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Analysis of institutional authors

Garcia Samartin, Jorge FranciscoAuthorCruz Ulloa, ChristyanAuthorDel Cerro, JaimeAuthorBarrientos, AntonioAuthor

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April 15, 2024
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Active robotic search for victims using ensemble deep learning techniques

Publicated to:Machine Learning: Science And Technology. 5 (2): 025004- - 2024-06-01 5(2), DOI: 10.1088/2632-2153/ad33df

Authors: Garcia-Samartin, Jorge F; Cruz Ulloa, Christyan; del Cerro, Jaime; Barrientos, Antonio

Affiliations

Univ Politecn Madrid, Ctr Automat & Robot, Consejo Super Invest Cient, Madrid 28006, Spain - Author

Abstract

In recent years, legged quadruped robots have proved to be a valuable support to humans in dealing with search and rescue operations. These robots can move with great ability in complex terrains, unstructured environments or regions with many obstacles. This work employs the quadruped robot A1 Rescue Tasks UPM Robot (ARTU-R) by Unitree, equipped with an RGB-D camera and a lidar, to perform victim searches in post-disaster scenarios. Exploration is done not by following a pre-planned path (as common methods) but by prioritising the areas most likely to harbour victims. To accomplish that task, both indirect search and next best view techniques have been used. When ARTU-R gets inside an unstructured and unknown environment, it selects the next exploration point from a series of candidates. This operation is performed by comparing, for each candidate, the distance to reach it, the unexplored space around it and the probability of a victim being in its vicinity. This probability value is obtained using a Random Forest, which processes the information provided by a convolutional neural network. Unlike other AI techniques, random forests are not black box models; humans can understand their decision-making processes. The system, once integrated, achieves speeds comparable to other state-of-the-art algorithms in terms of exploration, but concerning victim detection, the tests show that the resulting smart exploration generates logical paths-from a human point of view-and that ARTU-R tends to move first to the regions where victims are present.

Keywords

Computer visionEnsamble deep learningLegged robotsRandom forestRandom forestsSearch and rescue robotsSensing and perception

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Machine Learning: Science And Technology due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 20/135, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary Sciences.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-24:

  • WoS: 1
  • Scopus: 2

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-24:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 16.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 30 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 0.5.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/85967/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 35
  • Downloads: 8

Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GARCIA SAMARTIN, JORGE FRANCISCO) and Last Author (BARRIENTOS CRUZ, ANTONIO).

the author responsible for correspondence tasks has been Garcia-Samartin, Jorge F.