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About

João Pedro Carvalho de Souza is graduated in Electrical Engineering with specialization in Electronic Systems by the Federal University of Juiz de Fora (2015) and is Master in Electrical Engineering - Energy Systems - by the same institution (2018). During his career, he worked in research and development (R&D) in Brazil related to his areas of interest: robotics, ROS, unmanned aerial vehicles, development of software and firmware, artificial intelligence and intelligent systems. He also worked in electronic projects in the scope of embedded systems and digital electronics. Currently, he works as a research associate in the laboratory CRISS - Center for Industrial Robotics and Intelligent Systems - INESCTEC.

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002
Publications

2019

AdaptPack Studio: Automatic Offline Robot Programming Framework for Factory Environments

Authors
Castro, A; Souza, JP; Rocha, L; Silva, MF;

Publication
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
The brisk and dynamic environment that factories are facing, both as an internal and an external level, requires a collection of handy tools to solve emerging issues in the industry 4.0 context. Part of the common challenges that appear are related to the increasing demand for high adaptability in the organizations' production lines. Mechanical processes are becoming faster and more adjustable to the production diversity in the Fast Moving Consumer Goods (FMCG). Concerning the previous characteristics, future factories can only remain competitive and profitable if they have the ability to quickly adapt all their production resources in response to inconstant market demands. Having previous concerns in focus, this paper presents a fast and adaptative framework for automated cells modeling, simulation and offline robot programming, focused on palletizing operations. Established as an add-on for the Visual Components (VC) 3D manufacturing simulation software, the proposed application allows performing fast layout modeling and automatic offline generation of robot programs. Furthermore, A* based algorithms are used for generating collision-free trajectories, discretized both in the robot joints space and in the Cartesian space. The software evaluation was tested inside the VC simulation world and in the real-world scenario. Results have shown to be concise and accurate, with minor displacement inaccuracies due to differences between the virtual model and the real world. © 2019 IEEE.

2019

Converting Robot Offline Programs to Native Code Using the AdaptPack Studio Translators

Authors
Souza, JP; Castro, A; Rocha, L; Relvas, P; Silva, MF;

Publication
19th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2019

Abstract
The increase in productivity is a demand for modern industries that need to be competitive in the actual business scenario. To face these challenges, companies are increasingly using robotic systems for end-of-line production tasks, such as wrapping and palletizing, as a mean to enhance the production line efficiency and products traceability, allowing human operators to be moved to more added value operations. Despite this increasing use of robotic systems, these equipments still present some inconveniences regarding the programming procedure, as the time required for its execution does not meet the current industrial needs. To face this drawback, offline robot programming methods are gaining great visibility, as their flexibility and programming speed allows companies to face the need of successive changes in the production line set-up. However, even with a great number of robots and simulators that are available in market, the efforts to support several robot brands in one software did not reach the needs of engineers. Therefore, this paper proposes a translation library named AdaptPack Studio Translator, which is capable to export proprietary codes for the ABB, Fanuc, Kuka, and Yaskawa robot brands, after their offline programming has been performed in the Visual Components software. The results presented in this paper are evaluated in simulated and real scenarios. © 2019 IEEE.

2019

Autonomous Landing of UAV Based on Artificial Neural Network Supervised by Fuzzy Logic

Authors
Carvalho de Souza, JPC; Marques Marcato, ALM; de Aguiar, EP; Juca, MA; Teixeira, AM;

Publication
JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS

Abstract
Autonomous Unmanned Aerial Vehicles (UAVs) become an important field of research in which multiple applications can be designed, such as surveillance, deliveries, and others. Thus, studies aiming to improve the performance of these vehicles are being proposed: from new sensing solutions to more robust control techniques. Additionally, the autonomous UAV has challenges in flight stages as the landing. This procedure needs to be performed safely with a reduced error margin in static and dynamic targets. To solve this imperative issue, many applications with computer vision and control theory have been developed. Therefore, this paper presents an alternative method to train a multilayer perceptron neural network based on fuzzy Mamdani logic to control the landing of a UAV on an artificial marker. The advantage of this method is the reduction in computational complexity while maintaining the characteristics and intelligence of the fuzzy logic controller. Results are presented with simulation and real tests for static and dynamic landing spots. For the real experiments, a quadcopter with an onboard computer and ROS is used.

2018

Direct-DRRT*: A RRT improvement proposal

Authors
Coelho, FO; Carvalho, JP; Pinto, MF; Marcato, AL;

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
The present work aims at the development of a new heuristic approach named Direct-DRRT . This new algorithm is an improvement of the DRRT* method, which is the fusion between RRT * and DRRT. This improvement aims at the mobile robot autonomous planning considering less memory and computational time for a route design. The results show the efficiency of our approach compared to the other methods, presenting less processing time and a signification reduced number of nodes and paths. © 2018 IEEE.

2018

EKF and computer vision for mobile robot localization

Authors
Coelho, FO; Carvalho, JP; Pinto, MF; Marcato, AL;

Publication
13th APCA International Conference on Control and Soft Computing, CONTROLO 2018 - Proceedings

Abstract
The autonomous robotic system accurate localization is a challenging step in robot navigation field once the mobile device should avoid dangerous situations, such as unsafe conditions and collisions. In this context, the present paper proposes a localization method using the Extended Kalman Filter (EKF) to fuse the information coming from two different sensors (i.e. odometry and computer vision). The localization results present with known and unknown starting points and are tested in a simulated environment. © 2018 IEEE.