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In the context of Leonardo-LABS research on autonomous and intelligent systems a prototyping simulation stack has been recently settled to allow the development of core autonomy functions for future Leonardo rotary wing unmanned platforms. In order to increase the safety and reliability of UAV(s) some requirements, shared among all Leonardo-LHD platforms, have been identified as key enablers for future autonomous flight. This lead the selection of the 4 streams currently under development: GOA (Ground Object Awareness), ATA (Air Traffic Awareness), ATOL(Autonomous Take-Off and Landing) and ANav (Alternative/GNSS-denied navigation). The proposed simulation solution relies on Ansys®AVxcelerate for simulating sensors output subsequently interfaced to a widely diffused robotic middle-ware (ROS2) allowing a rapid development of perception pipelines heading to a full SIL capability and to test algorithms under one-to-one correspondence with real sensors output. In this work the overall software architecture will be discussed together with the integration into the simulation of a UAV’s flight dynamic and IMU models. Such integration makes use of the Bullet Physics C++ SDK and leverages the above mentioned connection with the ROS2 stack. AVxcelerate has been recently adopted in the automotive industry for supporting autonomous car development as it allows to model and simulate multiple sensors like visible-cameras, LWIR-cameras, various LiDAR models (both flashing and rotating) and mmWave array radars. Moreover among the most advantageous features of Ansys solution there is the capability of modeling the interaction of the light’s spectrum with matter and the possibility of quickly and effectively set sensors properties (cameras optics, radar antennas, LiDAR rays geometry and mechanical behavior, etc.). In the last section of this work will be also presented the so called visual-inertial navigation case of study (belonging to the ANav set of autonomy functions). It will be shown how we are currently addressing the navigation problem of a UAV via a monocular visible camera and IMU system relying on simulated data and will be shown how the problem is addressed leveraging the software architecture and multi-physic and multi-spectral modeling described in this work.

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Multi-physics and Multi-spectral Sensors Simulator for Autonomous Flight Functions Development

  • 会议论文

  • 发表日期：

    2025年

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  • Matteo Baiguera,
  • Marco Ciarambino

摘要

In the context of Leonardo-LABS research on autonomous and intelligent systems a prototyping simulation stack has been recently settled to allow the development of core autonomy functions for future Leonardo rotary wing unmanned platforms. In order to increase the safety and reliability of UAV(s) some requirements, shared among all Leonardo-LHD platforms, have been identified as key enablers for future autonomous flight. This lead the selection of the 4 streams currently under development: GOA (Ground Object Awareness), ATA (Air Traffic Awareness), ATOL(Autonomous Take-Off and Landing) and ANav (Alternative/GNSS-denied navigation). The proposed simulation solution relies on Ansys®AVxcelerate for simulating sensors output subsequently interfaced to a widely diffused robotic middle-ware (ROS2) allowing a rapid development of perception pipelines heading to a full SIL capability and to test algorithms under one-to-one correspondence with real sensors output. In this work the overall software architecture will be discussed together with the integration into the simulation of a UAV’s flight dynamic and IMU models. Such integration makes use of the Bullet Physics C++ SDK and leverages the above mentioned connection with the ROS2 stack. AVxcelerate has been recently adopted in the automotive industry for supporting autonomous car development as it allows to model and simulate multiple sensors like visible-cameras, LWIR-cameras, various LiDAR models (both flashing and rotating) and mmWave array radars. Moreover among the most advantageous features of Ansys solution there is the capability of modeling the interaction of the light’s spectrum with matter and the possibility of quickly and effectively set sensors properties (cameras optics, radar antennas, LiDAR rays geometry and mechanical behavior, etc.). In the last section of this work will be also presented the so called visual-inertial navigation case of study (belonging to the ANav set of autonomy functions). It will be shown how we are currently addressing the navigation problem of a UAV via a monocular visible camera and IMU system relying on simulated data and will be shown how the problem is addressed leveraging the software architecture and multi-physic and multi-spectral modeling described in this work.