ORION: A simulation environment for spacecraft formation flight, capture, and orbital robotics

Wilde, Markus; Kaplinger, Brian; Go, Tiauw Hiong; Gutiérrez, Héctor; Kirk, Daniel

doi:10.1109/aero.2016.7500575

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…HIL data were captured on the ORION testbed [36] in the Autonomy Lab at Florida Tech. The test bed consisted of a planar, Cartesian maneuver kinematics simulator, shown in Figure 2, with a workspace of 5.5 m × 3.5 m. The simulator was equipped with a 2 DOF motion table that can translate at a maximum speed of 0.25 m/s with a maximum acceleration of 1 m/s 2 .…”

Section: Methodsmentioning

confidence: 99%

Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting

Nguyen,

Sandidge,

Mahendrakar

et al. 2024

Aerospace

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The accelerating deployment of spacecraft in orbit has generated interest in on-orbit servicing (OOS), inspection of spacecraft, and active debris removal (ADR). Such missions require precise rendezvous and proximity operations in the vicinity of non-cooperative, possibly unknown, resident space objects. Safety concerns with manned missions and lag times with ground-based control necessitate complete autonomy. This requires robust characterization of the target’s geometry. In this article, we present an approach for mapping geometries of satellites on orbit based on 3D Gaussian splatting that can run on computing resources available on current spaceflight hardware. We demonstrate model training and 3D rendering performance on a hardware-in-the-loop satellite mock-up under several realistic lighting and motion conditions. Our model is shown to be capable of training on-board and rendering higher quality novel views of an unknown satellite nearly 2 orders of magnitude faster than previous NeRF-based algorithms. Such on-board capabilities are critical to enable downstream machine intelligence tasks necessary for autonomous guidance, navigation, and control tasks.

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Section: Methodsmentioning

confidence: 99%

Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting

Nguyen,

Sandidge,

Mahendrakar

et al. 2024

Aerospace

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“…ORION, USA. The Florida Institute of Technology developed the Orbital Robotic Interaction, On-orbit Servicing, and Navigation (ORION) laboratory to test spacecraft GNC systems for proximity manoeuvres and autonomous or telerobotic capture [27]. The ORION simulator uses the commercial-off-the-shelf Litepanels Hilio D12 LED panel to generate a light source sufficiently bright to exceed the dynamic range of common optical sensors while providing a narrow beam angle.…”

Section: Epos Germany the European Proximity Operationsmentioning

confidence: 99%

Lessons from a Space Lab: An Image Acquisition Perspective

Pauly,

Jamrozik,

del Castillo

et al. 2023

International Journal of Aerospace Engineering

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The use of deep learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the “SnT Zero-G Lab,” for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focuses on the image acquisition equipment in a space lab: background materials, cameras, and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.

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“…The Florida Institute of Technology developed the Orbital Robotic Interaction, On-orbit servicing, and Navigation (ORION) laboratory to test spacecraft GNC systems for proximity manoeuvres and autonomous or telerobotic capture (Wilde et al, 2016). The ORION simulator uses the commercial-off-the-shelf Litepanels Hilio D12 LED panel to generate a light source sufficiently bright to exceed the dynamic range of common optical sensors while providing a narrow beam angle.…”

Section: Orion Usamentioning

confidence: 99%

Lessons from a Space Lab -- An Image Acquisition Perspective

Pauly¹,

Jamrozik²,

Castillo³

et al. 2022

Preprint

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The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation, when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the 'SnT Zero-G Lab', for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focus on the image acquisition equipment in a space lab: background materials, cameras and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.

show abstract

ORION: A simulation environment for spacecraft formation flight, capture, and orbital robotics

Cited by 21 publications

References 34 publications

Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting

Characterizing Satellite Geometry via Accelerated 3D Gaussian Splatting

Lessons from a Space Lab: An Image Acquisition Perspective

Lessons from a Space Lab -- An Image Acquisition Perspective

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