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Throughout their life cycle, space launch vehicles impact their local and global environments on Earth and in space. Given the space industry’s projected growth, recent literature suggests that the atmospheric consequences of these activities are understudied and insufficiently addressed. Rockets uniquely emit combustion gases and particles into distinct layers of the atmosphere, inducing effects that include perturbations of ozone chemistry and of Earth’s energy balance through radiative forcing. International environmental regulations do not presently address rocket emissions and only scarce, isolated policies exist at the national level. Additional research on the impact of space launches, including new in situ measurements coupled with global atmospheric models, is required to inform policymaking and future mitigation. The development of an actionable and collaborative sustainability index for launch vehicles could serve as a basis for future regulations or incentivize the sector towards more sustainable designs by making emissions reduction a competitive advantage.

In this study, we introduce a framework for planning and assessing multi-spacecraft asteroid deflection campaigns. In the scenario considered, a near-Earth asteroid (NEA) is nudged away from gravitational keyholes via a kinetic impactor (KI) technique, lest its passage should incur an Earth collision in the future. An asteroid orbiter or/and an impactor is/are used in the precursor stage to obtain uncertain information about the target asteroid, whose launch date and trajectory are optimized using Chebyshev's method and a genetic algorithm. The KI mass is optimized through Monte Carlo simulations to improve the robustness of the method and achieve the minimum required probability of success (PoS). Case scenarios targeting Apophis and Bennu substantiate the utility of the framework in optimizing different deflection campaign architectures and making decisions amongst them via newly proposed visualization methods.

This paper presents an improved technique for predicting wet mass, dry mass, end-of-life power, and launch-configuration volume for Earth observation satellites based on inputs of mission type, pay...

High resolution, high frequency, multi-spectral remotely-sensed Earth Observation (EO) data is increasingly available. Challenges of ensuring radiometric data quality and consistency of data products, however, have not been widely recognized. As new products and applications rapidly emerge, and decisions linked to public safety, well-being, and environmental assessments are at stake, the issue of EO data quality and quantification of errors and their impacts has gained increasing salience. This paper presents a generalizable decision-theoretic framework for quantifying the value of data quality (partly through improved calibration), and demonstrates its application for a water quality monitoring case. Results from the particular case of freshwater monitoring show that after 30% or more probability of occurrence of a harmful algal bloom, remote sensing is useful to have regardless of the data quality level. However, factors such as cost of remote sensing data (in this case, comparing internal processing vs. external processing of tasked satellite imagery) will affect the threshold at which remote sensing value is positive regardless of data quality level.

The spacecraft early conceptual design effort often involves picking components from a catalog or database, combined with discretized decisions concerning operations, like the choice of ground station, type of orbit (LEO or MEO, syn-synchronous or polar), and, in the case of constellations, the number of planes and satellites per plane. One common strategy is to exhaustively enumerate all combinations, filter out unfeasible ones, and plot all resulting designs in the objective space where a Pareto frontier might be found between competing performance metrics. However, as the number of combinations grows exponentially in the number of options, this strategy quickly becomes limited. Based on the optimization field of mixed-integer nonlinear programming, a new method has shown promise in overcoming the challenges resulting from a large number of options. This paper enhances the previous method describing how to incorporate operational aspects of design: choice of ground station, the timing of communication downlinks, and modeling eclipse time windows. We apply the method to a simple Earth observation case study and evaluate the new optimization problems' computational performance as a function of time discretization level.

The Mars Oxygen ISRU Experiment (MOXIE) is an instrument traveling to Mars onboard NASA's Perseverance rover. It will demonstrate, for the first time ever, in-situ resource utilization (ISRU) on the surface of another celestial body. MOXIE will utilize the carbon dioxide atmosphere of Mars to create oxygen as a demonstration of a planned larger mission. The instrument itself will produce oxygen at roughly 0.5 percent of the scale that would be necessary to support a human mission to Mars. A scaled-up version of MOXIE would be sent to Mars twenty-six months ahead of the first human mission and would aim to produce approximately 3 kg/hr of oxygen while in operation. This production rate would fully fuel the oxidizer portion of a Mars Ascent Vehicle prior to the first crew landing on Mars, which would enable that crew to return to Earth. This is a key capability to reduce mission risk by providing a safe return option on Mars prior to the crew arriving. Additionally, the system could provide oxygen for life support systems and habitation pressure. The intent of this paper is to describe a model that has been created to optimize the design of this scaled ISRU plant. It takes lessons learned from the MOXIE project and combines them with parameters and constraints of a planned human mission to systematically identify optimal design solutions. The extensibility of MOXIE is formulated through a multiobjective optimization problem for early-stage conceptual design. The objective functions minimize the power and mass required to build this ISRU system by changing operating conditions and system architecture while satisfying a set of constraints. The subsystems modeled for this problem include carbon dioxide acquisition and compression (CAC) to compress the Mars atmosphere, solid oxide electrolysis (SOE) to produce oxygen from carbon dioxide, and liquefaction to prepare the oxygen for storage. Additionally, the power, electronics, and heat exchange systems are simulated to capture gas transfer and control mechanisms. The model is built in MATLAB and uses Simulink as a framework. Results from this multiobjective optimization study and an analysis on the scalability of the MOXIE instrument show that an ISRU system that produces 22,717 kg of oxygen over 14 months would have a mass of 7,512 kg and a power requirement of 19,526 W. These results provide NASA and other agencies with an optimized design of a scaled ISRU system and its potential to reduce cost and risk as they prepare for a human mission to Mars.

© 2020 The MITRE Corporation. All Rights Reserved. The reuse of proven designs is a common practice in complex engineering systems. The contextual differences between missions, however, mean that design reuse is rarely a “copy and paste” effort – some amount of rework and adaptation is required. In the space industry, many projects adopt unstructured, non-systematic processes for assessing potential reuse scenarios. There is a need for a standardized and repeatable methodology for determining the technical and programmatic impacts of these scenarios before they are pursued. We claim that MBSE is an ideal environment within which to base such a methodology. We demonstrate development of and application to a sample problem of an MBSE Design Reuse Methodology. First, we build out the methodology logically and agnostic to MBSE implementation methods; then, we explore and select options for conducting the logical process within the MBSE environment. A Reuse Profile guides application of the methodology to the Mission of Interest – these specifications leverage the semantic richness of SysML to both describe the Mission of Interest and the candidate reuse missions/elements, as well as to coordinate the analytical efforts assessing technical feasibility and programmatic impacts. A sample problem – exploring design reuse for the robotic arm element of a robotic, multi-purpose lunar rover – is selected to demonstrate the capabilities of the tool while remaining relevant to the exploration objectives of the Artemis program. Results suggest that leveraging designs of past rover missions may yield a 15% improvement in systems engineering effort (measured in Person-Months). This work forms part of a larger effort towards building MBSE methods and theory to provide decision support leading to more strategic and sustainable reuse of successful designs.

On-orbit assembly missions typically involve humans-in-the-loop and use large custom-built robotic arms designed to service existing modules. A proposed concept of on-orbit robotic assembly of modularized CubeSat components within a spacecraft locker eliminates the need for humans-in-the loop. The spacecraft locker supports use cases such as rapidly placing failed nodes within a constellation of satellites and providing sensing and propulsion capabilities in Low Earth Orbit. Despite the recent proliferation of small satellites, there are few planned demonstrations of on-orbit assembly and few demonstrations of on-orbit servicing. Key gaps challenges of in-space assembly of small satellites are (1) the lack of standardization of electromechanical CubeSat components for compatibility with commercial robotic assembly hardware, and (2) testing and modifying commercial robotic assembly hardware. In this work, we focus on testing and modifying: we develop an optimization process for a robotic assembly model to integrate small satellites in space. Our process focus is on the optimization of the on-orbit assembly time of small satellites. We use Commercial-Off-The-Shelf (COTS) robot arms to snap together components in a spacecraft, while minimizing humans-in-the-loop. Assembly time is the selected performance metric as it is critical to the assertion that building small satellites on-orbit results in reduced budget and satellite development time on Earth. We minimize on-orbit small satellite assembly time by optimizing assembly time with the Genetic Algorithm, which use dexterous robotic arms to assemble components, without any negative effects on the attitude and control system. We implement a robot arm assembly model in Python, using Inverse Kinematics. We use a Genetic Algorithm-based optimization scheme, with time as the objective function, and three constraints: robot assembly volume, power consumption, and peak power. Design variables such as joint damping, motor force (torque), position gain and velocity gain are used to model grasping a component and moving the component to the satellite assembly area of the spacecraft. The robot arms are required to be within a tolerance defined based on the 300 mm x 300 mm x 500 mm assembly area. In simulation, we observe that using a given baseline servo motor (7 V) at high proportional gains results in optimal assembly time of approximately 10-20 seconds per component assembly, compared to roughly double this time per component for a 1U CubeSat weighing 2 kg. However, we expect this improvement to result in 25% higher power consumption. Using a high gain value with a lower voltage (5 V) motor results in oscillations and additional time required to dampen out to within the given tolerance, and results in increased assembly time. The benchmarked small satellite assembly time with a human-in-the-loop requires 50 weeks to 90 months of component assembly and integration time on Earth. We anticipate that on-orbit assembly capability optimized for a 1 U functional CubeSat with 30 W of total power, would reduce the assembly time by an order of magnitude. With robotic arm models, for a 1 U CubeSat assembly, we show up to 42% saving benefit in robotic assembly time.

With the emerging democratization of space, Earth Observation (EO) imagery is becoming increasingly important to a variety of industries. However, it remains difficult and expensive to build constellations that achieve continuous and high-quality global coverage. Reconfiguring a satellite constellation into different orbital planes to change its observational performance is traditionally a fuel intensive procedure. The concept of a reconfigurable constellation (ReCon) accounts for J2 perturbation effects when making fuel efficient maneuvers to shift a satellite’s ground track. ReCon reduces the cost of high revisit frequency, high-quality resolution, EO constellations compared to nonreconfigurable constellations by reducing the number of satellites required to achieve repeated observations of a given ground event on demand. This paper first explores the sensitivities of ReCon’s performance against uncertainties in reconfiguration demand, design costs, and imagery value. The sensitivity analysis reveals that in cases of extremely low demand, ReCon fails to provide a cost-effective solution in terms of events responded to per dollar spent. In cases of high demand ReCon fails to meet demand altogether. A Monte Carlo analysis over a range of demand scenarios shows using a staged deployment for ReCon offers a flexible, cost-effective solution to the uncertainties in the demand of EO imagery. Deferring launch costs to the future, through a staged deployment, not only provides flexibility in constellation design, but also allows the designer to capitalize on the continuation of lowering launch costs and increasing launch opportunities. Staging the deployment of constellations also allows for the satellites’ technology to evolve over time, facilitating the capture of higher value imagery and further enhancing the capabilities of ReCon. Implementing the option to deploy additional satellites in stages makes ReCon significantly better equipped to respond to the uncertainty in the demand of space assets.

© 2020 The MITRE Corporation. All Rights Reserved. Traditional Earth-observing satellite constellations must preselect orbits to match established mission objectives, inherently limiting the capability of the system to respond to dynamic events. A more flexible approach to constellation design which maneuvers satellites to new orbits throughout the mission lifetime can enable the tracking of mobile targets; such a concept will be explored and evaluated in the following paper. Currently, an existing approach for creating a reconfigurable constellation of satellites (ReCon) demonstrates some increased functionality. However, the repeating ground track (RGT) orbits used are only well-suited for static ground targets (i.e. particular latitude and longitude points) as they are defined by their repetitious passes over the particular points on the globe; inherently these orbits are not ideal for tracking mobile targets such as ships, oil spills, hurricanes, and other weather events. By addressing this gap and designing the constellation to follow these mobile targets, data return can be of higher quality and quantity leading toward improved predictions of future movements. Traditional non-maneuverable satellites may of course capture mobile feature data; however, the quality and quantity of data returned is driven by the satellites’ orbits which were not designed for the particular mobile target. A proposed concept of operations that models possible sequential satellite maneuvers as a graph has shown promise for tracking mobile targets in the case of a single satellite. In the following paper, this concept is further explored by the addition of an optimizer to this method, applied to both a single satellite and a constellation of maneuverable satellites, like ReCon. Results for a tropical storm case study indicate that with relatively low amounts of delta-V per satellite, many possible maneuver combinations are found which result in improved accesses with greater total access time and closer passes. Trade-offs are shown between total target access time, mean distance to targets upon access, and total delta-V used. The performance of increased constellation size is also explored. By having satellites dynamically respond to mobile targets such as hurricanes, satellite imagery and remote sensing can be of better quality and even contribute to better predictions for developing storms.