Unmanned aerial vehicles (UAVs) have gained immense popularity for their versatility and diverse applications. However, this increased usage has raised concerns about the safety and security of UAVs, emphasizing the critical role of their Inertial Measurement Units (IMUs) in ensuring accurate orientation and position data. IMU faults, including both Accelerometer faults and Gyrometer faults, can lead to severe consequences, such as mission failures, collisions, or loss of control. This study addresses the need to enhance UAV resilience in urban airspace by exploring the impact of various IMU faults. A comprehensive fault model is introduced in this paper, covering a range of faults from hardware malfunctions to external attacks. Through extensive fault injection experiments in a simulated environment, the study assesses the effects of different fault types and durations on mission outcomes, providing valuable insights for developing resilient and fault-tolerant UAV systems. Evaluation metrics, including inner and outer bubble violations, missions completed, flight duration, and distance traveled, offer a comprehensive understanding of IMU fault impacts in dynamic operational scenarios. Results reveal that longer injection durations, particularly at 30 seconds, increase bubble violations and significantly reduce mission completion rates. Accelerometer faults, such as “Accelerometer Freeze” and “Accelerometer Random” exhibit reduced mission completion rates of 42.5% and 5%, respectively. Gyrometer faults, especially “Gyrometer Minimum” and “Gyrometer Random” lead to the lowest mission completion rates (2.5%). Additionally, IMU faults (where the fault affects both the Accelerometer and Gyrometer), notably “IMU Minimum”, “IMU Freeze”, and “IMU Random” result in complete mission failures, highlighting the importance of understanding specific fault characteristics. These insights can contribute to developing fault tolerance mechanisms and resilient UAV systems in complex and dynamic environments.

Unmanned Aerial Vehicles (UAVs) are susceptible to various hazards (e.g., software or hardware failures, communication failures, or security attacks) that may hinder mission completion or compromise safety by violating the separation minima (i.e., the minimum distance that must be maintained between UAVs in order to ensure safe and efficient operations). To address this issue, this paper proposes a new machine learning-based fault-tolerant mechanism for UAV flight controllers that tolerates GPS-related faults. These faults are of paramount importance (i.e., accidental faults and/or security attacks that eventually cause failures in the GPS function/data), as accurate positioning and tracking are essential to assure safe operation in UAVs. The proposed machine learning models were built using 884,410 data records from 1,985 flight logs collected from the PX4 public repository. The trained models are used to predict the expected position of the UAV during a mission, and separation minima are used as a threshold to detect the GPS hazards by comparing it with the distance between two consecutive position values. When a hazard is detected (i.e., the distance is higher than separation minima), the predicted values by machine learning models are fed into the flight controller’s position estimator (i.e., an Extended Kalman Filter (EKF)). To evaluate the effectiveness of this approach, validation experiments were conducted on several realistically defined missions while being exposed to different types of failure conditions (e.g., GPS signal loss or GPS Spoofing), both with and without using the proposed fault-tolerant mechanism. The results show a remarkable reduction in safety violations (the number of separation minima violations was reduced from 94 to 1). Additionally, the proposed mechanism demonstrated a notable improvement in the distance traveled by UAV and the duration of the flight mission in failure conditions, showing its ability to mitigate faults effectively. These findings support the effectiveness of the proposed fault tolerance mechanism in enhancing UAV safety in the presence of issues caused by GPS.

Unmanned Ariel Vehicles (UAVs) are recently being studied and worked upon to make them safe and secure for the upcoming expected growth of UAVs in civilian airspace. These efforts resulted in services such as Unmanned Aircraft System Traffic Management (UTM) or U-space in Europe, providing services to regularize and organize (pre-flight), monitor/track (during the flight) drones in civilian airspace while avoiding collisions. The primary source of information for tracking drones during flight is GPS positioning data, which is used and filtered (after being fused with the other sensors' data) by the flight controller software to estimate the vehicle position, velocity, and orientation. Extended Kalman Filter (EKF), which is used in most open-source flight controllers such as PX4, is responsible for doing this estimation. This makes EKF a critical component of the whole system. This paper aims to study the reliability of flight controllers and their core component, namely EKF, in the presence of GPS-related failures. To do so, we injected faults (i.e., we emulated failures indeed) on GPS raw data ranging from small noises to complete failure (missing GPS signals) and GPS spoofing to study their impact on EKF estimation and on the system as a whole. We observed that for small faults (e.g., Fixed Small Noise or Freeze Values), EKF is efficient and can tolerate/compensate the faults, whereas there is a gap in the filter for handling bigger anomalies (e.g., Invalid Values or Random Values) in the GPS data. Our research also clearly demonstrates that GPS faults lasting 30 seconds or more have a noticeable effect, which represents a clear vulnerability since GPS can be subject of cyber attacks such as spoofing. The quantification of the impact of GPS-related failures in the PX4 is an essential step to measure and improve the reliability of UAVs' flight controller software.