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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRSDate not supplied

Cislunar Trajectory Design and Maneuver Autonomy for NASA's Moon to Mars Architecture

NASA’s Moon to Mars architecture is an ambitious roadmap of manned cislunar and deep space exploration. The extensive amount of orbital assets required will place a significant burden on ground-based resources, such as communication networks and operations facilities. Spacecraft autonomy is essential for maintaining a vast number of complex missions beyond Earth orbit. To achieve full autonomy, spacecraft must be able to employ methods of robust maneuver design without an explicit dependence on commands sent from the ground. This level of autonomy is needed not only for stationkeeping, but also for outbound transfers. To address the need of spacecraft maneuver design autonomy, this work investigates the use of neural networks (NNs) in a supervised learning environment. A supervised learning approach for NNs allows for a curated training data set, consisting exclusively of perturbations applied to a desired mission concept of operations (ConOps). The proposed approach allows humans on the ground to design a specific mission ConOps before flight, then employ NNs to fly the mission robustly and autonomously. This investigation numerically tests maneuver autonomy in four highly sensitive regions of flight: orbit raising, translunar injection burns, powered lunar flybys, and invariant manifold insertion burns. These straining cases are contextualized by testing them in a demonstration mission, targeting an Earth-Moon L3 orbit. The study first establishes feasibility by automating impulsive burn maneuvers. However, some guidance algorithms will need more intensive commands, such as inertial pointing and angular rates. To validate this method, NN maneuver autonomy is applied to a finite burn model of the demonstration mission. The use of sequential, mission specific maneuvers provide an appropriate testbed to demonstrate the robustness of a NN trained on feasible perturbed states. Moreover, these scenarios provide preliminary proof-of-concept for fully autonomous missions that execute maneuvers without dependence upon explicit command uplinks. As a result, the technological advancement proposed in this work may significantly ease the strain on ground-based mission operations. This would enable complex and autonomous mission execution in cislunar and deep space regimes, filling a technology gap required to support future manned missions.

NASA↗
NASA NTRS2017

Overview of the Mission Design Reference Trajectory for NASA's Asteroid Redirect Robotic Mission

The National Aeronautics and Space Administration's (NASA's) recently cancelled Asteroid Redirect Mission was proposed to rendezvous with and characterize a 100 m plus class near-Earth asteroid and provide the capability to capture and retrieve a boulder off of the surface of the asteroid and bring the asteroidal material back to cislunar space. Leveraging the best of NASA's science, technology, and human exploration efforts, this mission was originally conceived to support observation campaigns, advanced solar electric propulsion, and NASA's Space Launch System heavy-lift rocket and Orion crew vehicle. The asteroid characterization and capture portion of ARM was referred to as the Asteroid Redirect Robotic Mission (ARRM) and was focused on the robotic capture and then redirection of an asteroidal boulder mass from the reference target, asteroid 2008 EV5, into an orbit near the Moon, referred to as a Near Rectilinear Halo Orbit where astronauts would visit and study it. The purpose of this paper is to document the final reference trajectory of ARRM and the challenges and unique methods employed in the trajectory design of the mission.

low thrust↗
NASA NTRS2005

Evolutionary Computing for Low-thrust Navigation

The development of new mission concepts requires efficient methodologies to analyze, design and simulate the concepts before implementation. New mission concepts are increasingly considering the use of ion thrusters for fuel-efficient navigation in deep space. This paper presents parallel, evolutionary computing methods to design trajectories of spacecraft propelled by ion thrusters and to assess the trade-off between delivered payload mass and required flight time. The developed methods utilize a distributed computing environment in order to speed up computation, and use evolutionary algorithms to find globally Pareto-optimal solutions. The methods are coupled with two main traditional trajectory design approaches, which are called direct and indirect. In the direct approach, thrust control is discretized in either arc time or arc length, and the resulting discrete thrust vectors are optimized. In the indirect approach, a thrust control problem is transformed into a costate control problem, and the initial values of the costate vector are optimized. The developed methods are applied to two problems: 1) an orbit transfer around the Earth and 2) a transfer between two distance retrograde orbits around Europa, the closest to Jupiter of the icy Galilean moons. The optimal solutions found with the present methods are comparable to other state-of-the-art trajectory optimizers and to analytical approximations for optimal transfers, while the required computational time is several orders of magnitude shorter than other optimizers thanks to an intelligent design of control vector discretization, advanced algorithmic parameterization, and parallel computing.

optimization↗
NASA NTRS2019

Refining Lucy Mission Delta-V During Spacecraft Design Using Trajectory Optimization Within High-Fidelity Monte Carlo Maneuver Analysis

Recent advances linking medium-fidelity trajectory optimization and high-fidelity trajectory propagation/maneuver design software with Monte Carlo maneuver analysis and parallel processing enabled realistic statistical delta-V estimation well before launch. Completing this high-confidence, refined statistical maneuver analysis early enabled release of excess delta-V margin for increased dry mass margin for the Lucy Jupiter Trojan flyby mission. By 3.3 years before launch, 16 of 34 TCMs had 1000 re-optimized trajectory design samples, yielding tens of m/s lower 99%-probability delta-V versus targeting maneuvers to one optimal trajectory. One year later, 1000 re-optimized samples of all deterministic maneuvers and subsequent flybys further lowered estimated delta-V.

Trojan↗
NASA NTRS2012

General Methodology for Designing Spacecraft Trajectories

A methodology for designing spacecraft trajectories in any gravitational environment within the solar system has been developed. The methodology facilitates modeling and optimization for problems ranging from that of a single spacecraft orbiting a single celestial body to that of a mission involving multiple spacecraft and multiple propulsion systems operating in gravitational fields of multiple celestial bodies. The methodology consolidates almost all spacecraft trajectory design and optimization problems into a single conceptual framework requiring solution of either a system of nonlinear equations or a parameter-optimization problem with equality and/or inequality constraints.

Condon, Gerald↗
NASA NTRSDate not supplied

Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories

Designing interplanetary missions is an iterative process, with a tight coupling between mission analysis and vehicle design. The ability to simultaneously process both analyses early on can provide significant benefits in both the concept formulation and in overall mission feasibility. To implement such a simultaneous analysis process, there is a need to model interplanetary trajectories rapidly while maintaining an acceptable level of fidelity. Many simple patched conics tools make too many assumptions to satisfactorily address all relevant constraints and objectives, while full-fidelity trajectory design tools often require more time and expertise than is available in early conceptual studies. Hence, some form of middle-ground modeling tool would provide value. This paper introduces the development of such a tool using Python. This tool, Keplerian Analysis for Versatile Evaluation of Arbitrary Trajectories (KAVEAT), implements a patched conics method that enforces continuity of state (time, mass, position, velocity) through all transitions between various spheres of influence on the trajectory. The trajectory is constructed in an object-oriented manner, dynamically assembling unique maneuvers such as departure burns, heliocentric transfers (ballistic or with electric propulsion), and planetary flybys (powered or unpowered). Each maneuver has its own specific set of basic inputs needed to define it. The tool then takes these defined maneuvers, strings them together creating all the interdependencies required, and solves for the ideal trajectory using a gradient-based optimizer.

Katherine T McBrayer↗
NASA NTRS1991

Advances in robust flight design

Current launch vehicle trajectory design philosophies, generally based on maximizing payload capability, result in an expensive and time-consuming iteration in trajectory design for each mission. However, for a launch system that is not performance-driven, a flight design that is robust to variations in missions and provides single-engine-out capability can be highly cost-effective. This philosophy has led to the development of two flight design concepts to reduce recurring costs: standard trajectories and command multiplier steering. Preliminary analyses of these two concepts had proven the feasibility and showed encouraging results in applications to an Advanced Launch System vehicle. Recent progress has demonstrated the effective and efficient integration of the two concepts with minimal payload penalty.

Wong, Kelvin K.↗
NASA NTRS1982

The development and application of aerodynamic uncertainties in the design of the entry trajectory and flight control system of the Space Shuttle Orbiter

In connection with the decision to conduct with the Shuttle an orbital, manned mission on its first launch, certain problems arose related to mission safety, which had to be provided without the benefit of either a graduated flight test program or an initial unmanned flight concept. In an attempt to overcome these problems, the philosophy was adopted to provide a reasonable estimate of maximum possible errors in the preflight predicted aerodynamics. The flight control system (FCS) was to be certified on the basis of the estimated errors prior to STS-1. A set of 'worst case' aerodynamic uncertainties, defined as variations, was developed. As part of the first flight certification, variations, combined with other system uncertainties, were used to 'stress' the FCS through a multitude of simulations. Attention is given to an Orbiter description, the entry mission, the correlation of aerodynamic uncertainties, the application of aerodynamic variation in FCS and trajectory design, and the flight test results.

Gamble, J. D.↗
NASA NTRSDate not supplied

A History of Orion Mission Design, Copernicus Software Development, and the Artemis I Trajectory

This paper describes the history of the on-orbit trajectory design and optimization for the Orion spacecraft at NASA JSC, from the initial design through the execution of the Artemis I test flight. In parallel, the Copernicus trajectory optimization tool was also being developed and was the main tool used for Orion trajectory design during this period. Finally, the paper gives an overview of the Artemis I trajectory that was flown during the Artemis I mission from November 16 - December 11, 2022.

Orion↗
NASA NTRS1990

Interactive orbital proximity operations planning system

An interactive graphical planning system for on-site planning of proximity operations in the congested multispacecraft environment about the space station is presented. The system shows the astronaut a bird's eye perspective of the space station, the orbital plane, and the co-orbiting spacecraft. The system operates in two operational modes: (1) a viewpoint mode, in which the astronaut is able to move the viewpoint around in the orbital plane to range in on areas of interest; and (2) a trajectory design mode, in which the trajectory is planned. Trajectory design involves the composition of a set of waypoints which result in a fuel-optimal trajectory which satisfies all operational constraints, such as departure and arrival constraints, plume impingement constraints, and structural constraints. The main purpose of the system is to present the trajectory and the constraints in an easily interpretable graphical format. Through a graphical interactive process, the trajectory waypoints are edited until all operational constraints are satisfied. A series of experiments was conducted to evaluate the system. Eight airline pilots with no prior background in orbital mechanics participated in the experiments. Subject training included a stand-alone training session of about 6 hours duration, in which the subjects became familiar with orbital mechanics concepts and performed a series of exercises to familiarize themselves with the control and display features of the system. They then carried out a series of production runs in which 90 different trajectory design situations were randomly addressed. The purpose of these experiments was to investigate how the planning time, planning efforts, and fuel expenditures were affected by the planning difficulty. Some results of these experiments are presented.

Grunwald, Arthur J.↗
NASA NTRSDate not supplied

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Christopher J Sullivan↗
NASA NTRS2021

Exploring Transfers Between Earth-Moon Halo Orbits via Multi-Objective Reinforcement Learning

Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.

Mashiku, Alinda K.↗
NASA NTRS1975

Atmosphere explorer /AE-C/ adaptive mission design and trajectory performance

The preflight mission design and in-flight execution are presented to illustrate the actual utilization of the adaptive nature of this mission. Spacecraft systems (propulsion, attitude, accelerometer) and software (maneuver program) features which contribute to the adaptive trajectory flexibility are discussed. Descriptions of procedures used to handle low perigee maneuver operations (to 130 km), for achieving ground track control and orbit phasing, for making a transition from elliptical to circular orbits, and for circular orbit restoration and maintenance are presented in detail. The 85 maneuvers made during the first 17 months of the mission are summarized.

Barbieri, R. W.↗
NASA NTRS2019

Designing Low-Thrust Enabled Trajectories for a Heliophysics Smallsat Mission to Sun-Earth L5

A small satellite deployed to Sun-Earth L5 could serve as a low-cost platform to observe solar phenomena such as coronal mass ejections. However, the small satellite platform introduces significant challenges in the trajectory design process via limited thrusting capabilities, power and operational constraints, and fixed deployment conditions. To address these challenges, a strategy employing dynamical systems theory is used to design a low-thrust-enabled trajectory for a small satellite to reach the Sun-Earth L5 region. This procedure is demonstrated for a small satellite that launches as a secondary payload with a larger spacecraft destined for a Sun-Earth L2 halo orbit.

Stuart, Jeffrey↗
NASA NTRS1988

Optimization of aeroassisted orbital transfer - Current status

This paper presents the theory of optimal aeroassisted orbital transfer, with special consideration given to the transfer about a central body with an atmosphere, with propulsive maneuvers in space modeled as instantaneous changes in the velocity vector. It is shown that there are four potentially optimal transfer modes, two aeroassisted and two all-propulsive, for each point in the two-dimensional transfer space. Aeroassisted orbital transfer introduces a strong coupling between the trajectory design and the vehicle design; a trajectory that minimizes fuel mass without attention to heating may require the vehicle to have a heavy thermal protection system. It is emphasized that, if aeroassisted transfer is to be preferred to all-propulsive transfer, it must offer a reduction in fuel mass greater than the increase in thermal protection mass.

Mease, K. D.↗