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.

Exploring the Moon and Mars are crucial steps in advancing space exploration. Numerous missions aim to land and research in various lunar locations, some of which possess challenging surfaces with unchanging features. Some of these areas are cataloged as lunar light plains. Their main characteristics are that they are almost featureless and reflect more light than other lunar surfaces. This poses a challenge during navigation and landing. This paper compares traditional feature matching techniques, specifically scale-invariant feature transform and the oriented FAST and rotated BRIEF, and novel machine learning approaches for dense feature matching in challenging, unstructured scenarios, focusing on lunar light plains. Traditional feature detection methods often need help in environments characterized by uniform terrain and unique lighting conditions, where unique, distinguishable features are rare. Our study addresses these challenges and underscores the robustness of machine learning. The methodology involves an experimental analysis using images that mimic lunar-like landscapes, representing these light plains, to generate and compare feature maps derived from traditional and learning-based methods. These maps are evaluated based on their density and accuracy, which are critical for effective structure-from-motion reconstruction commonly utilized in navigation for landing. The results demonstrate that machine learning techniques enhance feature detection and matching, providing more intricate representations of environments with sparse features. This improvement indicates a significant potential for machine learning to boost hazard detection and avoidance in space exploration and other complex applications.

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.

In early 2022, Intuitive Machines' NOVA-C Lander will touch down on the lunar surface becoming the first commercial endeavor to visit a celestial body. NOVA-C will deliver six payloads to the lunar surface with various scientific and engineering objectives, ushering in a new era of commercial space exploration and utilization. However, to safely accomplish the mission, the NOVA-C lander must ensure its landing site is free of hazards larger than 30 cm and the slope of local terrain at touchdown is less than 10 degrees off vertical. To accomplish this, NOVA-C utilizes Intuitive Machines' precision navigation system, coupled with machine vision algorithms for scene reduction and landing site characterization. A unique aspect to the NOVA-C approach is the real-time nature of the hazard detection and avoidance algorithms--which are performed 400 meters above and down range of the intended landing site and completed within 15 seconds. In this paper, we review the theoretical foundations for the hazard detection and avoidance algorithms, describe the practical challenges of implementation on the NOVA-C flight computer, and present test and analysis results.

Although passive mitigation measures have been put into place to ensure the long-term sustainability of space activ-ities, numerous studies have shown that in order to stabilize the growth of the orbital debris population, active removal of the largest debris in Earth orbit, such as abandoned spacecraft and rocket bodies, is still necessary. In active debris removal scenarios, the target is generally uncooperative and therefore non-communicative (e.g., through some easily recognizable arti-ficial markers or transponder) with the servicing spacecraft and cannot exchange information and safely perform the docking or berthing operations. Additionally, essential information on the form and inertia characteristics of the space target may have altered due to the prolonged period in orbit (e.g., due to exhaustion of fuel, collisions, or explosions). Additionally, unknown objects such as large debris pieces will have unknown inertia properties. This paper proposes a method of inertia esti-mation of a rotating target assuming a torque-free environment. Estimation is performed through the use of Particle Swarm Optimization (PSO) utilizing attitude observations of the target (quaternion data). The solution space for PSO particles is an $\mathbb{R}^{6}$ vector that can be mapped to inertia tensor matrix space, which represents an estimate of the target's (symmetric) inertia tensor. Attitude motion is propagated using Euler's equations to generate estimated measurements which are then compared to experimental attitude measurements for validation.

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 method of near real-time detection and tracking of resident space objects (RSOs) using a convolutional neural network (CNN) and linear quadratic estimator (LQE) is proposed. Advances in machine learning architecture allow the use of low-power/cost embedded devices to perform complex classification tasks. In order to reduce the costs of tracking systems, a low-cost embedded device will be used to run a CNN detection model for RSOs in unresolved images captured by a gray-scale camera and small telescope. Detection results computed in near real-time are then passed to an LQE to compute tracking updates for the telescope mount, resulting in a fully autonomous method of optical RSO detection and tracking.

Ahead of the United States' crewed return to the moon in 2024, Intuitive Machines, under a NASA Commercial Lunar Payload Services contract, will land their Nova-C lunar lander in October 2021. At 30 meters altitude during the terminal descent, EagleCam will be deployed, and will capture and transmit the first-ever third-person images of a spacecraft making an extraterrestrial landing. This paper will focus on the structural design, modeling, and impact analysis of a 1.5U CubeSat payload to withstand a ballistic, soft-touch landing on the lunar surface.

There has been an exponential increase in planned lunar missions with the advent of commercial missions and future manned missions. Understanding the lunar environment is crucial to providing information for future missions that will land on the moon and other celestial bodies. The necessity to navigate freely, without loss of sensing capability is paramount to many of these explorations. Some of the vehicles that will pioneer these missions will encounter various natural hazards and awareness challenges–such as lunar dust and regolith. Due to their electrical and mechanical properties, the constituent particles will tend to adhere to the surface of the exploratory craft and the on-board equipment. This behavior poses a significant threat as the charged dust can cause vital electronics to short or render experimental instruments unusable due to accumulation and electrical discharges, as experienced by the Apollo crews on each visit to the Moon. This paper will focus on the design and experimentation of an autonomous control system that will detect regolith using the deep learning computer vision architecture MobileNetV2, and remove it using an electrodynamic dust shield covering a camera lens. This entire system provides an active method to detect optical obstructions, assess, and then remove them in order to regain navigation and/or scientific capabilities.

This paper focuses on the development of a ground-based test-bed to analyze the complexities of contact dynamics between multibody systems in space. The test-bed consists of an air-bearing platform equipped with a 7 degrees-of-freedom (one degree per revolute joint) robotic arm which acts as the servicing satellite. The dynamics of the manipulator on the platform is modeled as an aid for the analysis and design of stabilizing control algorithms suited for autonomous on-orbit servicing missions. The dynamics are represented analytically using a recursive Newton-Euler multibody method with D-H parameters derived from the physical properties of the arm and platform. In addition, Product of Exponential (PoE) method is also employed to serve as a comparison with the D-H parameters approach. Finally, an independent numerical simulation created with the SimScape modeling environment is also presented as a means of verifying the accuracy of the recursive model and the PoE approach. The results from both models and SimScape are then validated through comparison with internal measurement data taken from the robotic arm itself.

In this paper, an autonomous method of satellite detection and tracking in images is implemented using optical flow. Optical flow is used to estimate the image velocities of detected objects in a series of space images. Given that most objects in an image will be stars, the overall image velocity from star motion is used to estimate the image's frame-to-frame motion. Objects seen to be moving with velocity profiles distinct from the overall image velocity are then classified as potential resident space objects. The detection algorithm is exercised using both simulated star images and ground-based imagery of satellites. Finally, this algorithm will be tested and compared using a commercial and an open-source software approach to provide the reader with two different options based on their need.

This work utilizes a MobileNetV2 Convolutional Neural Network (CNN) for fast, mobile detection of satellites, and rejection of stars, in cluttered unresolved space imagery. First, a custom database is created using imagery from a synthetic satellite image program and labeled with bounding boxes over satellites for "satellite-positive" images. The CNN is then trained on this database and the inference is validated by checking the accuracy of the model on an external dataset constructed of real telescope imagery. In doing so, the trained CNN provides a method of rapid satellite identification for subsequent utilization in ground-based orbit estimation.

Robotic and human lunar landings are a focus of future NASA missions. Precision landing capabilities are vital to guarantee the success of the mission, and the safety of the lander and crew. During the approach to the surface there are multiple challenges associated with Hazard Relative Navigation to ensure safe landings. This paper will focus on a passive autonomous hazard detection and avoidance sub-system to generate an initial assessment of possible landing regions for the guidance system. The system uses a single camera and the MobileNetV2 neural network architecture to detect and discern between safe landing sites and hazards such as rocks, shadows, and craters. Then a monocular structure from motion will recreate the surface to provide slope and roughness analysis.

The interest in returning to the Moon for research and exploration has increased as new tipping point technologies are providing the possibility to do so. One of these initiatives is the Artemis program by NASA, which plans to return humans by 2024 to the lunar surface and study water deposits on the surface. This program will also serve as a practice run to plan the logistics of sending humans to explore Mars. To return humans safely to the Moon, multiple technological advances and diverse knowledge about the nature of the lunar surface are needed. This paper will discuss the design and implementation of the flight software of EagleCam, a CubeSat camera system based on the free open-source core Flight System (cFS) architecture developed by NASA's Goddard Space Flight Center. EagleCam is a payload transported to the Moon by the Commercial Lunar Payload Services Nova-C lander developed by Intuitive Machines. The camera system will capture the first third-person view of a spacecraft performing a Moon landing and collect other scientific data such as plume interaction with the surface. The complete system is composed of the CubeSat and the deployer that will eject it. This will be the first time WiFi protocol is used on the Moon to establish a local communication network.

This work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize the difference between actual and estimated observations and knowledge of the chief's position with known CR3BP dynamics to determine the deputy's initial state. Convergence is achieved through limiting particle starting positions to feasible positions based on the known chief position, and sensor constraints. Parallel and GPU processing methods are used to improve computation time and provide an accurate initial state estimate for a variety of cislunar orbit geometries.

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.

The renewed interest in lunar exploration has triggered the development of novel commercial lunar payloads in recent years. As the design paradigm is shifting from multiple-year design and development of payload using customized devices, to rapid design and prototyping using commercial-off-the-shelf (COTS) components, end-to-end testing is essential to ensure success of the mission. Thermal-vacuum (TVAC) testing is one of the key tests performed on the lunar systems. TVAC testing is an experiment to verify the readiness of the hardware and software under the extreme circumstances the payload will experience in the space environment. This paper focuses on the TVAC testing of a hypothetical small wireless lunar sensing payload's communications system, consisting of an electrical power and communications subsystems, on-board computer, and house-keeping and scientific sensors. The experiment follows NASA guidelines and standards to confirm the design requirements and their verification plan. The payload is considered part of a short-duration sensing and telemetry mission in a particular location near-Lunar-surface environment. Therefore, it is designed to withstand the computed thermal and vacuum requirements for the mission conditions: a temperature range from 15 degrees C to 50 degrees C and an atmospheric pressure of 10(-4) Pa.