Autonomous Drones

Divya B V, Anup Rao K, Ashish K Jacob, Vaishnav Pramod and Pattabi Ram

School of Electrical & Electronics Engineering, REVA University, Bangalore, India

Keywords: Autonomous Drone, Microcontroller-Based Control, Signal Mimicry, Raspberry Pi Zero, Quadcopter,

Cost-Effective Drone Control, Remote Control Emulation.

Abstract: This paper presents an innovative and cost-effective approach to autonomous drone systems, utilizing

microcontroller-based signal mimicry achieved through PWM signal analysis and recreation. The study

investigates the feasibility and effectiveness of this technique, demonstrating its potential for applications in

surveillance, logistics, disaster response, and environmental monitoring. By leveraging microcontrollers, the

system offers adaptability, scalability, and seamless integration with existing infrastructures, catering to

diverse operational needs. This paper contributes to the advancement of autonomous drone technology,

showcasing the potential of microcontroller-based signal emulation for cost-effective and adaptable drone

control. Additionally, emphasis is placed on the transformative potential of autonomous drone systems,

highlighting their capabilities in real-time data acquisition, machine learning, and collaborative swarm

intelligence across various industries.

1 INTRODUCTION

The exploration of autonomous drone control

methods has led to the investigation of integrating

microcontroller-based signal mimicry. This research

delves into various aspects of autonomous drones,

focusing specifically on Raspberry Pi Zero and other

cost-effective components. A comprehensive

investigation encompasses real-world performance,

adaptability, ethical considerations, and the unique

contributions of autonomous drone systems.

Various studies contribute to the advancement of

autonomous drone technology. Research on altitude

control algorithms and measurement fault diagnosis

provides essential insights for ensuring the

effectiveness of autonomous drone systems (Mung

and Lee, 2016), (Mung and et al. , 2017). Work on

deep reinforcement learning for navigation aligns

with the emphasis on innovative approaches in

autonomous drone control discussed in this

paper(Hodge, Hawkins, et al. , 2017). Investigations

into GNSS-based systems for delivery drones

highlight the importance of efficient navigation

techniques, relevant to the applications discussed in

this study (Hodge, Hawkins, et al. , 2017).

Additionally, proposed strategies for safer navigation

complement the focus on cost-effective and adaptable

drone control presented in this paper. These studies

collectively contribute to the progress of autonomous

drone technology, providing insights and solutions

that align with the objectives and findings of this

research.

This section explores the construction of the

experimental drone and the process of reading and

analyzing the signals generated by the transceiver. It

begins by outlining the conventional quadcopter

configuration, followed by a detailed description of

the components employed in the drone construction,

including information on the motors, electronic speed

controllers (ESCs), frame, propellers, and flight

controller. Additionally, details regarding the

transmitter and receiver used for wireless control of

the drone are provided.

Following the construction details, the intriguing

process of analyzing the transceiver's signals is

explored. Here, the Arduino UNO, a versatile

microcontroller, is leveraged to capture and interpret

the signals with accuracy. Discussion focuses on the

specific PWM pins utilized and their frequencies,

emphasizing their role in capturing crucial

information. A block diagram is presented to visually

represent the signal processing pipeline and how the

signals are received and processed by the Arduino.

The analysis extends beyond simply capturing the

signals. It demonstrates how to effectively read and

interpret these signals using the Arduino UNO's

914

B V, D., K, A. R., Jacob, A. K., Pramod, V. and Ram, P.

Autonomous Drones.

DOI: 10.5220/0013734900004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 3, pages 914-919

ISBN: 978-989-758-763-4

capabilities and a well-conceived approach. This

paves the way for subsequent analysis and decision-

making, enabling the extraction of valuable

information and precise control over the drone's

movements. The process is showcased through

figures depicting the signal behavior when the

transceiver is turned on and off, as well as how the

signal alters with adjustments to the throttle on the

controller. Such analysis is crucial for ensuring

optimal performance and identifying any potential

issues with the control system.

2 QUADCOPTER

CONSTRUCTION AND

PROBLEM FORMULATION

Quadcopters, a prominent type of UAVs, have

captured researchers' interest due to their exceptional

features. These include high maneuverability,

reliability, versatile applications, and cost-

effectiveness. With their four rotors arranged

symmetrically, quadcopters excel in navigating

complex environments with precision. Their compact

size and straightforward design make them ideal for

various tasks such as aerial photography,

surveillance, search and rescue missions, and package

delivery. This versatility and efficiency make

quadcopters a compelling choice for both research

and practical applications (Ozbek, Onkol, Garcia,

2018).

Various studies explore advancements in

autonomous drone technology. One study introduces

an optimization framework for vision-based

autonomous drone navigation, enhancing tasks like

surveillance and environmental monitoring (Navardi,

Shiri, et al. , 2016). Another research effort focuses

on autonomous drone delivery systems, potentially

revolutionizing last-mile logistics (Kannan;, Min,

Hong, 2019). A separate study discusses continuous

maneuver control and data capture scheduling of

autonomous drones in wireless sensor networks,

aiming to optimize data acquisition efficiency(Li, Ni,

Hong, 2019). Additionally, a proposed resource-

efficient online target detection system utilizes

autonomous drones, enhancing IoT applications

(Wang, Gu, et al. , 2020). Furthermore, research

examines the stability of small-scale UAVs under

PID control with added payload mass, contributing to

the understanding of drone stability (Pounds, Bersak,

et al. , 2019). Another study discusses resilient

control design for intelligent vehicle lateral motion

regulation, offering insights applicable to drone

control systems (Chang, Liu, et al. , 2017). Finally,

visual servoing techniques for micro quadrotors

landing on ground platforms are explored, addressing

challenges in drone precision landing (Huang,

Chiang, et al. , 2022).

In this section of the paper, the investigation into

autonomous drone control methods delved into the

integration of microcontroller-based signal mimicry.

Various aspects of autonomous drones were

explored, focusing on Raspberry Pi Zero and other

inexpensive components. The findings cover real-

world performance, adaptability, ethical

considerations, and the unique contributions of

autonomous drone systems.

In this section, the construction of the drone for

experimentation and reading the signals created by

the transceiver for recreation will be briefly

discussed.

2.1 Drone Construction

In this Subsection, Figure 1 depicts the conventional

quadcopter configuration, comprising four rotors

mounted on arms extending from a central body. Each

rotor is powered by a brushless DC (BLDC) motor,

generating downward thrust forces to achieve vertical

lift. The magnitude and direction of the thrust forces

are adjustable by varying the motor speeds.

Figure 1: Quadcopter Configuration

The drone system employed for our

experimentation utilized cost-effective components

to minimize expenses. The motors were 1000

RPM/Volt BLDC motors, known for their high power

and efficiency. To control the motor speeds, four

electronic speed controllers (ESCs) were utilized.

The drone frame was an f450 model, commonly

preferred by DIY drone builders. Plastic propellers

Autonomous Drones

915

were chosen for their combination of lightness and

durability. The flight controller was a KK2.1.5

model, a popular open-source option. Lastly, for

wireless control of the drone, a Flysky transmitter and

receiver were used.

Figure 2: F450 drone frame and 1000 RPM/Volt BLDC

motor used in the construction of the drone

Figure 3: KK2.1.5 Flight Controller for stability of the

system and Flysky transmitter and receiver used further

down in section B

Figure 4: The experimental quadcopter drone, used for

experimentation and further research.

The constructed drone system is capable of

executing various maneuvers, including hovering,

forward and backward flight, and left and right turns.

It exhibited stability and ease of control, even in

windy conditions.

2.2 Reading Receiver Signal

In this section, the procedure of analyzing the signals

generated by the transceiver of the drone system will

be investigated and examined. To accomplish this,

the Arduino UNO, a versatile microcontroller known

for its widespread use in various DIY projects, was

employed.

Specifically, designated Pulse Width Modulation

(PWM) pins on the Arduino UNO, including pins 3,

5, 6, 9, 10, and 11, were utilized for capturing and

interpreting the signals transmitted by the transceiver

with accuracy. These PWM pins have frequencies of

490 Hz, except for pins 5 and 6, which have

frequencies of 980 Hz. They are essential for

capturing and interpreting the signals transmitted by

the transceiver accurately.

Figure 5 presents a simplified block diagram

illustrating the flow of PWM signals from the

transceiver to the Arduino UNO. This diagram serves

as a visual representation of the signal processing

pipeline, facilitating a comprehensive understanding

of how the signals are received and processed by the

Arduino

Figure 5: Block Diagram of the Basic Reading Process of a

Transceiver Signal

Figure 6: The Arduino UNO is able to read pulse width

modulation (PWM) signals emanating from the transceiver.

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3 MAIN RESULT

3.1 Signal Analysis

By strategically leveraging the capabilities of the

Arduino UNO and employing a well-conceived

approach to signal acquisition, one can effectively

read and interpret the signals produced by the

transceiver of the drone system. This lays the

foundation for subsequent analysis and decision-

making, enabling the extraction of valuable

information and precise control over the drone's

movements.

Figure 7: Detection of the signal when the transceiver was

turned ON.

Figure 8: Alterations in the signal when the throttle on the

transceiver was adjusted to the maximum.

The analysis presented in Figures 7-11 focuses on

channel 2, corresponding to the throttle of the drone

controller. This involves capturing and plotting the

signal over time to understand its behavior.

The y-axis represents the signal, while the x-axis

represents time. The graph visually illustrates how the

signal changes in response to movements of the

remote controller stick, allowing assessment of

throttle channel stability, responsiveness, and overall

performance.

Similar analyses are conducted for all four

channels of the transceiver. By comparing signal-to-

time graphs of different channels, any discrepancies

or inconsistencies in behavior can be identified. This

ensures optimal functioning of all channels and the

drone controller.

Figure 9: Alterations in the signal as the throttle on the

transceiver was varied in an upward and downward

direction

Figure 10: Alterations in the signal when the throttle on the

transceiver was adjusted to the minimum.

Figure 11: Changes seen in the signal when the transceiver

was turned OFF.

Channel 2 analysis provides insights into throttle

control, showing how the signal modulates to adjust

the drone's speed and altitude. Evaluation includes

assessing throttle response smoothness, presence of

sudden signal changes, and overall control system

stability.

Analyzing all channels' signal-to-time graphs

offers a comprehensive understanding of the drone

controller's performance. This analysis is essential for

optimizing controller behavior and ensuring reliable

drone operation.

In summary, examining Channel 2 involves

capturing and graphically representing the signal over

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time to assess throttle channel performance. Similar

analyses across all channels aid in identifying

discrepancies and optimizing controller behavior for

efficient drone operation.

3.2 Recreation

In this section of the project, the process of achieving

simple automation of a quadcopter by analyzing the

signals produced by the transceiver and recreating

them using a microcontroller is delved into. This

approach aims to provide a cost-effective and

customizable solution for controlling the quadcopter

without relying on expensive commercial remotes.

To accomplish this objective, the Raspberry Pi

Zero microcontroller was utilized, widely recognized

for its adaptability and extensive application in DIY

projects. The Raspberry Pi Zero serves as the central

processing unit of the automated system, interpreting

signals received from the ground personal computer

(PC) of the transceiver and generating appropriate

control commands for the quadcopter, as depicted in

Figure 12.

The process began with thorough analysis of the

signals transmitted by the transceiver. Using the

Arduino UNO and its PWM pins, specific patterns

and frequencies associated with different control

inputs, such as throttle, yaw, pitch, and roll, were

identified. This analysis provided an understanding of

the communication protocol between the transceiver

and the quadcopter.

Based on the signal analysis, a Python script was

developed to emulate the transceiver's signals. The

script generates PWM signals with varying duty

cycles, corresponding to the desired control inputs.

The Raspberry Pi Zero's GPIO pins are utilized to

output these PWM signals, providing precise control

over the quadcopter's movements.

Figure 12: Block Diagram of the Basic emulating Process

of the Transceiver Signal

By combining signal analysis with

microcontroller emulation, simple automation of the

quadcopter was achieved. This method offers a

flexible and cost-effective alternative to commercial

remote controls, enabling customization and

integration with other systems.

4 CONCLUSION

Our investigation into microcontroller-based

autonomous drone systems yielded promising results,

demonstrating the feasibility and effectiveness of this

approach. A cost-effective drone was successfully

constructed using readily available components, and

our process for reading, analyzing, and emulating the

transceiver signals was established. This enabled the

achievement of basic automation of the quadcopter

through the Raspberry Pi Zero microcontroller,

serving as a customizable alternative to traditional

remote controls.

The key findings of our study can be summarized

as follows:

Effective signal analysis and emulation: The

Arduino UNO was successfully employed to capture

and interpret the transceiver signals, paving the way

for their subsequent emulation using the Raspberry Pi

Zero.

Cost-effective and adaptable solution: The use of

readily available components and our

microcontroller-based approach offer a cost-effective

and adaptable solution for autonomous drone control

compared to traditional methods.

Potential for diverse applications: The developed

system lays the groundwork for further exploration

and potential applications in various fields, including

surveillance, logistics, disaster response, and

environmental monitoring.

While this paper focused on basic automation, it

serves as a valuable stepping stone for further

development of autonomous drone systems. Future

research can explore more complex maneuvers,

integrate sensor data for environment awareness, and

investigate the application of machine learning

algorithms for enhanced decision-making

capabilities. Additionally, ethical considerations

regarding data privacy, safety, and responsible drone

use must be continually addressed as the technology

advances.

In conclusion, this paper has demonstrated the

potential of microcontroller-based signal emulation

for cost-effective and adaptable autonomous drone

control. This technology has the potential to

significantly impact various industries and pave the

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way for innovative solutions in the future of drone

technology.

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