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Safe to Fly? Real-Time Flight Mission Feasibility Assessment for Drone Package Delivery Operations

Authors Abenezer Taye , Austin Coursey , Marcos Quinones-Grueiro , Chao Hu, Gautam Biswas , Peng Wei

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ACM Subject Classification
  • Computing methodologies → Control methods
  • Computing methodologies → Model development and analysis
Keywords
  • Battery Modeling
  • Neural ODE
  • Unmanned Aerial Vehicles

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Abstract

Ensuring flight safety for small unmanned aerial systems (sUAS) requires continuous in-flight monitoring and decision-making, as unexpected events can alter power consumption and deplete battery energy faster than anticipated. Such events may result in insufficient battery capacity to complete a mission, thereby compromising flight safety. In this paper, we present an online feasibility assessment and contingency management framework that continuously monitors the aircraft’s battery state and the energy required to complete the flight in real-time, which enables informed decision-making to enhance flight safety. The framework consists of two main components: power consumption prediction and battery voltage trajectory prediction. The power consumption prediction is conducted using a model that is based on momentum theory, while the voltage trajectory prediction is performed using a Neural Ordinary Differential Equation (Neural ODE)-based data-driven model. By integrating these two components, the framework evaluates the feasibility of a flight mission in real time and determines whether to proceed with the mission or initiate rerouting. We evaluate the framework’s performance in a drone delivery scenario in the Dallas–Fort Worth (DFW) area, where the aircraft encounters an unexpected energy depletion event mid-flight. The proposed framework is tasked with assessing the feasibility of completing the mission and, if necessary, rerouting the aircraft for an emergency landing. The results demonstrate that the framework accurately and efficiently detects energy insufficiencies in real-time and re-routes the aircraft to a [3] predefined emergency landing site.

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Abenezer Taye, Austin Coursey, Marcos Quinones-Grueiro, Chao Hu, Gautam Biswas, and Peng Wei. Safe to Fly? Real-Time Flight Mission Feasibility Assessment for Drone Package Delivery Operations. In 36th International Conference on Principles of Diagnosis and Resilient Systems (DX 2025). Open Access Series in Informatics (OASIcs), Volume 136, pp. 8:1-8:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.DX.2025.8

Author Details

Abenezer Taye
  • Mechanical and Aerospace Engineering Department, School of Engineering and Applied Science, George Washington University, Washington DC, USA
Austin Coursey
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Marcos Quinones-Grueiro
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Chao Hu
  • School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT, USA
Gautam Biswas
  • Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN, USA
Peng Wei
  • Mechanical and Aerospace Engineering Department, School of Engineering and Applied Science, George Washington University, Washington DC, USA

Funding

This work was supported by NASA award #80NSSC21M0087-21-S06.

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