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About

I am a researcher at the Centre for Robotics and Autonomous Systems (CRAS). In 2008 I received the degree of MS in Electrical and Computer Engineering from the University of Porto. In 2017 I received the degree of PhD in Electrical and Computer Engineering from the University of Porto. My thesis focused in search mission planning using autonomous underwater vehicles. My research activity is mainly focused on robotics in research topics such as path planning and artificial intelligence. I also work as a systems engineer on our vehicles and develop software as required.

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001
Publications

2017

Accounting for uncertainty in search operations using AUVs

Authors
Abreu, N; Cruz, N; Matos, A;

Publication
2017 IEEE OES International Symposium on Underwater Technology, UT 2017

Abstract
Traditional coverage path planners create lawnmower-type paths in the operating area completely ignoring the uncertainty in the vehicle's position. However, in the presence of significant uncertainty in localization estimates, one can no longer guarantee that the vehicle will cover all the area according to plan. Aiming to bridge this gap, we present a coverage path planning technique for search operations which takes into account the vehicle's position and detection performance uncertainties and tries to minimize this uncertainty along the planned path. The objective is to plan paths, using a localization error model as input, to reduce as much uncertainty as possible and to minimize the extra path length (swath overlap) while satisfying mission feasibility constraints. We introduce an algorithm that calculates what will be the best moments for bringing the vehicle to surface to ensure a bounded position error. We also consider time and energy constraints that may influence the planned trajectory as path overlap is increased to account for uncertainty. Additionally we challenge the assumption frequently seen in coverage algorithms where two observations of the same target are considered independent. © 2017 IEEE.

2017

Case-based replanning of search missions using AUVs

Authors
Abreu, N; Matos, A;

Publication
OCEANS 2017 - ABERDEEN

Abstract
Autonomous underwater vehicles (AUVs) are increasingly being used to perform search operations but its capabilities are limited by the efficiency of the planning process. The objective of the paper is to propose new survey planning methods for AUVs. In particular, the problem of multi-objective search mission planning with an AUV navigating in known or unknown 3D environments is studied. The vehicle should completely cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. The approach presented here differs from other CPP methods in that paths for coverage are generated based on a coverage map that is actively maintained as the vehicle executed its mission. Our replanning approach borrows ideas from case-based reasoning (CBR) in which old problem and solution information helps solve a new problem. The resulting combination takes advantage of both paradigms where our evolutionary approach in conjunction with an artificial neural network (ANN), presented earlier, delivers robustness and adaptive learning while the case-based component speeds up the replanning process. The experiments show that the online algorithm was able to successfully replan missions in varied scenarios and guarantee full area coverage while minimizing resource consumption.

2014

Minehunting Mission Planning for Autonomous Underwater Systems Using Evolutionary Algorithms

Authors
Abreu, N; Matos, A;

Publication
Unmanned Systems

Abstract

2014

Using evolutionary algorithms to plan automatic minehunting operations

Authors
Abreu, N; Matos, A;

Publication
ICINCO 2014 - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics

Abstract
While autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations, the capability of these systems is limited by the efficiency of the planning process. In this paper we study the problem of multiobjective MCM mission planning with an AUV. In order to overcome the inherent complexity of the problem, a multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure based on simulated annealing (SA), aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The results show that the proposed strategy can efficiently identify a higher quality solution set and solve the mission planning problem.

2013

Measuring underwater noise with high endurance surface and underwater autonomous vehicles

Authors
Silva, A; Matos, A; Soares, C; Alves, JC; Valente, J; Zabel, F; Cabral, H; Abreu, N; Cruz, N; Almeida, R; Ferreira, RN; Ijaz, S; Lobo, V;

Publication
2013 OCEANS - SAN DIEGO

Abstract
This paper describes the results of AcousticRobot'13 - a noise measurement campaign that took place off the Portuguese Coast in May 2013, using two high endurance autonomous vehicles capable of silent operation (an underwater glider and an autonmomous sailing vessel) equipped with hydrophones, and a moored hydrophone that served as reference. We show that the autonomous vehicles used can provide useful measurements of underwater noise, and describe the main advantages and shortcomings that became evident during the campaign.