RETINA: a hardware-in-the-loop optical facility with reduced optical aberrations
The increasing interest in spacecraft autonomy and the complex tasks to be accomplished by the spacecraft raise the need for a trustworthy approach to perform Verification & Validation of Guidance, Navigation, and Control algorithms. In the context of autonomous operations, vision-based navigation algorithms have established themselves as effective solutions to determine the spacecraft state in orbit with low-cost and versatile sensors. Nevertheless, detailed testing must be performed on ground to understand the algorithm's robustness and performance on flight hardware. Given the impossibility of testing directly on orbit these algorithms, a dedicated simulation framework must be developed to emulate the orbital environment in a laboratory setup. This paper presents the design of a low-aberration optical facility called RETINA to perform this task. RETINA is designed to accommodate cameras with different characteristics (e.g., sensor size and focal length) while ensuring the correct stimulation of the camera detector. A preliminary design is performed to identify the range of possible components to be used in the facility according to the facility requirements. Then, a detailed optical design is performed in Zemax OpticStudio to optimize the number and characteristics of the lenses composing the facility's optical systems. The final design is compared against the preliminary design to show the superiority of the optical performance achieved with this approach. This work presents also a calibration procedure to estimate the misalignment and the centering errors in the facility. These estimated parameters are used in a dedicated compensation algorithm, enabling the stimulation of the camera at tens of arcseconds of precision. Finally, two different applications are presented to show the versatility of RETINA in accommodating different cameras and in simulating different mission scenarios.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hardware-In-The-Loop Training of a 4f Optical Correlator with Logarithmic Complexity Reduction for CNNs
This work evaluates a forward-only learning algorithm on the MNIST dataset with hardware-in-the-loop training of a 4f optical correlator, achieving 87.6% accuracy with O(n2) complexity, compared to backpropagation, which…
Feasibility Assessment of an Optically Powered Digital Retinal Prosthesis Architecture for Retinal Ganglion Cell Stimulation
Clinical trials previously demonstrated the notable capacity to elicit visual percepts in blind patients affected with retinal diseases by electrically stimulating the remaining neurons on the retina. However, these impl…
Retina : Low-Power Eye Tracking with Event Camera and Spiking Hardware
This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework integrates a directly trained Spiking Neuron Network (SNN) reg…
Pupil DetectionPupil TrackingGenerating retinal flow maps from structural optical coherence tomography with artificial intelligence
Despite significant advances in artificial intelligence (AI) for computer vision, its application in medical imaging has been limited by the burden and limits of expert-generated labels. We used images from optical coher…
Forecasting Loss of Signal in Optical Networks with Machine Learning
Loss of Signal (LOS) represents a significant cost for operators of optical networks. By studying large sets of real-world Performance Monitoring (PM) data collected from six international optical networks, we find that …
BIG-bench Machine LearningClassification on Time Series with Missing DataTime Series Classification