Range Safety Flight Elevation Limit Calculation Software

This program was developed to fill a need within the Wallops Flight Facility workflow for automation of the development of vertical plan limit lines used by flight safety officers during the conduct of expendable launch vehicle missions. Vertical plane present-position-based destruct lines have been used by range safety organizations at numerous launch ranges to mitigate launch vehicle risks during the early phase of flight. Various ranges have implemented data submittal and processing workflows to develop these destruct lines. As such, there is significant prior art in this field. The ElLimits program was developed at NASA's Wallops Flight Facility to automate the process for developing vertical plane limit lines using current computing technologies. The ElLimits program is used to configure launch-phase range safety flight control lines for guided missiles. The name of the program derives itself from the fundamental quantity that is computed - flight elevation limits. The user specifies the extent and resolution of a grid in the vertical plane oriented along the launch azimuth. At each grid point, the program computes the maximum velocity vector flight elevation that can be permitted without endangering a specified back-range location. Vertical plane x-y limit lines that can be utilized on a present position display are derived from the flight elevation limit data by numerically propagating 'streamlines' through the grid. The failure turn and debris propagation simulation technique used by the application is common to all of its analysis options. A simulation is initialized at a vertical plane grid point chosen by the program. A powered flight failure turn is then propagated in the plane for the duration of the so-called RSO reaction time. At the end of the turn, a delta-velocity is imparted, and a ballistic trajectory is propagated to impact. While the program possesses capability for powered flight failure turn modeling, it does not require extensive user inputs of vehicle characteristics (e.g., thrust and aerodynamic data), nor does it require reams of turn data after the traditional fashion of the Air Force ranges. The program requires a nominal trajectory table (time, altitude, range, velocity, and flight elevation) and makes heavy use of it to initialize and model a failure turn

An Autonomous Flight Safety System

The Autonomous Flight Safety System (AFSS) being developed by NASA s Goddard Space Flight Center s Wallops Flight Facility and Kennedy Space Center has completed two successful developmental flights and is preparing for a third. AFSS has been demonstrated to be a viable architecture for implementation of a completely vehicle based system capable of protecting life and property in event of an errant vehicle by terminating the flight or initiating other actions. It is capable of replacing current human-in-the-loop systems or acting in parallel with them. AFSS is configured prior to flight in accordance with a specific rule set agreed upon by the range safety authority and the user to protect the public and assure mission success. This paper discusses the motivation for the project, describes the method of development, and presents an overview of the evolving architecture and the current status

Reforming Range of Motion: The Use of the Pilates Reformer in a Female with Postoperative Adhesive Capsulitis

The purpose of this case report is to describe the rehabilitation outcomes of a 62-year-old female with post operative adhesive capsulitis using Pilates-based intervention in conjunction with standard Physical Therapy.https://soar.usa.edu/flsaspring2018/1015/thumbnail.jp

Loosely Coupled GPS-Aided Inertial Navigation System for Range Safety

The Autonomous Flight Safety System (AFSS) aims to replace the human element of range safety operations, as well as reduce reliance on expensive, downrange assets for launches of expendable launch vehicles (ELVs). The system consists of multiple navigation sensors and flight computers that provide a highly reliable platform. It is designed to ensure that single-event failures in a flight computer or sensor will not bring down the whole system. The flight computer uses a rules-based structure derived from range safety requirements to make decisions whether or not to destroy the rocket

The NASA Wallops Arc-Second Pointer (WASP) System for Precision Pointing of Scientific Balloon Instruments and Telescopes

The National Aeronautics and Space Administrations (NASA) Wallops Flight Facility (WFF), part of the Goddard Space Flight Center (GSFC), has developed a unique pointing control system for instruments aboard scientific balloon gondolas. The ability to point large telescopes and instruments with arc-second accuracy and stability is highly desired by multiple scientific disciplines, such as Planetary, Earth Science, Heliospheric and Astrophysics, and the availability of a standardized system supplied by NASA alleviates the need for the science user to develop and provide their own system. In addition to the pointing control system, a star tracker has been developed with both daytime and nighttime capability to augment the WASP and provide an absolute pointing reference. The WASP Project has successfully completed five test flights and one operational science mission, and is currently supporting an additional test flight in 2017, along with three science missions with flights scheduled between 2018 and 2020. The WASP system has demonstrated precision pointing and high reliability, and is available to support scientific balloon missions

GN and C Design Overview and Flight Test Results from NASA's Max Launch Abort System (MLAS)

The National Aeronautics and Space Administration (NASA) Engineering and Safety Center (NESC) designed, developed and flew the alternative Max Launch Abort System (MLAS) as risk mitigation for the baseline Orion spacecraft launch abort system (LAS) already in development. The NESC was tasked with both formulating a conceptual objective system (OS) design of this alternative MLAS as well as demonstrating this concept with a simulated pad abort flight test. The goal was to obtain sufficient flight test data to assess performance, validate models/tools, and to reduce the design and development risks for a MLAS OS. Less than 2 years after Project start the MLAS simulated pad abort flight test was successfully conducted from Wallops Island on July 8, 2009. The entire flight test duration was 88 seconds during which time multiple staging events were performed and nine separate critically timed parachute deployments occurred as scheduled. Overall, the as-flown flight performance was as predicted prior to launch. This paper provides an overview of the guidance navigation and control (GN&C) technical approaches employed on this rapid prototyping activity. This paper describes the methodology used to design the MLAS flight test vehicle (FTV). Lessons that were learned during this rapid prototyping project are also summarized

Range Safety for an Autonomous Flight Safety System

The Range Safety Algorithm software encapsulates the various constructs and algorithms required to accomplish Time Space Position Information (TSPI) data management from multiple tracking sources, autonomous mission mode detection and management, and flight-termination mission rule evaluation. The software evaluates various user-configurable rule sets that govern the qualification of TSPI data sources, provides a prelaunch autonomous hold-launch function, performs the flight-monitoring-and-termination functions, and performs end-of-mission safin

Learning Mazes with Aliasing States: An LCS Algorithm with Associative Perception

Learning classifier systems (LCSs) belong to a class of algorithms based on the principle of self-organization and have frequently been applied to the task of solving mazes, an important type of reinforcement learning (RL) problem. Maze problems represent a simplified virtual model of real environments that can be used for developing core algorithms of many real-world applications related to the problem of navigation. However, the best achievements of LCSs in maze problems are still mostly bounded to non-aliasing environments, while LCS complexity seems to obstruct a proper analysis of the reasons of failure. We construct a new LCS agent that has a simpler and more transparent performance mechanism, but that can still solve mazes better than existing algorithms. We use the structure of a predictive LCS model, strip out the evolutionary mechanism, simplify the reinforcement learning procedure and equip the agent with the ability of associative perception, adopted from psychology. To improve our understanding of the nature and structure of maze environments, we analyze mazes used in research for the last two decades, introduce a set of maze complexity characteristics, and develop a set of new maze environments. We then run our new LCS with associative perception through the old and new aliasing mazes, which represent partially observable Markov decision problems (POMDP) and demonstrate that it performs at least as well as, and in some cases better than, other published systems

Guidance, Navigation and Control (GN and C) Design Overview and Flight Test Results from NASA's Max Launch Abort System (MLAS)

The National Aeronautics and Space Administration Engineering and Safety Center designed, developed and flew the alternative Max Launch Abort System (MLAS) as risk mitigation for the baseline Orion spacecraft launch abort system already in development. The NESC was tasked with both formulating a conceptual objective system design of this alternative MLAS as well as demonstrating this concept with a simulated pad abort flight test. Less than 2 years after Project start the MLAS simulated pad abort flight test was successfully conducted from Wallops Island on July 8, 2009. The entire flight test duration was 88 seconds during which time multiple staging events were performed and nine separate critically timed parachute deployments occurred as scheduled. This paper provides an overview of the guidance navigation and control technical approaches employed on this rapid prototyping activity; describes the methodology used to design the MLAS flight test vehicle; and lessons that were learned during this rapid prototyping project are also summarized

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems