The Effectiveness of Augmented Reality for Astronauts on Lunar Missions

The uses of Augmented Reality (AR) and Head-Up Displays (HUDs) are becoming more prominent in industries such as aviation, automotive and medicine. An AR device such as the Microsoft Hololens can project holograms onto the user’s natural field of view to assist with completion of a variety of tasks. Unfortunately, only a little research and development has begun in the space sector for astronauts using these HUDs. Future lunar missions could incorporate AR for astronauts to ease task load and improve accuracy. The study evaluated the usability, subjective workload, and task performance of 22 participants using the Microsoft HoloLens to complete tasks that are analogous those completed by astronauts on a lunar mission, including navigation, rock sample collection, and maintenance tasks. Results from the usability survey, NASA TLX, and task performance evaluation suggested that AR supports astronaut missions with reduced workload, and minimizing task errors. Usability data information collected from the participants sought to improve on the User Interface (UI), and confirmed the aforementioned results. The researcher concluded that further research must be conducted to test the development of interfaces along with the usability aspect by the National Aeronautics and Space Administration (NASA) astronauts

A Machine Learning Approach Towards Analyzing Impact of Surface Weather on Expect Departure Clearance Times in Aviation

Commercial air travel in the United States has grown significantly in the past decade. While the reasons for air traffic delays can vary, the weather is the largest cause of flight cancellations and delays in the United States. Air Traffic Control centers utilize Traffic Management Initiatives such as Ground Stops and Expect Departure Clearance Times (EDCT) to manage traffic into and out of affected airports. Airline dispatchers and pilots monitor EDCTs to adjust flight blocks and flight schedules to reduce the impact on the airline’s operating network. The use of time-series data mining can be used to assess and quantify the impact of surface weather variables on EDCTs. A major hub airport in the United States, Charlotte Douglas International Airport, was chosen for the model development and assessment, and Vector Autoregression and Recurrent Neural Network models were developed. While both models were assessed to have demonstrated acceptable performance for the assessment, the Vector Autoregression outperformed the Recurrent Neural Network model. Weather variables up to six hours before the prediction time period were used to develop the proposed lasso regularized Vector Autoregression equation. Precipitation values were assessed to be the most significant predictors for EDCT values by the Vector Autoregression and Recurrent Neural Network models

Evaluating Small UAS Near Midair Collision Risk Using AeroScope and ADS-B

As small unmanned aircraft systems (sUAS) continue to proliferate in the National Airspace System (NAS), near midair collisions are becoming more common. In late 2017, the National Transportation Safety Board released a report detailing the first confirmed midair collision between a sUAS and manned aircraft in the United States. In February 2018, a video of a sUAS maneuvering around a passenger jetliner on approach to a Las Vegas airport went viral on YouTube. Just months later, a helicopter instructor pilot reported performing evasive maneuvers to avoid colliding with a sUAS, resulting in a non-fatal crash. From 2014 to 2018 the Federal Aviation Administration (FAA) recorded 6,117 reports of near encounters between manned and unmanned aircraft within the NAS (Government Accountability Office [GAO], 2018). In their report, the GAO (2018) highlighted the need for additional operational data to aid the FAA’s management of safety risks posed by unmanned aircraft. The purpose of this study was to evaluate aviation interference and safety hazards caused by unmanned aircraft at an airport in Class C airspace. Using a passive RF sUAS detection device known as the AeroScope, the authors collected sUAS operations data for 13 days at Daytona Beach International Airport in Florida. While the study was limited to DJI-manufactured sUAS, the results yielded detailed operational information on 190 sUAS flights that had been conducted during the sampling period. The authors identified several operator behaviors including preferred sUAS models, flight days and times, common operating locations, and operational altitudes. Operational data was compared against published FAA UAS Facility Maps (UASFM) to examine potential risk areas. Additionally, sUAS detections were compared against historical ADS-B information to examine for potential midair collisions, yielding several notable case studies. The authors evaluated the effectiveness of existing geofencing infrastructure and provided recommendations for integration with the Low Altitude Authorization and Notification Capability (LAANC) system. The paper culminates with a proposal for integrating LAANC usage data into existing aviation information sharing infrastructure to improve manned pilot situational awareness of sUAS activity within the NAS