VDMNav: Software Architecture for Aerodynamically Constrained Navigation on Small Fixed-Wing Drones

Navigation of drones is predominantly based on sensor fusion algorithms. Most of these algorithms make use of some form of Bayesian filtering with a majority employing an Extended Kalman Filter (EKF), wherein inertial measurements are fused with a Global Navigation Satellite System (GNSS), and other...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 9; no. 3; pp. 2869 - 2876
Main Authors: Laupre, Gabriel Francois, Sharma, Aman, Skaloud, Jan
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerodynamics
Algorithms
Drones
Dynamic models
Extended Kalman filter
Field robots
Global navigation satellite system
Inertial sensing devices
Kinematics
Multisensor fusion
Navigation
Navigation systems
Real time
Real-time systems
Robot sensing systems
Run time (computers)
Sensors
Software
Software architecture
software architecture for robotic and automation
software-hardware integration for robot systems
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Navigation of drones is predominantly based on sensor fusion algorithms. Most of these algorithms make use of some form of Bayesian filtering with a majority employing an Extended Kalman Filter (EKF), wherein inertial measurements are fused with a Global Navigation Satellite System (GNSS), and other sensors, in a kinematic framework to yield a navigation solution (position, velocity, attitude, and time). However, the long-term accuracy of this solution is exacerbated during the absence of satellite positioning, especially for small drones with low-cost MEMS inertial sensors. On the other hand, a recently proposed vehicle dynamic model (VDM)-based navigation system has shown significant improvement in positioning accuracy during the absence of a satellite positioning solution, although in a mostly offline setting. In this article, we present the software architecture of its real-time implementation using Robot Operating System (ROS) that separates and interfaces its core from a particular hardware. The presented implementation asynchronously handles different sensor data in a modular fashion and allows i) adapting the underlying aerodynamic model, ii) including complementary sensors, and iii) reducing the dimensionality of the EKF state space at run-time without compromising the navigation performance. The real-time performance of the proposed software architecture is evaluated during long GNSS absences of up to eight minutes and compared to that of inertial coasting.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3358758