modern autonomous robots in order to enable effective completion of complex tasks over possibly large distances in low structured environments. In this paper, a Extended Kalman Filter is used in order to implement self-localization. This is done by merging odometry and localization information, when available. The used landmarks are colored poles that can be recognized while the robot moves around performing normal tasks. This paper models measurements with very different characteristics in distance and angle to markers and shows results of the self-localization method. Results of simulations and real robot tests are shown.

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This paper presents a architecture control and model identification of a onmi-Directional Mobile Robot It is divided into the three stages. Stage one proposes a procedure for dynamic model identification and control of the "motor + reduction + encoder" process of the Robot's Motors. Second, proposes the identification of a dynamic model for the whole mobile robot considering it as a multi-variable system. Third, presents a algorithm for perfect trajectory tracking of Omni-Directional Mobile Robots, based on restriction on motor's velocities. This algorithm combines the restriction on motor's velocities and the kinematic model of mobile robot to generate ideal drive velocities for the mobile robot to follow the trajectories correctly with the best possible performance.