Aloft: Self-Adaptive Drone Controller Testbed

Calum Corrie Imrie*, Rhys Howard, Divya Thuremella, Nawshin Mannan Proma, Tejas Pandey, Paulina Barbara Wiktoria Lewinska, Ricardo Cannizzaro, Richard David Hawkins, Colin Paterson, Lars Kunze, Victoria J. Hodge

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Aerial drones are increasingly being considered as a valuable tool for inspection in safety critical contexts. Nowhere is this more true than in mining operations which present a dynamic and dangerous environment for human operators. Drones can be deployed in a number of contexts including efficient surveying as well as search and rescue missions. Operating in these dynamic contexts is challenging however and requires the drones control software to detect and adapt to conditions at run-time.

To help in the development of such systems we present Aloft, a simulation supported testbed for investigating self-adaptive controllers for drones in mines.
Aloft, utilises the Robot Operating system (ROS) and a model environment using Gazebo to provide a physics-based testing. The simulation environment is constructed from a 3D point cloud collected in a physical mock-up of a mine and contains features expected to be found in real-world contexts.

Aloft allows members of the research community to deploy their own self-adaptive controllers into the control loop of the drone to evaluate the effectiveness and robustness of controllers in a challenging environment. To demonstrate our system we provide a self-adaptive drone controller and operating scenario as an exemplar. The self-adaptive drone controller provided, utilises a two-layered architecture with a MAPE-K feedback loop. The scenario is an inspection task during which we inject a communications failure. The aim of the controller is to detect this loss of communication and autonomously perform a return home behaviour. Limited battery life presents a constraint on the mission, which therefore means that the drone should complete its mission as fast as possible. Humans, however, might also be present within the environment. This poses a safety risk and the drone must be able to avoid collisions during autonomous flight.

In this paper we describe the controller framework and the simulation environment and provide information on how a user might construct and evaluate their own controllers in the presence of disruptions at run-time.
Original languageEnglish
Title of host publicationSEAMS '24: Proceedings of the 19th Symposium on Software Engineering for Adaptive and Self-Managing Systems
Publication statusPublished - 16 Apr 2024
Event19th International Conference on Software Engineering for Adaptive and Self-Managing Systems - Lisbon, Portugal
Duration: 15 Apr 202416 Apr 2024


Conference19th International Conference on Software Engineering for Adaptive and Self-Managing Systems
Abbreviated titleSEAMS 2024

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