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George Gorospe

Publications and source records attributed to George Gorospe.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2025

Considerations for Optimal Sensor Placement for Higher Accuracy Object Localization for Urban Air Mobility

Previous research into object localization has shown that sensor placement and alignment plays an important role in achieving higher accuracy levels of the estimated location of a tracked Urban Air Mobility Vehicle. In general, a near-orthogonal intersection between the ground node observation vectors results in the highest accuracy due to a smaller overlapping uncertainty region between both. This applies to triangulation by means of ground node camera angle observations as well as trilateration by means of ground node distance measurements. However, this simple concept is not easily fulfilled with a network of a limited number of static ground nodes and a moving object to be localized. This case study performs sensitivity analyses and explores practical ways on how to achieve higher estimate accuracy levels in this context.

sensor placement↗
NASA NTRS2023

Vibration Anomaly Indicator in UAVs in presence of Wind

One of the critical factors affecting flight safety of unmanned aerial vehicles (UAVs) is the amount of vibration they are exposed during a flight. For UAVs under remote operation, vehicle stabilization and navigation is typically achieved by estimating its attitude and position using onboard miniature sensors such as accelerometers, gyroscopes, and GPS via an onboard autopilot. Since precise control of the UAV relies heavily on the attitude sensing, the vibration levels need to be as low as possible in order to minimize the signal noise. Incorrect sensor data can lead to uncertain state estimation causing the multirotor to drift from its desired position. Moreover, high vibrations can induce faults in the safety-critical components of the UAV such as its on-board sensors, motors and propellers. Hence, it is important to monitor the vibration levels during a UAV flight. This paper specifically looks into effect of wind on the vibrations recorded by the autopilot system in an octocopter. Using data from experimental flights under varying wind conditions, we aim to classify between a nominal and anomalous vibration level and define a safety metric known as the Vibrational Anomaly Indicator (VAI) for small UAV systems. Further, we will study effect of high vibrations on the inertial measurement unit (IMU) of an octocopter under laboratory set-up and compute the VAI from IMU measurements. Results would demonstrate the utility of VAI as an health indicator for unmanned flights either in presence of winds or from degraded on-board IMU sensor.

Vibration↗
NASA NTRS2023

An Analysis of Barriers Preventing the Widespread Adoption of Predictive and Prescriptive Maintenance in Aviation

The aviation industry has long recognized the potential benefits of predictive maintenance, a maintenance strategy that leverages sensor and operational data to predict the future degradation of components. Prescriptive maintenance takes this a step further and considers the entire aviation ecosystem to schedule maintenance actions optimally. With the ability to reduce maintenance costs by up to 30%, as reported by the Department of Energy, these maintenance strategies have been identified to be an important investment to reduce a airline costs. However, despite great interest and technological advances in areas such as diagnostics, prognostics, sensing, computation, and machine learning, the adoption of predictive and prescriptive maintenance has not been widely applied in aviation. To shed light on this issue, we conducted an analysis of the barriers preventing or limiting the adoption of predictive and prescriptive maintenance in aviation. Through discussions with subject matter experts across industry, academia, standards bodies, and government, we identified five key challenges: complexity of prediction; validation, safety assurance, and regulatory challenges; cost of adoption; difficulty in quantifying impact and informing decisions; and data availability, quality, and ownership challenges. This study provides a detailed overview of these barriers and areas where stakeholders could invest to overcome them, aiming to support the scaled adoption of predictive and prescriptive maintenance in aviation.

Christopher Teubert↗
NASA NTRS2023

Distributed Ground Sensor Fusion Based Object Tracking for Autonomous Advanced Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage. Dropouts of individual sensors affect the accuracy of the tracking results, which agrees with expectations for partial coverage, when full localization is not achievable anymore. Finally, a study is performed to identify which parameters have the largest impact on the fit error.

Autonomous↗
NASA NTRS2020

Experimental Validation of Model-Based Prognostics for Pneumatic Valves

Because valves control many critical operations, they are prime candidates for deployment of prognostic algorithms. But, similar to the situation with most other components, examples of failures experienced in the field are hard to come by. This lack of data impacts the ability to test and validate prognostic algorithms. A solution sometimes employed to overcome this shortcoming is to perform run-to-failure experiments in a lab. However, the mean time to failure of valves is typically very high (possibly lasting decades), preventing evaluation within a reasonable time frame. Therefore, a mechanism to observe development of fault signatures considerably faster is sought. Described here is a testbed that addresses these issues by allowing the physical injection of leakage faults (which are the most common fault mode) into pneumatic valves. What makes this testbed stand out is the ability to modulate the magnitude of the fault almost arbitrarily fast. With that, the performance of end-of-life estimation algorithms can be tested. Further, the testbed is mobile and can be connected to valves in the field. This mobility helps to bring the overall process of prognostic algorithm development for this valve a step closer to validation. The paper illustrates the development of a model-based prognostic approach that uses data from the testbed for partial validation.

Chetan S Kulkarni↗
NASA NTRS2020

Vibration-based health characterization of multiple IMUs in UAV applications

Inertial measurement units (IMUs) are vital in UAV navigation for vehicle attitude and position estimation, especially in the frequently-occurring case of temporary loss of GPS capabilities. Degradation in these units could yield inaccurate position estimates, resulting in incorrect input for vehicle control systems. A wide variety of these units exist to meet the needs of UAV operators, general aviation, and military vehicles. In recent decades, lightweight and inexpensive MEMS sensors have grown in popularity with respect to UAVs. Studies into the failure modes of MEMS devices suggest that vibration is one of the leading causes of MEMS degradation. MEMS sensors are generally encased in their own hermetic packaging, as their small size and sensitivity cause them to be particularly vulnerable to air damping and particulate contamination when exposed. Exposure to excessive vibration can cause cracking in this packaging as well as damage to connecting electronics, with recorded results that cause sensors to act outside of their specifications, potentially providing unreliable input to controllers. A prior study from the Politecnico di Torino looked into the degradation of MEMS IMUs, specifically the AXIS-AIS402, for aerospace and vibrating environments. In the study, the authors found that after exposing MEMS IMUs to simulated vibrations from working conditions of aerospace applications, the sensors showed increased noise and increased bias instability. At NASA Ames, several widely-used MEMS IMUs were selected for a new IMU degradation study, comparing accelerometer performance before and after exposure to similar levels of vibration simulating aerospace conditions. The IMUs were selected from the Pixhawk autopilot systems, hobby sUAS, or prior experimental studies at NASA. Additionally, the industrial grade VectorNav-100 IMU was selected as a ground truth reference point to which the performance of the test sensors can be compared. In the study, each sensor is exposed to functional levels of UAV vibration as replicated on a single-frequency vibratory table in the NASA Ames SHARP Laboratory, and each accelerometer is characterized for bias, drift, and noise characteristics both before and after vibratory exposure. The focus of this study is to monitor the health of the IMU sensors and determine if sensor degradation can be detected from output error and frequency spectrum analysis, rather than from microscopic examination of the sensors. In this case, sensor degradation and faulty data can be more easily detectable by non-experts hoping to use these sensors as an important piece of an integrated package. In addition, specific sensor degradation modes potentially leading to in-flight hazards could be identified, such as accelerated levels of drift and altered temperature response. The overall goal of this work is to increase levels of safety for future users of these sensors and to provide a clear analysis of sensor limitations currently lacking in most sensor specification documentation.

IMUs↗
NASA NTRS2017

A Virtual Laboratory for Aviation and Airspace Prognostics Research

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

LVC-DE↗
NASA NTRSDate not supplied

3D Representation of UAV-obstacle Collision Risk Under Off-nominal Conditions

Safe operations of autonomous unmanned aerial vehicles (UAVs) in low-altitude airspace with beyond visual line-of-sight (BVLOS) flights demand robust risk monitoring of airspace as well as of people and property on ground. One of the safety critical factors for UAV flights is the risk of collision with static and dynamic obstacles in proximity to its flight path. This paper presents a detailed formulation of risk of obstacle collision incorporating the effects of off-nominal conditions introduced by component failures, degraded controllability and environmental disturbances such as wind gusts. The risk is represented in terms of a matrix with rows corresponding to the likelihood of occurrence of collision and columns representing severity of collision to the vehicle and surrounding structures. Risk likelihood is generated using a Bayesian Belief Network (BBN) that compiles knowledge from related Failure Modes and Effects Analysis (FMEAs) and Subject Matter Experts (SMEs) to determine the probability of collision based on on-board sensor measurements indicative of vehicle health and controllability. Risk severity is computed utilizing a point-mass 3D kinematic model of the vehicle in presence of wind. The proposed risk factor is demonstrated on real flight data from experimental flights of an octocopter at NASA Langley Research Center in presence of simulated obstacles and wind conditions. Effect of varying wind conditions, level of controllability and obstacle measurement noise on the risk factor is demonstrated. The proposed approach enables risk-informed decision making for timely mitigation of current and future unsafe events in autonomous systems.

risk analysis↗
NASA NTRSDate not supplied

Examining the Role of IMU Health Characterization and Monitoring in UAS Safety

Inertial measurement units (IMUs) can be vital for vehicle attitude and position estimation in unmanned aerial vehicles (UAVs). Degradation in these units could result in incorrect position estimates that are utilized in vehicle control, trajectory prediction, and other critical systems; however, the modes and effects of degradation in these IMUs are not well understood. In order to quantify the risk posed by degradation in these sensors, a study was conducted on the types of IMUs prevalent in the commercial market, their known failure modes, and the applicability to health monitoring methods for risk reduction and increased safety in an increasingly autonomous airspace. First, use cases and failure modes of the various types and reliability of commercially available IMUs were reviewed, and knowledge gaps and issues in the field were identified. Noting that inexpensive and lightweight MEMS (Microelectromechanical Systems) IMUs are some of the most commonly used but least reliable sensors in sUAS (small Unmanned Aerial Systems), five inexpensive MEMS IMUs were chosen for a performance evaluation study, selected from the Pixhawk autopilot systems, hobby sUAS, and prior NASA experimental studies. For each of these IMUs, a 10-minute bias test and a 12-hour drift test were performed for the accelerometers in a benchtop setting, using a BeagleBone Black for data collection. Using these results, the sensors’ performance is compared to their reported specifications, and the utility of implementing diagnostic methods for MEMS IMUs for research and commercial applications is evaluated. The paper concludes with a planned study for the evaluation of vibration-induced degradation for the selected sensors.

IMUs↗
NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from Smart Spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs and mathematical formalisms to demonstrate operation. The proposed framework will be evaluated on a regional AAM flight scenario and will focus on two initial applications: (1) GPS-free navigation supporting precision approach and landing (PAL), and (2) surveillance and conformance monitoring of aircraft in vertiport airspaces. Such approaches show promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Structurally-Adaptive↗
NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.

distributed sensing↗
NASA NTRSDate not supplied

A Simulation Architecture for Air Traffic Over Urban Environments Supporting Autonomy Research in Advanced Air Mobility

As part of its research, NASA investigates concepts, aircraft, and operations related to Advanced Air Mobility (AAM). One of the most challenging scenarios for AAM will be enabling safe routine access near densely populated urban centers. AAM flight operations over a regional area require a moderately high-fidelity simulation capability to develop and evaluate autonomy technologies in the urban environment. This paper aims to describe a system to simulate flight operations around regions such as the San Francisco-Oakland Bay area at a moderately-high scale (tens to hundreds of flights) that incorporates detailed vehicle models and control necessary to support research in airborne autonomy. The flight vehicle utilizes NASA AAM concept vehicle dynamics integrated with a custom flight management system and flight control system to simulate all flight phases accurately. The simulation incorporates a detailed simulated urban environment and includes glass cockpit displays to monitor aircraft operations. Simulation models integrate to simulate air and ground-based sensors, such as Radar and LiDAR. As a commercially available rendering engine, X-Plane 11 is used as the renderer to simulate vision-based sensors (such as onboard and ground-based cameras) with a detailed graphical model of a city at different times of the day and weather conditions. This paper presents the simulation and software architecture used for simulating AAM traffic over this urban region. This system enables the evaluation of NASA research concepts in autonomy for urban AAM operations on the path toward flight test evaluation.

Distributed sensing↗
NASA NTRSDate not supplied

Concepts for Distributed Sensing and Collaborative Airspace Autonomy in Advanced Urban Air Mobility

Emerging concepts for advanced urban air mobility envision responsive air transportation capabilities that will safely move people and cargo in locations presently underserved by aviation. Expanding aviation services to these locales, particularly for high-density autonomous flight operations over urban centers, will require advances beyond the state-of-the-art techniques for airborne sensing. The emerging field of distributed sensing and ‘smart spaces’ – where sensing, processing, communication, and actuation are embedded in the environment in which agents are acting – may provide attractive alternatives over traditional aviation solutions. This paper outlines the challenges and opportunities for distributed sensing and smart space concepts to meet the emerging needs of advanced urban operations in the national airspace. We present an overview of distributed sensing concepts and research currently being investigated under this endeavor.

Distributed sensing↗
NASA NTRSDate not supplied

A Structurally-Adaptive Framework for Distributed Airborne Sensing over Real-time Collaborative Information Sharing Networks

The emergence and maturation of wireless communication technologies continue to transform the aviation industry and are enabling new solutions to challenges faced by NASA’s Advanced Air Mobility (AAM) initiative. AAM is leading towards high-density autonomous aircraft operations in areas underserved by traditional aviation, such as over densely populated urban centers. In this paper, we build on concepts from distributed sensing and smart spaces - where sensing, processing, and communication are embedded in an environment, and agents are operating within the space can exploit these capabilities in real-time through collaborative information sharing networks. Building from these concepts, we propose a framework to enable a dynamic, topologically-adaptive, and distributed estimation system for man-rated aviation to address challenges faced by autonomous AAM operations. This paper presents the initial concept of operations and system design for this framework, presents a mathematical formulation for abstraction of the problem, identifies requirements and constraints for operation, and presents algorithmic constructs to demonstrate operation. The initial framework design will focus on supporting precision navigation and independent surveillance supporting conformance monitoring of aircraft in airspace corridors and vertiport airspaces. Preliminary results from this framework shows promise in addressing gaps in current technologies needed to enable future AAM concepts, while promising greater capabilities, performance, robustness, and safety over current aviation systems and operations.

Distributed sensing↗
NASA NTRSDate not supplied

T^3 Autonomous Systems Student Overview

The Autonomous Systems (AS) Discipline is NASA's investment in the continued development of autonomy technologies in aerospace each year. Through sustained long-term development, researchers and scientists within AS identify and work towards knowledge and technology gaps limiting the application of autonomy technology in the future of aviation. This presentation gives a short overview of the advanced air mobility problem area and how AS work is currently contributing to NASA's effort to make advanced air mobility a safe, efficient, and equitable reality.

Autonomy↗
NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗