Control of Autonomous Aerial Vehicles to Transport a Medical

Supplies

RICARDO YAURI1,2, SANTIAGO FERNANDEZ1, ANYELA AQUINO3,4

1Facultad de Ingeniería,

Universidad Tecnológica del Perú,

Lima,

PERU

2Universidad Nacional Mayor de San Marcos,

Lima,

PERU

3Institut National des Sciences Appliquées de Rennes,

Rennes,

FRANCE

4Telecom Sudparis,

FRANCE

Abstract: - Public health surveillance must guarantee the safety of people by limiting human mobility, in cases

of isolation, through product deliveries, making it necessary to use drones to guarantee safety because they play

a crucial role in several sectors. The literature review highlights the benefits of automation in-home delivery

using drones, focusing on time efficiency and competitiveness in various sectors, and provides crucial design

parameters to ensure its implementation in urban areas using different control techniques. A contribution was

proposed to a solution that aims to realize the honeycomb design, which drones create during flight, controlled

by a flight and delivery algorithm in a simulation environment applying an iterative methodology and

continuous transport tests. medical burden. The results indicate a qualitative advance in the successful creation

of simulated terrain, although the lack of numerical data on takeoffs and landings suggests the need for

additional quantitative measurements. The current results support the efficiency of drones in route planning,

precise management of medical cargo, and reduction of delivery time is numerical evidence that reinforces the

robustness of the solution. In conclusion, this study developed a functional prototype to control drones with a

flight planning algorithm and a swarm formation system for the transport of medical supplies in urban

environments, although the need for future research to implement artificial intelligence technologies is noted.

that improve transportation efficiency.

Key-Words: - Drones, medical supplies, transportation, simulation, Honeycomb, UAV.

Received: May 29, 2023. Revised: November 15, 2023. Accepted: December 19, 2023. Published: January 18, 2024.

1 Introduction

Contemporary society grapples with a significant

dilemma concerning the efficient surveillance of

public health and health emergencies across various

nations. The core of this issue lies in the limited

resources, insufficient technology, and the lack of

coordination between health agencies and

governments.

Contributing to the care of people's health and

well-being is necessary in events of sanitary

isolation, which has generated that the delivery of

products directly to the doors of homes has

increased, [1], [2]. Health authorities support

reducing people's mobility to safeguard their well-

being during events such as pandemics, [3].

An important solution has been the use of

drones (UAVs), which is useful in different civil

and military applications due to their reliability in

various tasks. These solutions incorporate many

functionalities, such as the integration of cameras

and radars. This allows the collection and analysis

of data, which can be applied in traffic surveillance,

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

E-ISSN: 2224-2678

Volume 23, 2024

emergency response, and aerial monitoring, [4], [5],

[6]. Additionally, there has been a substantial surge

in the demand for unmanned drones to provide

support in the realm of medical emergencies, [7],

[8].

Hence, a comprehensive examination of the

package loading procedure is imperative, as it

involves the implementation of novel trajectories,

enhancements in algorithms, and flight optimization

through simulations across diverse urban settings.

Furthermore, due to the rise of electronic commerce,

there is a need for efficient transportation of

packages, which has led numerous companies to

explore technologies to improve the efficiency of

transportation, trying to solve the problem of flying

autonomous aerial vehicles in formation, [9], [10],

[11].

The literature review shows that the proposed

solutions have a common approach to using drones

in the automatic delivery of items at home,

automating processes, saving time, and improving

competitiveness in fields such as surveillance,

tracking, telecommunications, and delivery of

medical supplies. This literature review allows us to

obtain design parameters for the drone, considering

economic and legal aspects to guarantee its

circulation in any city.

For all the above, this research raises the

following research question: How to carry out the

control process of drones in formation to transport a

medical supply in a simulation environment? Given

this research question, the objective to be met is to

design a control system for autonomous aerial

vehicles using a flight and delivery configuration

algorithm, the creation of a simulation environment,

and the design of a honeycomb system with control

systems.

The innovative value of this research lies in the

exhaustive analysis of the parameters and functions

of delivery drones, considering so many technical

aspects of simulation, proposing an innovative

"honeycomb" design, controlled by a configuration

algorithm for flight and Delivery. This paper is

structured in the following chapters: Section 2

presents related papers; and Section 3 describes the

concepts used for drone flight simulation processes.

Section 4 details the implementation of the system.

Finally, the results are presented in Section 5, and

the conclusions are presented in Section 6

2 Literature Review

A bibliographic review of research articles was

conducted using specific keywords related to the

topic. This literature review made it possible to

identify existing theories, concepts, solutions, and

systems in the field of drones and automated

delivery. From this review, the most relevant

theories and concepts have been explored and

selected, which are applied to improve control and

automation in the delivery of medical supplies.

Development methodologies usually use PID

controls for UAVs in various environments. Some

research compares traditional PID control with a

new non-smooth control strategy (improving

precision by 2.03 degrees), [12], [13], while others

use a mechanism for stable flight control in a Hexa-

rotor UAV with two PID controllers, [14].

Furthermore, the Flux Guided method that enables

efficient displacement using electrical flow in

leader-follower UAVs with PID control is also

explored (circling a target in times of 0.52 and 0.88

seconds), [15].

In the case of other types of PID controls, some

research, [16], [17], [18] (PID diffuse gain provides

a better response with 10% overshoot and 15-second

settling time) choose to use the Parrot "AR Drone"

drone to implement them (The result shows that the

PSO technique adjusts the PID controllers better,

saving time). In [16], a gradient descent approach

with automatic adjustment of the PID control is

used, while in [17], LabVIEW software is used to

modify the control and improve the flight position

according to a predefined path. Other investigations,

[15], [19], implemented a PID control called Flux

Guided, where its advantage is highlighted by

significantly reducing the degrees of freedom by

requiring only nodes at the limit of the surface area

(LQR controller has 0.0% overshoot and 4.1%

settling time).

UAVs can be used in surveillance tasks, tree

counting, and humidity studies, [20], [21]. They

demonstrate their usefulness in data collection

processes, using, in some cases, a UAV hexacopter

with brushless motors to control takeoff and

movement, [14], or using lightweight materials and

brushless motors, [13]. For simulation processes,

some research used Parrot AR drones to evaluate

two types of different PID controls (one based on

fuzzy gain and the other on gradient descent

autotuning), [16], [18], [21], with simulations to

compare their performance in navigation by

waypoints.

The integration of Internet of Things

technologies with drones allows for strengthening

their use for the automatic delivery of items,

automating processes, allowing remote control, and

saving time, with a significant impact on the

delivery industry, [18], [22], [23], (It is

demonstrated that a wireless data rate of 100 Gbps

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

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can be achieved at frequencies below 20 GHz). Its

results focus on proposing a drone flight control

design, in a simulation environment.

3 Unmanned Autonomous Air

Vehicles

Drones, or unmanned aerial vehicles (UAVs), are

autonomous aircraft that can fly without human

intervention and be operated remotely from ground

control stations or through autopilot and sensors

such as global positioning systems. These UAVs

have various applications, including wireless

coverage, military use, medical applications, and

transportation of goods, being a more economical

option than manned systems in several situations,

[4], [24].

Applications of UAVs include Multi-UAV

Cooperation, where multiple drones collaborate for

common goals, improving efficiency in sectors such

as agriculture; UAV-to-VANET Collaborations,

which streamline traffic surveillance by identifying

accidents more quickly than conventional

techniques, [5], [25]. In [26], drone control is

classified into several categories: manual control,

where the pilot uses a remote control or a mobile

application, [27]; automatic control with a

predefined route, [28]; sensor control to improve the

precision of UAV movement, [29]; and artificial

intelligence control using machine learning

algorithms and swarm control, [30].

3.1 UAVs Transport Methods

There are several ways to transport cargo with

drones, including attaching packages directly to the

drone, using containers, and delivering using a

dedicated system. Some drones are designed to

carry heavy or bulky loads, while others can carry

multiple packages simultaneously, [25].

Furthermore, according to [31], drones represent

an alternative to current land transportation

methods, allowing traffic to be avoided and delivery

times to be reduced. Figure 1 illustrates how a drone

performs inspections on high-voltage towers and

cables, facilitating maintenance work, [32].

3.2 Simulation Tools

For drone simulation and synthetic data generation,

there are essential tools that allow you to create

detailed and realistic virtual environments. These

tools play a vital role in providing detailed

information about objects, sensors, and three-

dimensional simulations. Together, these solutions

form a comprehensive process to diversify

simulation pools and assess the gap between

simulation and reality in drone detection, [33].

Among some tools, we have:

 Unreal engine. is a game engine that is used

with other modules and software to generate

synthetic data in virtual simulations, [34].

 AirSim. is an open-source drone simulation

software for creating realistic 3D environments,

facilitating the generation of synthetic data to

train deep learning models.

Fig. 1: Tower inspection using drones, [32]

4 Proposed System

The proposed solution involves a honeycomb design

that the drones will create during flight, controlled

by an algorithm for flight and delivery in a

simulation environment. This process begins with

the flight controller that sends commands to the

actuators, moving the medical load and facilitating

the interaction with sensors and navigation control

for communication with the logistics area (Figure

2).

To develop the system, Kanban and Scrum

methodologies were applied, performing iterations

at each stage to allow continuous adjustments before

moving forward. An iterative approach was adopted,

performing multiple cycles of design,

implementation, verification, and maintenance

within each phase. Additionally, rapid prototyping

was used to create agile versions of the system and

obtain early feedback. The incremental development

divided the project into progressive and functional

phases, adapting to the needs of medical cargo

transportation. Continuous feedback and early,

continuous testing ensured the quality of the system,

identifying problems and making corrections

throughout the process.

4.1 Formation Flight Configuration

Algorithm

The flight configuration algorithm is responsible for

defining the starting point and destination for

package delivery. Initially, a connection is

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

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established with AirSim (an application specialized

in simulating drone flights) and then the specific

route of the drone is programmed, which includes

the collection of the package at the point of origin,

followed by the flight to the destination. Once the

delivery is complete, the drone lands safely, thus

ending its mission (Figure 3).

Fig. 2: System diagram

Fig 3: Flight configuration diagram

The complexity of the algorithm is determined

by the number of drones, being efficient in setting

up a honeycomb pattern in the simulation. The

algorithm sets the initial position in the form of a

hexagon for each drone using a for loop,

highlighting that the calculation involves constant

mathematical operations per drone, and the drones

are then moved to their initial and final positions

using moveToPositionAsync(). In Visual Studio

Code a script called “primera_test.py” was

implemented, the connection to the simulator was

started, and the control of the drone was enabled

through the AirSim API. Next, the drone takes off

and is guided from an initial position entered to a

target position and after a pause of 5 seconds, the

drone lands and the control is deactivated, thus

ending the connection with the simulator (Figure 4).

Fig. 4: Algorithm flowchart

4.2 Simulation Environment to Transport a

Medical Supply

To facilitate the transportation of medical cargo

using autonomous aerial vehicles, a simulation

environment was designed using Unreal Engine,

which offers a highly realistic 3D virtual space. In

this environment, drone and medical payload

models were precisely incorporated and control

algorithms were applied.

Creating a drone simulation environment in

Unreal Engine is crucial to evaluate and improve

system performance in a safe and controlled context.

This process involves downloading specific content

to configure key elements in the editor. In this case,

the Epic Games Launcher software is used, and the

"Landscape Mountains" option is selected, followed

by configuring AirSim plugins in the project.

Making sure to adjust the location of the

"PlayerStart" and setting the GameMode Override

to AirSimGameMode are critical steps to ensure

proper drone behavior. Furthermore, it is essential to

optimize the editor settings, avoiding overloading

the CPU and ensuring smooth performance. The

creation of a specific mountain environment is

shown in Figure 5 and can be modified to simulate

different flight and payload control scenarios for the

drone.

4.3 Honeycomb System using a Control

Process

The honeycomb system plays a fundamental role in

allowing the configuration of a hexagonal formation

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

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of drones through coordinated control systems. It is

essential to develop an algorithm that facilitates this

swarm flight control for coordinated flights

requiring precise instructions, maintaining their

positions in the hexagonal formation.

Fig. 5: Simulation environment

Mutual communication between drones is

essential to exchange data and adjust flight as

necessary and a control system ensures smooth and

efficient formation flight, which is essential for the

success of drone transport missions, including

activities such as takeoff, flight, landing, and

understanding the origin and destination of each

drone (Figure 6).

Figure 7 shows the beginning of the drone

simulation process in the AirSim program,

simultaneously with Visual Studio Code, which

houses the code responsible for automating the

drone's actions, according to the instructions in

Table 1. This process is the fundamental beginning

of the process, highlighting the integration of the

code and the real-time simulation of the drone.

The instructions in Table 1 are used to calculate

and configure the initial position of multiple drones

in a honeycomb formation in a flight simulation

program. First, it calculates honeycomb positions

for the drones, then takes them off and moves them

to their respective starting positions. This allows the

coordinated flight of multiple drones in a hexagonal

formation to be simulated in the simulation

environment.

Fig. 6: Honeycomb System Diagram

Fig. 7: Drone motion simulation environment

Table 1. Honeycomb System Instructions

Line

Instruction

# Calculate honeycomb position

center_position = airsim.Vector3r(0, 0, 0)

radius = 5.0

angle_deg = 60.0

angle_rad = math.radians(angle_deg)

# Set initial position. honeycomb. Ascend

for drone_name, drone_data in drones.items():

client = clients[drone_name]

posicion_inicial = drone_data["posicion_inicial"]

# Calculate honeycomb position

angle = drones.keys().index(drone_name) * angle_rad

x = center_position.x_val + radius * math.cos(angle)

y = center_position.y_val + radius * math.sin(angle)

z = posicion_inicial.z_val + 5.0

# Despegar y mover a la posición inicial

client.takeoffAsync().join()

client.moveToPositionAsync(x, y, z, 5).join()

The algorithm depicted in Table 1 initiates by

establishing a central point for the hexagon at

coordinates (0, 0, 0) and configuring a hexagon with

a radius of 5.0 units. A 60.0-degree angle separates

each point of the hexagon. Subsequently, through a

for loop, the initial drone positions are computed in

the shape of a hexagon, utilizing the drone's index

from the dictionary. Trigonometric functions, along

with the angle and radius, determine the "x" and "y"

coordinates, while the "z" coordinate is elevated 5

meters above the starting point. To transition the

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

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Volume 23, 2024

drones to their initial hexagonal positions, the

moveToPositionAsync() method is employed.

It is crucial to adjust the settings.json file within

Visual Studio to customize simulator parameters, as

exemplified in a segment of the file outlined in

Table 2. The customization encompasses drones

denoted as "Drone1" to "Drone5," each furnished

with a specialized sensor named "MyDistanceX"

designed for distance measurement. Furthermore,

the configuration version is stipulated as "1.2" on

line 2, ensuring compatibility with the anticipated

structure mandated by the simulator. In addition, the

simulation mode is defined as "Multirotor" in line 3,

which allows simulating vehicles with multiple

propellers, such as quadcopters, allowing complex

flights and specific studies on these aircraft. This

enriches the user experience and facilitates more

accurate and realistic investigations.

5 Results

5.1 Flight Configuration Algorithm

The use of swarm algorithm was chosen for its

computational efficiency, which depends on the

number of drones to be used. In addition, it can

calculate the initial positions in a hexagon, being

efficient in generating a honeycomb flight pattern.

Table 2. Drone 1 settings in settings.json file

Line

Instruction

"SeeDocsAt": "https://github.... ",

"SettingsVersion": 1.2,

"SimMode": "Multirotor",

"Vehicles": {

"Drone1": {

"VehicleType": "SimpleFlight",

"X": 0,

"Y": 4,

"Z": -2,

"Yaw": -180,

"Sensors": {

"MyDistance1": {

"SensorType": 5,

"Enabled": true,

"NumberOfChannels": 4,

"PointsPerSecond": 10000,

"X": 0,

"Y": 0,

"Z": 0,

The connection with AirSim software ensures

an efficient process from collection to delivery. As a

result, a code was generated resulting from the

implementation of an algorithm (Table 3) that

shows the flight configuration and distribution

process in autonomous flight formation,

representing only the final product of an extensive

simulation and testing process. This detailed

approach ensures real-world system safety and

effectiveness, highlighting the importance of

rigorous testing in the development of autonomous

technologies.

In the drone flight simulation code, the

statement, client.takeoffAsync().join(), allows the

drone to take off in the simulator, preparing it for

flight. The second instruction is

client.moveToPositionAsync(x, y, z, speed).join(),

guide the drone to a specific position defined by

three-dimensional coordinates (x, y, z) with a given

speed. These instructions are essential for

simulating precise drone movements, providing a

fundamental basis for the evaluation of flight

algorithms before their implementation in the real

world (Table 3).

5.2 Simulation Environment Creation

A coordinated flight control system was

implemented to control cargo transported through

three distinct areas: a mountainous terrain, a city,

and an urbanized area. Although the simulation

accurately represents the natural and urban

environments, it does not include details about the

takeoff and landing of the drone, nor does it show

the final location of the medical cargo after the

mission (Figure 8).

Table 3. Drone flight configuration

Line

Instruction

import airsim

import time

…

# Drone setup

client.enableApiControl(True)

client.armDisarm(True)

client.takeoffAsync().join()

…

# Take off and fly to the target position

client.moveToPositionAsync(posicion_inicial.x_val,

posicion_inicial.y_val, posicion_inicial.z_val, 5).join()

client.moveToPositionAsync(posicion_objetivo.x_val,

posicion_objetivo.y_val, posicion_objetivo.z_val, 5).join()

Fig. 8: Simulation environment of an urbanization

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Ricardo Yauri, Santiago Fernandez, Anyela Aquino

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5.3 Honeycomb System for Drones

In Figure 9, the transport of medical cargo in a city

is simulated, where efficient route planning is

conducted and adapted to changing conditions,

where it is highlighted, that drones manage to

reduce delivery times and avoid collisions, which

suggests a successful performance. In Figure 10, the

visual representation shows how drones operate in a

more complex environment, with urban structures

that imitate a more densely populated area,

evidencing adaptability to changing conditions in

this context. During testing, the drones

demonstrated efficient route planning and

adaptability to changing conditions in the virtual

environment, reducing delivery times and avoiding

collisions.

The interaction of the drone for the delivery of

medical cargo allowed a reliable landing, to ensure

the care of supplies and care of patients. On the

other hand, managing variations in supplies to be

delivered for different weights demonstrates the

need for the robustness of the honeycomb system. In

addition, updates to the AirSim program generated

the possibility of adaptation to a simulation

environment, allowing a realistic experience that

can be improved in other research such as medical

logistics.

6 Conclusions

In this article, a prototype was implemented to

evaluate drones with a flight planning algorithm and

swarm formation. The appropriate use of this

technology was evidenced by the evaluation of its

efficiency in the transportation of medical loads in

urban environments and cities.

Fig. 9: Swarm formation in the city

Fig. 10: Swarm formation in an urbanization

The design and implementation of the

simulation environment were suitable for test flights

with drones, using AirSim which was important for

autonomous systems. The delivery algorithm was

integrated into this environment, analyzing its

behavior through flight tests.

Despite the results achieved, it must be

considered that the approach and type of load

prevent the results from being generalized.

Furthermore, the use of laboratory simulations

avoids generalizing to actual transportation

conditions, indicating the need for field testing to

evaluate their effectiveness in the real world. This

study does not address aspects of regulation and

legislation necessary to implement this technology

in urban areas. Future work can investigate the

feasibility of implementing artificial intelligence

technologies in autonomous aerial vehicles in

training, to improve efficiency and safety in

transportation.

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WSEAS TRANSACTIONS on SYSTEMS

DOI: 10.37394/23202.2024.23.8

Ricardo Yauri, Santiago Fernandez, Anyela Aquino

E-ISSN: 2224-2678

Volume 23, 2024