Results for “Contingency planning” — Page 13

NASA NTRS2010

Collective Intelligence and Three Aspects of Planning in Organizations: A NASA Example

For many complex sociotechnical systems, planning seems to require explicit coordination; certainly, in executing a plan the activities of different actors must be tightly coupled. However, distributing the needed planning information can be very burdensome and error prone, because different groups need different collections of information, updated or kept current on different time cycles. Further, the information needed to form successful plans is often highly distributed, and while feedback about the success of prior plans may exist, it may not be available to those in a position of using this to improve plans or to detect and resolve other problems in the system (Weick, 1995). Tools to support various aspects of planning have been developed, and can provide a huge benefit to the individuals working on that aspect. To be tractable, most solutions address a quite bounded slice of work, isolating it from the larger context. Prospective planning takes place over multiple, nested cycles of decision making. This builds a plan that specifies activities of different granularity. "Subplans" may specify multiple parallel activities by different groups and individuals, as well as sequential, nested actions by a single actor. Planning produces valuable, sharable, external, representations: in addition to prospective use, plans support retrospective assessment and also action in the present. Viewing planning in a larger context - both temporal and organizational -- enables noticing what one does not know and generating more systemic and effective solutions. Viewing a problem as one of collective intelligence invites thinking about the larger organizational context. Many approaches to supporting collective intelligence do not support execution of highly contingent actions, distributed across many players, and hence provide incomplete support for planning. However, CI technology maybe helpful in managing the processes of gathering information for decision making in planning and of distributing plan information to various actors, needed on various time cycles.

Billman, Dorrit↗

NASA NTRS1980

Space Shuttle/Orbiter EVA and EVA provisions

EVA objectives, procedures, and equipment for the Shuttle are reviewed. The EVA will occur as a planned excursion, to complete a mission objective, or on a contingency basis as support for the mission or to effect repairs to the Orbiter or its payload. Configurations for the placement of the airlock for EVA with and without Spacelab payloads are discussed, along with the various EVA tasks which could be expected as necessary for mission completion. Handholds have been placed in strategic positions on the RMS and along the payload doors, and a safety tether has been incorporated with line extension out to 25 ft. Off-the-shelf tools such as needlenose pliers, forceps, diagonal cutters, etc. are carried as standard equipment for the repair of malfunctioning equipment and doorlatches. Finally, attention is given to EVA lighting, communication, life-support, and work station restraint systems.

Goodman, J. R.↗

NASA NTRSDate not supplied

An Approach to Reasoning Service Migration in Data and Reasoning Fabric (DRF) Implementation

In this paper we consider service placement problem for Data and Reasoning Fabric (DRF) enabled airspace operations assuming a fixed cloud/edge infrastructure with allocated computing, storage and power resources, where cloud/edge servers, and communication stations are in a wired connected network, while vehicles use a wireless network for communication. The objective is to automatically select the best location for the requested service execution, which achieves minimum cost while satisfying the user quality of service (QoS) and available resources constraints. To this end, we estimate for each potential location the response time, consumed energy and total cost; formulate an optimization problem for cost minimization given the users QoS constraints and network infrastructure limitations; and solve it using nonlinear programming tools. The approach is applied to trajectory re-planning use case to handle a no-fly zone contingency in real time.

Vahram Stepanyan↗

NASA NTRS2007

Rapid Contingency Simulation Modeling of the NASA Crew Launch Vehicle

The NASA Crew Launch Vehicle is a two-stage orbital launcher designed to meet NASA's current as well as future needs for human space flight. In order to free the designers to explore more possibilities during the design phase, a need exists for the ability to quickly perform simulation on both the baseline vehicle as well as the vehicle after proposed changes due to mission planning, vehicle configuration and avionics changes, proposed new guidance and control algorithms, and any other contingencies the designers may wish to consider. Further, after the vehicle is designed and built, the need will remain for such analysis in the event of future mission planning. An easily reconfigurable, modular, nonlinear six-degree-of-freedom simulation matching NASA Marshall's in-house high-fidelity simulator is created with the ability to quickly perform simulation and analysis of the Crew Launch Vehicle throughout the entire launch profile. Simulation results are presented and discussed, and an example comparison fly-off between two candidate controllers is presented.

Betts, Kevin M.↗

NASA NTRS2004

Manned Spacecraft Landing and Recovery

As recent history has tragically demonstrated, a successful space mission is not complete until the crew has safely returned to earth and has been successfully recovered. It is noted that a safe return to earth does not guarantee a successful recovery. The focus of this presentation will be a discussion of the ground operation assets involved in a successful recovery. The author's experience in land and water-based recovery of crewed vehicles and flight hardware at Kennedy Space Center (KSC), Edwards Air Force Base, international landing sites, and the Atlantic Ocean provides for some unique insight into this topic. He has participated in many aspects of Space Shuttle landing and recovery operations including activation of Transatlantic Abort Landing (TAL) sites and Emergency Landing Sites (ELS) as an Operations Test Director, execution of post landing convoy operations as an Orbiter Move Director, Operations Test Director, and Landing and Recovery Director, and recovery of solid rocket boosters, frustum and their parachutes 140 miles offshore in a wide range of sea states as a Retrieval Diver/Engineer. The recovery operations for the Mercury, Gemini, and Apollo were similar from a landing and recovery perspective in th t they all were capsules with limited "flying" capability and had a planned End of Mission (EOM) in an ocean with a descent slowed by parachutes. The general process was to deploy swim teams via helicopters to prepare the capsule for recovery and assist with crew extraction when required. The capsule was then hoisted onto the deck of a naval vessel. This approach required the extensive use and deployment of military assets to support the primary landing zone as well as alternate and contingency locations. The Russian Soyuz capsule also has limited "flying" capability; however, the planned EOM is terrestrial. In addition to use of parachutes to slow the reentry descent, soft-landing rockets on the bottom of the vehicle are employed to cushion the landing. The recovery forces are deployed via helicopters and the capsule is transported by a specialized all-terrain vehicle. The Space Shuttle Orbiter landing and recovery process is considerably different. The added lift capability and maneuverability allow the Orbiter to land at an exact location/runway for a nominal EOM. This allows for a timely response of recovery/contingency rescue forces, centralized staging of personnel and equipment, and assured access by ground vehicles. The well defined landing zone also provides for far more options when selecting landing sites for EOM and emergency returns and the relatively large cross-range capability increases the number of landing opportunities at the preferred sites.

Hammel, Don↗

NASA NTRS1996

STS-77 Space Shuttle Mission Report

The STS-77 Space Shuttle Program Mission Report summarizes the Payload activities as well as the: Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME) systems performance during the seventy-seventh flight of the Space Shuttle Program, the fifty-second flight since the return-to-flight, and the eleventh flight of the Orbiter Endeavour (OV-105). STS-77 was also the last flight of OV-105 prior to the vehicle being placed in the Orbiter Maintenance Down Period (OMDP). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-78; three SSME's that were designated as serial numbers 2037, 2040, and 2038 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-080. The RSRM's, designated RSRM-47, were installed in each SRB and the individual RSRM's were designated as 360TO47A for the left SRB, and 360TO47B for the right SRB. The STS-77 Space Shuttle Program Mission Report fulfills the Space Shuttle Program requirement as documented in NSTS 07700, Volume VII, Appendix E. The requirement stated in that document is that each organizational element supporting the Program will report the results of their hardware (and software) evaluation and mission performance plus identify all related in-flight anomalies. The primary objectives of this flight were to successfully perform the operations necessary to fulfill the requirements of Spacehab-4, the SPARTAN 207/inflatable Antenna Experiment (IAE), and the Technology Experiments Advancing Missions in Space (TEAMS) payload. Secondary objectives of this flight were to perform the experiments of the Aquatic Research Facility (ARF), Brilliant Eyes Ten-Kelvin Sorption Cryocooler Experiment (BETSCE), Biological Research in Canisters (BRIC), Get-Away-Special (GAS), and GAS Bridge Assembly (GBA). The STS-77 mission was planned as a 9-day flight plus 1 day, plus 2 contingency days, which were available for weather avoidance or Orbiter contingency operations. The sequence of events for the STS-77 mission is shown in Table 1, and the Space Shuttle Vehicle Management Office Problem Tracking List is shown in Table 11. The Government Fumished Equipment/Flight Crew Equipment (GFE/FCE) Problem Tracking List is shown in Table II. Appendix A lists the sources of data, both formal and informal, that were used to prepare this report. Appendix B provides the definition of acronyms and abbreviations used throughout the report. All times during the flight are given in Greenwich mean time (G.m.t.) and mission elapsed time (MET). The six-person crew for STS-77 consisted of John H. Casper, Col., U. S. Air Force, Commander; Curtis L. Brown, Jr., Lt. Col., U. S. Air Force, Pilot; Andrew S. W. Thomas, Civilian, Ph.D., Mission Specialist 1; Daniel W. Bursch, CDR., U. S. Navy, Mission Specialist 2; Mario Runco, Jr., Civilian, Mission Specialist 3; and Marc Gameau, Civilian, PhD, Mission Specialist 4.

Fricke, Robert W., Jr.↗

NASA NTRS1996

STS-78 Space Shuttle Mission Report

The STS-78 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the seventy-eighth flight of the Space Shuttle Program, the fifty-third flight since the return-to-flight, and the twentieth flight of the Orbiter Columbia (OV-102). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-79; three SSME's that were designated as serial numbers 2041, 2039, and 2036 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-081. The RSRM's, designated RSRM-55, were installed in each SRB and the individual RSRM's were designated as 360L055A for the left SRB, and 360L055B for the right SRB. The STS-78 Space Shuttle Program Mission Report fulfills the Space Shuttle Program requirement as documented in NSTS 07700, Volume 7, Appendix E. The requirement stated in that document is that each organizational element supporting the Program will report the results of their hardware (and software) evaluation and mission performance plus identify all related in-flight anomalies. The primary objective of this flight was to successfully perform the planned operations of the Life and Microgravity Spacelab experiments. The secondary objectives of this flight were to complete the operations of the Orbital Acceleration Research Experiment (OARE), Biological Research in Canister Unit-Block II (BRIC), and the Shuttle Amateur Radio Experiment II-Configuration C (SAREX-II). The STS-78 mission was planned as a 16-day, plus one day flight plus two contingency days, which were available for weather avoidance or Orbiter contingency operations. The sequence of events for the STS-78 mission is shown in Table 1, and the Space Shuttle Vehicle Management Office Problem Tracking List is shown in Table 2. The Government Furnished Equipment/Flight Crew Equipment (GFE/FCE) Problem Tracking List is shown in Table 3. The Marshall Space Flight Center (MSFC) Problem Tracking List is shown in Table 4. Appendix A lists the sources of data, both formal and informal, that were used to prepare this report. Appendix B provides the definition of acronyms and abbreviations used throughout the report. All times during the flight are given in Greenwich mean time (G.m.t.) and mission elapsed time (MET).

Fricke, Robert W., Jr.↗

NASA NTRS2016

Unmanned Aircraft Systems Traffic Management (UTM) Safely Enabling UAS Operations in Low-Altitude Airspace

Unmanned Aircraft System (UAS) Traffic Management (UTM) Enabling Civilian Low-Altitude Airspace and Unmanned Aircraft System Operations What is the problem? Many beneficial civilian applications of UAS have been proposed, from goods delivery and infrastructure surveillance, to search and rescue, and agricultural monitoring. Currently, there is no established infrastructure to enable and safely manage the widespread use of low-altitude airspace and UAS operations, regardless of the type of UAS. A UAS traffic management (UTM) system for low-altitude airspace may be needed, perhaps leveraging concepts from the system of roads, lanes, stop signs, rules and lights that govern vehicles on the ground today, whether the vehicles are driven by humans or are automated. What system technologies is NASA exploring? Building on its legacy of work in air traffic management for crewed aircraft, NASA is researching prototype technologies for a UAS Traffic Management (UTM) system that could develop airspace integration requirements for enabling safe, efficient low-altitude operations. While incorporating lessons learned from the today's well-established air traffic management system, which was a response that grew out of a mid-air collision over the Grand Canyon in the early days of commercial aviation, the UTM system would enable safe and efficient low-altitude airspace operations by providing services such as airspace design, corridors, dynamic geofencing, severe weather and wind avoidance, congestion management, terrain avoidance, route planning and re-routing, separation management, sequencing and spacing, and contingency management. One of the attributes of the UTM system is that it would not require human operators to monitor every vehicle continuously. The system could provide to human managers the data to make strategic decisions related to initiation, continuation, and termination of airspace operations. This approach would ensure that only authenticated UAS could operate in the airspace. In its most mature form, the UTM system could be developed using autonomicity characteristics that include self-configuration, self-optimization and self-protection. The self-configuration aspect could determine whether the operations should continue given the current andor predicted windweather conditions. NASA envisions concepts for two types of possible UTM systems. The first type would be a Portable UTM system, which would move from between geographical areas and support operations such as precision agriculture and disaster relief. The second type of system would be a Persistent UTM system, which would support low-altitude operations and provide continuous coverage for a geographical area. Either system would require persistent communication, navigation, and surveillance (CNS) coverage to track, ensure, and monitor conformance. What is NASA doing to test the technologies? NASA's near-term goal is the development and demonstration of a possible future UTM system that could safely enable low-altitude airspace and UAS operations. Working alongside many committed government, industry and academic partners, NASA is leading the research, development and testing that is taking place in a series of activities called Technology Capability Levels (TCL), each increasing in complexity. UTM TCL1 concluded field testing in August 2015 and is undergoing additional testing at an FAA site.

UTM Low-altitude↗

NASA NTRSDate not supplied

Accuracy Assessment of Two Gps Fidelity Prediction Services in Urban Terrain

Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.

Andrew Moore↗

NASA NTRSDate not supplied

Accuracy Assessment of Two GPS Fidelity Prediction Services in Urban Terrain

Low altitude flight in urban areas is susceptible to degraded GNSS-based navigation system performance due to terrain interference with radio signals from orbital positioning satellites. Predictive navigation performance fidelity tools are needed a) in preflight planning to assist in the creation of safe flight paths and b) in-flight to provide contingency management agents with the navigation risk of proximal flight corridors. Two navigation fidelity prediction services are validated by comparison with over 6000 readings from GNSS sensors collected along a five-mile path through urban areas of Corpus Christi, Texas, on three dates in 2022. Predictions are based on satellite line of sight through 3D terrain data collected in 2018. Each service predicts a set of navigation fidelity metrics over a user-specified time period. One metric estimated by both is the number of visible satellites. A direct comparison of the number of predicted visible satellites with the number sensed by the receiver is used to validate the prediction services. Results show an exact match in the number of predicted satellites for 60% of the measurements, and a match within +/- 4 satellites for 95% of the measurements. As expected, agreement improves away from vertical blocking terrain. Most cases of mismatch are due a lower predicted count than measured (false negatives), and can be accounted for by receiver pickup of stray signals caused by multipath propagation. About 10% of mismatches are false positives and are mostly accounted for by foliage effects. The two services predict visibility of the same set of satellites 80% of the time, differ by two or less satellites 95% of the time, and can compute predictions for one hour of observations in one minute or less. Validation is analyzed statistically and in detailed case studies of selected observation times. The prediction services validated in this study run fast enough for preflight safety planning. The more stringent challenge of inflight navigation fidelity prediction for contingency management requires both a speedup of the current level of modeling and equally fast stray signal modeling.

Andrew J. Moore↗

NASA NTRS1974

Operations analysis (study 2.1). Contingency analysis

Future operational concepts for the space transportation system were studied in terms of space shuttle upper stage failure contingencies possible during deployment, retrieval, or space servicing of automated satellite programs. Problems anticipated during mission planning were isolated using a modified 'fault tree' technique, normally used in safety analyses. A comprehensive space servicing hazard analysis is presented which classifies possible failure modes under the catagories of catastrophic collision, failure to rendezvous and dock, servicing failure, and failure to undock. The failure contingencies defined are to be taken into account during design of the upper stage.

Source record↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS1992

Real-time contingency handling in MAESTRO

A scheduling and resource management system named MAESTRO was interfaced with a Space Station Module Power Management and Distribution (SSM/PMAD) breadboard at MSFC. The combined system serves to illustrate the integration of planning, scheduling, and control in a realistic, complex domain. This paper briefly describes the functional elements of the combined system, including normal and contingency operational scenarios, then focusses on the method used by the scheduler to handle real-time contingencies.

Britt, Daniel L.↗

NASA NTRSDate not supplied

Air Traffic Management Response to Vertiport Closure

Increasing quantities of urban air mobility operations requires consideration of various contingencies associated with novel operational tempos and airspace management practices. This paper explores a contingency scenario in which a single vertiport closes unexpectedly during high tempo operations. Dynamic re-planning is explored as a function performed both by operators using common operating procedures and through emergency assistance from air traffic control. Implications for airspace capacity, infrastructure requirements, and vehicle energy reserves are discussed.

UAM↗

NASA NTRSDate not supplied

Air Traffic Management Response to Vertiport Closure

Increasing quantities of urban air mobility operations requires consideration of various contingencies associated with novel operational tempos and airspace management practices. This paper explores a contingency scenario in which a single vertiport closes unexpectedly during high tempo operations. Dynamic re-planning is explored as a function performed both by operators using common operating procedures and through emergency assistance from air traffic control. Implications for airspace capacity, infrastructure requirements, and vehicle energy reserves are discussed.

UAM↗

NASA NTRS1987

Geometric reasoning

Cognitive robot systems are ones in which sensing and representation occur, from which task plans and tactics are determined. Such a robot system accomplishes a task after being told what to do, but determines for itself how to do it. Cognition is required when the work environment is uncontrolled, when contingencies are prevalent, or when task complexity is large; it is useful in any robotic mission. A number of distinguishing features can be associated with cognitive robotics, and one emphasized here is the role of artificial intelligence in knowledge representation and in planning. While space telerobotics may elude some of the problems driving cognitive robotics, it shares many of the same demands, and it can be assumed that capabilities developed for cognitive robotics can be employed advantageously for telerobotics in general. The top level problem is task planning, and it is appropriate to introduce a hierarchical view of control. Presented with certain mission objectives, the system must generate plans (typically) at the strategic, tactical, and reflexive levels. The structure by which knowledge is used to construct and update these plans endows the system with its cognitive attributes, and with the ability to deal with contingencies, changes, unknowns, and so on. Issues of representation and reasoning which are absolutely fundamental to robot manipulation, decisions based upon geometry, are discussed here, not AI task planning per se.

Woodbury, R. F.↗

NASA NTRSDate not supplied

Performance Impacts to the NASA Artemis II Trajectory Correction Burn Placement

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

GN&C↗