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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 NTRS2020

Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined probability distributions. This paper addresses on PSTN Dynamic Controllability (DC) robustness measure, i.e. the execution success probability of a network under dynamic control.We consider PSTNs where the probability distributions of the contingent edges are ordinary distributed (e.g. non-parametric, non-symmetric). We introduce the concepts of dispatching protocol (DP) as well as DP-robustness, the probability of success under a predefined dynamic policy.We propose a fixed-parameter pseudo-polynomial time algorithm to compute the exact DP-robustness of any PSTN under \textit{NextFirst} protocol, and apply to various PSTN datasets, including the real case of planetary exploration in the context of the Mars 2020 rover, and propose an original structural analysis.

Saint-Guillain, Michael↗
NASA NTRS1999

Autonomous Rovers for Mars Exploration

Rovers will play a critical role in the exploration of Mars. Near-term mission plans call for long traverses over unknown terrain, robust navigation and instrument placement, and reliable operations for extended periods of time. Longer-term missions may visit multiple science sites in a single day and perform opportunistic science data collection, as well as complex scouting, construction, and maintenance tasks in preparation for an eventual human presence. The Pathfinder mission demonstrated the potential for robotic Mars exploration but at the same time indicated the need for more rover autonomy. The highly ground-intensive control with infrequent communication and high latency limited the effectiveness of the Sojourner rover. When failures occurred, Sojourner often sat idle for extended periods of time, awaiting further commands from earth. In future missions, the tasks will be more complex and extended; hence there will be even more situations where things do not go exactly as planned. Significant advances in rover autonomy are needed to cope with increasing task complexity and greater execution uncertainty. Towards this end, we have designed an on-board executive architecture that incorporates robust operation, resource utilization, and failure recovery. In addition, we have designed ground tools to produce and refine contingent schedules that take advantage of the on-board architecture's flexible execution characteristics. Together, the on-board executive and the ground tools constitute an integrated rover autonomy architecture. This work draws from our experience with the Deep Space One autonomy experiment, with enhancements to ensure robust operation in the face of the unpredictable, complex environment that the rover will encounter on Mars. The rover autonomy architecture is currently being developed and deployed on the Marsokhod rover platform at NASA Ames Research Center. The capabilities of the rover autonomy architecture to support autonomous operations will be demonstrated concretely in upcoming field tests.

Anderson, Corin↗
NASA NTRS2003

Agent Based Software for the Autonomous Control of Formation Flying Spacecraft

Distributed satellite systems is an enabling technology for many future NASA/DoD earth and space science missions, such as MMS, MAXIM, Leonardo, and LISA [1, 2, 3]. While formation flying offers significant science benefits, to reduce the operating costs for these missions it will be essential that these multiple vehicles effectively act as a single spacecraft by performing coordinated observations. Autonomous guidance, navigation, and control as part of a coordinated fleet-autonomy is a key technology that will help accomplish this complex goal. This is no small task, as most current space missions require significant input from the ground for even relatively simple decisions such as thruster burns. Work for the NMP DS1 mission focused on the development of the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. NMRA integrates traditional real-time monitoring and control with components for constraint-based planning, robust multi-threaded execution, and model-based diagnosis and reconfiguration. The complexity of using an autonomous approach for space flight software was evident when most of its capabilities were stripped off prior to launch (although more capability was uplinked subsequently, and the resulting demonstration was very successful).

How, Jonathan P.↗
NASA NTRS1997

An Autonomous Spacecraft Agent Prototype

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. This architecture integrates traditional real-time monitoring and control with constraint-based planning and scheduling, robust multi-threaded execution, and model-based diagnosis and reconfiguration.

New Millennium Remote Agent↗
NASA NTRS2001

Robust planning with imperfect models

Most approaches to robust automony with respect to planning and execution are focused on either providing models that allow for flexibility or providing techniques for changing models to improve performance. We take these techniques into consideration, but focus the majority of our work on robust autonomous planning and execution with imperfect models.

planning scheduling autonomy↗
NASA NTRSDate not supplied

Mission Design Considerations for a Low-Thrust Spacecraft

Developing an executable low-thrust trajectory for use in a spaceflight mission requires the design and optimization of a deterministic trajectory as well as the validation that the selected architecture is robust to some set of uncertainties, execution errors, and potential contingencies. Uncertainty in the ability of the spacecraft and its launch vehicle to execute a trajectory as well as potential deviations such as in-flight anomalies combine with design-to constraints and requirements to complicate the optimization problem. The approach by which robust mission design was accomplished for the initial capability of NASA’s Gateway is presented as well as associated results.

NRHO↗
NASA NTRSDate not supplied

Mission Design Considerations for a Low-Thrust Spacecraft

Developing an executable low-thrust trajectory for use in a spaceflight mission requires the design and optimization of a deterministic trajectory as well as the validation that the selected architecture is robust to some set of uncertainties, execution errors, and potential contingencies. Uncertainty in the ability of the spacecraft and its launch vehicle to execute a trajectory as well as potential deviations such as in-flight anomalies combine with design-to constraints and requirements to complicate the optimization problem. The approach by which robust mission design was accomplished for the initial capability of NASA’s Gateway is presented as well as associated results.

mission design↗
NASA NTRSDate not supplied

Performance Impacts to the NASA Artemis II Trajectory Correction Burn Placement

As NASA embarks to return humans to the lunar vicinity with the upcoming Artemis II mission, the selected free return trajectory taking the crew to the Moon in the Orion spacecraft is impacted by the execution of small trajectory correction burns to ensure the spacecraft stays on course for a successful return to Earth. The placement of these nominally zero translational maneuvers must account for the crew schedule, navigation tracking constraints, spacecraft venting, thermal and communication requirements, and a host of other programmatic factors. Understanding the influence the placement of these periodic burn corrections have on the integrated GN\&C performance can provide valuable insight to mission controllers and trajectory planning processes to untangle the complex trade space considered for both baseline and contingency scenarios. This sensitivity information can also be utilized to facilitate the optimized placement of these burns. This paper utilizes several techniques to systematically generate the performance impacts to the NASA Artemis II trajectory correction burn placement and demonstrate how to derive optimized locations that make the system robust to crew activity, maneuver execution errors, navigation uncertainty, orbit insertion errors, disturbance accelerations, and other system limitations.

GN&C↗
NASA NTRSDate not supplied

Sensitivity of Optimal Midcourse Correction Scheduling for Robust Cislunar Trajectory Design

A new approach to optimal trajectory design is the determination of optimal trajectories that are robust to initial trajectory dispersions, navigation errors, maneuver execution errors, and environment modeling errors. This paper investigates the sensitivity of cislunar robust optimal trajectory design to launch date, duration of navigation measurement passes, and navigation measurement frequency. For a given cislunar trajectory from translunar injection (TLI) to lunar orbit insertion (LOI), the optimal locations of midcourse corrections, also known as trajectory correction maneuvers (TCM) are determined by minimizing the final 3-σ ∆v subject to a final 3-σ position dispersion constraint for a given launch date, specified measurement pass duration prior to each maneuver, and measurement frequency. Optical navigation (OpNav) is assumed, and OpNav field-of-view (FOV) and lighting constraints are employed. These constraints turn out to be important elements of the problem. The sensitivity of the optimal TCM locations are then investigated by varying the launch date, duration of OpNav measurement passes, and the OpNav measurement frequency, and then re-optimizing the locations of the TCMs. Given the problem parameters provided herein, results show that while the optimal TCM locations with respect to TLI vary greatly from one launch date to another, their locations with respect to LOI are nearly invariant over a 2-month launch window. Results also show that in all cases the optimal location of the last TCM is found to be at the point where the OpNav lunar FOV constraint is first violated. For all other TCMs, OpNav measurement pass duration and measurement frequency can have a moderate to large affect on the optimal TCM locations.

Linear Covariance Analysis↗
NASA NTRS1990

A memory efficient user interface for CLIPS micro-computer applications

The goal of the Integrated Southern Pine Beetle Expert System (ISPBEX) is to provide expert level knowledge concerning treatment advice that is convenient and easy to use for Forest Service personnel. ISPBEX was developed in CLIPS and delivered on an IBM PC AT class micro-computer, operating with an MS/DOS operating system. This restricted the size of the run time system to 640K. In order to provide a robust expert system, with on-line explanation, help, and alternative actions menus, as well as features that allow the user to back up or execute 'what if' scenarios, a memory efficient menuing system was developed to interface with the CLIPS programs. By robust, we mean an expert system that (1) is user friendly, (2) provides reasonable solutions for a wide variety of domain specific problems, (3) explains why some solutions were suggested but others were not, and (4) provides technical information relating to the problem solution. Several advantages were gained by using this type of user interface (UI). First, by storing the menus on the hard disk (instead of main memory) during program execution, a more robust system could be implemented. Second, since the menus were built rapidly, development time was reduced. Third, the user may try a new scenario by backing up to any of the input screens and revising segments of the original input without having to retype all the information. And fourth, asserting facts from the menus provided for a dynamic and flexible fact base. This UI technology has been applied successfully in expert systems applications in forest management, agriculture, and manufacturing. This paper discusses the architecture of the UI system, human factors considerations, and the menu syntax design.

Sterle, Mark E.↗
NASA NTRS2020

Using Flexible Execution, Replanning, and Model Parameter Updates to Address Environmental Uncertainty for a Planetary Lander

When planning in unknown environments, a planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution. Productivity in the face of these challenges requires an integrated approach to planning and execution that is capable of reacting to variation while still maximizing overall utility. We examine this problem in the context of the Europa Lander mission concept. We propose a planning and execution framework that responds to feedback using three techniques: (1) flexible execution, (2) periodic replanning, and (3) online model parameter and utility updates. The efficacy of each of these techniques is examined through simulation of the Europa Lander concept, showing higher utility achievement compared to baseline approaches.

Chien, Steve A.↗
NASA NTRS1991

Development, simulation validation, and wind tunnel testing of a digital controller system for flutter suppression

Flutter suppression (FS) is one of the active control concepts being investigated by the AFW program. The design goal for FS control laws was to increase the passive flutter dynamic pressure by 30 percent. In order to meet this goal, the FS control laws had to be capable of suppressing both symmetric and antisymmetric flutter instabilities simultaneously. In addition, the FS control laws had to be practical and low-order, robust and capable of real time execution within the 200 hz. sampling time. The purpose here is to present an overview of the development, simulation validation, and wind tunnel testing of a digital controller system for flutter suppression.

Hoadley, Sherwood Tiffany↗
NASA NTRS1997

Execution of Multidisciplinary Design Optimization Approaches on Common Test Problems

A class of synthetic problems for testing multidisciplinary design optimization (MDO) approaches is presented. These test problems are easy to reproduce because all functions are given as closed-form mathematical expressions. They are constructed in such a way that the optimal value of all variables and the objective is unity. The test problems involve three disciplines and allow the user to specify the number of design variables, state variables, coupling functions, design constraints, controlling design constraints, and the strength of coupling. Several MDO approaches were executed on two sample synthetic test problems. These approaches included single-level optimization approaches, collaborative optimization approaches, and concurrent subspace optimization approaches. Execution results are presented, and the robustness and efficiency of these approaches an evaluated for these sample problems.

Balling, R. J.↗
NASA NTRS2002

Autonomous planning and scheduling on the TechSat 21 mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

AI architectures applications intelligent agents m↗
NASA NTRS2002

Autonomous operations through onboard artificial intelligence

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE TechSat 21↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattem recognition to radically increase science retum by enabling intelligent downlink selection and autonomous retargeting. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE spacecraft ↗
NASA NTRS2003

The autonomous sciencecraft constellations

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecraft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE AI technolo↗
NASA NTRS2002

Spacecraft autonomy using onboard processing for a SAR constellation mission

The Autonomous Sciencecraft Experiment (ASE) will fly onboard the Air Force TechSat 21 constellation of three spacecraft scheduled for launch in 2006. ASE uses onboard continuous planning, robust task and goal-based execution, model-based mode identification and reconfiguration, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in a hybrid multi-layer control architecture to enable a virtual spacecruft science agent. Demonstration of these capabilities in a flight environment will open up tremendous new opportunities in planetary science, space physics, and earth science that would be unreachable without this technology.

Autonomous Sciencecraft Experiment ASE virtual spa↗