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Your search keyword '"Cobano, Jose A."' showing total 19 results
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19 results on '"Cobano, Jose A."'

1. Skyeye Team at MBZIRC 2020: A team of aerial and ground robots for GPS-denied autonomous fire extinguishing in an urban building scenario

Author

Martinez-Rozas, Simon, Rey, Rafael, Alejo, David, Acedo, Domingo, Cobano, Jose Antonio, Rodriguez-Ramos, Alejandro, Campoy, Pascual, Merino, Luis, and Caballero, Fernando

Subjects
Computer Science - Robotics
Abstract

The paper presents a framework for fire extinguishing in an urban scenario by a team of aerial and ground robots. The system was developed to address Challenge 3 of the 2020Mohamed Bin Zayed International Robotics Challenge (MBZIRC). The challenge required to autonomously detect, locate and extinguish fires on different floors of a building, as well as in its surroundings. The multi-robot system developed consists of a heterogeneous robot team of up to three Unmanned Aerial Vehicles (UAV) and one Unmanned Ground Vehicle (UGV). We describe the main hardware and software components for UAV and UGVplatforms and also present the main algorithmic components of the system: a 3D LIDAR-based mapping and localization module able to work in GPS-denied scenarios; a global planner and a fast local re-planning system for robot navigation; infrared-based perception and robot actuation control for fire extinguishing; and a mission executive and coordination module based on Behavior Trees. The paper finally describes the results obtained during the competition, where the system worked fully autonomously and scored in all the trials performed. The presented system ended in 7th position out of 20 teams in the Challenge3 competition and in 5th position (out of 17 teams) in the Challenge 3 entry to the Grand Finale (Grand Challenge) of MBZIRC 2020 competition., Comment: The presents paper was accepted in the journal "Field Robotics" as part of MBZIRC'20 special Issues

Published
2021

2. Data Fusion of RADAR and LIDAR for Robot Localization Under Low-Visibility Conditions in Structured Environments

Author

Alejo, David, Rey, Rafael, Cobano, José Antonio, Caballero, Fernando, Merino, Luis, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Tardioli, Danilo, editor, Matellán, Vicente, editor, Heredia, Guillermo, editor, Silva, Manuel F., editor, and Marques, Lino, editor

Published
2023
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4. Architecture of a Flight Endurance Enhancement System for Maritime Operations with Fixed Wing UAS

Author

Rodríguez, Leopoldo, Cobano, Jose A., Ollero, Anibal, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Ollero, Anibal, editor, Sanfeliu, Alberto, editor, Montano, Luis, editor, Lau, Nuno, editor, and Cardeira, Carlos, editor

Published
2018
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19. Small UAS-Based Wind Feature Identification System Part 1: Integration and Validation.

Author

Rodriguez Salazar, Leopoldo, Cobano, Jose A., and Ollero, Anibal

Subjects
*DRONE aircraft, *WIND shear, *ATMOSPHERIC models, *PREDICTION models, *APPROXIMATION theory
Abstract

This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1Hz and a wind map can be updated at 0.4Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s. [ABSTRACT FROM AUTHOR]

Published
2017
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