Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “planning under uncertainty”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Options and Uncertainties in Planetary Defense: Mission Planning and Vehicle Design for Flexible Response

This paper is part of an integrated study by NASA and the NNSA to quantitatively understand the response timeframe should a threatening Earth-impacting near-Earth object (NEO) be identified. The two realistic responses considered are the use of a spacecraft functioning as either a kinetic impactor or a nuclear explosive carrier to deflect the approaching NEO. The choice depends on the NEO size and mass, the available response time prior to Earth impact, and the various uncertainties. Whenever practical, the kinetic impactor is the preferred approach, but various factors, such as large uncertainties or short available response time, reduce the kinetic impactor's suitability and, ultimately, eliminate its sufficiency. Herein we examine response time and the activities that occur between the time when an NEO is recognized as being a sufficient threat to require a deflection and the time when the deflection impulse is applied to the NEO. To use a kinetic impactor for successful deflection of an NEO, it is essential to minimize the reaction time and maximize the time available for the impulse delivered to the NEO by the kinetic impactor to integrate forward in time to the eventual deflection of the NEO away from Earth impact. To shorten the response time, we develop tools to survey the profile of needed spacecraft launches and the possible mission payloads. We further present a vehicle design capable of either serving as a kinetic impactor, or, if the need arises, serving as a system to transport a nuclear explosive to the NEO. These results are generated by analyzing a specific case study in which the simulated Earth-impacting NEO is modeled very closely after the real NEO known as 101955 Bennu (1999 RQ36). Bennu was selected for our case study in part because it is the best-studied of the known NEOs. It is also the destination of NASA's OSIRIS-REx sample return mission, which is, at the time of this writing, enroute to Bennu following a September 2016 launch.

characterization↗
NASA NTRS2017

Options and Uncertainties in Planetary Defense: Mission Planning and Vehicle Design for Flexible Response

This paper is part of an integrated study by NASA and the NNSA to quantitatively understand the response timeframe should a threatening Earth-impacting near-Earth object (NEO) be identified. The two realistic responses considered are the use of a spacecraft functioning as either a kinetic impactor or a nuclear explosive carrier to deflect the approaching NEO. The choice depends on the NEO size and mass, the available response time prior to Earth impact, and the various uncertainties. Whenever practical, the kinetic impactor is the preferred approach, but various factors, such as large uncertainties or short available response time, reduce the kinetic impactor's suitability and, ultimately, eliminate its sufficiency. Herein we examine response time and the activities that occur between the time when an NEO is recognized as being a sufficient threat to require a deflection and the time when the deflection impulse is applied to the NEO. To use a kinetic impactor for successful deflection of an NEO, it is essential to minimize the reaction time and maximize the time available for the impulse delivered to the NEO by the kinetic impactor to integrate forward in time to the eventual deflection of the NEO away from Earth impact. To shorten the response time, we develop tools to survey the profile of needed spacecraft launches and the possible mission payloads. We further present a vehicle design capable of either serving as a kinetic impactor, or, if the need arises, serving as a system to transport a nuclear explosive to the NEO. These results are generated by analyzing a specific case study in which the simulated Earth-impacting NEO is modeled very closely after the real NEO known as 101955 Bennu (1999 RQ36). Bennu was selected for our case study in part because it is the best-studied of the known NEOs. It is also the destination of NASA's OSIRIS-REx sample return mission, which is, at the time of this writing, enroute to Bennu following a September 2016 launch.

Brent W. Barbee↗
NASA NTRS2010

Energy Management of the Multi-Mission Space Exploration Vehicle Using a Goal-Oriented Control System

Safe human exploration in space missions requires careful management of limited resources such as breathable air and stored electrical energy. Daily activities for astronauts must be carefully planned with respect to such resources, and usage must be monitored as activities proceed to ensure that they can be completed while maintaining safe resource margins. Such planning and monitoring can be complex because they depend on models of resource usage, the activities being planned, and uncertainties. This paper describes a system - and the technology behind it - for energy management of the NASA-Johnson Space Center's Multi-Mission Space Exploration Vehicles (SEV), that provides, in an onboard advisory mode, situational awareness to astronauts and real-time guidance to mission operators. This new capability was evaluated during this year's Desert RATS (Research and Technology Studies) planetary exploration analog test in Arizona. This software aided ground operators and crew members in modifying the day s activities based on the real-time execution of the plan and on energy data received from the rovers.

Braman, Julia M. B.↗
DOE OSTI2025

Dynamic Charging Rendezvous and Motion Planning for a Multi-AGV Team Including a Mobile Charging Host

Teams of automated battery-powered electric vehicles have the potential to execute complex mission tasks in off-road environments for agriculture, military, and other applications. Limited onboard energy reserves hinder their adoption in large-scale resource-constrained environments, where recharging is a necessity. It may be infeasible to install a network of static charging stations in off-road environments. For this reason, dedicated mobile host vehicles with charging capabilities are proposed as a means to increase range and capabilities of the multivehicle team. Here, in this study, we consider an ad hoc planning framework, where results from a high-confidence trajectory planner are leveraged to plan charging rendezvous between a host and other worker vehicles in a receding horizon fashion to provide high confidence that energy reserves will not be prematurely exhausted. The core problem is posed so as to minimize the impact of recharging on the mission in terms of task delays, overall energy utilization, and costs of fast charging. Through extensive Monte Carlo simulations of an off-road mission, we show a decrease in task delays without substantial increases in energy needs by updating the charging rendezvous plan during the mission. However, if updates are made too often, model mismatch may cause unnecessary cycling and mission failure.

Energy constraints↗
NASA NTRS2016

Self-Aware Vehicles: Mission and Performance Adaptation to System Health

Advances in sensing (miniaturization, distributed sensor networks) combined with improvements in computational power leading to significant gains in perception, real-time decision making/reasoning and dynamic planning under uncertainty as well as big data predictive analysis have set the stage for realization of autonomous system capability. These advances open the design and operating space for self-aware vehicles that are able to assess their own capabilities and adjust their behavior to either complete the assigned mission or to modify the mission to reflect their current capabilities. This paper discusses the self-aware vehicle concept and associated technologies necessary for full exploitation of the concept. A self-aware aircraft, spacecraft or system is one that is aware of its internal state, has situational awareness of its environment, can assess its capabilities currently and project them into the future, understands its mission objectives, and can make decisions under uncertainty regarding its ability to achieve its mission objectives.

Gregory, Irene M.↗
NASA NTRS2016

Deja Vu All Over Again: The Reappearance of Supernova Refsdal

In Hubble Space Telescope (HST) imaging taken on 2014 November 10, four images of supernova (SN) Refsdal(redshift z1.49) appeared in an Einstein-cross-like configuration (images S1S4) around an early-type galaxy in the cluster MACS J1149.5+2223 (z0.54). Almost all lens models of the cluster have predicted that the SN should reappear within a year in a second host-galaxy image created by the clusters potential. In HST observations taken on 2015 December 11, we find a new source at the predicted position of the new image of SN Refsdal approximately 8 from the previous images S1S4. This marks the first time the appearance of a SN at a particular time and location in the sky was successfully predicted in advance! We use these data and the light curve from the first four observed images of SN Refsdal to place constraints on the relative time delay and magnification of the new image (SX) compared to images S1S4. This enables us, for the first time, to test blind lens model predictions of both magnifications and time delays for a lensed SN. We find that the timing and brightness of the new image are consistent with the blind predictions of a fraction of the models. The reappearance illustrates the discriminatory power of this blind test and its utility to uncover sources of systematic uncertainty. From planned HST photometry, we expect to reach a precision of 12 on the time delay between S1S4 and SX.

galaxies: clusters: general – galaxies: individu↗
NASA NTRS2018

Hybridized Agile Software Development of Flight Control Team Tools for International Space Station's Payload Operations Integration Center

Ground systems operations at the National Aeronautics and Space Administration's (NASA) Payload Operations and Integration Function (POIF) is increasing, via the High Operations Tempo (HOT) initiative, its ability to support more science activities with a fourth crew member on the International Space Station (ISS). The Flight Control Team's need to support this increased crew presence mandated the need for a series of software tools to better handle the increasing pace of payload science operations. The overall need was clear from the outset, but the full scope and user experience for each tool, were not as well understood, so establishing a fixed set of initial requirements was not feasible. An Agile Software Development (ASD) paradigm takes advantage of uncertainty, and plans for it, so it was deemed the most appropriate approach to create room for exploring novel concepts, and to mount a rapid and flexible response to inevitiably changing requirements. It facilitated the need for unprecedented collaboration between the Product Team (i.e. users from the Flight Control Team) and the Development Team (i.e. POIC systems engineers, developers, testers). This is a process shift in the development, test, and release of software from one that is prescriptive to one that is adaptive, which is necessary for these tools to have longevity. The application of ASD to the product development lifecycle permitted the timely incorporation of customer feedback, and, allowed for continuous quality improvements. This resulted in a suite of tools that are efficient, user-friendly, and enable POIF ground systems to support the increasing pace of payload science operations. ASD is not as much a set of prescriptive processes as it is a shift in mindset; one that moves from planning against change, to planning for change, thereby iteratively growing software towards user-defined value.

Albers, Cerese M.↗
NASA NTRS2020

Partially Observable Games for Secure Autonomy

Technology development efforts in autonomy and cyber-defense have been evolving independently of each other, over the past decade. In this paper, we report our ongoing effort to integrate these two presently distinct areas into a single framework. To this end, we propose the two-player partially observable stochastic game formalism to capture both high-level autonomous mission planning under uncertainty and adversarial decision making subject to imperfect information. We show that synthesizing sub-optimal strategies for such games is possible under finite-memory assumptions for both the autonomous decision maker and the cyber-adversary. We then describe an experimental testbed to evaluate the efficacy of the proposed framework.

Ames, Aaron D.↗
DOE OSTI2021

Data-driven optimization of mixed-integer bi-level multi-follower integrated planning and scheduling problems under demand uncertainty

The coordination of interconnected elements across the different layers of the supply chain is essential for all industrial processes and the key to optimal decision-making. Yet, the modeling and optimization of such interdependent systems are still burdensome. Here we address the simultaneous modeling and optimization of medium-term planning and short-term scheduling problems under demand uncertainty using mixed-integer bi-level multi-follower programming and data-driven optimization. Bi-level multi-follower programs model the natural hierarchy between different layers of supply chain management holistically, while scenario analysis and data-driven optimization allow us to retrieve the guaranteed feasible solutions of the integrated formulation under various demand considerations. We address the data-driven optimization of this challenging class of problems using the DOMINO framework, which was initially developed to solve single-leader single-follower bi-level optimization problems to guaranteed feasibility. This framework is extended to solve single-leader multi-follower stochastic formulations and its performance is characterized by well-known single and multi-product process scheduling case studies. Through our data-driven algorithmic approach, we present guaranteed feasible solutions to linear and nonlinear mixed-integer bi-level formulations of simultaneous planning and scheduling problems and further characterize the effects of the scheduling level complexity on the solution performance, which spans over several hundred continuous and binary variables, and thousands of constraints.

42 ENGINEERING↗
NASA NTRS2005

V and V of ISHM Software for Space Exploration

NASA has established a far-reaching and long-term program for robotic and manned exploration of the solar system, beginning with missions to the moon and Mars. The Crew Transportation System (CTS), a key system for space exploration, imposes four requirements' that ISHM addresses. These requirements have a wide range of implications for V&V and certification of ISHM. There is a range of time-criticality for ISHM actions, from prognostication, which is often (but not always) non-time-critical, to time-critical state estimation and system management under off-nominal emergency conditions. These are externally imposed requirements on ISHM that are subject to V&V. - In addition, a range of techniques are needed to implement an ISHM. The approaches to ISHM are described elsewhere. These approaches range from well-understood algorithms for low-level data analysis, validation and reporting, to AI techniques for state estimation and planning. The range of techniques, and specifically the use of AI techniques such as reasoning under uncertainty and mission planning (and re-planning), implies that several V&V approaches may be required. Depending on the ISHM architecture, traditional testing approaches may be adequate for some ISHM functionality. The AI-based approaches to reasoning under uncertainty, model-based reasoning, and planning share characteristics typical of other complex software systems, but they also have characteristics that set them apart and challenge standard V&V techniques. The range of possible solutions to the overall ISHM problem impose internal challenges to V&V. The V&V challenges increase when hard real-time constraints are imposed for time-critical functionality. For example, there is an external requirement that impending catastrophic failure of the Launch Vehicle (LV) at launch time be detected and life-saving action be taken within two seconds. In this paper we outline the challenges for ISHM V&V, existing approaches and analogs in other software application areas, and possible new approaches to the V&V challenges for space exploration ISHM.

Markosian, Lawrence↗
DOE OSTI2021

Distributionally Safe Path Planning: Wasserstein Safe RRT

In this paper, we propose a Wasserstein metric-based random path planning algorithm. Wasserstein Safe RRT (W-Safe RRT) provides finite-sample probabilistic guarantees on the safety of a returned path in an uncertain obstacle environment. Vehicle and obstacle states are modeled as distributions based upon state and model observations. Additionally, we define limits on distributional sampling error so the Wasserstein distance between a vehicle state distribution and obstacle distributions can be bounded. This enables the algorithm to return safe paths with a confidence bound through combining finite sampling error bounds with calculations of the Wasserstein distance between discrete distributions. W-Safe RRT is compared against a baseline minimum encompassing ball algorithm, which ensures balls that minimally encompass discrete state and obstacle distributions do not overlap. The improved performance is verified in a 3D environment using single, multi, and rotating non-convex obstacle cases, with and without forced obstacle error in adversarial directions, showing that W-Safe RRT can handle poorly modeled complex environments.

42 ENGINEERING↗
DOE OSTI2025

Visual‐Analytics Bridge Complexity and Accessibility for Robust Urban Water Planning

Urban water resources planning is complicated by unprecedented uncertainty in supply and demand. Real‐world planning often simplifies the full range of uncertainty faced by a system into a limited set of deterministic scenarios to enhance accessibility for decision‐makers and the public. However, overlooking uncertainty can expose the system to failures. On the other end of the spectrum, academically developed tools for scenario analysis rigorously quantify the combined effects of multiple sources of uncertainty, but the practical application of these models is limited by the challenges of information visualization and communication of results. In short, municipal water supply planners lack access to planning frameworks that effectively integrate a rigorous treatment of uncertainty with accessible, user‐friendly visual and interactive tools to enhance user accessibility. In this work, we fill this gap by proposing Visual‐Robust Decision Making, and demonstrate an application for the city of Santa Barbara (SB), CA. Santa Barbara faces multiple uncertainties from pending state and federal regulations to changing hydrology and water demand. The city seeks to increase its water portfolio robustness by expanding its seawater desalination plant, but must decide how much capacity to add. We introduce computational tools that assess uncertainty across nine uncertain drivers identified with the help of water planners in SB. To allow public participation in the desalination expansion decision, we develop interactive visual‐analytics to aid decision‐makers and stakeholders in navigating complex scenario analysis outcomes. Our results quantify the tradeoffs between increased capacity and system robustness and aim to enhance participation and uncertainty characterization of urban water planning efforts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2023

Analysis of Strategic Conflict Management Approaches as Applied to Simulated UAM Operations

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Strategic Conflict Management↗
NASA NTRS2021

A sampling-based optimization approach to handling environmental uncertainty for a planetary lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Zilberstein, Shlomo↗
NASA NTRS2021

A Sampling-Based Optimization Approach to Handling Environmental Uncertainty for a Planetary Lander

Planning for unknown environments presents a number of technical challenges. The planner must ensure robustness to unknown phenomena and manage unpredictable variation in execution, all while operating in a capacity that maximizes its objective. Productivity in the face of these challenges re-quires an integrated approach to planning and execution that is capable of accomplishing goals, reacting to variation, and maximizing overall utility. We examine this problem in the context of a Europa Lander concept mission. We model the problem as a hierarchical task network, framing it as a utility maximization problem constrained on a depletable energy resource. We propose an uncertainty–sensitive deterministic planning framework that utilizes periodic replanning to better handle model uncertainty and variable execution. We demonstrate the efficacy of our framework through simulations of a Europa Lander concept mission in which our algorithm out-performs several baseline approaches in both utility maximization and robustness

Chien, Steve↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗