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UAV sensor failures dataset: Biomisa arducopter sensory critique (BASiC).

Muhammad Waqas Ahmad¹, Muhammad Usman Akram¹

¹Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.

|February 2, 2024

Summary

This summary is machine-generated.

A new dataset, Biomisa Arducopter Sensory Critique (BASiC), addresses unmanned aerial vehicle (UAV) sensor failures. This resource aids in developing robust deep learning models for safer autonomous flights.

Keywords:

ArduPilot Autonomous flights Autopilot Mission planner Sensors failures dataset Software in the loop UAV Unmanned aerial vehicle

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Area of Science:

Robotics and Autonomous Systems
Aerospace Engineering
Data Science

Background:

Unmanned aerial vehicles (UAVs) depend on sensor data for navigation and control.
Sensor failures or cyber-attacks altering data can lead to unsafe flight conditions and crashes.
Existing datasets lack comprehensive UAV sensor failure analysis capabilities.

Purpose of the Study:

To introduce the Biomisa Arducopter Sensory Critique (BASiC) dataset for UAV sensor failure analysis.
To provide a resource for developing and testing fault-handling mechanisms in UAV autopilots.
To enhance the safety and reliability of autonomous UAV operations through advanced data analysis.

Main Methods:

The ArduPilot platform was used for experiments with Software in the Loop (SITL) simulations.
The BASiC dataset was created, comprising 70 autonomous flights (over 7 hours) with pre-failure, post-failure, and no-failure data.
Simulations included six critical sensor failures: GPS, remote control, accelerometer, gyroscope, compass, and barometer.

Main Results:

The BASiC dataset offers over 3 hours of pre-failure and post-failure data for each simulated sensor failure scenario.
It provides a comprehensive collection of time-series sensor data under various failure conditions.
The dataset enables detailed analysis of sensor performance degradation and recovery.

Conclusions:

The BASiC dataset is a valuable resource for the research community studying UAV sensor failures.
It facilitates the development and validation of deep learning models for time-series signal analysis in autonomous systems.
This dataset can significantly contribute to improving the safety and reliability of mission-critical UAV flights.