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Pedro Gutiérrez

dblp:56/210 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2Systems, architecture and hardware · 1Graphics, computer vision, multimedia, augmented reality and games · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Legged, aerial and field robots · 56% Robot manipulation · 28% Robot navigation and mapping · 17%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
aerial robots		 0.112006
Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks · ICRA 2006
Robotics › Robot manipulation › parameter identification
dynamics identification		 0.112006
Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks · ICRA 2006
Robotics › Legged, aerial and field robots › aerial robots
unmanned aerial vehicle		 0.112006
Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks · ICRA 2006
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
position and orientation estimation		 0.012006
Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks · ICRA 2006
Robotics › Robot navigation and mapping › state estimation
vehicle state estimation		 0.012006
Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks · ICRA 2006

Methods — techniques the papers use, named apart from their topics

supervised neural network · 0.1radial basis function network · 0.1multi-layer perceptron · 0.1
YearPublicationVenuePosition
2006 Image Compression by a Time Enhanced Self Organizing Map
Pascual Campoy Cervera, Pedro Gutiérrez
 CIARP2
2006 Unmanned Aerial Vehicle (UAV) Modelling based on Supervised Neural Networks
abstract
This paper proposes the utilization of hybrid models of supervised neural networks for the modelling of dynamic systems. Particularly, as an example of a system, an autonomous helicopter or UAV is identified in both attitude and position systems. The evaluation of the model is done by comparing the radial basis and multilayer perceptron with the real system
Rodrigo San Martin, Antonio Barrientos, Pedro Gutiérrez, Jaime del Cerro
 ICRA3