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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Veröffentlicht in:IEEE robotics and automation letters Jg. 9; H. 3; S. 2869 - 2876
Hauptverfasser: Laupre, Gabriel Francois, Sharma, Aman, Skaloud, Jan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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
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Abstract 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.
AbstractList 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.
Author Laupre, Gabriel Francois
Sharma, Aman
Skaloud, Jan
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Snippet 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...
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SubjectTerms 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
Title VDMNav: Software Architecture for Aerodynamically Constrained Navigation on Small Fixed-Wing Drones
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