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All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

NASA HAT Lab Activities

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N). This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT). This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust).

multi-vehicle control↗

NASA NTRSDate not supplied

m:N Working Group

multi-vehicle control↗

NASA NTRSDate not supplied

Future Operations

multi-vehicle control↗

NASA NTRSDate not supplied

m:N Operations and Future

The principles and reasons for employing Human Autonomy Teaming is discussed. Application of these techniques to multi-vehicle control is described. In several operational environments from small drone delivery to air taxi to autonomous cargo, many companies will need technologies that will allow for the operation of an unmanned aircraft (UAS or eVTOL) by a small crew of individuals that are located in a remote network operations center. That is, Multiple operators supervising an increasing Number of vehicles (M:N).This will require a new control/supervisory paradigm where the supervisors team with automation to achieve their joint tasks; Human Autonomy Teaming (HAT).This task will follow the HAT philosophy and tenants (e.g., trust, bi-directional communication, pilot directed interfaces). It will also develop and employ specific HAT tools (e.g., playbook, working agreements, predictive timeline displays, transparent interfaces to build trust). This presentation discusses the barriers and requirements for future m:N operations.

multi-vehicle control↗

NASA NTRSDate not supplied

Identifying Common Coordination Procedures across Extensible Traffic Management (xTM) to Integrate xTM Operations into the National Airspace System

New categories of missions and vehicle types, such as drone delivery services, on-demand air taxi, and high-altitude long-endurance (HALE) vehicles are being proposed to operate using a novel, highly automated information exchange infrastructure and a community-based, cooperative traffic management concept. Collectively, these new operations are called Extensible Traffic Management (xTM). As these xTM vehicles become more prevalent, their operations will increasingly overlap with existing conventional aircraft and with each other. In order to seamlessly co-exist with current conventional aircraft operations, new coordination procedures, tools and services will be needed to integrate xTM into the future National Airspace System (NAS). In our prior work, we have identified a set of use cases for xTM interactions with air traffic control (ATC), categorized across different xTM operations based on trigger events. Events consisted of ones such as nominal xTM vehicle transition into the ATC environment or an off-nominal emergency landing situation. In this paper, we have extended the prior work to identify commonalities in the coordination procedures across xTM, as well as differences that are specific to the individual xTM operations. The overall results showed that two types of xTM-ATC interactions were prevalent: 1) xTM vehicles transitioning between xTM and ATC operational environments; 2) xTM vehicles being allowed to continue xTM operations in areas that are normally controlled by ATC. The results also suggested that emergency and rare off-nominal events may need specialized procedures for each vehicle type. The overall results suggest that there is a pathway to define a common method of handling and integrating diverse xTM operations in the future NAS, but there need to be procedures for individualized handling of xTM vehicles in infrequent, safety-critical events.

Extensible Traffic Management (xTM)↗

DOE OSTI2025

Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics

Aerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗

DOE OSTI2024

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Here, results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

Drone energy consumption↗

NASA NTRS2019

Predicting Quadcopter Drone Noise Using the Lattice Boltzmann Method

The market for new vertical takeoff and landing vehicles, including autonomous urban air taxis and drones for applications such as package delivery, imaging, and surveillance, is growing rapidly. However, aerodynamic noise continues to be the biggest roadblock to community acceptance and adoption. To predict the aerodynamic noise generated by an isolated quadcopter drone, derived from from first principles, we used the Lattice Boltzmann flow solver within NASA’s Launch Ascent and Vehicle Aerodynamics (LAVA) solver framework. The solver’s computational efficiency, and the complete absence of labor-intensive manual volume mesh generation in the workflow, are key to making routine aeroacoustic analysis of urban air taxis and drones from first principles possible.

Cadieux, Francois↗

NASA NTRSDate not supplied

Use of Autonomous Vehicles in Emergency Situations - Wildfire Planning and Mitigation

Unmanned vehicles can be useful in emergency situations for many applications such as surveillance, access to harsh environments, and delivery of supplies. However, efforts to use drones in these situations has not been well-coordinated. This project is looking at multiple aspects of the problem with sub-teams addressing: establishing a method and database network to identify and communicate resources for deployment during major incidents and disasters; identifying how drones and other aviation assets can be useful in wildfire-specific emergencies, including mitigation and prevention planning; and considering safety, rules, and behavior of autonomous vehicles.

Autonomous Vehicles↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. There exists a gamut of approaches to solving merging and intersection crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to solving these problems that is based on distributed cooperation between the UAVs/UASs and the infrastructure. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing UAVs/UASs to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them.

Distributed↗

NASA NTRSDate not supplied

Achieving Equilibrium for Dense, Integrated, Vehicle Navigation

Drone usage has proliferated in recent years with many applications that have market-changing potential. Applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. In this paper, the term drone is used to mean both Unmanned Aerial Vehicle and small Unmanned Aircraft System vehicles. Flight infrastructure can currently only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay atone airport sends ripple effects through the system, causing more delays and missed connections. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the NAS is expected to skyrocket to millions, potentially congesting the airspace resulting in possible separation violations. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is a safety critical property for drones in the airspace. This paper addresses separation in time and in distance for high volume corridors (en-route) and lanes(on ground). The requirements and necessary conditions for maintaining proper separation and reaching maximum throughput for a given corridor/lane are addressed assuming unidirectional corridors/lanes where an aircraft arrives from one side and departs from the opposite side. Simulation results are presented that show the presented solution guarantees a set of drones to reach the equilibrium state by adjusting their speed based on their distance to the aircraft in front of them. The equilibrium state is defined as a state when a set of n aircraft moving at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as a state when the aircraft cannot move at their maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach using a large number of drones and evaluate the scalability of the proposed solution.

Mahyar R. Malekpour↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRSDate not supplied

A Strategic Approach for Dense, Integrated, Vehicle Navigation

Drone usage has been on the rise in recent years with applications that include parcel delivery, wildlife protection, precision farming, law enforcement, and industrial inspection, just to name a few. Once regulations and safety policies are put in place to allow for the widespread use of unmanned drones, the number of aircraft in the National Airspace System (NAS) is expected to skyrocket to millions, potentially congesting the airspace which increases the likelihood of separation violations and possibly incidents. Currently, flight infrastructure can only support a few thousand aircraft flying over the United States National Airspace System (NAS) at any given time. A delay at one airport can send ripple effects throughout the system, causing more delays and missed connections. In air traffic control, separation is the concept of keeping an “ownship” aircraft outside a minimum distance from “intruder” aircraft to reduce the risk of the aircraft colliding, as well as preventing accidents due to secondary factors, such as wake turbulence. Maintaining proper separation is often a safety critical property for fixed-wing drones in the airspace. This paper addresses drone separation in time and distance for high volume corridors (en route) and lanes (on ground), merging as well as crossing intersections of multiple corridors/lanes. In this paper, the term drone is applied to both Unmanned Aerial Vehicle (UAV) and small Unmanned Aircraft System (UAS) vehicles operating autonomously. There exists a gamut of approaches to the merging and crossing problems. At one end of the extreme are the conservative yet low cost and verifiable solutions of today that deal with two drones at a time. At the other end are complex Machine Learning-based solutions with high computing requirements for fully autonomous drones of the future that are expected to handle all contentions. This paper presents a feasible and verifiable strategic approach to these problems that is based on distributed cooperation between the drones and the infrastructure. Three phases of the strategic approach (Prepare, Adjust, Commit) are presented. Simulation results are presented that show the proposed approach is stable and resilient to induced perturbations and guarantees a set of fixed-wing drones to merge and cross intersections by adjusting their speed based on their distance to the aircraft in front of them while remaining in the equilibrium state. The equilibrium state is defined as the state when a set of n aircraft move at a relatively constant speed and uniform spacing from each other in a congested system. A congested system is defined as the state when at least one aircraft cannot move at its maximum allowed speed. Unlike existing centralized and pre-planned approaches, the proposed solution is fully distributed and enables autonomous aircraft to decide to adjust their speed and distance with respect to the preceding aircraft, dynamically. Simulation results are presented that assess the feasibility of the approach.

Distributed↗

NASA NTRS2024

Uncrewed Aerial Systems for Emergency Medical First Response: A Market Research Report

This report presents the findings from market research conducted for NASA’s Aerial Aid Convergent Aeronautics Solutions (CAS) exploration project, which aims to assess the current state of the market and technological readiness for Uncrewed Aerial Systems (UAS) for medical emergency first response. The research reveals a robust and rapidly growing market for UAS, with a notable emerging sector for Drones as First Responders (DFR). Despite this growth, DFR applications are currently limited by regulatory, technical, and other challenges, which restrict their use primarily to manned remote video surveillance, and therefore are primarily employed by police units. To our knowledge, there is no evidence of UAS being utilized by medical first responders for scene assessment. Limited evidence exists for closely related applications; however, these are mostly confined to pilot programs for the delivery of medical supplies or equipment. Although there has been discussion around fully autonomous DFR applications for medical purposes such as UAS ambulances or patient transport drones, these applications are generally not yet operational in practice. The technology for full autonomy, especially in guidance and control, has seen significant advancements, and recent Federal Aviation Administration (FAA)regulations are likely to accelerate adoption. Computer vision algorithms for fully autonomous medical emergency response scene surveillance are primed for advancement and deployment. A notable gap likely exists between advancements in computer vision research and what is being integrated in the commercial DFR sector. This gap is primarily due to challenges such as quality assurance for autonomous systems, the availability of application-specific training datasets for computer vision algorithms, regulatory constraints, and public perception and privacy concerns.

Joshua M Fody↗

DOE OSTI2024

Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles

The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.

47 OTHER INSTRUMENTATION↗

NASA NTRS2015

Operator Informational Needs for Multiple Autonomous Small Vehicles

With the anticipated explosion of small unmanned aerial vehicles, it is highly likely that operators will be controlling fleets of autonomous vehicles. To fulfill the promise of autonomy, vehicle operators will not be concerned with manual control of the vehicle; instead, they will deal with the overall mission. Furthermore, the one operator to many vehicles is becoming a constant meme with various industries including package delivery, search and rescue, and utility companies. In order for an operator to concurrently control several vehicles, his station must look and behave very differently than the current ground control station instantiations. Furthermore, the vehicle will have to be much more autonomous, especially during non-normal operations, in order to accommodate the knowledge deficit or the information overload of the operator in charge of several vehicles. The expected usage increase of small drones requires presenting the operational information generated by a fleet of heterogeneous autonomous agents to an operator. NASA Langley Research Center's Autonomy Incubator has brought together researchers in various disciplines including controls, trajectory planning, systems engineering, and human factors to develop an integrated system to study autonomy issues. The initial human factors effort is focusing on mission displays that would give an operator the overall status of all autonomous agents involved in the current mission. This paper will discuss the specifics of the mission displays for operators controlling several vehicles.

Trujillo, Anna C.↗

NASA NTRSDate not supplied

Predicting Quadcopter Noise With The Lattice-Boltzmann Method

From small delivery Unmanned Aircraft Systems (UAS) to short-haul building-to-building aircraft, urban air mobility is gaining traction as a new approach to tackle the problem of transportation in densely populated areas. NASA’s vision for vertical lift vehicles is to capitalize on and improve unique capabilities to greatly benefit the United States’ growing civil flight requirements. This vision is embodied in the Revolutionary Vertical Lift Technology (RVLT)project [1]. Beyond safety, one of the chief concerns of communities where drones are becoming more popular is the noise they generate. Noise will undoubtedly be one of the major obstacles to public acceptance of any new urban air mobility technology. The ability to predict the acoustics of new conceptual aircraft with multiple rotors and complex fuselages is critical to enable the creation of quieter designs. The objective of this research is to build up a better physical understanding of the noise generated by a typical quadcopter drone and what it takes to predict it from first principles using computational fluid dynamics (CFD) with the Lattice-Boltzmann method (LBM). The specific goals are to establish best practices to predict multi-rotor and vehicle interaction noise with LBM, validate these predictions by comparing to wind tunnel measurements, and assess the computational cost necessary to obtain accurate predictions.

Francois Cadieux↗