CRAS - Other Publications

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    Autonomous Underwater Vehicles
    ( 2011) Nuno Cruz
    Autonomous Underwater Vehicles (AUVs) are remarkable machines that revolutionized the process of gathering ocean data. Their major breakthroughs resulted from successful developments of complementary technologies to overcome the challenges associated with autonomous operation in harsh environments. Most of these advances aimed at reaching new application scenarios and decreasing the cost of ocean data collection, by reducing ship time and automating the process of data gathering with accurate geo location. With the present capabilities, some novel paradigms are already being employed to further exploit the on board intelligence, by making decisions on line based on real time interpretation of sensor data. This book collects a set of self contained chapters covering different aspects of AUV technology and applications in more detail than is commonly found in journal and conference papers. They are divided into three main sections, addressing innovative vehicle design, navigation and con
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    The European Project Semester at ISEP Learning to Learn Engineering
    ( 2013) Benedita Malheiro ; Manuel Santos Silva ; Ribeiro,MC ; Guedes,P ; Ferreira,P
    The European Project Semester at ISEP (EPS@ISEP) is a one semester project-based learning programme addressed to engineering students from diverse scientific backgrounds and nationalities. The students, organized in multicultural teams, are challenged to solve real world multidisciplinary problems, accounting for 30 ECTU. The EPS package, although focused on project development (20 ECTU), includes a series of complementary seminars aimed at fostering soft, project-related and engineering transversal skills (10 ECTU). This paper presents the study plan, resources, operation and results of the EPS@ISEP that was created in 2011 to apply the best engineering education practices and promote the internationalization of ISEP. The results show that the EPS@ISEP students acquire during one semester the scientific, technical and soft competences necessary to propose, design and implement a solution for a multidisciplinary problem.
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    Fast 3D Map Matching Localisation Algorithm
    ( 2013) Pinto,M ; António Paulo Moreira ; Aníbal Matos ; Héber Miguel Sobreira ; Filipe Neves Santos
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    Lecture Notes in Electrical Engineering: Preface
    ( 2015) António Paulo Moreira ; Aníbal Matos ; Germano Veiga
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    The MARES AUV, A Modular Autonomous Robot for Environment Sampling
    ( 2009) Nuno Cruz ; Patrícia Ramos ; Aníbal Matos
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    Object recognition using laser range finder and machine learning techniques
    ( 2013) Andry Maykol Pinto ; Luís Freitas Rocha ; António Paulo Moreira
    In recent years, computer vision has been widely used on industrial environments, allowing robots to perform important tasks like quality control, inspection and recognition. Vision systems are typically used to determine the position and orientation of objects in the workstation, enabling them to be transported and assembled by a robotic cell (e.g. industrial manipulator). These systems commonly resort to CCD (Charge-Coupled Device) Cameras fixed and located in a particular work area or attached directly to the robotic arm (eye-in-hand vision system). Although it is a valid approach, the performance of these vision systems is directly influenced by the industrial environment lighting. Taking all these into consideration, a new approach is proposed for eye-on-hand systems, where the use of cameras will be replaced by the 2D Laser Range Finder (LRF). The LRF will be attached to a robotic manipulator, which executes a pre-defined path to produce grayscale images of the workstation. With this technique the environment lighting interference is minimized resulting in a more reliable and robust computer vision system. After the grayscale image is created, this work focuses on the recognition and classification of different objects using inherent features (based on the invariant moments of Hu) with the most well-known machine learning models: k-Nearest Neighbor (kNN), Neural Networks (NNs) and Support Vector Machines (SVMs). In order to achieve a good performance for each classification model, a wrapper method is used to select one good subset of features, as well as an assessment model technique called K-fold cross-validation to adjust the parameters of the classifiers. The performance of the models is also compared, achieving performances of 83.5% for kNN, 95.5% for the NN and 98.9% for the SVM (generalized accuracy). These high performances are related with the feature selection algorithm based on the simulated annealing heuristic, and the model assessment (k-fold cross-validation). It makes possible to identify the most important features in the recognition process, as well as the adjustment of the best parameters for the machine learning models, increasing the classification ratio of the work objects present in the robot's environment.
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    Preface
    ( 2018) Manuel Santos Silva ; Virk,GS ; Tokhi,MO ; Benedita Malheiro ; Ferreira,P ; Guedes,P ; 5855 ; 5655
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    Preface
    ( 2018) Manuel Santos Silva ; Virk,GS ; Tokhi,MO ; Benedita Malheiro ; Ferreira,P ; Guedes,P ; 5855 ; 5655
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    Pushing for Higher Autonomy and Cooperative Behaviors in Maritime Robotics
    ( 2019) Curtin,TB ; Nuno Cruz ; Djapic,V ; Potter,JR ; Kirkwood,WJ ; 5155
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    Revisiting Lucas-Kanade and Horn-Schunck
    ( 2013) Andry Maykol Pinto ; António Paulo Moreira ; Paulo José Costa ; Miguel Velhote Correia
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    Robot@Factory: Localization Method Based on Map-Matching and Particle Swarm Optimization
    ( 2013) Andry Maykol Pinto ; António Paulo Moreira ; Paulo José Costa
    This paper presents a novel localization method for small mobile robots. The proposed technique is especially designed for the Robot@Factory which is a new robotic competition presented in Lisbon 2011. The real-time localization technique resorts to low-cost infra-red sensors, a map-matching method and an Extended Kalman Filter (EKF) to create a pose tracking system that is well-behaved. The sensor information is continuously updated in time and space through the expected motion of the robot. Then, the information is incorporated into the map-matching optimization in order to increase the amount of sensor information that is available at each moment. In addition, a particle filter based on Particle Swarm Optimization (PSO) relocates the robot when the map-matching error is high. Meaning that the map-matching is unreliable and robot is lost. The experiments conducted in this paper prove the ability and accuracy of the presented technique to localize small mobile robots for this competition. Therefore, extensive results show that the proposed method have an interesting localization capability for robots equipped with a limited amount of sensors.
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    Self-localisation of indoor mobile robots using multi-hypotheses and a matching algorithm
    ( 2013) Pinto,M ; Héber Miguel Sobreira ; António Paulo Moreira ; Hélio Mendonça ; Aníbal Matos
    This paper proposes a new, fast and computationally light weight methodology to pinpoint a robot in a structured scenario. The localisation algorithm performs a tracking routine to pinpoint the robot's pose as it moves in a known map, without the need for preparing the environment, with artificial landmarks or beacons. To perform such tracking routine, it is necessary to know the initial position of the vehicle. This paper describes the tracking routine and presents a solution to pinpoint that initial position in an autonomous way, using a multi-hypotheses strategy. This paper presents experimental results on the performance of the proposed method applied in two different scenarios: (1) in the Middle Size Soccer Robotic League (MSL), using artificial vision data from an omnidirectional robot and (2) in indoor environments using 3D data from a tilting Laser Range Finder of a differential drive robot (called RobVigil). This paper presents results comparing the proposed methodology and an Industrial Positioning System (the Sick NAV350), commonly used to locate Autonomous Guided Vehicles (AGVs) with a high degree of accuracy in industrial environments.