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A

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License and use

Citations

3

Altmetrics

Article has an altmetric score of 1

Grant support

The research leading to these results has received funding from RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by "Programas de Actividades I+D en la Comunidad de Madrid", and cofunded by Structural Funds of the EU.

Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Arias Perez, PedroCorresponding AuthorCampoy, PascualAuthor

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January 2, 2023
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A Middleware Infrastructure for Programming Vision-Based Applications in UAVs

Publicated to:Drones. 6 (11): 369-369 - 2022-11-01 6(11), DOI: 10.3390/drones6110369

Authors: Arias-Perez, Pedro; Fernandez-Conde, Jesus; Gomez, David Martin; Canas, Jose M; Campoy, Pascual

Affiliations

Rey Juan Carlos Univ, Dept Telemat Syst & Computat, Madrid 28942, Spain - Author
Univ Carlos III Madrid, Intelligent Syst Lab, Calle Butarque 15, Madrid 28911, Spain - Author
Univ Politecn Madrid, Ctr Automat & Robot, Comp Vis & Aerial Robot Grp, Madrid 28006, Spain - Author

Abstract

Unmanned Aerial Vehicles (UAVs) are part of our daily lives with a number of applications in diverse fields. On many occasions, developing these applications can be an arduous or even impossible task for users with a limited knowledge of aerial robotics. This work seeks to provide a middleware programming infrastructure that facilitates this type of process. The presented infrastructure, named DroneWrapper, offers the user the possibility of developing applications abstracting the user from the complexities associated with the aircraft through a simple user programming interface. DroneWrapper is built upon the de facto standard in robot programming, Robot Operating System (ROS), and it has been implemented in Python, following a modular design that facilitates the coupling of various drivers and allows the extension of the functionalities. Along with the infrastructure, several drivers have been developed for different aerial platforms, real and simulated. Two applications have been developed in order to exemplify the use of the infrastructure created: follow-color and follow-person. Both applications use techniques of computer vision, classic (image filtering) or modern (deep learning), to follow a specific-colored object or to follow a person. These two applications have been tested on different aerial platforms, including real and simulated, to validate the scope of the offered solution.

Keywords

Aerial roboticsComputer visionDeep learningIndustry, innovation and infrastructureMiddleware

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Drones 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, 2022, it was in position 14/34, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Remote Sensing.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.2, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jul 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-07-27, the following number of citations:

  • WoS: 1
  • Scopus: 2
  • Open Alex: 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 2025-07-27:

  • 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: 6.
  • 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: 7 (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: 0.25.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 9 - Industry, innovation and infrastructure, with a probability of 63% according to the mBERT algorithm developed by Aurora University.

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 (Arias-Perez, Pedro) and Last Author (CAMPOY CERVERA, PASCUAL).

the author responsible for correspondence tasks has been ARIAS PÉREZ, PEDRO.