Engineering topics
Restrepo, Carolina
Publications and source records attributed to Restrepo, Carolina.
NASA SPLICE Project: Developing the Next Generation Hazard Detection System
The SPLICE project is continuing NASA's efforts to develop precision landing GN&C technologies for future lander missions. One of those technologies is the next generation Hazard Detection System (HDS), which consists of a new Hazard Detection LIDAR and Hazard Detection Algorithms. The HDS is a modular system that will be adapted to fit specific mission needs in the future. This abstract presents the design approach and current challenges for the SPLICE HDS.
NASA SPLICE Project: Development and Testing of Precision Landing GN&C Technologies
This presentation shows NASA's progress in developing precision landing and hazard avoidance technologies during the last few years through its ALHAT, COBALT and now SPLICE projects. The talk shows how these projects intend to use active optical sensors to map landing surfaces and identify hazards for future lander missions. The talk will be given at an Entry Descent and Landing session at a NASA Technical Interchange Meeting on active optical sensor systems.
Cobalt: Development and Maturation of GN&C Technologies for Precision Landing
The CoOperative Blending of Autonomous Landing Technologies (COBALT) instrument is a terrestrial test platform for development and maturation of guidance, navigation and control (GN&C) technologies for precision landing. The project is developing a third-generation Langley Research Center (LaRC) navigation doppler lidar (NDL) for ultra-precise velocity and range measurements, which will be integrated and tested with the Jet Propulsion Laboratory (JPL) lander vision system (LVS) for terrain relative navigation (TRN) position estimates. These technologies together provide precise navigation knowledge that is critical for a controlled and precise touchdown. The COBALT hardware will be integrated in 2017 into the GN&C subsystem of the Xodiac rocket-propulsive vertical test bed (VTB) developed by Masten Space Systems, and two terrestrial flight campaigns will be conducted: one open-loop (i.e., passive) and one closed-loop (i.e., active).
Flight Testing a Real-Time Hazard Detection System for Safe Lunar Landing on the Rocket-Powered Morpheus Vehicle
The Hazard Detection System (HDS) is a component of the ALHAT (Autonomous Landing and Hazard Avoidance Technology) sensor suite, which together provide a lander Guidance, Navigation and Control (GN&C) system with the relevant measurements necessary to enable safe precision landing under any lighting conditions. The HDS consists of a stand-alone compute element (CE), an Inertial Measurement Unit (IMU), and a gimbaled flash LIDAR sensor that are used, in real-time, to generate a Digital Elevation Map (DEM) of the landing terrain, detect candidate safe landing sites for the vehicle through Hazard Detection (HD), and generate hazard-relative navigation (HRN) measurements used for safe precision landing. Following an extensive ground and helicopter test campaign, ALHAT was integrated onto the Morpheus rocket-powered terrestrial test vehicle in March 2014. Morpheus and ALHAT then performed five successful free flights at the simulated lunar hazard field constructed at the Shuttle Landing Facility (SLF) at Kennedy Space Center, for the first time testing the full system on a lunar-like approach geometry in a relevant dynamic environment. During these flights, the HDS successfully generated DEMs, correctly identified safe landing sites and provided HRN measurements to the vehicle, marking the first autonomous landing of a NASA rocket-powered vehicle in hazardous terrain. This paper provides a brief overview of the HDS architecture and describes its in-flight performance.
Tool for Rapid Analysis of Monte Carlo Simulations
Designing a spacecraft, or any other complex engineering system, requires extensive simulation and analysis work. Oftentimes, the large amounts of simulation data generated are very difficult and time consuming to analyze, with the added risk of overlooking potentially critical problems in the design. The authors have developed a generic data analysis tool that can quickly sort through large data sets and point an analyst to the areas in the data set that cause specific types of failures. The first version of this tool was a serial code and the current version is a parallel code, which has greatly increased the analysis capabilities. This paper describes the new implementation of this analysis tool on a graphical processing unit, and presents analysis results for NASA's Orion Monte Carlo data to demonstrate its capabilities.
Pattern Recognition for a Flight Dynamics Monte Carlo Simulation
The design, analysis, and verification and validation of a spacecraft relies heavily on Monte Carlo simulations. Modern computational techniques are able to generate large amounts of Monte Carlo data but flight dynamics engineers lack the time and resources to analyze it all. The growing amounts of data combined with the diminished available time of engineers motivates the need to automate the analysis process. Pattern recognition algorithms are an innovative way of analyzing flight dynamics data efficiently. They can search large data sets for specific patterns and highlight critical variables so analysts can focus their analysis efforts. This work combines a few tractable pattern recognition algorithms with basic flight dynamics concepts to build a practical analysis tool for Monte Carlo simulations. Current results show that this tool can quickly and automatically identify individual design parameters, and most importantly, specific combinations of parameters that should be avoided in order to prevent specific system failures. The current version uses a kernel density estimation algorithm and a sequential feature selection algorithm combined with a k-nearest neighbor classifier to find and rank important design parameters. This provides an increased level of confidence in the analysis and saves a significant amount of time.
Tool for Rapid Analysis of Monte Carlo Simulations
Designing a spacecraft, or any other complex engineering system, requires extensive simulation and analysis work. Oftentimes, the large amounts of simulation data generated are very di cult and time consuming to analyze, with the added risk of overlooking potentially critical problems in the design. The authors have developed a generic data analysis tool that can quickly sort through large data sets and point an analyst to the areas in the data set that cause specific types of failures. The Tool for Rapid Analysis of Monte Carlo simulations (TRAM) has been used in recent design and analysis work for the Orion vehicle, greatly decreasing the time it takes to evaluate performance requirements. A previous version of this tool was developed to automatically identify driving design variables in Monte Carlo data sets. This paper describes a new, parallel version, of TRAM implemented on a graphical processing unit, and presents analysis results for NASA's Orion Monte Carlo data to demonstrate its capabilities.
Preliminary Study Using Forward Reaction Control System Jets During Space Shuttle Entry
Failure or degradation of the flight control system, or hull damage, can lead to loss of vehicle control during entry. Possible failure scenarios are debris impact and wing damage that could result in a large aerodynamic asymmetry which cannot be trimmed out without additional yaw control. Currently the space shuttle uses aerodynamic control surfaces and Reaction Control System jets to control attitude. The forward jets are used for orbital maneuvering only, while the aft jets are used for yaw control during entry. This paper develops a controller for using the forward reaction control system jets as an additional control during entry, and assesses its value and feasibility during failure situations. Forward-aft jet blending logic is created, and implemented on a simplified model of the space shuttle entry flight control system. The model is validated and verified on the nonlinear, six degree-of-freedom Shuttle Engineering Simulator. A rudimentary human factors study was undertaken using the forward cockpit simulator at Johnson Space Center, to assess flying qualities of the new system and pilot workload. Results presented in the paper show that the combination of forward and aft jets provides useful additional yaw control, in addition to potential fuel savings and the ability to balance the use of the fuel in the forward and aft tanks to meet availability constraints of both forward and aft fuel tanks. Piloted simulation studies indicated that using both sets of jets while flying a damaged space shuttle reduces pilot workload, and makes the vehicle more responsive.