Person:
Besada Portas, Eva

First Name

Eva

Last Name

Besada Portas

Affiliation

Universidad Complutense de Madrid

Faculty / Institute

Ciencias Físicas

Department

Arquitectura de Computadores y Automática

Area

Ingeniería de Sistemas y Automática

Identifiers

Full item page

Search Results

Now showing 1 - 10 of 29

Evolutionary trajectory planner for multiple UAVs in realistic scenarios
(IEEE-INST Electrical Electronics Engineers Inc, 2010-08) Besada Portas, Eva; Torre Cubillo, Luis de la; Cruz García, Jesús Manuel de la; Andrés Toro, Bonifacio de
This paper presents a path planner for multiple unmanned aerial vehicles (UAVs) based on evolutionary algorithms (EAs) for realistic scenarios. The paths returned by the algorithm fulfill and optimize multiple criteria that 1) are calculated based on the properties of real UAVs, terrains, radars, and missiles and 2) are structured in different levels of priority according to the selected mission. The paths of all the UAVs are obtained with the multiple coordinated agents coevolution EA (MCACEA), which is a general framework that uses an EA per agent (i.e., UAV) that share their optimal solutions to coordinate the evolutions of the EAs populations using cooperation objectives. This planner works offline and online by means of recalculating parts of the original path to avoid unexpected risks while the UAV is flying. Its search space and computation time have been reduced using some special operators in the EAs. The successful results of the paths obtained in multiple scenarios, which are statistically analyzed in the paper, and tested against a simulator that incorporates complex models of the UAVs, radars, and missiles, make us believe that this planner could be used for real-flight missions.
Cloud DEVS-based computation of UAVs trajectories for search and rescue missions
(Taylor & Francis Ltd, 2022-04-05) Bordón Ruiz, Juan B.; López Orozco, José Antonio; Besada Portas, Eva
This paper presents a new Cloud-deployable DEVS-based framework for optimising UAV trajectories and sensor strategies in target-search missions. DEVS provides it with a well-established, flexible, and verifiable modelling strategy to include different models for the UAV, sensor, and target dynamics; the target and sensor uncertainty; and the optimising process. Its Cloud deployability speeds up the evaluations/simulations required to optimise this NP-hard problem, which involves computationally heavy models when solving real-world missions. The framework, designed to handle different types of target-search missions, currently optimises, using a multi-objective Genetic Algorithm, free-shape trajectories of multiple UAVs,eqquiped with several static/movable sensors to detect a target within a search area. It is implemented in xDEVS and deployable over a set of containers in the Google Cloud Platform. The results show that our deployment policy speeds up the computation up to 3.35 times, letting the operator simultaneously optimise several search strategies for agiven scenario.
Semiphysical modelling of the nonlinear dynamics of a surface craft with LS-SVM
(Hindawi Publishing Corporation, 2013) Moreno Salinas, David; Chaos, Dictino; Besada Portas, Eva; López Orozco, José Antonio; Cruz García, Jesús Manuel de la; Aranda, Joaquín
One of the most important problems in many research fields is the development of reliable mathematical models with good predictive ability to simulate experimental systems accurately. Moreover, in some of these fields, as marine systems, these models play a key role due to the changing environmental conditions and the complexity and high cost of the infrastructure needed to carry out experimental tests. In this paper, a semiphysical modelling technique based on least-squares support vector machines (LS-SVM) is proposed to determine a nonlinear mathematical model of a surface craft. The speed and steering equations of the nonlinear model of Blanke are determined analysing the rudder angle, surge and sway speeds, and yaw rate from real experimental data measured from a zig-zag manoeuvre made by a scale ship. The predictive ability of the model is tested with different manoeuvring experimental tests to show the good performance and prediction ability of the model computed.
Artificial Intelligence Techniques for Automatic Detection of Peri‑implant Marginal Bone Remodeling in Intraoral Radiographs
(Springer, 2023-07-01) Vera González, Vicente; Besada Portas, Eva; Pajares Martínsanz, Gonzalo; Gómez Silva, María José; Aliaga Vera, Ignacio Joaquín; Pedrera Canal, María; Vera, María; López-González, Clara Isabel; Gascó, Esther
Peri-implantitis can cause marginal bone remodeling around implants. The aim is to develop an automatic image processing approach based on two artificial intelligence (AI) techniques in intraoral (periapical and bitewing) radiographs to assist dentists in determining bone loss. The first is a deep learning (DL) object-detector (YOLOv3) to roughly identify (no exact localization is required) two objects: prosthesis (crown) and implant (screw). The second is an image understanding-based (IU) process to fine-tune lines on screw edges and to identify significant points (intensity bone changes, intersections between screw and crown). Distances between these points are used to compute bone loss. A total of 2920 radiographs were used for training (50%) and testing (50%) the DL process. The mAP@0.5 metric is used for performance evaluation of DL considering periapical/bitewing and screws/crowns in upper and lower jaws, with scores ranging from 0.537 to 0.898 (sufficient because DL only needs an approximation). The IU performance is assessed with 50% of the testing radiographs through the t test statistical method, obtaining p values of 0.0106 (line fitting) and 0.0213 (significant point detection). The IU performance is satisfactory, as these values are in accordance with the statistical average/standard deviation in pixels for line fitting (2.75/1.01) and for significant point detection (2.63/1.28) according to the expert criteria of dentists, who establish the ground-truth lines and significant points. In conclusion, AI methods have good prospects for automatic bone loss detection in intraoral radiographs to assist dental specialists in diagnosing peri-implantitis.
Multisensor fusion for linear control systems with asynchronous, Out-Of-Sequence and erroneous data
(Pergamon-Elsevier Science LTD, 2012-03) Besada Portas, Eva; López Orozco, José Antonio; Besada, Juan; Cruz García, Jesús Manuel de la
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
Multisensor out of sequence data fusion for estimating the state of discrete control systems
(IEEE-Inst Electrical Electronics Engineers INC, 2009-07) Besada Portas, Eva; López Orozco, José Antonio; Cruz García, Jesús Manuel de la; Besada, Juan A.
The fusion center of a complex control system estimates its state with the information provided by different sensors. Physically distributed sensors, communication networks, pre-processing algorithms, multitasking, etc, introduce non-systematic delays in the arrival of information to the fusion center, making the information available Out-Of-Sequence (OOS). For real-time control systems, the state has to be efficiently estimated with all the information received so far. So, several solutions of the OOS problem for dynamic Multiple-Input Multiple-Output (MIMO) discrete control systems traditionally solved by the Kalman Filter (KF) have been proposed recently. This paper presents two new streamlined algorithms for the linear and non-linear case. IFAsyn, the linear algorithm, is equivalent to other optimal solutions but more general, efficient and easy to implement. EIFAsyn, the nonlinear one, is a new solution of the OOS problem in the Extended KF (EKF) framework.
Minimum time search in uncertain dynamic domains with complex sensorial platforms
(MDPI AG, 2014-08-04) Lanillos Pradas, Pablo; Besada Portas, Eva; López Orozco, José Antonio; Cruz García, Jesús Manuel de la
The minimum time search in uncertain domains is a searching task, which appears in real world problems such as natural disasters and sea rescue operations, where a target has to be found, as soon as possible, by a set of sensor-equipped searchers. The automation of this task, where the time to detect the target is critical, can be achieved by new probabilistic techniques that directly minimize the Expected Time (ET) to detect a dynamic target using the observation probability models and actual observations collected by the sensors on board the searchers. The selected technique, described in algorithmic form in this paper for completeness, has only been previously partially tested with an ideal binary detection model, in spite of being designed to deal with complex non-linear/non-differential sensorial models. This paper covers the gap, testing its performance and applicability over different searching tasks with searchers equipped with different complex sensors. The sensorial models under test vary from stepped detection probabilities to continuous/discontinuous differentiable/non-differentiable detection probabilities dependent on distance, orientation, and structured maps. The analysis of the simulated results of several static and dynamic scenarios performed in this paper validates the applicability of the technique with different types of sensor models.
Localization of non-linearly modeled autonomous mobile robots using out-of-sequence measurements
(MDPI AG, 2012-03) Besada Portas, Eva; López Orozco, José Antonio; Lanillos Pradas, Pablo; Cruz García, Jesús Manuel de la
This paper presents a state of the art of the estimation algorithms dealing with Out-of-Sequence (OOS) measurements for non-linearly modeled systems. The state of the art includes a critical analysis of the algorithm properties that takes into account the applicability of these techniques to autonomous mobile robot navigation based on the fusion of the measurements provided, delayed and OOS, by multiple sensors. Besides, it shows a representative example of the use of one of the most computationally efficient approaches in the localization module of the control software of a real robot (which has non-linear dynamics, and linear and non-linear sensors) and compares its performance against other approaches. The simulated results obtained with the selected OOS algorithm shows the computational requirements that each sensor of the robot imposes to it. The real experiments show how the inclusion of the selected OOS algorithm in the control software lets the robot successfully navigate in spite of receiving many OOS measurements. Finally, the comparison highlights that not only is the selected OOS algorithm among the best performing ones of the comparison, but it also has the lowest computational and memory cost.
Enhancing EJsS with extension plugins
(MDPI, 2021-02) Chacón Sombría, Jesús; Besada Portas, Eva; Carazo Barbero, Gonzalo; López Orozco, José Antonio
Easy JavaScript Simulations (EJsS) is an open-source tool that allows teachers with limited programming experience to straightforwardly bundle an interactive computer science or engineer simulation in an HTML+ JavaScript webpage. Its prominent place in Physics (where it has won several prizes) should not hinder its application in other fields (such as building the front-end of remote laboratories or learning analytics) after having adapted part of the functionality of EJsS to them. To facilitate the future inclusion of new functionalities in EJsS, this paper presents a new version of this tool that allows the enhancement of EJsS, letting it incorporate new tools and change its graphical user interface, by means of extension plugins (special software libraries). To illustrate the benefits of this distributable self-contained non-intrusive strategy, the paper (a) discusses the new methodological possibilities that the Plugins bring to EJsS developers and users, and (b) presents three plugins: one to support the plugin management and the others to easily set up a streamlined remote laboratory. Moreover, the paper also presents the main characteristics of that remote lab to allow readers take advantage of EJsS and the three plugins to set up new online experiments for their students quickly.
Hierarchical mission planning with a GA-optimizer for unmanned high altitude pseudo-satellites
(MDPI AG, 2021-03) Kiam, Jane Jean; Besada Portas, Eva; Schulte, Axel
Unmanned Aerial Vehicles (UAVs) are gaining preference for mapping and monitoring ground activities, partially due to the cost efficiency and availability of lightweight high-resolution imaging sensors. Recent advances in solar-powered High Altitude Pseudo-Satellites (HAPSs) widen the future use of multiple UAVs of this sort for long-endurance remote sensing, from the lower stratosphere of vast ground areas. However, to increase mission success and safety, the effect of the wind on the platform dynamics and of the cloud coverage on the quality of the images must be considered during mission planning. For this reason, this article presents a new planner that, considering the weather conditions, determines the temporal hierarchical decomposition of the tasks of several HAPSs. This planner is supported by a Multiple Objective Evolutionary Algorithm (MOEA) that determines the best Pareto front of feasible high-level plans according to different objectives carefully defined to consider the uncertainties imposed by the time-varying conditions of the environment. Meanwhile, the feasibility of the plans is assured by integrating constraints handling techniques in the MOEA. Leveraging historical weather data and realistic mission settings, we analyze the performance of the planner for different scenarios and conclude that it is capable of determining overall good solutions under different conditions.