In February 2024, the Polaris Dawn mission - a privately crewed spaceflight aboard a SpaceX Dragon capsule - launched into an elliptical Earth orbit with an apogee of $\mathbf{1, 4 0 0}$ kilometers, marking the furthest distance traveled by a crewed spacecraft since NASA's Apollo missions. During their transit, the crew passed through regions of the Van Allen radiation belts, which are zones characterized by elevated levels of charged particle radiation. In collaboration with SpaceX and the Polaris program, Embry-Riddle's Space Technologies Lab integrated a payload, named LLAMAS, onto the interior wall of the Dragon capsule to capture footage of the Polaris Dawn crew throughout the mission. This paper will describe the measured effects of radiation on the LLAMAS camera sensors by examining the density of hot pixels throughout the duration of the spaceflight.

Wide Field-of-View (WFOV) cameras are commonly adopted in applications such as autonomous driving and space-craft navigation due to their ability to capture large portions of the environment, enhancing situational awareness and improving the likelihood of detecting reference features like stars for celestial navigation and star tracking. However, wide-angle optics introduce severe nonlinear distortion, especially toward the image periphery, posing challenges for accurate perception, limiting the effectiveness of star-based attitude estimation methods that rely on global plate solving. These challenges are further exacerbated under occlusion or glare scenarios, where global imaging solutions become impractical. To overcome these limitations, this work presents a three-stage framework that combines omnidirectional distortion correction with tile-wise plate solving to improve the reliability and accuracy of star tracking in wide-angle imagery. An omnidirectional fisheye distortion model first rectifies the raw image, which is then subdivided into virtually reprojected tiles that reduce distortion by covering a smaller field with a pinhole perspective. Each tile is independently plate-solved by matching detected stars to a known star catalog, enabling localized estimation of pointing vectors. Experimental results on clear-sky conditions and glare-obstructed datasets demonstrate the robustness of the proposed approach, confirming its potential as a resilient navigation solution for wide-angle star tracking.

CubeSat functionality is often limited by not only volume but also power. As such, there have been multiple advances in devices to store energy with higher energy density. 18650 Lithium-Ion Cells are a common battery used for low current draw use cases, making them an economical and approachable candidate as primary batteries for powering this type of small spacecraft. However, it is known that the performance and efficiency will vary according to different environmental conditions, in particular, temperature-requiring thorough testing of the batteries at extreme conditions. This paper will explore environmental tests to verify a power system based on 18650 batteries for a mission duration and to rate them for human spaceflight. The main tests that were conducted included discharge tests at various temperatures and cyclic testing corresponding to the environment in which the batteries will be used. From this performance, data is collected on the battery's performance at a range of temperatures, discharge rates, and environmental conditions.

Ahead of the United States’ Artemis crewed mission returning to the moon in 2024, Intuitive Machines, under the NASA Commercial Lunar Payload Services contract, is set to launch their Nova-C lunar lander in mid-February 2024, with a landing expected in late Februrary. EagleCam, a deployable payload onboard this mission, was delivered in November of 2021 with the initial intention of launching soon thereafter. Given that the long-term shelf life of the CubeSat was not taken into account initially as a requirement, charging and battery health telemetry are only available when the payload is activated. To ensure the battery health within the CubeSat, alternative methods of charging and analysis were developed. This paper will elucidate the challenges that emerged due to the postponed launch timeline, and the invaluable lessons learned, and will present data collected from the mission itself.

Ahead of the United States' Artemis crewed mission returning to the moon in 2024, Intuitive Machines, under the NASA Commercial Lunar Payload Services contract will land their Nova-C lunar lander in early 2023. When this lunar lander reaches an altitude of 30 meters over the lunar surface during the terminal descent, EagleCam will be deployed. This Cube-Sat will capture, and transmit via WiFi, the first-ever, third-person images of a commercial spacecraft performing a lunar landing. This paper will illustrate the CubeSat design, avionics selection, and testing for different off-the-shelf and space-rated components, with a special focus on the science obtained by the different sensors.

A large number of robotic and human-assisted missions to the Moon and Mars are forecast. NASA's efforts to learn about the geology and makeup of these celestial bodies rely heavily on the use of robotic arms. The safety and redundancy aspects will be crucial when humans will be working alongside the robotic explorers. Additionally, robotic arms are crucial to satellite servicing and planned orbit debris mitigation missions. The goal of this work is to create a custom Computer Vision (CV) based Artificial Neural Network (ANN) that would be able to rapidly identify the posture of a 7 Degree of Freedom (DoF) robotic arm from a single (RGB-D) image - just like humans can easily identify if an arm is pointing in some general direction. The Sawyer robotic arm is used for developing and training this intelligent algorithm. Since Sawyer's joint space spans 7 dimensions, it is an insurmountable task to cover the entire joint configuration space. In this work, orthogonal arrays are used, similar to the Taguchi method, to efficiently span the joint space with the minimal number of training images. This ``optimally'' generated database is used to train the custom ANN and its degree of accuracy is on average equal to twice the smallest joint displacement step used for database generation. A pre-trained ANN will be useful for estimating the postures of robotic manipulators used on space stations, spacecraft, and rovers as an auxiliary tool or for contingency plans.