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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
DOE OSTI2021

Optimization of battery swapping infrastructure for e-commerce drone delivery

Drone delivery is widely-researched to alter the current e-commerce delivery convention for providing short delivery lead times. Yet, flight range of drones, constrained by the available battery technology, sets a milestone toward realizing the sole-drone delivery. To tackle with the flight range limitation, locating automated battery swapping machines (ABSM) have been proposed and a few studies modeled the problem. Using the ABSMs, drones can be loaded with fully-charged batteries along the route to a demand location. Here, we introduce a mixed-integer nonlinear program to model the problem. The objective of the program is to optimally select ABSM locations, determine the delivery-mode choices (drone-only, truck-only, and mixed delivery) of demand locations, find drone delivery routes, and approximate the baseline requirements for the number of drones and batteries needed. The program minimizes the overall delivery system costs including: ABSM, delivery, drone ownership, battery inventory, and service congestion. A cutting-plane method is developed to find exact solutions in finite iterations. Computational experiments showed that the method quickly yields the optimal solution to instances with less than 60 ABSM candidates and 20 demand locations. A case study shows that the optimal drone delivery infrastructure can save almost 20% cost compared to the conventional truck-only delivery. Sensitivity analyses were conducted to reveal the impact of key parameters in the decision-making and found that a decrease in the ABSM and drone costs highly affect the system cost.

25 ENERGY STORAGE↗
DOE OSTI2021

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.

drone battery swapping↗
DOE OSTI2023

Drones, Delivery Robots, Driverless Cars, and Intelligent Curbs for Increasing Energy Productivity of First/Last Mile Goods Movement

The objective of the project is to use empirical testing, life cycle assessment, and systems analysis to research and demonstrate energy improvement when using goods delivery drones, eVTOL, ground delivery robots and automated vehicles compared to a baseline network. The research will also develop proof-of-concept testing, a model, and simulation for a smart curb space as an intelligently-managed urban delivery zone demonstrating additional improvement in energy productivity.

Scherer, Sebastian↗
DOE OSTI2023

Comparing Regional Energy Consumption for Direct Drone and Truck Deliveries

Drone delivery, once thought of as fictitious, is becoming a reality with the efforts of both forward-looking enterprises and supportive government policies. This emerging mode of e-commerce delivery raises many concerns. One important concern is the energy efficiency of direct delivery drones compared with conventional delivery trucks at a regional systems level. Here, in this study, we develop and apply methods to quantify the regional energy impacts of drone delivery, then we assess these impacts and compare them with the impacts of truck delivery. To study this problem, we develop an optimization model that determines an optimal set of fulfillment centers (FCs) with variable service capacities that allow drones to make direct e-commerce deliveries. We adopt two drone delivery energy estimation models from the literature and use them as inputs to demonstrate the potential range of energy needs. We also develop another optimization model to account for the energy consumption of diesel trucks (DTs) and battery electric vehicles (BEVs). We test the models using validated simulation data for the Chicago metropolitan area in the U.S. to quantify the energy implications of these three delivery modes. For drone delivery, we further extend our analyses by considering the impact of wind speed and flight patterns. Our results show that direct delivery drones require 15.8% more energy than BEVs on an average windy day, and they need 15% more energy than DTs on a very windy day. We provide essential parameter values for reproducibility and list relevant open problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Designing a drone delivery network with automated battery swapping machines

Drones are projected to alter last-mile delivery, but their short travel range is a concern. In this study, we propose a drone delivery network design using automated battery swapping machines (ABSMs) to extend ranges. The design minimizes the long-term delivery costs, including ABSM investment, drone ownership, and cost of the delivery time, and locates ABSMs to serve a set of customers. We build a mixed-integer nonlinear program that captures the nonlinear waiting time of drones at ABSMs. To solve the problem, we create an exact solution algorithm that finds the globally optimal solution using a derivative-supported cutting-plane method. To validate the applicability of our program, we conduct a case study on the Chicago Metropolitan area using cost data from leading ABSM manufacturer and geographical data from the planning and operations language for agent-based regional integrated simulation (more commonly known as POLARIS). A sensitivity analysis identifies that ABSM service times and costs are the key parameters impacting the long-term adoption of drone delivery.

25 ENERGY STORAGE↗
NASA NTRS2023

Implementation of A Drone Delivery System for Healthcare in Mexico: International Precedents and Domestic Considerations

One of the Mexican healthcare system’s principal problems is providing access to its rural communities. Drone delivery has repeatedly proven to be a compelling and cost-effective solution to this problem elsewhere, yet there is currently no scholarship connecting global developments in drone technology to problems in the Mexican healthcare system. This paper is to outline lessons learned from drone systems in several countries, providing information on the potential target communities, technical drone and nest specs, goods of delivery, and regulation of a drone system for healthcare in Mexico.

Drone↗
DOE OSTI2025

Optimizing Energy For Delivery Drones - A Comprehensive Tool Set For Drone Energy Calculation And Drone Fleet Optimization

This tool is intended to be deployed for potential customers to compare the energy profiles across various drone types/classes. The primary factors considered were design of the drone, the weight of the drone, the weight of the payload, and how the drone is flown. It has energy comparison metrics like "Drone (A) vs Drone (B) ", "Drone vs Ground Vehicle", "Drone Energy from delivery via landing versus hovering". It also includes the ability to determine the number of drones and batteries needed to optimally deliver goods from a chosen location to a set of destinations.

Mendadhala, Rohit [Idaho National Laboratory (INL)↗
NASA NTRSDate not supplied

The Potential of Medical Drones: An Analysis of Current and Future Use Cases

Modern Application of Medical-Based Drone Delivery Drones have been used advantageously by militaries for nearly a century, but their uses in civilian life are still mostly cutting-edge, if not theoretical. After a decade of bold proclamations, Amazon’s “PrimeAir” drone delivery system is still in the stage of “preparing” for deliveries, while the public awaits for start ups like SkyDrop (formerly Flirtey) to follow through on impressive promises. Despite the well-publicized disappointment so far in commercial drone delivery, medical drone delivery has already proven itself practical and cheap in several countries, and it promises to expand in the coming years. Drones are uniquely suited to make valuable and urgent deliveries to remote areas, quickly transporting medical supplies where road transportation is prohibitively slow or not available at all. Drones have been used notably to deliver AEDs for out-of-hospital cardiac arrest, frequently beating first-responders to the scene; to deliver blood when there is none on hand at hospitals; to deliver vaccines to an island nation with little transportation infrastructure; and to respond flexibly to medical emergencies in a war zone. Economics make the delivery of food and other cheap goods by drone unattractive in the near-term, but the value and time-sensitivity of medical deliveries mean that drones are already saving lives in healthcare. “We believe the value of new technology is most valuable where it is clearly needed...that’s why we wanted to focus on drones delivering medicine and not delivering pizzas, ”said one executive of a drone system manufacturer. The immediate prospects for the expansion of medical drone use are many; however, they do not exist without their own drawbacks and challenges. Most obvious is the limited range of current commercially-available drones, most of which are isolated to a perimeter of roughly 18 miles. Technological know-how presents another barrier to integration of medical drones on a larger scale. Reports from the United Nations frequently cite a“skill deficit”—a prohibitively low number of qualified drone operators in low-and moderate-income countries (LMICs). Another perhaps more discreet speed bump in global drone development and usage are the various regulations on drone usage. Drone technology has developed so quickly that many states, out of an excess of caution, have nearly snuffed out the fledgling industry with regulation. There also exist significant concerns over the security of private citizens, the efficacy of medical deliveries, and the costs of drone operation. It is these last three barriers which this study will seek to overcome. Put simply, the prospect for human development in LMICs from drone-based medical delivery is far too great to disregard. As of 2020, 3.4 billion people live in rural communities, containing fewer than 5,000 people/km^2. Often lacking infrastructure, these communities are largely isolated from their more populated, urban counterparts. In drones lies the potential to reshape the geographic and developmental distinctions that divide the global population. This development must, therefore, begin first and foremost with advancement in regional well-being and life expectancy. Life expectancy makes up a key facet of human development. The United Nations relies on it as a key indicator of a state’s health. Lars Kunze of the Dortmund University Department of Economic sex plains this as a matter of physical capital accumulation. The longer people live, the more they save as opposed to spend. The more they save, the more which eventually gets invested in themselves and the community as a whole. In providing medical products via drone, it is the intention of this study to enable communities with the means and incentives for long-run savings and investment for future economic development. Through a close analysis of Vanuatu, Rwanda, Tanzania, and Ukraine—four states where drones are currently used to deliver medical supplies—this study develops a framework that LMICs in general and Mexico and particular can adopt and to use medical drones in difficult-to-reach communities for the sake of long-run human developmental initiatives.

Ryan Teoh↗
NASA NTRSDate not supplied

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗
NASA NTRS2021

A Cognitive Walkthrough of Multiple Drone Delivery Operations

Advances of early twenty-first century aviation and transportation technologies provide opportunities for enhanced aerial projects, and the overall integration of unmanned aircraft systems (UAS) into the National Airspace System (NAS) has applications across a wide range of operations. Through these, remote operators have learned to manage several UAS at the same time in a variety of operational environments. The present work details a component piece of an ongoing body of research into multi-UAS operations. Beginning in early 2020, NASA has collaborated with Uber Technologies to design and develop concepts of operations, roles and responsibilities, and ground control station (GCS) concepts to enable food delivery operations via multiple, small UAS (sUAS). A cognitive walkthrough was chosen as the method for data collection. This allowed information to be gathered from UAS subject matter experts (SMEs) that could further mature designs for future human-in-the-loop (HITL) simulations; in addition, it allowed information to be collected remotely during the stringent restrictions of the COVID-19 pandemic. Consequently, the described cognitive walkthrough activity utilized remote data collection protocols mediated through the usage of programs designed for presentation and telecommunications. Scenarios were designed, complete with airspace, contingencies, and remedial actions, to be presented to the SMEs. Information was collected using a combination of rating scales and open-ended questions. Results received from the SMEs revealed expected hazards, workloads, and information concerns inherent in the contingency scenarios. SMEs also provided insight into the design of GCS tools and displays as well as the duties and relationships of human operators (i.e., monitors) and automation (i.e., informers and flight managers). Implications of these findings are discussed.

unmanned aircraft systems↗
NASA NTRSDate not supplied

A Remote, Human-in-the-Loop Evaluation of a Multiple-Drone Delivery Operation

Over time, advances in unmanned aircraft systems (UAS) have enabled a shift in the operational paradigm from one operator managing one aircraft to that of multiple operators working together to manage multiple aircraft. This shift has highlighted the need for effective human-autonomy teaming methods to maintain manageable workload levels for operators as well as high standards of system performance and safety. This paper presents a study aimed at evaluating whether automation can help operators manage workload during small UAS (sUAS) package delivery scenarios featuring contingency situations. These contingency situations, resulting from unplanned UAS Volume Reservations (UVRs), required flight path reroutes for multiple aircraft simultaneously. The study manipulated the number of aircraft affected by the UVRs and the level of automation support. The presence of terrain conflicts was also controlled within each scenario. Due to the COVID-19 pandemic, subjects were not able to gain direct access to the Ground Control System (GCS). Therefore, the study was conducted using a subject-surrogate paradigm that required subjects to relay commands through a verbal protocol from remote locations outside of the lab to a researcher surrogate who had direct control of the GCS interfaces at the lab location. Results show that the automated support condition was associated with faster reroute response times, more efficient reroute maneuvers, and significantly lower levels of perceived workload than the manual reroute condition. However, the automation support level did not significantly impact pilots’ ability to avoid the UVR successfully; pilots were overwhelmingly capable of avoiding the UVR in all conditions. The presence of terrain conflicts primarily impacted pilot performance by leading to multiple uploads per vehicle, which was not typically required when pilots only needed to maneuver laterally. Although subjects did not have direct control over the GCS, subjective ratings indicate that the displays under test provided them with sufficient information to manage their aircraft and promptly respond to the unplanned UVRs. Overall, the objective and subjective data strongly suggest that the verbal protocol and subject-surrogate paradigm were effective methods for collecting data remotely amid the COVID-19 pandemic.

multi-UAS↗
NASA NTRSDate not supplied

Traffic Flow Analysis for Package Delivery Drones using a Queueing Model

A key component of the small unmanned aircraft systems traffic management ecosystem is the design of scalable algorithms for strategic deconfliction of drones prior to takeoff. In this work, we focus on efficient flow management of drones on a network of intersecting edges subject to two kinds of spacing constraints: 1) between any two adjacent vehicles on an edge and 2) between any two vehicles on two different edges arriving one after the other at an intersection. The spacing is designed to enable non-intersection of operational volumes corresponding to two different vehicles thereby properly separating the vehicles inside each volume. For simplicity, we assume a constant ground speed for the drones and fixed dimensions for the operational volume blocks. The deconfliction is managed by adjusting the takeoff time of the drones, thereby regulating their arrival time at various crossing waypoints in the network. This framework allows us to study the maximum flow (throughput) of vehicles on a network of edges connecting depots to drop off sites subject to the temporal spacing constraints. The departure scheduling of individual drones results in a combinatorial optimization problem. To alleviate this, we solve a max-flow formulation and use queueing theory to simplify the analysis and provide upper bounds to the underlying optimization problem for individual drone departure scheduling. Our results indicate that throughput drops rapidly after the density of drones in the network passes the max-flow limits.

Alexey A Munishkin↗
NASA NTRS2017

A Multi-Modality Mobility Concept for a Small Package Delivery UAV

This paper will discuss a different approach to the typical notional small package delivery drone concept. Most delivery drone concepts employ a point-to-point aerial delivery CONOPS (Concept of Operations) from a warehouse directly to the front or back yards of a customers residence or a commercial office space. Instead, the proposed approach is somewhat analogous to current postal deliveries: a small aerial vehicle flies from a warehouse to designated neighborhood VTOL (Vertical Take-Off and Landing) landing spots where the aerial vehicle then converts to a "roadable" (ground-mobility) vehicle that then transits on sidewalks and/or bicycle paths till it arrives to the residence/office drop-off points. This concept and associated platform or vehicle will be referred in this paper as MICHAEL (Multimodal Intra-City Hauling and Aerial-Effected Logistics) concept. It is suggested that the MICHAEL concept potentially results in a more community friendly "delivery drone" approach.

UAV↗
DOE OSTI2024

Droning on to Delivery: Examining the Energy Impacts of Using Drones for Moving Goods

The demand for fast, localized delivery has grown significantly in recent years. Whether for cheesy snacks, prepared food, medical solutions, or business deliveries, fast and efficient delivery is increasingly a demand and differentiator. Drone delivery in the freight sector offers to revolutionize last-mile logistics and improve services. This research analyzes the impacts of drone energy for delivery operations, aiming to compare various types of drones (large and small, rotary and VTOL) and various types of business methods. The project executes novel open-air and laboratory-based testing to look at the impacts of weights, operations, temperatures and weather conditions. The study combines this real-world experimental data with fleet optimization mathematical models to assess energy consumption of different scenarios as well as examine the minimum fleet size and additional battery requirements. These fleet optimization models compare energy between types of deployments with existing delivery methods. The model was also extended to look at mixed fleets of aerial drones and ground vehicles to accommodate different restrictions on drones or when weather prohibits their use. The analysis provided insights into factors such as drone design, payload weight, flight distance, weather conditions and operational parameters and impacts of each. It showed how to combine different types of vehicles to reduce energy and improve services. And it showed that drone speed, routing restrictions, and unfavorable weather significantly influence energy consumption. Our open experiment data and optimization models can assist stakeholders and industry in understanding drone package delivery and offers keys to improving deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRSDate not supplied

Aerial Vehicle Routing and Scheduling for UAS Traffic Management: A Hybrid Monte Carlo Tree Search Approach

We present the Multi-Route Weighted Package Delivery Problem (MRWPDP) and a scalable solution methodology as a major step towards enabling an airspace deconfliction service for drone delivery operations. The problem is motivated by Strategic deconfliction under the FAA’s “Unmanned Aircraft Systems Traffic Management” Concept of Operations. MRWPDP falls under a class of vehicle routing and scheduling problems, and as such is NP-Hard. In MRWPDP, a graph network is given which consists of depots, drop-off sites, and multiple routes connecting the two. In addition, routes are weighted by the associated ground risk and total travel distance for package delivery. The goal is to optimally schedule the departure time and assign routes to a known set of vehicles at the depot. We propose a heuristic solution to the problem by borrowing techniques from Mixed Integer Linear Programming (MILP), Constraint Programming, and Monte Carlo Tree Search (MCTS). The resulting hybrid framework is MCTS with Bound-and-Prune (BP) and rapid simulated updates (U), or MCTS-BP-U. This approach is able to quickly provide a feasible solution for MRWPDP, even for large problem instances up to 1000 vehicles. We provide a MILP formulation of MRWPDP and compare its performance against MCTS-BP-U in terms of solution quality. An agent-based model simulation is conducted as a final step to validate the efficacy of our approach.

air traffic scheduling↗