Search NASA⌕ Search

SEARCH · Search NASA

Results for “ELF”

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.

189 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

An Autonomy Architecture for Aerobot Exploration of the Saturnian Moon Titan

The Huygens probe arrived at Saturn's moon Titan on January 14, 2005, unveiling a world that is radically different from any other in the Solar system. The data obtained, complemented by continuing observations from the Cassini spacecraft, show methane lakes, river channels and drainage basins, sand dunes, cryovolcanos and sierras. This has lead to an enormous scientific interest in a follow-up mission to Titan, using a robotic lighter-than-air vehicle (or aerobot). Aerobots have modest power requirements, can fly missions with extended durations, and have very long distance traverse capabilities. They can execute regional surveys, transport and deploy scientific instruments and in-situ laboratory facilities over vast distances, and also provide surface sampling at strategic science sites. This paper describes our progress in the development of the autonomy technologies that will be required for exploration of Titan. We provide an overview of the autonomy architecture and some of its key components. We also show results obtained from autonomous flight tests conducted in the Mojave desert.

Titan↗
NASA NTRS2008

The Telesupervised Adaptive Ocean Sensor Fleet (TAOSF) Architecture: Coordination of Multiple Oceanic Robot Boats

Earth science research must bridge the gap between the atmosphere and the ocean to foster understanding of Earth s climate and ecology. Ocean sensing is typically done with satellites, buoys, and crewed research ships. The limitations of these systems include the fact that satellites are often blocked by cloud cover, and buoys and ships have spatial coverage limitations. This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in-situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended deployment autonomous ocean surface vehicles, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a sliding autonomy control architecture, where the operating mode of the vessels ranges from autonomous control to teleoperated human control. TAOSF increases data-gathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

sensor web↗
NASA NTRS2008

Mission Activity Planning for Humans and Robots on the Moon

A series of studies is conducted to develop a systematic approach to optimizing, both in terms of the distribution and scheduling of tasks, scenarios in which astronauts and robots accomplish a group of activities on the Moon, given an objective function (OF) and specific resources and constraints. An automated planning tool is developed as a key element of this optimization system.

task allocation↗
NASA NTRS2012

HURON (HUman and Robotic Optimization Network) Multi-Agent Temporal Activity Planner/Scheduler

HURON solves the problem of how to optimize a plan and schedule for assigning multiple agents to a temporal sequence of actions (e.g., science tasks). Developed as a generic planning and scheduling tool, HURON has been used to optimize space mission surface operations. The tool has also been used to analyze lunar architectures for a variety of surface operational scenarios in order to maximize return on investment and productivity. These scenarios include numerous science activities performed by a diverse set of agents: humans, teleoperated rovers, and autonomous rovers. Once given a set of agents, activities, resources, resource constraints, temporal constraints, and de pendencies, HURON computes an optimal schedule that meets a specified goal (e.g., maximum productivity or minimum time), subject to the constraints. HURON performs planning and scheduling optimization as a graph search in state-space with forward progression. Each node in the graph contains a state instance. Starting with the initial node, a graph is automatically constructed with new successive nodes of each new state to explore. The optimization uses a set of pre-conditions and post-conditions to create the children states. The Python language was adopted to not only enable more agile development, but to also allow the domain experts to easily define their optimization models. A graphical user interface was also developed to facilitate real-time search information feedback and interaction by the operator in the search optimization process. The HURON package has many potential uses in the fields of Operations Research and Management Science where this technology applies to many commercial domains requiring optimization to reduce costs. For example, optimizing a fleet of transportation truck routes, aircraft flight scheduling, and other route-planning scenarios involving multiple agent task optimization would all benefit by using HURON.

Hua, Hook↗
NASA NTRS2009

Decomposition Algorithm for Global Reachability Analysis on a Time-Varying Graph with an Application to Planetary Exploration

Hot air (Montgolfiere) balloons represent a promising vehicle system for possible future exploration of planets and moons with thick atmospheres such as Venus and Titan. To go to a desired location, this vehicle can primarily use the horizontal wind that varies with altitude, with a small help of its own actuation. A main challenge is how to plan such trajectory in a highly nonlinear and time-varying wind field. This paper poses this trajectory planning as a graph search on the space-time grid and addresses its computational aspects. When capturing various time scales involved in the wind field over the duration of long exploration mission, the size of the graph becomes excessively large. We show that the adjacency matrix of the graph is block-triangular, and by exploiting this structure, we decompose the large planning problem into several smaller subproblems, whose memory requirement stays almost constant as the problem size grows. The approach is demonstrated on a global reachability analysis of a possible Titan mission scenario.

Dijkstra's algorithm↗
NASA NTRS2010

Reconciling Scientific Aspirations and Engineering Constraints for a Lunar Mission via Hyperdimensional Interpolation

Virtually every NASA space-exploration mission represents a compromise between the interests of two expert, dedicated, but very different communities: scientists, who want to go quickly to the places that interest them most and spend as much time there as possible conducting sophisticated experiments, and the engineers and designers charged with maximizing the probability that a given mission will be successful and cost-effective. Recent work at NASA's Jet Propulsion Laboratory (JPL) seeks to enhance communication between these two groups, and to help them reconcile their interests, by developing advanced modeling capabilities with which they can analyze the achievement of science goals and objectives against engineering design and operational constraints. The analyses conducted prior to this study have been point-design driven. Each analysis has been of one hypothetical case which addresses the question: Given a set of constraints, how much science can be done? But the constraints imposed by the architecture team-e.g., rover speed, time allowed for extravehicular activity (EVA), number of sites at which science experiments are to be conducted- are all in early development and carry a great deal of uncertainty. Variations can be incorporated into the analysis, and indeed that has been done in sensitivity studies designed to see which constraint variations have the greatest impact on results. But if a very large number of variations can be analyzed all at once, producing a table that includes virtually the entire trade space under consideration, then we have a tool that enables scientists and mission architects to ask the inverse question: For a given desired level of science (or any other objective), what is the range of constraints that would be needed? With this tool, mission architects could determine, for example, what combinations of rover speed, EVA duration, and other constraints produce the desired results. Further, this tool would help them identify which technology-improvement investments would be likely to produce the largest or most important return. However, the number of variations that need to be considered for such analysis quickly balloons to an unwieldy size. If three variations are considered for each of six constraints-a very modest example-there are a total of 243 variations to consider. If it takes 40 minutes to compute each variation, as it does with HURON, our automated optimization system, then it would take 162 hours or nearly 7 days of round-the-clock computing to calculate the results. Adding further constraints or variations exponentially increases the amount of time that is needed.

Weisbin, Charles R.↗
NASA NTRS2010

Probabilistic Motion Planning of Balloons in Strong, Uncertain Wind Fields

This paper introduces a new algorithm for probabilistic motion planning in arbitrary, uncertain vector fields, with emphasis on high-level planning for Montgolfiere balloons in the atmosphere of Titan. The goal of the algorithm is to determine what altitude--and what horizontal actuation, if any is available on the vehicle--to use to reach a goal location in the fastest expected time. The winds can vary greatly at different altitudes and are strong relative to any feasible horizontal actuation, so the incorporation of the winds is critical for guidance plans. This paper focuses on how to integrate the uncertainty of the wind field into the wind model and how to reach a goal location through the uncertain wind field, using a Markov decision process (MDP). The resulting probabilistic solutions enable more robust guidance plans and more thorough analysis of potential paths than existing methods.

Wolf, Michael T.↗
NASA NTRS2010

Implications of Wind-Assisted Aerial Navigation for Titan Mission Planning and Science Exploration

The recent Titan Saturn System Mission (TSSM) proposal incorporates a montgolfiere (hot air balloon) as part of its architecture. Standard montgolfiere balloons generate lift through heating of the atmospheric gases inside the envelope, and use a vent valve for altitude control. A Titan aerobot (robotic aerial vehicle) would have to use radioisotope thermoelectric generators (RTGs) for electric power, and the excess heat generated can be used to provide thermal lift for a montgolfiere. A hybrid montgolfiere design could have propellers mounted on the gondola to generate horizontal thrust; in spite of the unfavorable aerodynamic drag caused by the shape of the balloon, a limited amount of lateral controllability could be achieved. In planning an aerial mission at Titan, it is extremely important to assess how the moon-wide wind field can be used to extend the navigation capabilities of an aerobot and thereby enhance the scientific return of the mission. In this paper we explore what guidance, navigation and control capabilities can be achieved by a vehicle that uses the Titan wind field. The control planning approach is based on passive wind field riding. The aerobot would use vertical control to select wind layers that would lead it towards a predefined science target, adding horizontal propulsion if available. The work presented in this paper is based on aerodynamic models that characterize balloon performance at Titan, and on TitanWRF (Weather Research and Forecasting), a model that incorporates heat convection, circulation, radiation, Titan haze properties, Saturn's tidal forcing, and other planetary phenomena. Our results show that a simple unpropelled montgolfiere without horizontal actuation will be able to reach a broad array of science targets within the constraints of the wind field. The study also indicates that even a small amount of horizontal thrust allows the balloon to reach any area of interest on Titan, and to do so in a fraction of the time needed by the unpropelled balloon. The results show that using the Titan wind field allows an aerobot to significantly extend its scientific reach, and that a montgolfiere (unpropelled or propelled) is a highly desirable architecture that can very significantly enhance the scientific return of a future Titan mission.

aerobot↗
NASA NTRS2015

Strategic Map for Exploring the Ocean-World Enceladus

Among the many "ocean worlds" of our solar system, Enceladus appears unique in its combination of astrobiologically relevant, exploration-worthy attributes: extensive liquid-water ocean with high-temperature hydrothermal activity, containing salts and organics expressed predictably into space. The Enceladus south polar plume allows direct access to telltale molecules, ions, isotopes, and potential cytofragments in space. Plume mass spectroscopy and sample return, in situ investigation of surface fallback deposits, direct vent exploration, and eventually oceanographic exploration can all be envisioned. However, building consensus to fund such ambitious exploration hinges on acquiring key new data. A roadmap is essential. It could start with cost-capped onramps: 1) flythrough analysis of the plume, following up on Cassini measurements with modern instruments; 2) sample return of plume material for analysis on Earth. A methodical mission sequence in which each step depends on emergent results from prior missions would push in situ oceanographic exploration into the second half of this century. Even for this scenario, prioritization by the next planetary Decadal Survey would be pivotal.

Cassini↗