Aloft: Self-Adaptive Drone Controller TestbedARTIFACT
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.
Mon 15 AprDisplayed time zone: Lisbon change
14:00 - 15:30 | Session 3: Unmanned Aerial Vehicles and LLMs Research Track / Artifact Track at Luis de Freitas Branco Chair(s): Gabriel A. Moreno Carnegie Mellon University Software Engineering Institute | ||
14:00 25mTalk | ADAM: Adaptive Monitoring of Runtime Anomalies in Small Uncrewed Aerial SystemsFULL Research Track Md Nafee Al Islam University of Notre Dame, Jane Cleland-Huang University of Notre Dame, Michael Vierhauser University of Innsbruck | ||
14:25 15mTalk | Towards Proactive Decentralized Adaptation of Unmanned Aerial Vehicles for Wildfire TrackingSHORT Research Track Enrique Vilchez University of Malaga, Javier Troya Universidad de Málaga, Spain, Javier Camara University of Málaga | ||
14:40 15mTalk | Wildfire-UAVSim: An Exemplar for Evaluation of Adaptive Cyber-Physical Systems in Partially-Observable EnvironmentsARTIFACT Artifact Track Enrique Vilchez University of Malaga, Javier Troya Universidad de Málaga, Spain, Javier Camara University of Málaga | ||
14:55 15mTalk | Aloft: Self-Adaptive Drone Controller TestbedARTIFACT Artifact Track Calum Imrie University of York, Rhys Howard University of Oxford, Divya Thuremella University of Oxford, Nawshin Mannan Proma University of York, Tejas Pandey University of York, Paulina Lewinska University of York, Ricardo Cannizzaro University of Oxford, Richard Hawkins University of York, Colin Paterson University of York, Lars Kunze University of Oxford, Victoria J. Hodge University of York | ||
15:10 15mTalk | Exploring the Potential of Large Language Models in Self-adaptive SystemsSHORT Research Track Jialong Li Waseda University, Japan, Mingyue Zhang Southwest University, NIANYU LI ZGC Lab, China, Danny Weyns KU Leuven, Zhi Jin Peking University, Kenji Tei Tokyo Institute of Technology | ||