Details
Name
Pedro Henrique MouraCluster
Industrial and Systems EngineeringRole
ResearcherSince
01st September 2018
Nationality
PortugalCentre
Robotics in Industry and Intelligent SystemsContacts
+351220413317
pedro.h.moura@inesctec.pt
2021
Authors
Terra F.; Rodrigues L.; Magalhaes S.; Santos F.; Moura P.; Cunha M.;
Publication
2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation, IRIA 2021
Abstract
2019
Authors
Moura, P; Ribeiro, D; dos Santos, FN; Gomes, A; Baptista, R; Cunha, M;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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.
2019
Authors
Moura, P; Costa, P; Lima, J; Costa, P;
Publication
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM-2018)
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.
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