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.

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.