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Chien, Steve

Publications and source records attributed to Chien, Steve.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Benchmarking Planning Applications on the Qualcomm Snapdragon

We benchmark several space planning/scheduling applications on the Qualcomm Snapdragon 855 Handheld Development Kit (HDK), a high performance embedded processor used in many mobile phones.We are flying 2 Snapdragon HDKs onboard the International Space Station (ISS) where they are hosted by the the Spaceborne Computer-2 by Hewlett Packard Enterprise linked by USB and 12V power delivery. We run computational benchmarks using three planner/ schedulers that are used for several space missions: Multi- Mission Executive (MEXEC), Compressed Large-scale Activity Scheduling and Planning (CLASP), and M2020 Ground Scheduler (Surrogate). We compare the Snapdragon performance to a performance baseline on Linux workstations. In addition, we are currently working on benchmarking the same applications on other space flight processors, such as the LEON4 Processor on the Sabertooth card, the LEON3 Processor on the Sphinx card, and the RAD750 processor.

Chien, Steve↗
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↗
NASA NTRS2021

Automated Scheduling of Federated Observations in the NOS Testbed

The advancement of remote and in-situ sensing technology, combined with the emergence of New Space ventures, is producing a variety of new measurements for Earth Science phenomena. New observation techniques must combine and leverage these federated systems. In developing this new paradigm, NASA’s Earth Science Technology Office is developing the New Observing Strategies (NOS) Testbed to validate and demonstrate new operations concepts. As a part of this effort, we have developed a planning and scheduling system to support automated retasking

Chien, Steve↗
NASA NTRS2021

An Efficient Approach for Scheduling Imaging Tasks Across a Fleet of Satellites

Dynamically retasking satellites in response to scientific alerts is challenging because the tasks and opportunities of one satellite can influence this of another. This abstract focuses on our high-level approach for scheduling imaging tasks across a constellation of satellite, which is subject to orbital and other practical constraints such as finding a feasible up-/down- link schedule. Our approach is inspired by combining insights from two existing approaches about the structure of these problems to create an efficient, new approach. We show that our approach stacks up favorably against two baselines–an optimal solver as well as a naive, greedy approach.

Maillard, Adrien↗
NASA NTRS2021

Analyzing the Efficacy of Flexible Execution, Replanning, and Plan Optimization for a Planetary Lander

Plan execution in unknown environments poses a number of challenges: uncertainty in domain modeling, stochasticity at execution time, and the presence of exogenous events. These challenges motivate an integrated approach to planning and execution that is able to respond intelligently to variation. We examine this problem in the context of the Europa Lander mission concept, and propose a planning and execution framework that responds to feedback and task failure using two techniques: flexible execution and replanning with plan optimization. We develop a theoretical framework to predict the value of each of these techniques, and we compare these predictions to empirical results generated in simulation. We demonstrate that an integrated approach to planning and execution that is grounded in flexible execution, replanning, and utility maximization will be an enabling technology for future tightly-constrained planetary surface missions.

Chien, Steve↗
NASA NTRS2021

Ground and Onboard Automated Scheduling for the Mars 2020 Rover Mission

The Mars 2020 Rover mission is using automated scheduling in two capacities. As of March 2021 a ground-based scheduler automatically schedules rover wake/sleep and preheats to support Mars 2020 daily operations. Included in the ground system is an explainable scheduling tool that allows users in-sight into the schedule developed by the automated scheduler. An onboard scheduler that will allow the rover autonomously better account for variations in execution is in development for use later in the mission.

Towey, Shannon↗
NASA NTRS2021

Using Intelligent Targeting to increase the science return of a Smart Ice Storm Hunting Radar

Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a radar intelligently targets ice storms based on information collected by a lookahead radiometer. Often space observations are performed by continuously collecting data from an instrument aimed at nadir (e.g. directly below the space platform). However, if the platform has the ability to assess science utility of features being overflown, an intelligent measurement scheme can improve science return. This can be achieved by controlling the on/off state of the instrument if it is not able to continuously operate (e.g. due to energy or thermal constraints), and by allowing the instrument to view off nadir if it has pointing capabilities.In the case of SMICES, power constraints and the rarity of storms means that with blind nadir targeting SMICES would collect a limited amount of ice storm radar data. The algorithms proposed acquire measurements to maximize acquired high interest storms while concurrently collecting a background sampling of all features. We use a cloud classification system to identify five different cloud types. Six algorithms ranging from “blind” to more selective are described and results from evaluation on a dataset of 13 ground swaths covering 72,399,600 km2 of data are presented. This data is from high quality science simulations that contain all five cloud types and multiple storms. When utilizing the radiometer’s lookahead and the full range of the radar the results show a 23.7x and 1.9x increase over the base algorithm in the most and second most important cloud types respectively.

Cooke, Caitlyn↗
NASA NTRS2021

Ground-based Automated Scheduling for Operations of the Mars 2020 Rover Mission

The National Aeronautics and Space Administration’s (NASA) Mars 2020 Rover, named Perseverance, landed on the surface of Mars in Jezero Crater on February 18, 2021. Since the landing, the rover’s activities have been planned with the aid of a ground-based automated scheduling system called Copilot. Automated scheduling is very rare for planetary rover missions. Historically humans have created a schedule manually and ensured that the schedule satisfied all constraints. Higher levels of automation in the system allows science planners to produce schedules for the rover more quickly. In addition to scheduling user-provided activities, Copilot generates and schedules two types of support activities: sleep activities and heating activities. Some activities require the CPU to be on as they execute, so Copilot schedules wakeups and shutdowns of the CPU at the appropriate times. Some activities require areas of the rover to be heated before they can execute, and that heating must be maintained throughout the duration of the activity. Copilot schedules the preheat and maintenance heating activities for the user-provided activities that require them. To facilitate Copilot usage, the Crosscheck tool shows the science planners how Copilot constructed a schedule. For activities that fail to be scheduled, Crosscheck gives information on the constraints that the activity would have violated. This gives the users insight into how to change the input activities and constraints in order to achieve a schedule that satisfies their goals.

Towey, Shannon↗
NASA NTRS2021

Using Automated Scheduling for Mission Design: A Case Study for EMIT

The Earth Surface Mineral Dust Source InvesTigation (EMIT) is an Earth Ventures-Instrument (EVI-4) mission to map the surface mineralogy of arid dust source regions. EMIT used automated scheduling technology to analyze aspects of the mission design. The automated scheduling technology was used to construct schedules which were then automatically analyzed with respect to science acquired. These analyses can be performed for a range of spacecraft hardware configurations, observation strategies, and science requirements. By studying the effects of changes on the above inputs, better hardware configurations, observation strategies, and science requirements can be formulated. The use of a pointing mirror on EMIT was under consideration, and this analysis aided in determining whether or not to keep it as part of the design of the instrument. Clouds will also have a large impact on the coverage of science targets achievable by the mission. Analysis was done on how clouds could impact the coverage achievable as well as the data volume. This analysis with clouds also aided in determining the coverage criteria for the mission. It was necessary to find a criteria that was achievable with some margin as well as satisfies the science goals of the mission.

Thompson, David R.↗
NASA NTRS2021

Scheduling and Operations of the Orbiting Carbon Observatory-3 Mission

This paper describes the development and use of an automated scheduling system for the National Aeronautics and Space Administration’s (NASA) Orbiting Carbon Observatory-3 (OCO-3) Mission. OCO-3 measures atmospheric carbon dioxide from space. Made from the spare instrument built as a backup to the Orbiting Carbon Observatory-2 (OCO-2), OCO-3 extends the rich set of data collected by OCO-2. OCO-3 is outfitted with an agile Pointing Mirror Assembly (PMA) that allows for more detailed types of observations and rapid mode transitions. The mission uses an adaptation of the Compressed Large-scale Activity Scheduling and Planning (CLASP) system for scheduling nominal operations, as well as a separate automated scheduling system developed for scheduling observations for the calibration of the PMA. CLASP is used to schedule the four types of observational modes: Nadir, Glint, Target, and Snapshot Area Map. OCO-3 has a variety of complex mission-specific geometric constraints that were incorporated into CLASP to produce schedules that ensure instrument safety.

Moy, Alan↗
NASA NTRS2021

Scheduling and Operations of the ECOSTRESS Mission

This paper describes the development and use of an automated scheduling system for the National Aeronautics and Space Administration’s (NASA) ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. Key to the success of the ECOSTRESS mission has been the use of automated scheduling in mission analysis pre-launch, and in successful operations where automated scheduling was deployed to address several operational challenges. ECOSTRESS uses an adaptation of the Compressed Large-scale Activity Scheduling and Planning (CLASP) system to automatically select science observations respecting area and point target priorities as well as visibility, illumination, onboard storage, and radiation constraints to satisfy high-level prioritized science campaigns. The ECOSTRESS scheduler was used pre-launch to predict the effectiveness of alternative formulations of science campaign definitions accounting for the impact of data volume, keepout, and orbit/illumination/visibility constraints to derive the initial operational science campaign definitions and priorities. The scheduler was then used after instrument checkout for operations. ECOSTRESS has faced multiple operational challenges relating to instrument firmware and hardware, and the scheduler has been updated several times to address these challenges. The instrument Mass Storage Units (MSUs) had operational issues, requiring the scheduler to plan for and schedule commands to handle intricacies of data management. After many months of operations, both MSUs on the instrument became non-functioning and the firmware of the instrument was updated to bypass the MSUs. A further update to the ECOSTRESS scheduler enabled the scheduler to operate in this new operations mode. The ECOSTRESS scheduler has also been updated to improve handling of along-track uncertainty inherent in International Space Station operations. The flexibility and ease of updating of the automated scheduler has been a significant contributor to successful operations of the ECOSTRESS mission.

Padams, Jordan↗