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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Practical Operational Readiness Gambits: Operations Training Simulations for the Curiosity Rover

The Mars Science Laboratory team had been puttingin effort to make a Training Venue to allow for parallel shadowtactical operations for trainees to actively work alongside theprime tactical operations personnel without affecting operations,since staffing constraints and shortened operations timelineswere straining the tactical process in supporting operationstrainees in the traditional way. The COVID- 19 Pandemic presentedfurther challenges in continuing on-console training forMSL operations trainees. The MSL operations team switched tofully remote operations, thus hampering the direct mentorshipa trainee would normally receive while on site at JPL. TheMars 2020 training team had developed a concept for roveroperations training simulations based on Johnson Space Center’sextensive simulations training program for astronauts andflight controllers. The MSL team borrowed this idea, and implementedthese training simulations which are called PracticalOperational Readiness Gambits (PORGs). The PORGs so farhave focused on the Science Planner and Rover Planner roles,which are two crucial roles in tactical operations that engagein key interactions throughout a shift. PORGs are based onactual sol scenarios that have occurred on Mars and follow thetactical operations process and timeline as closely as possible.However, unlike an operations shift, PORGs can slow down orpause to allow for more mentoring time. PORGs can focus onparticular skills to test the trainees on their understanding ofa concept. PORGs increase in complexity with each scenario toease the trainees into more typical tactical operations workloads.As more trainees join the MSL operations team, more roles arebeing incorporated into PORGs. There are plans to incorporatecertified operations personnel into PORGs to practice anomalyresponse situations. PORGs have become an essential part of theMSL operations training program and will continue even afterthe return to on-site operations at JPL.

Gajeway, Jocelyn↗
NASA NTRSDate not supplied

Lunar Dust Tolerance Testing of Representative Seals for Lunar Surface Hatch Seals

The Moon’s surface creates a uniquely challenging environment for mechanisms and materials. Electrostatic adhesion combined with a jagged particulate morphology makes lunar dust particularly destructive to the components and sub-systems of lunar surface assets. One component that will be acutely impacted by lunar dust is seals, particularly those on the hatches which will be opened and closed to allow extravehicular activities (EVA) and on docking systems that will connect surface assets and spacecraft together. Lunar dust on these seals can create leak paths for the pressurized atmosphere of a surface asset to escape. Quantifying the level of lunar dust contamination that is allowable for seals is of paramount importance for mission planners and asset designers. This paper covers dust tolerance testing that was conducted on representative hatch seals with the Uniform Dust Deposition System (UDDS) at NASA Glenn Research Center [1]. Sub scale (≈30 cm diameter) versions of seals for the Orion docking hatch and NASA Docking System (NDS were coated evenly with varying amounts of lunar dust simulant to evaluate its effect on seals leak rates. The resulting leak rate results from these flight-proven seal design can help planners and designers construct robust missions and products

N. Jimenez↗
NASA NTRS2022

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems

Space Communications Responsive to Events Across Missions (SCREAM): An Investigation of Network Solutions for Transient Science Space Systems The National Academies have prioritized the pursuit of new scientific discoveries using diverse and temporally coordinated measurements from multiple ground and space-based observatories. Networked communications can enable such measurements by connecting individual observatories and allowing them to operate as a cohesive and purposefully designed system. Timely data flows across terrestrial and space communications networks are required to observe transient scientific events and processes. Currently, communications to space-based observatories experience large latencies due to manual service reservation and scheduling procedures, intermittent signal coverage, and network capacity constraints. If space communications network latencies could be reduced, new discoveries about dynamic scientific processes could be realized. However, science mission and network planners lack a systematic framework for defining, quantifying and evaluating timely space data flow implementation options for transient scientific observation scenarios involving multiple ground and space-based observatories. This dissertation presents a model-based systems engineering approach to investigate and develop network solutions to meet the needs of transient science space systems. First, a systematic investigation of the current transient science operations of the National Aeronautics and Space Administration’s (NASA) Tracking and Data Relay Satellite (TDRS) space data network and the Neil Gehrels Swift Observatory resulted in a formal architectural model for transient science space systems. Two methods individual missions may use to achieve timely network services were defined, quantitatively modeled, and experimentally compared. Next, the architectural model was extended to describe two alternative ways to achieve timely and autonomous space data flows to multiple space-based observatories within the context of a purposefully designed transient science observation scenario. A quantitative multipoint space data flow modeling method based in queueing theory was defined. General system suitability metrics for timeliness, throughput, and capacity were specified to support the evaluation of alternative network data flow implementations. A hypothetical design study was performed to demonstrate the multipoint data flow modeling method and to evaluate alternative data flow implementations using TDRS. The merits of a proposed future TDRS broadcast service to implement multipoint data flows were quantified and compared to expected outcomes using the as-built TDRS network. Then, the architectural model was extended to incorporate commercial network service providers. Quantitative models for Globalstar and Iridium short messaging data services were developed based on publicly available sources. Financial cost was added to the set of system suitability metrics. The hypothetical design study was extended to compare the relative suitability of the as-built TDRS network with the commercial Globalstar and Iridium networks. Finally, results from this research are being applied by NASA missions and network planners. In 2020, Swift implemented the first automated command pipeline, increasing its expected gravitational wave follow-up detection rate by greater than 400%. Current NASA technology initiatives informed by this research will enable future space-based observatories to become interoperable sensing devices connected by a diverse ecosystem of network service providers.

Christopher J. Roberts↗
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 NTRS2020

Multi-Agent Motion Planning using Deep Learning for Space Applications

State-of-the-art motion planners cannot scale to a large number of systems. Motion planning for multiple agents is an NP (non-deterministic polynomial-time) hard problem, so the computation time increases exponentially with each addition of agents. This computational demand is a major stumbling block to the motion planner's application to future NASA missions involving the swarm of space vehicles. We applied a deep neural network to transform computationally demanding mathematical motion planning problems into deep learning-based numerical problems. We showed optimal motion trajectories can be accurately replicated using deep learning-based numerical models in several 2D and 3D systems with multiple agents. The deep learning-based numerical model demonstrates superior computational efficiency with plans generated 1000 times faster than the mathematical model counterpart.

Madani, Ramtin↗
NASA NTRSDate not supplied

Comparison of Acoustic Models and Trajectory Generation Methods for an Acoustically-Aware Aircraft

This paper presents a comparison of trajectory generation methodologies using acoustic source noise models of different fidelity for motion planning for an acoustically-aware aircraft subject to constraints on the vehicle dynamic performance, mission, and acoustic footprint of the vehicle at a set of (three-dimensional) observer locations. The performance of a pre-mission Bézier curve-based planner and a (near) real-time stochastic model predictive control planner are compared. Additionally, a comparison is made between the motion planning performance using a lower-fidelity acoustic model based on propeller tip Mach number and omni-directional sound power radiation, and a hemisphere-based higher-fidelity acoustic model. It is demonstrated that the asymmetry in hemisphere-based acoustic model can be exploited for improved flight path planning and trajectory-tracking performance in the presence of acoustic constraints.

Kasey A Ackerman↗
NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗
NASA NTRSDate not supplied

Space Nuclear Propulsion for Deep Space Science Missions

The use of nuclear thermal propulsion (NTP) 1 and nuclear electric propulsion (NEP) 2 systems on deep space science missions to the outer planets and into the interstellar medium 3 can yield significant spacecraft system and mission performance benefits and improvements relative to the use of conventional chemical propulsion systems. Several recent and ongoing programs are developing the technologies and systems required to realize a near-term deep space nuclear propulsion capability. NTP provides improved propulsion efficiencies compared to chemical propulsion, while also providing substantial thrust. This combination of high thrust and increased specific impulse (I_sp) provides high acceleration and extended thrusting periods, enabling greatly reduced trip-times on certain types of missions compared to various propulsive alternatives. For examples, compared to a baseline mission using chemical propulsion, NTP-powered missions to Jupiter or Uranus could deliver approximately 2.4-3.6 times more payload (in the case of Jupiter, the payload delivery is significantly larger than the Juno spacecraft). In this comparison, the higher end of the payload advantage is obtained when the trip time is held equal for the NTP-powered vehicle and a vehicle using a chemical propulsion departure stage. NTP systems are presently under development by multiple government agencies. NASA’s Space Nuclear Propulsion (SNP) project aims to demonstrate a hydrogen-fed NTP engine at 900 s specific impulse (I_sp) and approximately 10-15 klb_f of thrust. DARPA’s Demonstration Rocket for Agile Cislunar Operations (DRACO) program is targeting a demonstration of an NTP system in the cislunar space between the Earth and the Moon. An appropriately phased development plan that applies the development of the reactor technology for an NTP engine in this performance class and leverages mature, existing liquid rocket component hardware provides a path to a lower cost propulsion system that can be realized on a shorter development schedule. NEP, with high Isp in the 2,000-8,000 s range, can also provide advantages over chemical propulsion, including a much greater payload delivery mass and the flexibility for planners to trade between delivered mass and a wider window of mission trajectory options. Electric propulsion (EP) systems have demonstrated great utility, performing notably on the Dawn mission to enable rendezvous and orbital insertion at two separate bodies, Vesta and Ceres. An NEP-powered vehicle would have a similar capability to visit multiple bodies, loitering at each before moving to the next. A 10 kW_e NEP system provides a power- rich environment on the spacecraft that is simply not possible using present radioisotope power systems, giving mission planners more scientific instrument and communication hardware options. Several programs and projects are presently developing NEP systems and subsystems in the 10 kW_e power range, leveraging past reactor work and recent nuclear power generation risk-reduction demonstration activities such as the Demonstration Using Flattop Fission (DUFF) and the Kilopower Reactor Using Stirling TechnologY (KRUSTY). The goal of the Air Force Research Laboratory’s Joint Energy Technology Supplying On-Orbit Nuclear Power (JETSON) program is an in-space demonstration vehicle that has a 10 kW_e -class fission power source. These past and present efforts can be combined with the ongoing development of 10 kW_e -class electric propulsion systems (notably the NEXT-C ion thruster or the Hall-effect thrusters for Power and Propulsion Element of the Lunar Gateway) to provide a pathway to a low-cost, reliable NEP system for deep space science application.

Kurt A. Polzin↗
NASA NTRS2021

Sailing Towards an Expressive Scheduling Language for Europa Clipper

The mission planners for NASA's Europa Clipper deep-space mission use automated scheduling software to generate activity plans and command sequences for multiple instruments and subsystems before sending the sequences for execution on board. Both science and engineering planners must translate their intents into expressions of mission constraints, goals, and preferences that the scheduling engine understands. This paper describes the ongoing development of a dedicated domain-specific java-embedded language that can efficiently and accurately capture such concerns for the Europa Clipper mission, along with a user-interface companion for the language.

Schaffer, Steve↗
NASA NTRSDate not supplied

Planning Bias: Planning as a Source of Sampling Bias

Many data-driven planning methods are trained on data generated by planners. It is well known that many statistical learning methods are sensitive to sampling bias, and yet there has been little or no attention to planning as a sampling method and its role in introducing sampling bias into planner-generated training data. Recently, it has been demonstrated that A**,* in the presence of problems with variable heuristic error, prefers some solutions over other equally cost-optimal solutions. But, as we discuss in this paper, mitigation may not be as simple as resolving arbitrary tie-breaking by sampling from ties uniformly at random. In this paper, we formalize an intuition of planning bias. We focus on problems which output a single solution. Diverse planning only complicates the problem by generalizing it to bias in the set of sets; we show how it is subject to bias in the single solution. We make some useful observations about deterministic algorithms in contrast to non-deterministic algorithms. We explain how information entropy may be a good way to measure planning bias, and discuss some issues in evaluating practical approaches to measurement. We address the intuition that uniform random tiebreaking should mitigate bias; and sketch a novel approach to constructing an appropriate random distribution for duplicate detection during forward search for unbiased A*. Finally, we suggest directions for future work.

Planning Scheduling Algorithms↗
NASA NTRS2024

Uranus Global Reference Atmospheric Model (Uranus-GRAM) 2024: User Guide

Engineers and mission planners designing vehicles that pass through Uranus’ atmosphere require an atmospheric model that calculates the mean values and variations of atmospheric properties. The Uranus Global Reference Atmospheric Model(Uranus-GRAM)is an engineering- oriented model that provides this information based on data from Voyager observations. Uranus- GRAM is designed to offer mission planners the flexibility to select input parameters such as time, latitude, and longitude. Uranus-GRAM outputs atmospheric constituent data and mean values for atmospheric density, temperature, pressure, and zonal wind along a user defined path. Uranus-GRAM also provides dispersions of density and zonal wind. Uranus-GRAM is one option in the GRAM Suite that shares a common software core with the other planetary GRAMs while maintaining Uranus specific models. Additionally, documentation, including this User Guide, a Programmer’s Manual, and trajectory code interfaces has been made available with the software release. This Technical Memorandum summarizes the atmospheric data model in Uranus-GRAM and provides a guide for the user to obtain, set up, and run the code in various configurations. Section 2 describes the input atmospheric data files and how they are used in Uranus-GRAM. Section 3 explains the process to obtain the Uranus-GRAM code, the data files, and how to set up and run the program. Appendices A through E provide additional details regarding the Uranus-GRAM input and output files. Appendix F provides a history of Uranus-GRAM revisions.

atmospheric models↗
DOE OSTI2024

Fish Friendly Water Rules' Impact On the Power Grid with High VRE Levels

Columbia River basin multipurpose reservoirs are operated for hydropower production and many other purposes considering the aquatic habitat of the river basin. Specifically, the river basin fish population is a vital element for the tribal community of the river basin. When conventional thermal power plants are retired, hydropower flexibility balances the wind and solar variability of the power grid and provides grid services to maximize the high renewable power absorption. On the other hand, over-reliance on hydropower could harm the river basin fish habitat. For example, water release pulses during peak electricity demand hours and shorter-term water release variation could harm the fish population. Power grid planners, environmentalists, Columbia Basin tribes, hydro regulators, and water resource planners work together to understand the impacts of Columbia River basin operation in a fish-friendly way in the current power grid and power grid with higher renewable power share. We examine different levels of renewable share in the Western Interconnection to understand the multiple weather years and future climate projection and operating scenario impact on the power grid and water system. We measure power grid impacts for various water resources planning scenarios in terms of total system operating cost, system reliability indicators, changes in wind and solar generation and curtailments, local marginal prices, and revenue for hydropower producers. The study results inform reservoir operating rules decisions from hydropower power producers, system operators, other water users, tribes, environmentalists, and other stakeholders.

environmental↗
DOE OSTI2024

From Resilient and Ready to Used and Useful: Managing Temporal and Locational Uncertainty in Electrification, DER Adoption, and Climate Adaptation

Grid planning decisions involve weighing risks against benefits. The best decisions will facilitate development of electrical infrastructure that minimizes risk and maximizes benefits. With the rapidly evolving energy landscape, today's planner must discern new loads and demand cycles; embrace the operational complexity of climate risk; revise settled standards; and anticipate and manage the system impacts of distributed energy resource (DER) adoption. Only by managing the combined uncertainty of these dynamic processes can a planner hope to make effective decisions. Our analysis focuses on the management of temporal and locational uncertainty, and particularly on the risks presented to customers by the mismanagement of the factors that are the sources of these uncertainties.

climate↗
NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗
NASA NTRSDate not supplied

An Experimental System for Strategic Flight Path Management in Advanced Air Mobility

In the concept envisioned for Urban Air Mobility (UAM) operations, fleets of electric vertical takeoff and landing (eVTOL) vehicles would operate between vertiports distributed within a densely populated area. These operations would be largely independent from the existing air traffic control system and would place the responsibility for flight planning and aircraft separation on fleet operators. The fourth major level on the UAM Maturity Level scale, UML-4, relies on “collaborative and responsible” automation to enable operations in non-visual conditions with medium traffic density (hundreds of aircraft in one metropolitan region) and medium complexity. This level of service places many requirements on automation systems to assist the operators of these aircraft. NASA has developed the Autonomous Operations Planner (AOP), a reference prototype Flight Path Management automation system, and has modified AOP to support research of anticipated UML-4 operations. AOP creates a four-dimensional flight plan conforming to the constraints of these operations, evaluates and modifies the flight plan during flight as conditions and constraints evolve, and coordinates the flight plan with other airspace users and with service providers. This version of AOP has been integrated into the Sikorsky Autonomy Research Aircraft and used in a flight test activity. In this paper we discuss anticipated characteristics of UAM operations, modifications that were made to AOP to adapt to that environment or to support the flight test, and observations of software and aircraft performance during the flight test. The aircraft achieved four-dimensional conformance with the flight plan and AOP provided adequate planning in almost all cases. We discuss improvements that could be made to AOP to address deficiencies that were observed.

Autonomous Operations Planner↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2024

The ReSWARM microgravity flight experiments: Planning, control, and model estimation for on‐orbit close proximity operations

Abstract On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying “assembler” robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a “construction site” scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider.

Robotics↗
DOE OSTI2026

Interaction between the emerging components of online shopping and in-person activities: insights from a behavioral survey

The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.

Digital Divide↗