Abstract
To achieve fully autonomous operation for Unmanned Aerial Systems (UAS) it is necessary to integrate multiple and heterogeneous technical solutions (e.g., control-based methods, computer vision methods, automated planning, coordination algorithms, etc.). The combination of such methods in an operational system is a technical challenge that requires efficient architectural solutions. In a robotic engineering context, where productivity is important, it is also important to minimize the effort for the development of new systems. As a response to these needs, this paper presents Aerostack, an open-source software framework for the development of aerial robotic systems. This framework facilitates the creation of UAS by providing a set of reusable components specialized in functional tasks of aerial robotics (trajectory planning, self localization, etc.) together with an integration method in a multi-layered cognitive architecture based on five layers: reactive, executive, deliberative, reflective and social. Compared to other software frameworks for UAS, Aerostack can provide higher degrees of autonomy and it is more versatile to be applied to different types of hardware (aerial platforms and sensors) and different types of missions (e.g. multi robot swarm systems). Aerostack has been validated during four years (since February 2013) by its successful use on many research projects, international competitions and public exhibitions. As a representative example of system development, this paper also presents how Aerostack was used to develop a system for a (fictional) fully autonomous indoors search and rescue mission.
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Acknowledgements
This research work has been partially supported by the Spanish Ministry of Economy and Competitiveness through the project VA4UAV (Visual autonomy for UAV in Dynamic Environments), reference DPI2014-60139-R. The authors would like to thank, as well, the Consejo Superior de Investigaciones Cientificas (CSIC) of Spain for the JAE-Predoctoral scholarships of one of the authors and his funded research stays.
The authors would like to thank other members from the Computer Vision and Aerial Robotics (CVAR) research group and the Department of Artificial Intelligence (UPM) for their help in software programming and the development of fight experiments: David Palacios, Adrian Diaz-Moreno, Guillermo de Fermín, Alberto Camporredondo and Carlos Valencia.
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Sanchez-Lopez, J.L., Molina, M., Bavle, H. et al. A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework. J Intell Robot Syst 88, 683–709 (2017). https://doi.org/10.1007/s10846-017-0551-4
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DOI: https://doi.org/10.1007/s10846-017-0551-4