Assessment of Scheduling and Plan Execution of Apollo 14 Lunar Surface Operations

Although over forty years have passed since first landing on the Moon, there is not yet a comprehensive, quantitative assessment of Apollo extravehicular activities (EVAs). Quantitatively evaluating lunar EVAs will provide a better understanding of the challenges involved with surface operations. This first evaluation of a surface EVA centers on comparing the planned and the as-ran timeline, specifically collecting data on discrepancies between durations that were estimated versus executed. Differences were summarized by task categories in order to gain insight as to the type of surface operation activities that were most challenging. One Apollo 14 EVA was assessed utilizing the described methodology. Selected metrics and task categorizations were effective, and limitations to this process were identified

Integrating Human Performance Measures into Space Operations: Beyond Our Scheduling Capabilities?

Current planning and scheduling software tools for International Space Station (ISS) support different flight controller teams as they plan daily space operations. Planning and scheduling tools capabilities include integrating digitized ISS state inputs, evaluating their expected future states, and propagating them over time. Extensive, custom-made computational models of operations, of objectives, and of operational constraints help ISS flight controllers identify where scheduled events violate constraints. Based on the current capabilities of these tools, this paper proposes how human performance measures could be better integrated into planning and scheduling tools for space mission operations. Future integration of human performance measures could be applied to state inputs (in this case, the astronauts state) and to modeling human performance operational constraints & operational objectives (i.e., assigned activities) with parameters that are relevant to human performance measures. Gaps between the state-of-the-art for human performance modeling and planning tools for future exploration missions are identified

Level of Automation and Failure Frequency Effects on Simulated Lunar Lander Performance

A human-in-the-loop experiment was conducted at the NASA Ames Research Center Vertical Motion Simulator, where instrument-rated pilots completed a simulated terminal descent phase of a lunar landing. Ten pilots participated in a 2 x 2 mixed design experiment, with level of automation as the within-subjects factor and failure frequency as the between subjects factor. The two evaluated levels of automation were high (fully automated landing) and low (manual controlled landing). During test trials, participants were exposed to either a high number of failures (75% failure frequency) or low number of failures (25% failure frequency). In order to investigate the pilots' sensitivity to changes in levels of automation and failure frequency, the dependent measure selected for this experiment was accuracy of failure diagnosis, from which D Prime and Decision Criterion were derived. For each of the dependent measures, no significant difference was found for level of automation and no significant interaction was detected between level of automation and failure frequency. A significant effect was identified for failure frequency suggesting failure frequency has a significant effect on pilots' sensitivity to failure detection and diagnosis. Participants were more likely to correctly identify and diagnose failures if they experienced the higher levels of failures, regardless of level of automatio

Human-Automation Allocations for Current Robotic Space Operations: Space Station Remote Manipulator System

NASAs Human Research Programs Risk of Inadequate Design of Human and Automation/Robotic Integration (HARI) delineates the uncertainty surrounding crew work with automation and robotics in spaceflight. HARI is concerned with detrimental effects of ineffective user interfaces, system designs and/or functional task allocation on crew performance, potentially compromising mission success and safety. This risk arises because of limited experience with complex automation and robotics in spaceflight. One key knowledge gap within the HARI risk is related to function allocation

Shadow TUAV Single Operator Consolidation : Display Assessment

Currently, Shadow UAV operations require two people: the Air Vehicle Operator (AVO) and the Mission Payload Operator (MPO). A previous workload study demonstrated that it is possible to combine these two positions such that one person can assume both roles (Appendix A). However, to achieve this consolidation, improved displays in terms of usability and increased automated functionality will be necessary to keep the workload of the single operator to acceptable levels. To demonstrate the types of changes that will need to occur for successful AVO and MPO consolidation, this report focuses on display and automation improvements in the following three areas: systems management, vehicle situation awareness, and payload operations. For each of these areas, a previous display has either been designed or improved upon, always applying human factors design principles. Each of these display redesigns exemplifies how operator workload can be decreased, as well as improve overall mission capability

Mission Planning for Robotic and Human Exploration

Mission planning for spaceflight is a complex challenge for ground operators managing robotic or human missions. It requires integrating the needs of various disciplines and creating an effective, efficient, and safe plan to execute. NASA uses specialized tools to meet planning requirements and constraints. This talk will provide an overview of spaceflight mission planning as well as the software tools the Human-Computer Interaction Group has built to support scheduling and planning. Also, it identifies the future scheduling and planning challenges for human Mars missions and how we leverage Earth Analogs to learn about Mars operations

Evolving from Planning and Scheduling to Real-Time Operations Support: Design Challenges

Versions of Scheduling and Planning Interface for Exploration (SPIFe) have supported a variety of mission operations across NASA. This software tool has evolved and matured over several years, assisting planners who develop intricate schedules. While initially conceived for surface Mars missions, SPIFe has been deployed in other domains, where people rather than robotic explorers, execute plans. As a result, a diverse set of end-users has compelled growth in a new direction: supporting real-time operations. This paper describes the new needs and challenges that accompany this development. Among the key features that have been built for SPIFe are current time indicators integrated into the interface and timeline, as well as other plan attributes that enable execution of scheduled activities. Field tests include mission support for the Lunar CRater Observation and Sensing Satellite (LCROSS), NASA Extreme Environment Mission Operations (NEEMO) and Desert Research and Technology Studies (DRATS) campaigns

Lessons Learned from International Space Station Crew Autonomous Scheduling Test

In 2017, our team investigated and evaluated the novel concept of operations of astronaut self-scheduling (rescheduling their own timeline without creating violations) onboard International Space Station (ISS). Five test sessions were completed for this technology demonstration called Crew Autonomous Scheduling Test (CAST). For the first time in a spaceflight operational environment, an ISS crewmember planned, rescheduled, and executed their activities in real-time on a mobile device while abiding by flight and scheduling constraints. This paper discusses the lessons learned from deployment to execution

Playbook Data Analysis Tool: Collecting Interaction Data from Extremely Remote Users

Typically, user tests for software tools are conducted in person. At NASA, the users may be located at the bottom of the ocean in a pressurized habitat, above the atmosphere in the International Space Station, or in an isolated capsule on a simulated asteroid mission. The Playbook Data Analysis Tool (P-DAT) is a human-computer interaction (HCI) evaluation tool that the NASA Ames HCI Group has developed to record user interactions with Playbook, the group's existing planning-and-execution software application. Once the remotely collected user interaction data makes its way back to Earth, researchers can use P-DAT for in-depth analysis. Since a critical component of the Playbook project is to understand how to develop more intuitive software tools for astronauts to plan in space, P-DAT helps guide us in the development of additional easy-to-use features for Playbook, informing the design of future crew autonomy tools.P-DAT has demonstrated the capability of discreetly capturing usability data in amanner that is transparent to Playbooks end-users. In our experience, P-DAT data hasalready shown its utility, revealing potential usability patterns, helping diagnose softwarebugs, and identifying metrics and events that are pertinent to Playbook usage aswell as spaceflight operations. As we continue to develop this analysis tool, P-DATmay yet provide a method for long-duration, unobtrusive human performance collectionand evaluation for mission controllers back on Earth and researchers investigatingthe effects and mitigations related to future human spaceflight performance

Operational Assessment of Apollo Lunar Surface Extravehicular Activity

Quantifying the operational variability of extravehicular activity (EVA) execution is critical to help design and build future support systems to enable astronauts to monitor and manage operations in deep-space, where ground support operators will no longer be able to react instantly and manage execution deviations due to the significant communication latency. This study quantifies the operational variability exhibited during Apollo 14-17 lunar surface EVA operations to better understand the challenges and natural tendencies of timeline execution and life support system performance involved in surface operations. Each EVA (11 in total) is individually summarized as well as aggregated to provide descriptive trends exhibited throughout the Apollo missions. This work extends previous EVA task analyses by calculating deviations between planned and as-performed timelines as well as examining metabolic rate and consumables usage throughout the execution of each EVA. The intent of this work is to convey the natural variability of EVA operations and to provide operational context for coping with the variability inherent to EVA execution as a means to support future concepts of operations