This paper introduces a method for controlling UAV swarm formations and avoiding collisions using the Virtual Spring-Damper (VSD) approach. This approach draws upon classical mechanical spring-damper systems, employing displacement and velocities to generate attractive and repulsive forces. These forces help sustain swarm formation and prevent collisions in a decentralized manner. Extensive simulations provided a systematic approach to obtain the parameters involved in the spring-damper system. Flight tests were carried out to verify the proposed method. The experimental outcomes align with our simulations, demonstrating effectiveness in collision avoidance while preserving a specified flight formation.

This paper presents the development and implementation of a multi-UAV system focused on coverage path planning on multiple separated areas capable of re-planning the collective mission in case of unexpected events. For this purpose, we present a distributed-centralized architecture that uses heuristic and computationally efficient methods to perform the planning/re-planning and decision-making tasks during the control of the mission execution. We performed a computational evaluation of the algorithms, comparing them with other proposals, together with experiments in simulated and real flights. The results show that the system can distribute tasks equitably among the aircraft in an efficient way, even in the middle of the flight, when facing unexpected events; and show a higher computational efficiency when compared to multiple proposals in the state of the art.

In recent years, the use of unmanned aerial vehicles has spread across different fields of the industry due to their ease of deployment and minimal operational risk. Firefighting is a dangerous task for the humans involved, in which the use of UAVs presents itself as a good first-action protocol for a rapid response to an incipient fire because of their safety and speed of action. Current research is mainly focused on wildland fires, but fires in urban environments are barely mentioned in the bibliography. To motivate the research on this topic, ICUAS’22 organized an international competition inspired by this mission, with the challenge of a UAV traversing an area populated by obstacles, finding a target, and precisely throwing a ball to it. For this competition, the Computer Vision and Aerial Robotics (CVAR-UPM) team developed a solution composed of multiple modules and structured by a mission planner. In this paper, we describe our approach and the developed architecture that led us to be awarded the first prize in the competition.

The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner.

Robotics provides an increasing number of solutions for real-life problems, from autonomous driving to automatic cleaning, inspection and logistics. The demand of robotics education to train future engineers is also growing, both in regular degrees at universities and also in massive open online courses (MOOCs). Beyond theory lectures, robotics education typically requires hands-on and practiced robot programming to be effective and let the student develop the desired skills. This paper presents Unibotics, an open online learning platform that allows editing and running robot programs from the browser, and provides more than 20 academic units on service robotics, autonomous driving, drones, and mobile robotics. It uses state-of-the art open source robot simulator (Gazebo) and robot middleware (ROS), and so it is extensible with new exercises. It is intended as a tool for practical learning in robotics engineering university courses. The platform has been used by 130 students from four different university degrees. Furthermore, it has been experimentally validated at the Universidad Rey Juan Carlos (URJC) Master Degree in Computer Vision with 22 students.

Citation Aguado González, Esther ORCID: https://orcid.org/0000-0002-7860-9030, Alonso Martín, Fernando, Altares López, Sergio ORCID: https://orcid.org/0000-0002-0847-6113, Álvarez Pastor, Jesús, Amat Girbau, Josep, Arias Guadalupe, Janeth I., Arias Pérez, Sergio, Arias Pérez, Pedro ORCID: https://orcid.org/0000-0001-7166-9367, Armada Rodríguez, Manuel Ángel, Badesa Clemente, Francisco Javier ORCID: https://orcid.org/0000-0003-0149-6469, Bajo Collados, Daniel, Balaguer Bernaldo de Quirós, Carlos, Barbosa Meráz, Nancy Viviana, Barrientos Arellano, Atlas, Belmonte Cerdán, Elías, Beviá Ballesteros, Ismael, Blanco Ivorra, Andrea, Blanco Rojas, María D., Bolaños, Byron D., Brunete González, Alberto ORCID: https://orcid.org/0000-0001-9873-232X, Busque Nadal, Arnau, Cabanes Axpe, Itziar, Calvo Córdoba, Alberto ORCID: https://orcid.org/0000-0002-7772-2824, Cantalejo Escobar, David ORCID: https://orcid.org/0000-0001-8328-2231, Castillo Montoya, José Carlos, Carrasco Martínez, Sara, Casals Gelpí, Alicia, Cases Hurtado, Jesús, Castro González, Álvaro, Catalán Orts, José María, Copaci, Dorin S., Cortés Torres, Eliseo, Luis Moura, Duna De, Matías Martínez, Ainhoa De, Olmo Borrás, Carlos de, Delgado Oleas, Gabriel ORCID: https://orcid.org/0000-0003-0413-9520, Duque Domingo, Jaime, Echagüe Guardiola, Juan, Fabregat Jaén, Marc, Fernández Cortizas, Miguel ORCID: https://orcid.org/0000-0002-3822-075X, Fernández Irles, Clemente, Fernández Lozano, J. Jesús, Fernández Rodicio, Enrique, Fernández Saavedra, Roemi Emilia ORCID: https://orcid.org/0000-0003-0552-5407, Ferrando del Rincón, German, Ferre Pérez, Manuel ORCID: https://orcid.org/0000-0003-0030-1551, Fornas García, Sergio, Galán Cuenca, Álvaro, García González, David, García Aracil, Nicolás, García Cena, Cecilia Elisabet ORCID: https://orcid.org/0000-0002-1067-0564, García Cerezo, Alfonso J., García Gómez, Miguel ORCID: https://orcid.org/0000-0003-3148-1495, García Morales, Isabel, García Pérez, José Vicente, Gómez Bravo, Fernando, Gómez García-Bermejo, Jaime, Gómez Lambo, Virgilio Augusto, Gómez Ramos, Raúl, González de Santos, Pablo, Gracia Laso, Desirée Irene, Hernando Gutiérrez, Miguel ORCID: https://orcid.org/0000-0001-9997-0266, Herraiz Sala, Manuel, Herrera López, Juan María, Jara Bravo, Carlos A., Jiménez García, Luis Miguel, García Ripoll, Juan José, Naranjo Campos, Francisco José, Lipa, Gersom, Lledó Pérez, Luis Daniel, López Castellanos, José M., Lora Millán, Julio, Lukawsky, Bartek, Madrid Ruiz, Ericka Patricia, Malfaz Vázquez, María A., Mancisidor Barinagarrementeria, Aitziber, Mandow Andaluz, Antonio, Manrique Córdoba, Juliana, Mansilla Navarro, Paloma, Luna Aguirre, Marco ORCID: https://orcid.org/0000-0003-1036-7538, Marín Prades, Josep, Marín Prades, Raúl, Maroto Gómez, Marcos, Marqués Verdegal, Josep, Martí Avilés, José Vicente, Martín Batanero, Raúl, Martín González, Antonio, Molina González, Martín ORCID: https://orcid.org/0000-0001-7145-1974, Martínez Pascual, David, Martínez Sánchez, Juan Camilo, Melero Deza, Javier ORCID: https://orcid.org/0000-0002-1420-2960, Mengual Mesa, María, Merino Fidalgo, Sergio, Minguella Canela, Joaquim, Mollá Santamaría, Paula, Monje, Concepción A., Muñoz Yáñez-Barnuevo, Jorge, Muñoz Martínez, Víctor F., Navarro Cabello, Enrique ORCID: https://orcid.org/0000-0003-4824-4525, Navas Merlo, Eduardo, Ojeda Velázquez, Marta, Olivas González, Alejandro, Onorati, Teresa, Miranda Páez, Jesús, Campoy Cervera, Pascual ORCID: https://orcid.org/0000-0002-9894-2009, Payá Castelló, Luis, Peidró Vidal, Adrián, Pérez Saura, David ORCID: https://orcid.org/0000-0003-2571-3165, Pérez Segui, Rafael ORCID: https://orcid.org/0000-0002-9508-1055, Pérez Odriozola, Nerea, Pont Esteban, David ORCID: https://orcid.org/0000-0001-5889-7816, Ramos Rojas, Jaime, Ravina Vergara, Juan Manuel, Redondo Gallego, Violeta Isabel ORCID: https://orcid.org/0000-0003-0238-7280, Reinoso García, Óscar, Ribeiro Seijas, Angela Mª ORCID: https://orcid.org/0000-0001-5807-8132, Rocón de Lima, Eduardo ORCID: https://orcid.org/0000-0001-9618-2176, Rollón Rivas, Marcos, Romero Ante, Juan David, Rossi, Claudio ORCID: https://orcid.org/0000-0002-8740-2453, Sabater Navarro, José María, Salichs Sánchez-Caballero, Miguel A., Saltaren Pazmiño, Roque Jacinto ORCID: https://orcid.org/0000-0001-8009-5350, López Barajas, Salvador, San Juan Ferrer, Alejandro ORCID: https://orcid.org/0000-0002-7083-7610, Sánchez Martínez, Daniel, Sánchez-Girón Coca, Celia, Sánchez-Urán González, Miguel Ángel ORCID: https://orcid.org/0000-0001-6652-0090, Sanz Bravo, Ricardo ORCID: https://orcid.org/0000-0002-2381-933X, Sanz Valero, Pedro J., Serrano Balbontín, Andrés J., Soler Mora, Francisco José, Solís Jiménez, Alejandro, Sosa Méndez, Deira ORCID: https://orcid.org/0000-0002-3290-2039, Tegas, Nicola, Tejado Balsera, Inés, Torres Medina, Fernando, Trillo Legaz, Javier, Úbeda Castellanos, Andrés, Urueña de Castro, Héctor J., Vales Gómez, Yolanda, Vermander García, Patrick, Vicente Samper, José M., Vinagre Jara, Blas M., Francisco Viñas, Pablo, Zalama Casanova, Eduardo and Zhang, Kexin (2023). Jornadas Nacionales de Robótica y Bioingeniería 2023: Libro de actas. Fundación General de la Universidad Politécnica de Madrid. ISBN 978-84-09-51892-0. https://doi.org/10.20868/UPM.book.74896.

In recent years, the robotics community has witnessed the development of several software stacks for ground and articulated robots, such as Navigation2 and MoveIt. However, the same level of collaboration and standardization is yet to be achieved in the field of aerial robotics, where each research group has developed their own frameworks. This work presents Aerostack2, a framework for the development of autonomous aerial robotics systems that aims to address the lack of standardization and fragmentation of efforts in the field. Built on ROS 2 middleware and featuring an efficient modular software architecture and multi-robot orientation, Aerostack2 is a versatile and platform-independent environment that covers a wide range of robot capabilities for autonomous operation. Its major contributions include providing a logical level for specifying missions, reusing components and sub-systems for aerial robotics, and enabling the development of complete control architectures. All major contributions have been tested in simulation and real flights with multiple heterogeneous swarms. Aerostack2 is open source and community oriented, democratizing the access to its technology by autonomous drone systems developers.

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.

This article presents a full course for autonomous aerial robotics inside the RoboticsAcademy framework. This “drone programming” course is open-access and ready-to-use for any teacher/student to teach/learn drone programming with it for free. The students may program diverse drones on their computers without a physical presence in this course. Unmanned aerial vehicles (UAV) applications are essentially practical, as their intelligence resides in the software part. Therefore, the proposed course emphasizes drone programming through practical learning. It comprises a collection of exercises resembling drone applications in real life, such as following a road, visual landing, and people search and rescue, including their corresponding background theory. The course has been successfully taught for five years to students from several university engineering degrees. Some exercises from the course have also been validated in three aerial robotics competitions, including an international one. RoboticsAcademy is also briefly presented in the paper. It is an open framework for distance robotics learning in engineering degrees. It has been designed as a practical complement to the typical online videos of massive open online courses (MOOCs). Its educational contents are built upon robot operating system (ROS) middleware (de facto standard in robot programming), the powerful 3D Gazebo simulator, and the widely used Python programming language. Additionally, RoboticsAcademy is a suitable tool for gamified learning and online robotics competitions, as it includes several competitive exercises and automatic assessment tools.