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Results for “Autonomous Air Mobility”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

sensor fusion↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Fusion Based Object Tracking for Autonomous Urban Air Mobility Operations

Autonomous operations are a crucial aspect in the context of Urban Air Mobility and other emerging aviation markets. In order to enable this autonomy, systems must be able to build independently an accurate and detailed understanding of the own vehicle state as well as the surrounding environment, this includes detecting and avoiding moving objects in the sky, which can be cooperative (aircraft, UAM vehicles, etc.) as well as noncooperative (smaller drones, birds, ...). This paper focuses on the object tracking part that relies on adaptive multi-sensor fusion, taking into account specific properties and limitations of different sensor types. Results show the impact of dropouts of individual sensors on the accuracy of the tracking results for this adaptive sensor fusion approach.

object tracking↗
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 NTRS2018

Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service

This presents an initial implementation of an autonomous Urban Air Mobility network management and aircraft separation service for urban airspace that does 1) departure and arrival scheduling across the network, 2) continuous trajectory management to ensure safe separation between aircraft, and 3) seamless integration with traditional operations. The highly-autonomous AutoResolver algorithm developed for traditional aviation was extended to provide these capabilities. An evaluation of this initial implementation was conducted in fast-time simulations using a dense, two-hour traffic scenario with Urban Air Mobility aircraft flying between a network of 20 vertiports in the Dallas-Fort Worth metroplex. When the spatial separation was reduced from 0:3 nmi (nautical miles) to 0:1nmi, the total delay decreased by 7:3 percent...

Bosson, Christabelle S.↗
NASA NTRS2018

Simulation Evaluations of an Autonomous Urban Air Mobility Network Management and Separation Service

This paper presents an initial implementation of an autonomous Urban Air Mobility network management and aircraft separation service for urban airspace that does 1) departure and arrival scheduling across the network, 2) continuous trajectory management to ensure safe separation between aircraft, and 3) seamless integration with traditional operations. The highly-autonomous AutoResolver algorithm developed for traditional aviation was extended to provide these capabilities. An evaluation of this initial implementation was conducted in fast-time simulations using a dense, two-hour traffic scenario with Urban Air Mobility aircraft flying between a network of 20 vertiports in the Dallas-Fort Worth metroplex. When the spatial separation was reduced from 0:3nmi to 0:1nmi, the total de- lay decreased by 7:3%; when the temporal separation was reduced from 60s to 45s, the total delay decreased by 28:4%. The total number of conflict resolutions decreased by 26% and 17%, respectively. Furthermore, when a scheduling horizon greater than the duration of UAM flights was used (50min), most conflicts were resolved pre-departure producing ground delay. By comparison, when a shorter scheduling horizon was used (8min), most conflicts were resolved post-departure generating airborne delay. For all scheduling and separation constraints tested, AutoResolver prevented loss of separation from occurring. Urban Air Mobility operations have the ability to revolutionize how people and goods are transported and this paper presents initial research focusing on the high levels of autonomy required for an airspace system capable of scaling to handle significantly higher densities of aircraft.

Bosson, Christabelle S.↗
NASA NTRSDate not supplied

Adaptive Multi-Sensor Localization Information Fusion for Autonomous Urban Air Mobility Operations

An adaptive method is developed to iteratively fuse the information provided by multiple sensors to enable autonomous urban air mobility type operations. First, noisy and bias corrupted IMU readings are processed as soon as they arrive using kinematic equations represented in the vehicle's body frame. To correct the systems drift resulting from the integration, an information content measure is introduced to decide on the environment. For the cluttered environment the information provided by environmental sensors is counted as reliable and the drift correction as accurate. For the open space, the GPS data is counted as reliable, and the drift correction is done based on the GPS readings. The measurement noise effects are minimize using Iterated Extended Kalman Filter framework. The algorithm is implemented in the in-house developed FlightDeckz simulation environment using an IMU model, simulated video recorded from a camera mounted on the vehicle (for the purpose of this study, outside scenery was generated with XPlane), which flies in an urban environment, and GPS data generated from the environment's digital map.

Onboard Perception↗
NASA NTRSDate not supplied

Applications of Advanced Perception and Distributed Sensing Technology towards Autonomous Advanced Air Mobility Applications

Emerging concepts for Advanced Air Mobility (AAM) envisions responsive air transportation capabilities that can safely move people and cargo between places - including local, regional, intraregional, and urban - previously not served or underserved by aviation. Expanding traditional aviation services to these environments, particularly when autonomous operations are involved, face a number of challenges that will require advances beyond the state-of-the-art techniques for airborne sensing and perception, which goes beyond the limits of scalability and applicability of the current air transportation system infrastructure. Additionally, 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 and can be exploited by the agents through real-time wireless communication – may provide realistic near-time solutions to limitations imposed by traditional aviation techniques. This paper outlines the needs, challenges, and opportunities for advanced perception and distributed sensing techniques to meet the emerging needs for advanced AAM operations in the national airspace. A general roadmap for research, development, and maturation of perception and distributed sensing (P&DS) technologies is proposed to guide future development through verification, validation, certification into airborne systems. Through this analysis of challenges and the proposed roadmap for technology maturation, we hope to accelerate transition of advanced research techniques from other disciplines into this domain.

Autonomy↗
NASA NTRS2020

Designing and Training for Appropriate Trust in Increasingly Autonomous Advanced Air Mobility Operations: A Mental Model Approach: Version 1

To enable effective human-autonomy teaming (HAT) in Advanced Air Mobility (AAM) operations, the current paper presents a theoretical framework to design and train for appropriate trust in automation. The novel contribution of this work resides in connecting the construct of trust to mental models and showing how this method could be used to enable emerging HAT concepts such as Adaptive Trust Calibration. To contextualize this framework, in section 2 we discuss simplified vehicle operations (SVO) and remote vehicle operations (RVO), which are leading operational concepts within AAM. In section 3 we describe our perspective on automation and increasingly autonomous systems and present a brief discussion on human-automation interaction and human-autonomy teaming. In section 4 we provide a detailed discussion on the construct of trust in automation. In section 5 we present a framework that associates mental models with trust through principles of transparent design. Finally, in section 6 we present three descriptive models for designing and training for appropriate trust in increasingly autonomous systems.

Human-Autonomy Teaming↗
NASA NTRSDate not supplied

Benchmark Problem for Autonomous Urban Air Mobility

This paper introduces a Community Benchmark Problem (CBP) for Intelligent Contingency Management (ICM) for Urban Air Mobility (UAM) aircraft. The CBP aims to provide a common framework for measuring and comparing the progress of autonomy solutions for UAM aircraft in handling emergency situations. The paper proposes a methodology for defining and quantifying five measures of complexity that capture the challenges and requirements of ICM for UAM: Mission, Environmental, Autonomy, Decision-Making, and Mission Fault. In addition, it proposes a methodology for defining and quantifying mission risk acceptability with the same goals: Contingency Management, Mission Success, Operational, Mission Redefinition, and Environmental. We describe how to use these measures to track progress of the development of ICM capability, as well as to create scenarios and evaluate the performance of different autonomy solutions.

autonomy↗
NASA NTRSDate not supplied

Dynamic Vehicle Assessment for Intelligent Contingency Management for Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope prediction↗
NASA NTRSDate not supplied

Dynamic Vehicle Assessment for Intelligent Contingency Management of Urban Air Mobility Vehicles

New algorithms will be required to ensure passenger and bystander safety during the expected era of autonomous urban air mobility (UAM) aircraft. This paper examines an approach for assessing the vehicle capability to fly itself and to complete a mission safely. The concepts combine elements of system identification, adaptive control, flight dynamics, envelope predictions, and handling qualities, as well as human pilot intuition. The approach is applied to a simulation of a generic distributed electric propulsion urban air mobility-type aircraft, which was developed under the NASA Transformational Tools and Technologies (TTT) project, Autonomous Systems / Intelligent Contingency Management subproject.

flight envelope↗
NASA NTRS2021

Parametric Study of State-of-Charge for an Electric Aircraft in Urban Air Mobility

The envisioned concept of urban air mobility is anticipated to support passenger transportation, cargo delivery, and emergency services in major metropolitan areas with increasing autonomy levels in the future. Distributed electric propulsion powered electric vertical takeoff and landing aircraft are expected to enable urban air mobility. However, the low specific energy of onboard lithium-ion polymer batteries and wind conditions impose constraints on flight endurance. Therefore, to enable autonomous urban air mobility operations using electric aircraft, one of the critical steps from a safety and efficiency perspective is to understand how various operational and environmental conditions impact the state-of-charge of the onboard lithium-ion polymer batteries. This research performs a parametric study of the state-of-charge for a NASA-proposed conceptual multirotor aircraft flight in the urban environment. The parameters considered for the parametric analysis are cruise airspeed, cruise altitude, climb and descent profiles, wind conditions (wind magnitude, wind direction relative to the route, and wind magnitude uncertainty), and required time of arrival.

Urban Air Mobility↗
NASA NTRS2021

Parametric Study of State of Charge for an Electric Aircraft in Urban Air Mobility

The envisioned concept of urban air mobility is anticipated to support passenger transportation, cargo delivery, and emergency services in major metropolitan areas with increasing autonomy levels in the future. Distributed electric propulsion powered electric vertical takeoff and landing aircraft are expected to enable urban air mobility. However, the low specific energy of onboard lithium-ion polymer batteries and wind conditions impose constraints on flight endurance. Therefore, to enable autonomous urban air mobility operations using electric aircraft, one of the critical steps from a safety and efficiency perspective is to understand how various operational and environmental conditions impact the state-of-charge of the onboard lithium-ion polymer batteries. This research performs a parametric study of the state-of-charge for a NASA-proposed conceptual multirotor aircraft flight in the urban environment. The parameters considered for the parametric analysis are cruise airspeed, cruise altitude, climb and descent profiles, wind conditions (wind magnitude, wind direction relative to the route, and wind magnitude uncertainty), and required time of arrival.

Urban Air Mobility↗
NASA NTRSDate not supplied

AEGIS: Autonomous Entity Global Intelligence System for Urban Air Mobility

This paper presents a global intelligence system that synthesizes aerial vehicles’ real-time physical data, planned actions, and historical behavior into engineered data frames representing the collective state of the airspace and suitable for efficient machine learning consumption. These data frames are then learnt by a deep neural net to build a prediction model that estimates the expected evolution path of the current state, thereby identifying potential future conflicts. This approach lends itself to an automated early warning system that the aerial vehicles can implement onboard with a suitable edge computing module more efficiently and effectively than non-AI methods, and eventually take preventive or corrective measures towards self/collaborative resolution of the issues. Contrary to a centralized early warning system where all vehicles’ task-space eventually converges to a global optimum state, the presented distributed global intelligence system brings in a balance between local utility functions of each vehicle and the global operating framework. This contributes to effectively handle the potential massive scaling in urban air mobility in the near future.

Artificial Intelligence↗