Aerial Logistics in Hard-to-Reach Environments: Systematic Review

of the Use of Class 1 UAVs in Health Supply Distribution in Military

Operations and Other Context

Rodrigo Bomfim

, Pablo Gustavo Cogo Pochmann

and Eduardo Borba

Neves

Officers’ Improvement School (Escola de Aperfeiçoamento de Oficiais – EsAO),

Duque de Caxias Avenue, 2071, Rio de Janeiro-RJ, Brazil

Keywords: Unmanned Aerial Vehicles (UAVs), Health Supply Logistics, Constructive Simulation, COMBATER

Software, Military Doctrine, Hard-to-Reach Environments, Systematic Literature Review, PRISMA

Methodology, Class 1 Drones, Medical Supply Distribution.

Abstract: This study examines the potential integration of Class 1 Unmanned Aerial Vehicles (UAVs) into the

COMBATER simulation software, emphasizing their role in healthcare logistics within challenging

environments such as jungles and remote areas. A systematic literature review was conducted following

PRISMA guidelines, supported by the TREND quality assessment checklist. The analysis identified critical

operational parameters for UAV performance, including flight endurance, range, maximum speed, operational

altitude, and cargo capacity. These parameters were categorized by UAV class—Mini (<15 kg) and Small

(>15 kg)—to align with military doctrine and operational needs. The findings indicate that Mini drones are

ideal for unit-level operations, transporting lightweight items like medications and medical supplies, while

small drones are suited for brigade-level missions requiring the delivery of heavier and more complex

materials, such as blood products and human organs. Limitations include the heterogeneity of studies, the lack

of detailed meteorological data, and inconsistent reporting standards. To address these challenges, the study

highlights the importance of constructive simulation in testing UAV applications and refining their integration

into military operations. By incorporating UAV-specific data into COMBATER, this research contributes to

realistic scenario modelling, supporting military decision-making and advancing logistical efficiency. The

proposed framework provides a foundation for the strategic use of UAVs in military healthcare logistics,

offering insights into the development of military doctrine and the optimization of operations in complex

environments.

1 INTRODUCTION

The use of Unmanned Aerial Vehicles (UAVs), or

drones, in simulated scenarios offers promising

potential for assessing their logistical effectiveness in

challenging environments, such as jungle or remote

areas. In this context, Almeida et al. (2023) highlight

that the COMBATER software, widely employed by

the Brazilian Army, is a robust tool for modelling

complex operations and testing courses of action in

controlled environments. This functionality

significantly enhances decision-making processes

and tactical training. Although drone delivery not yet

https://orcid.org/0009-0003-1437-3078

https://orcid.org/0000-0003-3944-7953

https://orcid.org/0000-0003-4507-6562

implemented, the system could be adapted in the

future to analyse how drones might optimize the

distribution of medical supplies in hard-to-reach

areas, potentially improving outcomes for casualties

and the recovery of wounded personnel.

The integration of real-world data, such as range,

payload capacity, and operational conditions, into the

COMBATER algorithm will enable the creation of

more realistic scenarios to assess the feasibility of

using drones for transporting health supplies. Given

the critical importance of health supplies in sustaining

operations and ensuring timely medical care,

assessing their delivery via UAVs is a key focus of

Bomﬁm, R., Pochmann, P. G. C. and Neves, E. B.

Aerial Logistics in Hard-to-Reach Environments: Systematic Review of the Use of Class 1 UAVs in Health Supply Distribution in Military Operations and Other Context.

DOI: 10.5220/0013461400003970

In Proceedings of the 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2025), pages 15-26

ISBN: 978-989-758-759-7; ISSN: 2184-2841

this study. This approach would not only enhance

military training but also support strategic decision-

making, strengthening the Army's ability to address

the challenges of contemporary operations in remote

environments.

This study proposes exploring the possibility of

incorporating the delivery of medical supplies by

drones into the COMBATER algorithm, using real-

world data to parameterize the efficiency of this

logistical solution. Constructive simulation serves as

a cost-effective and controlled method to evaluate the

integration of UAVs in logistical processes,

minimizing risks and informing live simulation

strategies. Additionally, it aims to identify the most

suitable echelons to receive this technology in

constructive simulation before its deployment in live

simulation, ensuring that its adoption is grounded in

robust operational and technical evidence. This

initiative could also contribute to the evolution of

military doctrine by providing a data-driven

foundation for integrating UAVs into logistical

frameworks, enhancing operational efficiency and

readiness.

2 METHODS

2.1 Eligibility Criteria

This study employed a systematic literature review,

structured in alignment with the guidelines outlined

in the Preferred Reporting Items for Systematic

Reviews and Meta-Analyses (PRISMA) statement

(Page et al., 2021). The temporal scope included

publications on military doctrine from 1997 to the

present and scientific works published between 2014

and 2024. Four languages were considered for

inclusion: English, Portuguese, French, and Spanish.

Regarding the types of publications, the search

focused on book chapters, monographs, doctrinal

manuals, scientific articles, and technical standards.

2.2 Search Strategy

The primary objective of this review was to identify

and analyse studies focusing on the utilization of

drones, commonly referred to as RPA (Remotely

Piloted Aircraft) and UAV (Unmanned Aerial

Vehicles), for medical supply operations in jungle,

forest, or remote environments. The review explored

topics related to logistics, medical operations, and the

integration of technology in complex and challenging

settings.

In this study, the research focused on drones

classified as Class 1 (weighing up to 150 kg)

according to NATO standards (NATO, 2019). Class

1 drones are divided into three categories based on

their weight, maximum altitude, and range:

• Micro: drones weigh less than 2 kg, can reach

an altitude of up to 200 feet AGL (Above Ground

Level), and have a range of up to 5 km.

• Mini: drones weigh between 2 kg and 15 kg, can

fly up to 3,000 feet AGL, and have a range of up

to 25 km.

• Small: drones weigh between 15 kg and 150 kg,

can reach an altitude of up to 5,000 feet AGL, and

have a range of up to 50 km.

To structure the keywords used across various

databases for the research on drone applications in

medical supply logistics within forested

environments and military operations, we adopted the

PICO strategy (Population, Intervention,

Comparison, and Outcome). The PICO framework

aids in categorizing keywords into essential

components for systematic reviews, streamlining the

identification of studies that address specific aspects

of the topic (Nishikawa-Pacher, 2022).

To define and guide the search strategy in the

selected databases, this study adapted the PICO

framework to its specific research context.

• Problem (P): Focused on challenging

environments such as jungles and remote areas where

traditional logistics face accessibility issues

(keywords: "jungle," "forest," "remote area").

• Intervention (I): Examined the use of drones

(RPA/UAV) for transporting medical supplies in

hard-to-reach regions (keywords: "drone," "RPA,"

"UAV").

• Comparison (C): No direct comparison with

traditional methods; the focus is on the implicit

advantages of drones in these settings.

• Outcome (O): Aimed to improve logistics for

medical supplies (Class VIII), reducing response

times and risks (keywords: "medical," "medicine,"

"supply," "distribution," and "logistic.").

Building upon the defined PICO components, the

search string was developed according to the

established criteria, resulting in a set of Boolean terms

tailored to the capabilities of each digital database:

• Scopus, Web of Science, and PubMed: The

search string used was ((“drone” OR "rpa" OR "uav"

) AND ( "medical" OR "medicine" ) AND ( "supply"

OR "distribution" OR "logistic" ) AND ( "jungle" OR

"forest" OR "remote AND area" )), utilizing the

advanced search feature.

• Science Direct: Due to limitations in Boolean

operators within the database, the string ("drone" OR

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

"uav" OR "rpa") AND ("medical" OR "medicine")

AND ("supply" OR "distribution") AND ("jungle"

OR "forest") was applied in the advanced search

option.

• Defense Technical Information Center

(DTIC) and The Army University: In these

databases, the string ("uav" AND "forest" AND

"medical" AND "drone" AND "logistic") was

employed, as the lack of an advanced search option

constrained the number of applicable keywords.

• BDEx: As a Brazilian Army database, keyword

translation and the inclusion of the term SARP

(Sistemas de Aeronaves Remotamente Pilotadas)

were necessary, reflecting its official nomenclature

(Brasil, 2020).Due to limitations in search

capabilities, the string ("drone" AND "SARP" AND

"logística") was used to locate relevant studies

aligning with the specific objectives of this research.

2.3 Study Selection

During the identification phase, a total of 755

duplicate records were excluded from the initial set of

1,597 references. The remaining references

underwent a multi-stage screening process to ensure

alignment with the research objectives.

In the first screening, titles, keywords, and

abstracts were reviewed. References that were

misaligned with the study’s goals, such as those

focusing on artificial intelligence (AI), Internet of

Things (IoT), computer vision, robotics, or blockchain

in healthcare systems, were eliminated. Incomplete

references, such as indices, news, or abstracts without

full text, and those with search keywords appearing in

titles or abstracts but unrelated to the research

objectives, were also excluded. Additionally, records

using the acronym RPA to refer to Robotic Process

Automation rather than Remotely Piloted Aircraft

were removed.

The second screening addressed accessibility and

language. Studies not found or written in languages

other than English, French, Spanish, or Portuguese

were excluded.

In the third screening, the full texts of the

remaining studies were evaluated using a multi-

criteria analysis. Articles classified with very strong

adherence included at least four central concepts

(keywords) and were fully aligned with the research

objectives. Articles with strong adherence contained

at least three central concepts and demonstrated

coherence with the study’s aims, while those with

medium adherence covered at least two central

concepts and aligned with the research focus. No

articles were classified with weak adherence, and

articles with no adherence were excluded if they

lacked focus on the study’s scope and objectives, even

if they discussed drones.

2.4 Data Collection Process

The data collection process involved conducting

searches across seven pre-defined databases using

four proposed keyword combinations. These

databases were selected because they are well-known

and provided a higher number of studies aligned with

the objectives of the systematic review. After each

search, the retrieved data were imported and stored in

the EndNote software for organization and subsequent

analysis.

2.5 Quality Assessment of Studies

The quality of the methodologies employed in the

studies included in the review was assessed based on

the guidelines of the Transparent Reporting of

Evaluations with Nonrandomized Designs (TREND),

as described by Des Jarlais et al. (2004). This tool was

chosen due to its relevance in ensuring transparency in

the evaluation of studies with nonrandomized designs,

such as quasi-experimental and observational studies,

which are often utilized in contexts where randomized

clinical trials are unfeasible or unethical (Vallvé, C,

2005). The TREND statement provides a structured

checklist to evaluate aspects such as detailed

descriptions of interventions, theoretical foundations,

treatment allocation, consideration of confounding

variables, and the overall methodological robustness

(Des Jarlais et al., 2004).

The use of the TREND guidelines is also justified

by the need to ensure that the results of the studies are

accurate and consistent, making it easier to compare

findings across studies in meta-analyses and

systematic reviews. The evaluation looked at

important aspects, such as how clearly the sample

selection was explained, how treatments were

assigned, how potential factors that could affect the

results were considered, whether the results could be

applied to other situations, and how strong the overall

study design was. By following these steps, the studies

were carefully analyzed for their reliability and

accuracy. The results of this evaluation are shown in

Table 1.

Aerial Logistics in Hard-to-Reach Environments: Systematic Review of the Use of Class 1 UAVs in Health Supply Distribution in Military

Operations and Other Context

Table 1: Assessment of Study Quality Using the TREND Guideline.

Section/Topic

Study Study Study Study Study Study Study Study Study Study Study Study Study

Awad et at.,

2021

(Stanton,

2020)

Awad et at.,

2021

(Vodafone,

2019)

Awad et at.,

2021

(Hii et al.,

2019)

Awad et at.,

2021

(Ackerman

and Koziol,

2019)

Awad et at.,

2021

(Cheskes et

al., 2020)

Awad et at.,

2021

(Suas

News,

2014)

Ayamga et

at., 2021

(Sanfridsso

n et al.,

2019)

Banik et al.,

2023

(Adwibowo

, 2021)

Banik et al.,

2023

(Nur et al.,

2020)

Braun et al.,

2019

(DHL,

2018)

Braun et

al., 2019

(Howell et

al., 2015)

Euchi, 2021

(Amukele

et al.,

2017a)

Euchi, 2021

(Claesson

et al., 2017)

Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported?

Title and

Abstract

YES YES YES YES YES YES YES YES YES NO YES YES YES

Background YES YES YES YES YES YES YES YES YES NO YES YES YES

Participants NO NO NO YES YES NO YES NO NO NO NO YES YES

Interventions YES YES YES YES YES YES YES YES YES YES YES YES YES

Objectives YES YES YES YES YES YES YES YES YES NO YES YES YES

Outcomes NO NO YES YES YES NO YES YES YES NO YES YES YES

Sample Size NO NO NO NO YES NO NO NO YES NO NO YES YES

Assignment

Method

NO NO YES YES YES YES YES YES YES NO YES YES YES

Blinding

(masking)

NO NO NO NO NO NO NO NO NO NO NO NO NO

Unit of Analysis NO NO YES YES YES YES YES YES YES YES YES YES YES

Statistical

Methods

NO NO YES NO YES NO YES YES YES NO NO YES YES

Participant flow NO NO YES NO YES NO YES NA NA NO NO YES YES

Recruitment NO NO NA NA YES NO YES NO NO NO NO NO YES

Baseline Data NO NO NA NA NA NA YES NA NA NO NO YES NO

Baseline

equivalence

NO NO NA NA NA NA YES NA NA NO NO NO NO

Numbers

analyzed

NO NO YES YES YES NO YES NA NA NO NO YES YES

Outcomes and

estimation

NO NO YES YES YES YES YES YES YES YES YES YES YES

Ancillary

analyses

NO NO NO NO NO NO YES NO YES NO NO NO NO

Adverse events NO NO YES NO YES NO NO NO NO NO NO YES YES

Interpretation YES YES YES YES YES YES YES YES YES YES YES YES YES

Generalizability NO NO YES YES YES YES YES YES YES YES YES YES YES

Overall

Evidence

NO NO YES YES YES YES YES YES YES YES YES YES YES

Section/Topic

Study Study Study Study Study Study Study Study Study Study Study Study Study

Fakhruldd

in et al.,

2019

Fakhruldd

in et al.,

2019

(Claesson

et al.,

2016)

Flemons

et al.,

2022

Grote et

al., 2024

Mohd

Daud et

al., 2022

(Yakushij

i et al.,

2020)

Naor et

al., 2024

Poljak,

2020

(Mesar et

al., 2018)

Poljak,

2020

(Scalea et

al., 2018)

Sanz-

Martos,

2022

(Jain et

al., 2018)

Scott, J

and Scott,

C, 2018

Shao et

al., 2022

Sharma,

S. and

Sharma,

H, 2024

(The

Times of

India,

2023

)

Stierlin et

al., 2024

(Amukele

et al.,

2017b)

Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported? Reported?

Title and Abstract YES YES YES YES YES YES YES YES YES YES YES NO YES

Background

YES YES YES YES YES YES YES YES YES YES YES YES YES

Participants YES NO YES YES YES YES YES YES YES NO YES NO YES

Interventions YES YES YES YES YES YES YES YES YES YES YES YES YES

Objectives

YES YES YES YES YES YES YES YES YES YES YES NO YES

Outcomes YES YES YES YES YES YES YES YES YES YES YES NO YES

Sample Size YES YES YES YES YES YES YES YES NA NO NO NO YES

Assignment

Method

YES YES YES YES YES YES YES YES YES YES YES NO YES

Blinding

(masking)

NO NO NO NO NO NO NO NO YES NO NO NO NO

Unit of Analysis YES YES YES YES YES YES YES YES YES YES YES YES YES

Statistical

Methods

YES YES NO YES NO YES NO YES YES NO YES NO YES

Participant flow YES NO YES YES YES YES YES YES YES NO YES NO YES

Recruitment NO NO YES NO NO NO NO NO YES NO NO NO NO

Baseline Data

YES NA NO NO NA NA NO YES YES NO NO NO YES

Baseline

equivalence

NO NA NO NO NA NA NO NO YES NO NO NO YES

Numbers analyzed YES NA NO YES YES YES NO YES YES NO NO NO YES

Outcomes and

estimation

YES YES YES YES YES YES YES YES YES YES YES YES YES

Ancillary analyses NO NO NO NO NO NO YES NO YES NO YES NO YES

Adverse events NO NO YES NO YES NO YES NO YES NO NO NO NO

Interpretation

YES YES YES YES YES YES YES YES NO YES YES YES YES

Generalizability YES YES YES YES YES YES YES YES NO YES YES YES YES

Overall Evidence YES YES YES YES YES YES YES YES NO YES YES YES YES

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

3 RESULTS

3.1 Study Selection and Characteristics

The Fig. 1 presents the PRISMA flow diagram used to

illustrate the systematic selection process for this

review on medical supply operations using Class 1

UAVs in healthcare and military contexts. The process

includes four phases: Identification, Screening,

Eligibility, and Inclusion. Studies were identified

through databases and manual searches, followed by

removing duplicates and excluding those misaligned

with research objectives or limited to indexes. In the

eligibility phase, studies were rigorously assessed,

excluding those with superficial approaches or

irrelevant technologies. Ultimately, 97 studies were

included, ensuring a comprehensive synthesis aligned

with the review's objectives.

Following the systematic review using the

PRISMA method, a detailed analysis was conducted

on studies specifically addressing the characteristics

of drones used in medical supply operations in jungle,

forest, or remote environments. This process resulted

in the identification of 26 studies, 6 of which directly

mentioned the characteristics of the drones.

Additionally, the snowballing method was applied to

explore references and citations of the included

studies, leading to the identification of 20 additional

articles categorized as the "Corpus Static." This

corpus represents a fixed dataset derived from

reference analysis, comprising studies that specifically

addressed Class 1 UAV characteristics within their

scope of research (Wohlin, 2014).

3.2 Risk of Bias in Studies

Table 1 presents the risk of bias analysis for the 26

studies evaluated using the TREND method. The 5

studies marked in red exhibited low methodological

quality and a high risk of bias.

Some items from the TREND checklist are marked

as "not applicable (NA)" in certain studies because

these criteria may not align with the design or scope

of the evaluated research. For instance, most studies

do not directly involve human participants but focus

on logistical interventions or operational analyses,

which eliminates the need for descriptions of

demographic characteristics or blinding strategies.

Similarly, studies that examine technologies or

technical processes without traditional comparative

variables may not require advanced statistical methods

or causality analyses, thus justifying their exclusion in

specific evaluations.

Figure 1: PRISMA flow diagram illustrating the selection process.

Aerial Logistics in Hard-to-Reach Environments: Systematic Review of the Use of Class 1 UAVs in Health Supply Distribution in Military

Operations and Other Context

3.3 Results of Individual Studies

The Table 2 provides an overview of studies

analysing the application of drones in health logistics,

highlighting their capabilities and operational

contexts. It categorizes drones based on their

developers, models, configurations, and classification

according to NATO UAS guidelines (NATO, 2019),

detailing the operational environments (urban, rural,

remote, or controlled) and delivery methods, such as

ground landing, winch systems, or parachutes.

Additionally, it specifies the types of medical

materials transported, ranging from insulin and

vaccines to emergency medical equipment and

refrigerated medical cargo.

The technical performance indicators and load

capacities of drones used in health logistics are

presented in Table 3, which complements the analysis

provided in Table 2. It details information such as

flight endurance, operational range (per single battery

charge), maximum speed, altitude capabilities, and

load capacity for each model.

Table 2: General Characteristics of Studies on the Use of Drones in Health Logistics.

Study

(Corpus Static)

Drone Information (Developers,

Model, Configuration)

Country (Operation

Environmental)

Category

(Nato UAS

Classification)

Delivery method Type of Material Transported

Awad et at., 2021

(Hii et al., 2019)

DJI, Mavic Air, Multi-rotor-

quadcopter

United Kingdom

(Urban)

Mini (<15 kg) Ground landing Medications (Insulin)

Awad et at., 2021 (Stanton,

2020)

Volansi, VOLY C10, Multi-rotor -

quadcopter

USA

(Not provided)

Not provided Ground landing Vaccines

Awad et at., 2021

(Vodafone, 2019)

NUI Galway, Wingcopter 178

Heavy Lift, Hybrid

Irlanda

(Rural and Remote Areas)

Not provided Ground landing Medications (for diabetes)

Awad et at., 2021

(Ackerman and Koziol, 2019)

Zipline, Not provided, Fixed Wing

Rwanda (Mountainous,

Rural and Remote Areas)

Small (>15 kg) Parachute Blood products

Awad et at., 2021

(Cheskes et al., 2020)

Drone Delivery Canadian, Sparrow

X1000, Multi-rotor - octocopter

Canadian

(Rural and Remote Areas)

Not provided Ground landing

Automated External

Defibrillators (AED)

Awad et at., 2021

(Cheskes et al., 2020)

Indro Robotics, InDro M210C,

Multi-rotor - quadcopter

Canadian

(Rural and Remote Areas)

Not provided Ground landing

Automated External

Defibrillators (AED)

Awad et at., 2021

(Suas News, 2014)

TU Delft, Not provided, Multi-

rotor - octocopter

Netherlands

(Urban)

Mini (<15 kg) Ground landing

Automated External

Defibrillators (AED)

Ayamga et at., 2021

(Sanfridsson et al., 2019)

DJI, Inspire 1, Multi-rotor -

quadcopter

Sweden

(Urban and Rural)

Mini (<15 kg) Ground landing

Automated External

Defibrillators (AED)

Banik et al., 2023

(Adwibowo, 2021)

Not provided, Not provided,

Multi-rotor - quadcopter

Indonesia

(Mountainous and Rural)

Small (>15 kg) Ground landing Vaccines (COVID vaccine)

Banik et al., 2023

(Nur et al., 2020)

Not provided, "Drone B", Multi-

rotor - quadcopter

Not provided

(Urban and Rural)

Mini (<15 kg) Ground landing Not provided

Braun et al., 2019

(DHL, 2018)

DHL, Parcelcopter 4.0, Hybrid

Tanzania

(Remote Areas and Rural)

Not provided Ground landing Medications and Medical supplies.

Braun et al., 2019

(Howell et al., 2015)

Flirtey Corporation, Flirtey F2.4,

Multi-rotor - hexacopter

USA

(Urban and Rural)

Mini (<15 kg) Dropped by rope Medications and Medical supplies.

Euchi, 2021

(Amukele et al., 2017a)

DJI - Johns Hopkins Hospital,

S900, Multi-rotor - hexacopte

USA

(Controlled Environment)

Mini (<15 kg) Ground landing Blood products

Euchi, 2021

(Claesson et al., 2017)

Swedish Transportation Agency,

Not provided, Multi-rotor -

Octocopter

Sweden

(Urban and Rural)

Mini (<15 kg) Ground landing

Automated External

Defibrillators (AED)

Fakhrulddin et al., 2019

DJI, Phantom 3 Professional,

Multi-rotor - quadcopter

Iraq

(Urban)

Mini (<15 kg) Ground landing Medical supplies (First aid kit)

Fakhrulddin et al., 2019

(Claesson et al., 2016)

HEIGHT TECH GmbH & Co.

KG, Not provided, Multi-rotor -

Octocopter

Sweden

(Urban and Rural)

Not provided

Parachute, Cargo drop

and Ground landing

Automated External

Defibrillators (AED)

Flemons et al., 2022

DJI, Mavic Enterprise, Multi-rotor

- quadcopter

Canadian

(Rural and Remote Areas)

Mini (<15 kg)

Ground landing,

Winch system and

Cargo drop

Medical supplies (Medical devices, Personal

Protective Equipment (PPE), water bottles

and blankets)

Flemons et al., 2022

DJI, Matrice 300, Multi-rotor -

quadcopter

Canadian

(Rural and Remote Areas)

Mini (<15 kg)

Ground landing or

Winch system

Medical supplies (Medical devices, Personal

Protective Equipment (PPE), water bottles

and blankets)

Flemons et al., 2022

DJI, Matrice 600, Multi-rotor -

hexacopter

Canadian

(Rural and Remote Areas)

Mini (<15 kg)

Ground landing or

Winch system

Medical supplies (Medical devices, Personal

Protective Equipment (PPE), water bottles

and blankets)

Grote et al., 2024

Mugin UAVe Operation

Environment, Mugin-5 Pro,

Hybri

United Kingdom

(Urban and Rural)

Not provided

Ground landing or

Winch system

Medical samples (pathology samples)

Mohd Daud et al., 2022

(Yakushiji et al., 2020)

Mazex Co. Ltd., M1000, Multi-rotor

- quadcopter

Japan

(Urban and Remote Areas)

Small (>15 kg) Winch system

Medications, Medical supplies and

Automated External

Defibrillators (AED)

Naor et al., 2024 Gadfin, Spirit-One, Hybrid

Israel

(Urban and Remote Areas)

Small (>15 kg) Ground landing

Medications, Medical supplies, Blood

roducts and human organ

Naor et al., 2024 Gadfin, Spirit-HD, Hybrid

Israel

(Urban and Remote Areas)

Small (>15 kg) Ground landing

Medications, Medical supplies, Blood

roducts and human organ

Poljak, 2020

(Mesar et al., 2018)

Pulse Aerospace, Vapor 55, Rotary-

wing

USA

(Remote Areas)

Small (>15 kg) Ground landing

Medical supplies (tourniquets, bandages,

pain relievers) and Blood products

(tourniquets, bandages, pain relievers)

Poljak, 2020

(Scalea et al., 2018)

DJI - Universidade de Maryland,

M600, Multi-rotor - hexacopter

USA

(Urban and Rural)

Mini (<15 kg) Ground landing Human organs

Sanz-Martos, 2022 (Jain et

al., 2018)

Yuneec International, Yuneec

Tornado H920, Multi-rotor -

hexacopter

Canadian

(Controlled Environment)

Mini (<15 kg) Not provided

Medical supplies (Victim triage equipment

and sensors)

Scott, J and Scott, C, 2018

Flirtey, Not provided, Multi-rotor -

quadcopter

USA

(Remote Areas)

Mini (<15 kg) Winch system Medications

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

Scott, J and Scott, C, 2018

Matternet - UNICEF and Doctors

without Borders, Not provided,

Multi-rotor - quadcopter

Haiti, Dominican Republic,

Papua New

Guinea and Switzerland

(Urban and Rural)

Mini (<15 kg) Ground landing Medications and Blood products

Shao et al., 2022

Researchers from Chang Jung

Christian University e da Chunghwa

Telecom Co, Ltd., Not provided,

Multi-rotor - hexacopter

Taiwan

(Mountainous and Rural)

Mini (<15 kg) Not provided

Medications

Sharma, S. and Sharma, H,

2024

(The Times of India, 2023)

AIIMS-Rishikesh, AQUILA X2,

Hybrid

India

(Mountainous and Remote

Areas)

Mini (<15 kg) Ground landing

Medications (Anti-tuberculosis Drugs)

Stierlin et al., 2024

(Amukele et al., 2017b)

Latitude Engineering - Johns

Hopkins University School of

Medicine e a Mayo Clinic., HQ-40,

Hybri

USA

(Controlled Environment)

Mini (<15 kg) Ground landing Medical samples

Among the analysed studies, only six provided

data on ambient temperature during drone flights

(Amukele et al., 2017a, 2017b; Flemons et al., 2022;

Hii et al., 2019; Sanfridsson et al., 2019; Scalea et al.,

2018; Yakushiji et al., 2020). The recorded

temperatures varied significantly, ranging from

negative values, such as -1°C in tests conducted by

Hii et al. (2019), to a maximum of 36.2°C reported by

Mohd Daud et al. (2022). Wind speed was mentioned

in five studies (Flemons et al., 2022; Hii et al., 2019;

Scalea et al., 2018; Shao et al., 2022; Yakushiji et al.,

2020), while relative humidity was documented in

only two cases (Amukele et al., 2017; Hii et al.,

2019). Atmospheric pressure data were even scarcer,

being recorded solely by Hii et al. (2019), who

reported values between 1021 and 1022 mbar during

operations.

Among the 26 studies analyzed (Table 2), 24

(92.3%) reported the use of a single delivery method

per drone, while 2 studies (7.7%) described the

employment of multiple methods. In total, 31 drones

were reported, with ground landing being the

predominant method, observed in 64.5% (20 drones).

Additional methods included the winch system,

utilized in 6.5% (2 drones), and parachute delivery,

observed in 3.2% (1 drone). Combinations of

methods were employed in 9.7% (3 drones), such as

ground landing or winch system, while more complex

approaches, like parachute delivery, ground landing,

and cargo drop, were implemented in 6.5% (2

drones). Lastly, 6.5% (2 drones) did not provide

sufficient information about the delivery method

utilized.

Regarding operational environments, urban and

rural areas were the most frequently mentioned,

representing 30.8% (8 studies). Next, rural and

remote areas were observed in 11.5% (3 studies). The

mountainous environment was identified in various

combinations, including mountainous and remote

areas, mountainous and rural areas, or mountainous,

rural, and remote areas, totaling 15.4% (4 studies).

Exclusively urban and controlled environments were

equally reported, with 11.5% (3 studies) each.

Additionally, urban and remote areas were mentioned

in 7.7% (2 studies), and exclusively remote

environments were reported in 7.7% (2 studies).

Finally, 1 study (3.8%) did not specify the operational

environment.

Among the drones analyzed, the Mini (<15 kg)

category was the most frequently reported,

representing 58.1% (18 drones). This was followed

by the Small (>15 kg) category, accounting for 19.4%

(6 drones). Additionally, 22.6% (7 drones) were

classified as Not provided, indicating a lack of

specific weight-based categorization for a significant

portion of the sample.

In terms of materials transported, the most

frequently mentioned items were Automated External

Defibrillators (AEDs) and Medications, each reported

in 5 studies (19.2%). Medical Supplies were

mentioned in 4 studies (15.4%), while Vaccines,

Blood Products, and Medical Samples appeared in 2

studies each (7.7%). Unique cases included Human

Organs in 1 study (3.8%), and combinations such as

Medications and Medical Supplies in 2 studies (7.7%)

and Medications, Medical Supplies, Blood Products,

and Human Organs in 1 study (3.8%). Lastly, 1 study

(3.8%) did not specify the transported materials.

According to Table 3, the results indicated that the

flight endurance had a mean of 33.07 minutes and a

standard deviation of 36.36 minutes. The drones'

range showed a mean of 68.16 km, with a standard

deviation of 101.19 km. The average maximum speed

was 75.88 km/h, with a standard deviation of 39.61

km/h. For operational altitude, the mean found was

871.45 m, with a standard deviation of 1174.15 m.

Finally, the cargo capacity recorded a mean of 5.01

kg and a standard deviation of 4.24 kg.

Aerial Logistics in Hard-to-Reach Environments: Systematic Review of the Use of Class 1 UAVs in Health Supply Distribution in Military

Operations and Other Context

Table 3: Drone Flight Performance Metrics and Cargo Capacity.

Study (Corpus Static)

Drone Information (Developers, Model,

Configuration)

Flight

Endurance

(min)

Range (for a

single battery

charge) (km)

Maximal

Speed (km/h)

Altitude (m)

Cargo

capacity

(Kg)

Awad et at., 2021

(Hii et al., 2019)

DJI, Mavic Air, Multi-rotor-quadcopter 7 to 11 0.63 and 0.99 5.4 10 0.194

Awad et at., 2021

(Stanton, 2020)

Volansi, VOLY C10, Multi-rotor - quadcopter 60 80.46 Not provided Not provided 4.53

Awad et at., 2021

(Vodafone, 2019)

NUI Galway, Wingcopter 178 Heavy Lift, Hybrid 32 43.3 Not provided Not provided

Not

rovide

Awad et at., 2021

(Ackerman and Koziol,

2019)

Zipline, Not provided, Fixed Wing 45 160 128 400 to 500 1.75

Awad et at., 2021

(Cheskes et al., 2020)

Drone Delivery Canadian, Sparrow X1000, Multi-rotor -

octocopter

25 25 80 900 4.5

Awad et at., 2021

(Cheskes et al., 2020)

Indro Robotics, InDro M210C, Multi-rotor - quadcopter 25 25 55 1000 4

Awad et at., 2021

(Suas News, 2014)

TU Delft, Not provided, Multi-rotor - octocopter Not provided 100 Not provided Not provided 4

Ayamga et at., 2021

(Sanfridsson et al.,

2019)

DJI, Inspire 1, Multi-rotor - quadcopter 15 to 20 Not provided Not provided Not provided 2

Banik et al., 2023

(Adwibowo, 2021)

Not provided, Not provided, Multi-rotor - quadcopter 16 to 50 Not provided 54 to 80 Not provided 3 to 20

Banik et al., 2023

(Nur et al., 2020)

Not provided, "Drone B", Multi-rotor - quadcopter 15 12 Not provided Not provided 2

Braun et al., 2019

(DHL, 2018)

DHL, Parcelcopter 4.0, Hybrid 40 60 140 Not provided 4

Braun et al., 2019

(Howell et al., 2015)

Flirtey Corporation, Flirtey F2.4, Multi-rotor -

hexacopter

10 to 15 1.3 Not provided 152 2.3

Euchi, 2021

(Amukele et al., 2017a)

DJI - Johns Hopkins Hospital, S900, Multi-rotor -

hexacopter

26.5 Not provided 36 to 54 100 1,9

Euchi, 2021

(Claesson et al., 2017)

Swedish Transportation Agency, Not provided, Multi-

rotor - Octocopter

Not provided Not provided 75 Not provided

Not

rovide

Fakhrulddin et al., 2019 DJI, Phantom 3 Professional, Multi-rotor - quadcopter 3.75 Not provided Not provided Not provided

Not

rovide

Fakhrulddin et al., 2019

(Claesson et al., 2016)

HEIGHT TECH GmbH & Co. KG, Not provided, Multi-

rotor - Octocopter

8.5 10 70 Not provided

Not

rovide

Flemons et al., 2022 DJI, Mavic Enterprise, Multi-rotor - quadcopter < 30 8 Not provide

15 and 40 m < 1

Flemons et al., 2022 DJI, Matrice 300, Multi-rotor - quadcopter 30 to 45 15 Not provide

15 and 40 m 1 to 8

Flemons et al., 2022 DJI, Matrice 600, Multi-rotor - hexacopter 30 to 45 8 Not provide

15 and 40 m 1 to 8

Grote et al., 2024

Mugin UAVe Operation Environment, Mugin-5 Pro,

Hybri

Not provided 75 65 Not provided 5

Mohd Daud et al., 2022

(Yakushiji et al., 2020)

Mazex Co. Ltd., M1000, Multi-rotor - quadcopter Not provided Not provided 58 35 to 1100 17

Naor et al., 2024 Gadfin, Spirit-One, Hybri

Not provide

250 100 Not provide

Naor et al., 2024 Gadfin, Spirit-HD, Hybri

Not provide

400 100 Not provide

Poljak, 2020

(Mesar et al., 2018)

Pulse Aerospace, Vapor 55, Rotary-wing 20.77 12.27 34.03 Not provided 4.5

Poljak, 2020

(Scalea et al., 2018)

DJI - Universidade de Maryland, M600, Multi-rotor -

hexacopter

Not provided 4 to 5 67.6 30.5 a 61 9.1

Sanz-Martos, 2022 (Jain

et al., 2018)

Yuneec International, Yuneec Tornado H920, Multi-

rotor - hexacopter

24 0.7 Not provided 4000

Not

rovide

Scott, J and Scott, C,

2018

Flirtey, Not provided, Multi-rotor - quadcopter Not provided 32 Not provided Not provided 2

Scott, J and Scott, C,

2018

Matternet - UNICEF and Doctors without

Borders, Not provided, Multi-rotor - quadcopter

Not provided 10 40 Not provided 2

Shao et al., 2022

Researchers from Chang Jung Christian University e da

Chunghwa Telecom Co, Ltd., Not provided, Multi-rotor

- hexacopter

12 5.35 Not provided 1245 1

Sharma, S. and Sharma,

H, 2024

(The Times of India,

2023)

AIIMS-Rishikesh, AQUILA X2, Hybrid 30 40 Not provided Not provided 2

Stierlin et al., 2024

(Amukele et al., 2017b)

Latitude Engineering - Johns Hopkins University School

of Medicine e a Mayo Clinic., HQ-40, Hybrid

180 258 160 290 4

4 DISCUSSIONS

4.1 Evaluating UAVs in Healthcare:

Insights from a Systematic Review

The findings of this systematic review highlight the

operational versatility of Class 1 UAVs in healthcare

logistics, particularly in challenging environments

such as jungles, forests, and remote areas. Based on

the analysis of 26 studies, key metrics such as

endurance, range, speed, and cargo capacity were

identified as critical for selecting suitable drone

models for medical supply missions. The inclusion of

the snowballing method allowed the incorporation of

additional relevant studies (Corpus Static), enriching

the dataset with unique insights and reinforcing the

robustness of the methodology when combined with

PRISMA.

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

However, as noted in section 3.2 (Risk of Bias),

five of the 26 studies showed low methodological

quality and high risk of bias (DHL, 2018; Stanton,

2020; Suas News, 2014; The Times of India, 2023;

Vodafone, 2019). These sources failed to meet

several TREND criteria—such as clarity on sample

size, statistical methods, outcome reporting, and

participant flow—mainly because they are not peer-

reviewed papers, but descriptive reports from

websites. Although useful for contextual information,

they lack scientific rigor and are limited in supporting

data-driven decisions.

Overall, the RSL confirms the feasibility of

integrating drone-based medical supply delivery into

the COMBATER simulation algorithm. The

performance data compiled in Tables 2 and 3 provide

real-world parameters that can be used to model

realistic logistic scenarios within the simulator.

4.2 Identified Methodological

Limitations

This study highlighted several methodological

limitations that may influence the interpretation of the

findings and their applicability to the COMBATER

simulation software. One of the primary challenges

was the heterogeneity of the analysed studies, as the

experiments were conducted in diverse geographic

locations and with different types of drones and

equipment. This variability complicates the

generalization of results, as the performance of

drones may be context-specific, influenced by unique

environmental and operational factors.

Another significant limitation was the lack of

reported meteorological conditions in the studies.

Weather factors, such as wind speed, temperature,

humidity, and precipitation, are known to

significantly affect drone performance, particularly in

terms of range, endurance, and operational altitude.

Without this critical information, it becomes difficult

to comprehensively evaluate how drones operate

under various environmental conditions. This gap

limits the ability to model drone performance

realistically, especially in scenarios where adverse

weather conditions are a likely operational constraint.

Finally, the diversity in reporting standards

among the studies analysed also presented challenges.

The absence of consistent metrics, such as

standardized measures for flight endurance or

payload performance, hindered direct comparisons

and increased the reliance on averages that may not

fully capture the nuances of specific drone categories.

Addressing these methodological gaps in future

research will be essential to refine the parameters for

integration into COMBATER and ensure that

simulations are grounded in robust and

comprehensive data.

4.3 Delivery Methods and Drone

Categories in Healthcare Logistics

The analysis of delivery methods and operational

environments highlights an intrinsic relationship

between terrain conditions and the choice of

technology employed. The predominance of ground

landing (64.5%) in urban and rural areas reflects its

practicality and reliability in scenarios where terrain

access is relatively straightforward. Conversely, more

specific methods, such as winch (6.5%) and parachute

(3.2%), are used in situations that require adapted

solutions for inaccessible terrains or those that

minimize ground interaction, such as in remote or

mountainous regions.

Technical flexibility is demonstrated by combined

methods, such as ground landing and winch (9.7%),

which show potential for serving areas with mixed

characteristics, enhancing operational efficiency in

hard-to-reach locations. However, the lack of

information on delivery methods for 6.5% of the

drones, points to methodological gaps that could

compromise the practical applicability of the results.

These findings underscore the need to align the

choice of delivery method with the specificities of the

operational environment, maximizing logistical

efficiency and the effectiveness of operations in

challenging contexts.

The relationship between the drone category and

the type of material transported shows that Mini

drones (<15 kg), representing 58.1% of the analysed

drones (18 drones), are better suited for lightweight

and low-volume items, such as medications (19.2%)

and medical supplies (15.4%). This preference is

associated with their agility, lower operational costs,

and simplicity of operation, making them ideal for

short distances. On the other hand, small drones (>15

kg), which account for 19.4% of the sample (6

drones), are more appropriate for more complex

materials, such as blood products (7.7%), vaccines

(7.7%), and human organs (3.8%). These items

require greater payload capacity and transport

precision and are often linked to operations in remote

or mountainous areas. Additionally, combinations of

materials, such as medications, medical supplies,

blood products, and organs (7.7%), highlight the need

for more robust drones in the small category to meet

diverse logistical requirements.

Aerial Logistics in Hard-to-Reach Environments: Systematic Review of the Use of Class 1 UAVs in Health Supply Distribution in Military

Operations and Other Context

4.4 Recommendations for Future

Applications in COMBATER

As part of the proposed integration of drone data into

the COMBATER simulation software, and in

alignment with the Doctrinal and Operational

Constraints of the Brazilian Army (Brasil, 2024), it is

recommended that Mini drones (<15 kg) be employed

at the unit level, with operational responsibility

assigned to the Command and Support Company. In

contrast, small drones (>15 kg) should be allocated at

the brigade level, with the Logistics Battalion

responsible for their operation.

The analysis of Tables 2 and 3 enabled the

identification of specific parameters to be

incorporated into the COMBATER simulation

software to ensure accurate modeling. For Mini

drones (<15 kg), the predominant configuration is the

multi-rotor (quadcopter) type, used by 44% (8 out of

18) of the drones analyzed. Brazilian military

doctrine establishes a maximum range of 15 km line

of sight (LOS) and an operational altitude of up to 140

meters (Brasil, 2024). Additional parameters include

a flight endurance of 26.02 minutes, a maximum

speed of 73.93 km/h, and an average cargo capacity

of 3.63 kg. Recommended materials for transport in

this category include medications and medical

supplies, due to their lightweight and essential role in

unit-level logistical support.

For Small drones (>15 kg), a hybrid configuration

is recommended due to its superior flight endurance,

as evidenced in studies such as Amukele et al.

(2017b). Suggested parameters for this category,

consistent with military doctrine, include a range of

up to 50 km LOS and an operational altitude of up to

900 meters (Brasil, 2024). The data analyzed indicate

an average flight endurance of 61.5 minutes, a

maximum speed of 100 km/h, and an average cargo

capacity of 5 kg, based on the mean values across all

reviewed studies. In this category, recommended

cargo includes blood products, vaccines, and human

organs, which are heavier and more complex,

requiring more robust drones with higher operational

capacity.

The incorporation of UAVs into the COMBATER

simulator involves more than the mere inclusion of

technical parameters such as speed, range, or payload.

COMBATER operates as a constructive simulator,

based on doctrinal logic and AI-generated behaviors

rather than real-time physical replication. As

described by Almeida et al. (2023), simulated units in

COMBATER act autonomously according to

predefined behavior trees and doctrinal rules.

Therefore, representing UAV capabilities, such as

reconnaissance, aerial delivery, or coordination with

other elements, requires the development of specific

models within the simulator’s internal logic. Without

this modeling, the presence of UAVs would be

merely symbolic, with no meaningful impact on

simulation outcomes. In this context, the parameters

identified in the present study constitute a necessary

preliminary step toward adapting COMBATER’s

internal structure to enable more realistic

representations of UAV employment in future

simulation environments.

5 CONCLUSIONS

This study identified and analysed key operational

and doctrinal parameters related to the use of Class 1

Unmanned Aerial Vehicles (UAVs) in military

logistics, with a focus on environments such as jungle

operations. The systematic literature review,

conducted according to PRISMA guidelines, and

application of the TREND checklist enabled the

selection of relevant studies and extraction of

standardized technical data. These parameters form

the basis for modelling UAVs in simulation

environments.

Among the findings, mini drones (<15 kg) were

found suitable for unit-level operations, while small

drones (>15 kg) showed potential for brigade-level

missions. Suggested applications included the

transport of critical materials such as medications,

vaccines, and blood products. By identifying these

parameters, the study contributes to future

adjustments in the COMBATER simulation platform.

Rather than modifying the simulator directly, the

objective was to provide inputs that support doctrinal

modeling and realistic representation of UAV

capabilities. The integration of such data is expected

to enhance the system’s ability to simulate logistical

operations and decision-making more accurately.

However, the study faced limitations, such as the

heterogeneity of the analyzed studies, the absence of

detailed meteorological data, and inconsistent

reporting standards. These gaps hinder a

comprehensive understanding of UAV performance

under diverse conditions and highlight the need for

further research. In this context, constructive

simulation, as implemented in COMBATER, offers a

valuable tool to explore scenarios that are difficult to

replicate in real-world experiments.

SIMULTECH 2025 - 15th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

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