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About

José Lima (male) received the M.Sc. and PhD in Electrical and Computer Engineering on Faculty of Engineering of University of Porto, Portugal in 2001 and 2009. He joined the Polytechnic Institute of Bragança in 2002, and currently he is a Professor in the Electrical Engineering Department of that school. He is also a senior researcher in Centre for Robotics in Industry and Intelligent Systems group of the INESC-TEC (Institute for Systems and Computer Engineering of Porto, Portugal). He has published more than 60 papers in international scientific journals and conference proceedings. In addition, he participated in some autonomous mobile robotics competitions and applications. Moreover, his research interests are in the field of robotics and automation: simulation, path planning, image processing, localization, navigation, obstacle avoidance and perception. He participated in some national and FP7 funded projects such as Produtech, Grace, Arum, Carlos, Stamina and ColRobot.

Interest
Topics
Details

Details

005
Publications

2020

Path Planning Aware of Robot’s Center of Mass for Steep Slope Vineyards

Authors
Santos, L; Santos, F; Mendes, J; Costa, P; Lima, J; Reis, R; Shinde, P;

Publication
Robotica

Abstract
SummarySteep slope vineyards are a complex scenario for the development of ground robots. Planning a safe robot trajectory is one of the biggest challenges in this scenario, characterized by irregular surfaces and strong slopes (more than 35°). Moving the robot through a pile of stones, spots with high slope or/and with wrong robot yaw may result in an abrupt fall of the robot, damaging the equipment and centenary vines, and sometimes imposing injuries to humans. This paper presents a novel approach for path planning aware of center of mass of the robot for application in sloppy terrains. Agricultural robotic path planning (AgRobPP) is a framework that considers the A* algorithm by expanding inner functions to deal with three main inputs: multi-layer occupation grid map, altitude map and robot’s center of mass. This multi-layer grid map is updated by obstacles taking into account the terrain slope and maximum robot posture. AgRobPP is also extended with algorithms for local trajectory replanning during the execution of a trajectory that is blocked by the presence of an obstacle, always assuring the safety of the re-planned path. AgRobPP has a novel PointCloud translator algorithm called PointCloud to grid map and digital elevation model (PC2GD), which extracts the occupation grid map and digital elevation model from a PointCloud. This can be used in AgRobPP core algorithms and farm management intelligent systems as well. AgRobPP algorithms demonstrate a great performance with the real data acquired from AgRob V16, a robotic platform developed for autonomous navigation in steep slope vineyards.

2020

Wireless Sensor Network for Ignitions Detection: An IoT approach

Authors
Brito, T; Pereira, AI; Lima, J; Valente, A;

Publication
Electronics

Abstract
Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.

2020

Low Cost Binaural System Based on the Echolocation

Authors
Moreira, TFM; Lima, J; Costa, P; Cunha, M;

Publication
Advances in Intelligent Systems and Computing

Abstract
Ultrasonic sensors offers attractive features at an affordable cost. The main problem faced by the use of these devices is that the data obtained are not so easy to interpret, restricting their efficiency. This paper describes a binaural sensor system that is able to determine the coordinates of an object or a target in a two-dimensional space, focusing on mathematical and signal processing techniques to provide accurate measurements and increase the system reliability. The proposed work consists only of low cost components, which aims to demonstrate that improvement is possible. Experimental tests, performed in different scenarios, reported good accuracy and repeatability of the measurements. © 2020, Springer Nature Switzerland AG.

2019

Introduction to the Special Issue “Robotica 2016”

Authors
Cunha, B; Lima, J; Silva, M; Leitao, P;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract

2019

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

Authors
Sobreira, H; Costa, CM; Sousa, I; Rocha, L; Lima, J; Farias, PCMA; Costa, P; Paulo Moreira, AP;

Publication
Journal of Intelligent and Robotic Systems: Theory and Applications

Abstract
The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster. © 2018 Springer Science+Business Media B.V., part of Springer Nature

Supervised
thesis

2016

Projeto, modelo e construção de um manipulador com elevado grau de redundância

Author
Joaquim Manuel Costa Alves Duarte Ribeiro

Institution
UP-FEUP

2016

Sistema de Gestão de Energia Elétrica - DataLogger

Author
João Pedro Moreira da Cunha

Institution
IPB

2016

Desenvolvimento de um protótipo de um simulador de bloqueio do plexo braquial

Author
Stéphanie Coelho Monteiro

Institution
IPB

2016

Sistema Inteligente de Gestão de Energia Elétrica - Automação de uma habitação

Author
Pedro Gonçalo Guedes Lopes Praça

Institution
IPB

2016

Task Scheduling for Multiples Robots in an Industrial Environment

Author
Vítor Emanuel dos Santos Lousas Alves da Mota

Institution
UP-FEUP