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About

About

Nuno Cruz holds a MSc. in Digital Systems Engineering from UMIST, UK, and a PhD. in Electrical Engineering from the University of Porto, in Portugal. He is currently an Assistant Professor at the Faculty of Engineering of the University of Porto and a Coordinator at the Centre for Robotics and Autonomous Systems at INESC TEC. Nuno Cruz is an Associate Editor of the IEEE Journal of Oceanic Engineering and has over 100 publications in journals and proceedings of international conferences. He has been involved in the development and deployment of marine robotic vehicles for more than 20 years. He has led the design of multiple autonomous vehicles at the University of Porto and INESC TEC, namely the Zarco and Gama ASVs and the MARES and TriMARES AUVs. His current research interests include the development of strategies for the efficient use of autonomous vehicles at sea, including the concept of adaptive sampling.

More info in http://oceansys.fe.up.pt/

Interest
Topics
Details

Details

019
Publications

2022

An Autonomous System for Collecting Water Samples from the Surface

Authors
Pinto, AF; Cruz, NA; Ferreira, BM; Abreu, NM; Goncalves, CE; Villa, MP; Matos, AC; Honorio, LD; Westin, LG;

Publication
OCEANS 2022

Abstract

2022

Design and Experimental Tests of a Buoyancy Change Module for Autonomous Underwater Vehicles

Authors
Carneiro, JF; Pinto, JB; de Almeida, FG; Cruz, NA;

Publication
ACTUATORS

Abstract
Ocean exploration is of major importance for several reasons, including energy and mineral resource retrieval, sovereignty, and environmental concerns. The use of autonomous underwater vehicles (AUV) has thus been receiving increased attention from the scientific community. In this context, it has been shown that the use of buoyancy change modules (BCMs) can significantly improve the energy efficiency of an AUV. However, the literature regarding the detailed design of these modules is scarce. This paper contributes to this field by describing the development of an electromechanical buoyancy change module prototype to be incorporated into an existing AUV. A detailed description of the constraints and compromises existing in the design of the device components is presented. In addition, the mechanical design of the hull based on FEM simulations is described in detail. The prototype is experimentally tested in a shallow pool where its full functionality is shown. The paper also presents preliminary experimental values of the power consumption of the device and compares them with the ones provided by existing models in the literature.

2022

Genetic Algorithm to Solve Optimal Sensor Placement for Underwater Vehicle Localization with Range Dependent Noises

Authors
Villa, M; Ferreira, B; Cruz, N;

Publication
SENSORS

Abstract
In source localization problems, the relative geometry between sensors and source will influence the localization performance. The optimum configuration of sensors depends on the measurements used for the source location estimation, how these measurements are affected by noise, the positions of the source, and the criteria used to evaluate the localization performance. This paper addresses the problem of optimum sensor placement in a plane for the localization of an underwater vehicle moving in 3D. We consider sets of sensors that measure the distance to the vehicle and model the measurement noises with distance dependent covariances. We develop a genetic algorithm and analyze both single and multi-objective problems. In the former, we consider as the evaluation metric the arithmetic average along the vehicle trajectory of the maximum eigenvalue of the inverse of the Fisher information matrix. In the latter, we estimate the Pareto front of pairs of common criteria based on the Fisher information matrix and analyze the evolution of the sensor positioning for the different criteria. To validate the algorithm, we initially compare results with a case with a known optimal solution and constant measurement covariances, obtaining deviations from the optimal less than 0.1%. Posterior, we present results for an underwater vehicle performing a lawn-mower maneuver and a spiral descent maneuver. We also present results restricting the allowed positions for the sensors.

2022

Sonar-based Cable Detection for in-situ Calibration of Marine Sensors

Authors
Oliveira, AJ; Ferreira, BM; Diamant, R; Cruz, NA;

Publication
2022 IEEE/OES Autonomous Underwater Vehicles Symposium, AUV 2022

Abstract
In-situ calibration of marine sensors requires close-range positioning. In turn, localization relative to a given object of interest is necessary. This paper deals with the detection of a vertical cable hanging from a marine observatory implemented by means of a moored buoy. An algorithm composed of sequential image filtering, segmentation and template matching is proposed. Two approaches for generating the cable's acoustic image template are introduced. The performance of the approaches, obtained by comparison with ground-truth measurements, are illustrated over challenging cluttered acoustic images collected in a test tank. The results indicate a performance better than 74% of the best candidate to match the actual cable. © 2022 IEEE.

2022

On the localization of an acoustic target using a single receiver

Authors
Ferreira, B; Alves, J; Cruz, N; Graca, P;

Publication
Oceans Conference Record (IEEE)

Abstract
This paper addresses the localization of an unsynchronized acoustic source using a single receiver and a synthetic baseline. The enclosed work was applied in a real search of an electric glider that was lost at sea and later recovered, using the described approach. The search procedure is presented along with the localization methods and a metric based on the eigenvalues of the Fisher Information Matrix is used to quantify the expected uncertainty of the estimate. © 2022 IEEE.

Supervised
thesis

2021

Deepwater Intelligent Video Recorder

Author
Luís Páris Couto Venn Fonseca

Institution
UP-FEUP

2021

Efficent Verified MPC

Author
Manuel Luís Magalhães Duarte Correia

Institution
UP-FCUP

2021

Classificação automática de termogramas do pé diabético usando técnicas de machine learning

Author
Pedro Miguel Bento Teixeira

Institution
UP-FEUP

2021

Underwater Localization in Complex Environments

Author
Maria Sara Delgadinho Noronha

Institution
UP-FEUP

2021

Information-aware Feature-based Underwater Localization and Planning

Author
António José Ventura de Oliveira

Institution
UP-FEUP