Pedro Henrique Moura

Abstract

Abstract

Abstract
The world society needs to produce more food with the highest quality standards to feed the world population with the same level of nutrition. Microfarms and local food production enable growing vegetables near the population and reducing the operational logistics costs related to post-harvest food handling. However, it isn't economical viable neither efficient to have one person devoted to these microfarms task. To overcome this issue, we propose an open-source robotic solution capable of performing multitasks in small polyculture farms. This robot is equipped with optical sensors, manipulators and other mechatronic technology to monitor and process both biotic and abiotic agronomic data. This information supports the consequent activation of manipulators that perform several agricultural tasks: crop and weed detection, sowing and watering. The development of the robot meets low-cost requirements so that it can be a putative commercial solution. This solution is designed to be relevant as a test platform to support the assembly of new sensors and further develop new cognitive solutions, to raise awareness on topics related to Precision Agriculture. We are looking for a rational use of resources and several other aspects of an evolved, economically efficient and ecologically sustainable agriculture.

Abstract
Viticulturists need to obtain the estimation of productivity map during the grape vine harvesting, to understand in detail the vineyard variability. An accurate productivity map will support the farmer to take more informed and accurate intervention in the vineyard in line with the precision viticulture concept. This work presents a novel solution to measure the productivity during vineyard harvesting operation realized by a grape harvesting machine. We propose 2D LIDAR sensor attached to low cost IoT module located inside the harvesting machine, to estimate the volume of grapes. Besides, it is proposed data methodology to process data collected and productivity map, considering GIS software, expecting to support the winemakers decisions. A PCD map is also used to validate the method developed by comparison. © Springer Nature Switzerland AG 2019.

Abstract
The coordination problem in multi-AGV systems can be approached as an optimization problem and aims to make possible the execution of several tasks simultaneously, avoiding collision and deadlock situations and reducing the average execution time. Time Enhanced A* (TEA*) is one of the path planning algorithms developed for this purpose. This paper focus on the implementation of the TEA* for real industrial applications. In that context, a new approach was developed to complement the TEA* with the capacity to approximate the planning of the future positions for differential robots with its real behaviour.