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Abstract

An uncontrolled fire poses severe threats to both humans and the environment, making firefighting a perilous and complex task. Traditional fire suppression methods are inefficient, costly, and without thorough testing, leading to delays in gaining control over fire outbreaks. This paper presents a novel firefighting drone aimed at mitigating risks to firefighters by extinguishing fires and providing real-time imaging, gas concentration and fire location data monitoring. The proposed intelligent quadcopter utilizes the Pixhawk PX4 microcontroller for precise control and the Pixhawk Telemetry system for data processing. The proposed device is constructed from an ultra-strength S500 Quadcopter frame, NodeMCU, Arduino Nano, various gas sensors, a servo motor to extinguish the fire and a camera to detect fire events in real time. Equipped with an FPV camera and a video transmitter, it transmits live video feed to the ground, enabling efficient navigation using the Flysky I6X controller. The intended position and height of the drone are controlled using an adaptive optimization technique known as fuzzy-based backstepping control. This article demonstrates the effectiveness of the device by collecting and analyzing gas emissions data from controlled burns of various materials. The drone successfully measured concentrations of CO, CO₂, O₃, SO₂, and NO₂ in affected areas, providing valuable insights for firefighting operations. Different levels of gases have been measured depending on the concentration from burning alcohol, clothes, plastic materials, paper, leaves, and so on. The novelty of this work lies in the development and comprehensive analysis of an IoT-based firefighting drone conducting extensive real-time experiments.

Keywords

Drone flame sensor firefighting Pixhawk NodeMCU

Introduction

Fires pose significant threats globally, impacting both the environment and human lives. When sufficient heat is applied to a fuel source, combustion occurs, releasing volatile gases as covalent connections within the fuel are disrupted.¹ The consequences of fires extend beyond property damage, affecting local economies through tourism decline and long-term structural changes.² In 2019, 24,078 fire events were documented, resulting in 184 fatalities in Bangladesh.³ Tragically, a recent fire incident at a chemical store in Chittagong, Bangladesh’s major port, claimed the lives of 12 firefighters, marking the highest casualty toll reported by the Fire Department since 1981. Despite the best efforts, existing firefighting methods heavily rely on fallible human intervention, leading to limitations in effectiveness depending on the environment and specific circumstances. Moreover, such approaches may inadvertently contribute to the destruction of green forests and pose risks to human lives, especially firefighting personnel.⁴ Hence, introducing firefighting drones in fire-prone areas can address this challenge. Unmanned aerial vehicles (UAVs), commonly known as drones, have proven their utility across various domains, from routine tasks to high-risk operations, leveraging their flight capabilities primarily in military contexts. Over time, drones have found increasing applications in commercial sectors, film production, and recreational pursuits. This project aims to develop an IoT-based firefighting drone capable of autonomous fire control, data collection, and sensor monitoring. Leveraging the capabilities of firefighting drones enables early fire detection and access to fast and inaccessible areas for humans and ultimately saves lives.

Aydin et al.⁵ developed fire extinguishing balls and explored their enhanced effectiveness when used with drones and remote sensing technology. The authors applied remote sensing technology for monitoring, detecting, and assessing fire characteristics. This approach enables firefighters to deploy fire extinguishing balls from safe locations in the presence of wildfires, preventing the spread of fire into buildings and significant fuel resources. The proposed drone-based system facilitates flame length detection, assisting firefighters in extinguishing wildfires effectively and minimizing damage in both urban and wildland areas. Kinaneva et al.⁶ developed a forest firefighting drone incorporating artificial intelligence (AI) techniques. The authors employed two unmanned aerial vehicles (UAVs) equipped with AI capabilities, onboard processing, a graphical picture labeling framework implemented in Python, and a graphical user interface developed using Qt software. A neural network-based customized CNN model is utilized to construct the AI system. The proposed device demonstrates the ability to detect smoke or fire by employing computer vision techniques on the images and video frames captured by the drone cameras. The fixed-wing drone, characterized by an improved aerodynamic design and reduced carbon footprint, boasts a maximum take-off weight of approximately 15.9 kg and offers an impressive endurance rating. Furthermore, the rotary-wing drone is equipped to carry an infrared (IR) camera and a zoom camera simultaneously. Notably, the authors did not present any findings as the forest fire detection design is still in the developmental stage.

Cervantes et al.⁷ presented a cost-effective, timesaving, and easily controllable prototype drone designed to assist firefighting operations in various locations, ultimately contributing to life-saving efforts. The drone incorporates essential components such as an RC signal, temperature reading sensor, release signal mechanism, image capture capability, energy source, and ultrasonic sensors for enhanced functionality. The microcontroller utilized in this device is a NAZA processor, accompanied by additional equipment, including GPS, transmitter, receiver, and a servo motor for the dispenser. The drone incorporates six brushless motors for efficient lift and maneuverability of the entire system. For advanced communication, the authors employed the RC TGY-I6 system, a temperature reading sensor, an ultrasonic sensor for height measurement, a Go Pro camera, and an LED Display. The developed IoT-based firefighting drone demonstrates a remarkable weightlifting capacity of 13 kg, enabling it to carry significant payloads, and offers an extensive coverage area of 10 square meters for effective fire control. Hristov et al.⁸ presented a comprehensive system for early forest fire detection, combining the capabilities of multifunctional UAVs and LoRaWAN sensor networks. The authors employed three UAV models with, that is, the ALTi Transition VTOL UAV in their work. Fixed-wing UAVs are utilized for extended observation periods of 8–10 h, featuring a weight pull-off capacity averaging 16 kg, a thermal resolution of 640–480, and a 20× zoom capability. The authors further developed a LoRaWAN network using Raspberry Pi 3 embedded systems and Pycom LoPy modules as gateways, constituting their second LoRaWAN approach.

Hariveena et al.⁹ proposed the development of an internet-accessible device leveraging IoT technology. The device incorporates a variety of sensors, including PIR sensors, ultrasonic sensors, heat sensors, gas sensors, temperature sensors, and smoke sensors. Additionally, the authors utilized Raspberry Pi 3, a web camera, and DC motors, requiring a power supply to enable device movement. Operating systems such as Windows and Linux support the device’s display. Though the outcomes of this device are expected to involve significant potential in saving human lives and protecting valuable properties, the work did not report any experimental results. Jayapandian¹⁰ demonstrated the utilization of a firefighting drone device as an efficient and cost-effective solution to mitigate the frequency of fires. The author introduced the IoT-Base Cloud Enabled Smart Firefighting Drone, equipped with features such as GPS, UAV capabilities, a camera, Internet of Things integration, an extinguishing ball, fire sensors, and more.

Kanwar and Agilandeeswari¹¹ presented the development of a cost-effective firefighting robot that utilizes IoT technology to enhance human safety. The authors focused on monitoring the CO level of fires in their work. The system incorporates various advanced components such as the Android operating system, CO₂ gas, water pump, NodeMCU, MQTT Dashboard, MQ7 sensor, fire sensor, and MHZ-14 sensor. The paper provides detailed explanations of the fire levels and the corresponding extinguishing equipment, including the connection of MHZ14 and another sensor to the NodeMCU. By utilizing the MQTT protocol through an Android app, the authors demonstrated the control of the robot, pump activation, and monitoring of CO and CO₂ levels. When the CO gas level is below 500 ppm, the robot employs CO₂ gas as an extinguisher, whereas when the CO gas level exceeds 1000 ppm, it activates the water pump. The authors also outline future plans to optimize the system by replacing two manual water pumps with a self-operating pump, thus achieving cost savings. Chein et al.¹² designed an affordable firefighting robot with interfaces for home security applications equipped with an extinguisher and a lightweight metal frame. Various systems are integrated into the robot, including a flame sensor-based fire detection system and a modular obstacle detection system utilizing infrared and ultrasonic sensors. Experimental results demonstrate the robot’s autonomous capabilities, allowing it to navigate to different locations using IR and ultrasonic sensors. The robot achieves a top speed of 40 centimeters per second. In the initial experiment, the robot successfully moves toward the first goal, makes a right turn, and proceeds to the third goal. Subsequent investigations show that the robot swiftly adjusts its path to the left upon detecting obstacles on the right side. In the event of a fire, the robot moves to the location and employs the extinguisher to extinguish the flames. If no fire is detected, the system promptly triggers an alarm and sends a signal to the appliance module. The authors proposed future research involving the implementation of new algorithms for obstacle detection using IR and ultrasonic sensors.

Viegas et al.¹³ developed a UAV-based system to prevent forest fires using a multi-rotor configuration and water jet propulsion. The system consisted of distinct components such as wires, white paint, rotors, batteries, controllers, carbon fiber tubes, and a water nozzle. The authors proposed the versatile application of this technique for fire detection, monitoring, and burn spot mapping. They illustrated the performance of the water jet at different pressure levels, showcasing the corresponding forces exerted for both single and double-output scenarios. Additionally, the authors reported the effects of varying water jet powers, fire heights, and hose lengths. Wardihani et al.¹⁴ demonstrated the use of UAVs for real-time measurement of forest fires, aiming to prevent fire-related incidents by employing various components for fire detection. The authors utilized multiple sensors, a Raspberry Pi, and an APM flight controller. Four sensors are integrated into the APM, while a standalone temperature sensor is utilized. Additional components such as a compass, GPS, and IMU are also included. The Raspberry Pi processes data from the temperature sensor and GPS, transmitting it via the Transfer Control Protocol to web servers for visual logging on a dedicated website. The authors presented data on ten fire hotspots, using two flight timeouts to identify fires accurately. The first flight timeout of 0.2 s detects 7 out of 10 hotspots on the website, while the second flight timeout of 0.5 s detects nine hotspots, revealing a trade-off between shorter timeouts and data loss. The authors also implemented a method to mark fire positions on the website in real time.

Pavol et al.¹⁵ explored the potential application of UAVs in high-rise buildings. They used infrared cameras mounted on quadcopters and recording/transmitting devices for real-time monitoring. The infrared camera captured temperature conditions, which remained effective even after fire suppression. The authors conducted fire experiments and numerical FDS fire simulations to locate hidden fires. The objective of their research has made a significant impact in demonstrating the potential of UAVs in this field. Wang et al.¹⁶ designed a fire drone extinguishing system using the LM100 module, which consists of dissolving chemicals, and a vehicle. The drone features a system for continuous surveillance that includes laser, electromagnetic radar, video in high-definition, and ultraviolet video. It can be operated by hand or remotely. Finally, the authors used water under high pressure and sprayed dry particles from the pressure fire extinguisher apparatus.

Dampage et al.¹⁷ designed and implemented a system for detecting forest fires using wireless sensor networks and artificial intelligence techniques. The author utilized a wireless sensor DHT22 for measuring temperature and humidity in addition to light intensity and carbon monoxide. LDR and MQ9 are used to monitor carbon monoxide and light intensity. The authors presented machine learning and threshold ratio analysis to detect fire situations. The threshold ratio determines four environmental parameters during different situations morning, night, and afternoon. Additionally, machine learning techniques are applied using a dataset of 7000 samples for the four parameters. The analysis demonstrates that the probability values of all parameters are less than 5%, ensuring reliable fire detection. Rakib and Sarkar¹⁸ constructed an autonomous robot with a wooden base derived from a local Rashed tree. The robot incorporates a multisensor fire detection system (MSFDS) to detect and extinguish fires. The Arduino Uno microcontroller is utilized to operate the system effectively. By combining the performance of multiple sensors, the authors have developed a highly functional mobile robot. The inclusion of the LM35 temperature sensor enables the detection of significant temperature rises near the flame’s source, triggering appropriate system responses. The robot utilizes a water pump to extinguish fires by spraying water. The operational principle of this robot is straightforward and cost-effective, making it suitable for small-scale fire extinguishing and environmental control applications.

Hassanein et al.¹⁹ designed an autonomous robot equipped with various sensors, including flame, ultrasonic, proximity, and more, for effective fire detection and extinguishing. The robot incorporates a digital compass to locate the fire source accurately. Data from the digital compass is collected by an Arduino Mega microcontroller and transmitted to MATLAB via Bluetooth for further processing and operation. The authors utilize a live feed and map representation to enhance visibility and transparency, enabling humans to track the robot’s journey in extinguishing the fire. This user-friendly robot demonstrates versatility and can be deployed in both industrial and household settings. The authors reported that future enhancements and additional features could extend the robot’s functionality for broader applications. Shah et al.²⁰ implemented a prototype system to detect and combat fire and air pollution. The system is divided into two parts for circuit implementation. In the first part, a comparator circuit is utilized along with a Light Dependent Resistor (LDR), and a potentiometer is employed to set the voltage. The second part involves using LDRs and IR receivers for flame detection. The state-of-the-art 555 timing IC module is configured as a monostable device to obtain the desired output. The authors incorporated components such as the NOT gate, comparator circuit, Darlington pair, and NPN transistor in the system. The primary objective of this robot is to extinguish fires while fostering technological innovation. Its applicability extends beyond high-rise buildings, as it can find utility in various fields.

Ye et al.²¹ developed an automated firefighting robot with a specific focus on the STM32 embedded system. The authors provided a comprehensive description of the model’s construction, outlining each operational process step. The design incorporates key components such as the JY-901 attitude sensor, STM32 microcontroller, flame sensor, ultrasonic sensor, photoelectric sensor, and equation-based algorithms. The authors elucidate the overall control mechanism of the system, highlighting the implementation of a PID controller to ensure precise values and the utilization of three axes to control the robot chassis. The robot employs an ultrasonic sensor for navigation to avoid obstacles. The authors used the flame sensor, which promptly detects the presence of a fire source and signals the STM32 controller. Consequently, the robot moves toward the fire zone to extinguish the flames. While the authors provide a comprehensive explanation of the system’s construction and operation, no specific real experiments have been presented. Raafeek et al.²² devised an Internet-accessible firefighting robot that users can control via a mobile phone. The robot is equipped with a GSM module, ESP camera, and various sensors, including ultrasonic and flame sensors for fire detection. A water pump is employed in the system to extinguish fires. The Arduino Uno microcontroller serves as the central control system for the robot. Users can efficiently operate the firefighting robot using the Blynk software, which allows for seamless control and monitoring through a mobile phone. The prototype is cost-effective, making it suitable for deployment in both corporate and residential settings.

Abro et al.²³ designed a UAV using simulations based on Newton-Euler and Quaternion dynamics. Their research involved the implementation of advanced intelligent control laws, hybrid estimators, spectator designs, and quadrotor stabilization techniques. The authors examined the advantages of using quaternions, particularly in mitigating issues related to gimbal lock and discontinuities inherent in Euler angles. Some authors^24,25 discussed various unmodelled dynamic factors, that is, wind disturbance and rotor efficiency in drone design. Abro et al. studied the challenges associated with UAVs, specifically underactuated systems characterized by a higher degree of freedom and fewer control inputs. In response to these challenges, the authors introduced an innovative fuzzy-based backstepping control (FBSC) method, which places a strong emphasis on achieving superior trajectory tracking and reduced chattering. Through simulation results, FBSC demonstrates its potential for enhancing UAV stability, surpassing traditional backstepping control methods. The authors²⁶ developed an intelligent control concept for an underactuated quadrotor that combines a dual-loop one-dimensional fuzzy controller, adaptive sliding control, and trajectory recognition. This approach is aimed at stabilizing unmodeled variable dynamics and addressing instabilities.

From the above literature review, it becomes evident that significant research has been conducted on automatic fire control utilizing various advanced microcontrollers, sensing devices and artificial intelligence techniques. Drones have been effectively controlled and operated using artificial intelligence or controllers. However, most of these systems cannot assess gas quality or identify the root cause of a fire. Instead, they primarily focus on extinguishing fires using fire extinguisher balls. Very few articles provided real-time experimental results conducted by the developed prototype. This study develops a specialized firefighting drone with specific objectives, including fire analysis and localization, search and rescue operations, monitoring of hazardous variables, and the primary mission of fire control and suppression. Fixed firefighting drones, similar to automatic fire sprinklers and alarms, are implemented in densely populated high-risk areas to extinguish potential threats swiftly. Additionally, using these sophisticated sensors makes it feasible to identify the root causes of the fire events.

In this paper, a firefighting drone based on the Internet of Things (IoT) has been successfully designed and implemented. The Pixhawk microcontroller serves as the central control unit for operating the drone, while the NodeMCU microcontroller is responsible for managing the servo motor and various gas-sensing devices. The system incorporates a GPS module, enabling real-time tracking of the drone’s location. The Mission Planner software facilitates monitoring both the drone’s data and its live location. The study has made significant contributions in the following areas:

A prototype UAV system has been developed to assist firefighters in controlling and extinguishing flames employing an ultra-strength S500 Quadcopter frame. The proposed drone transmits live video stream to the ground via a video transmitter and an FPV camera, allowing for effective navigation using the Flysky I6X controller.

An important aspect of this work is the utilization of three microcontrollers, Pixhawk PX4, Arduino Nano and NodeMCU, for system control, command execution, and accuracy enhancement. The mechanical structure of the drone system has been specifically designed to meet the criteria for both autonomous operation and user input.

The sensor array plays a crucial role in the drone system’s functionality. MQ3, MQ4, MQ9, and MQ135 sensors and a servo motor are integrated to optimize the drone’s flight operations. Additionally, a specialized camera sensor is incorporated to provide the pilot with a first-person view of the mechanical system.

The firefighting drone’s software encompasses the controller’s functionality and handles all data processing tasks. The controller processes acquired data from various sensors to make it comprehensible for the user.

The most significant contribution of this research lies in developing a modular fire suppression system prototype utilizing IoT technology, which has global applicability. An in-depth exploration of fire science and experimental findings obtained in real-time using the designed prototype has been conducted, demonstrating a dedicated effort toward advancing the field of firefighting.

Section II discusses the implementation of the proposed system employing various hardware and software devices. Section III presents the outcomes of the conducted real field experiments. Section IV concludes the article.

Proposed system

In this section, the design and implementation of the proposed firefighting drone has been presented in detail.

The proposed firefighting system’s block diagram has been demonstrated in Figure 1. The subsequent paragraphs provide a description of the hardware and software needed to construct the firefighting drone.

Figure 1.

Block diagram of the proposed firefighting drone.

Hardware tools/components

There are two main components to the proposed firefighting quadcopter's design: the transmitter and the receiver. The GPS module, quadcopter frame, brushless motor, Pixhawk telemetry, 35A ESC, brushless propeller, camera, buzzer, video transmitter, monitor, controller, battery, and Pixhawk PX4 2.4.8 Flight controller microcontroller are all part of the receiver component. Conversely, the transmitter component is made up of a servo motor, a power module, an Arduino Nano R3, a flame sensor, MQ3, MQ4, MQ9, MQ135, DHT22, and a NodeMCU.

The Pixhawk PX4 2.4.8 Flight Controller and GPS: Figure 2 shows the drone system using the Pixhawk PX4 2.4.8 Flight Controller and GPS as the main microcontroller. This sophisticated controller supports the NuttX RTOS real-time operating system and has state-of-the-art 32-bit ARM Cortex-M4 high-performance processors. With eight RC channels and four serial ports, this sophisticated controlling tool allows for smooth control and communication. The controller supports both automated and manual modes, along with redundant power input and failover capabilities. Powered by a 32-bit STM32F427 Cortex M4 CPU with FPU and 128kb RAM, this controller delivers exceptional performance.¹² Furthermore, it integrates a built-in GPS module, providing constant visibility of the drone’s location.

Figure 2.

Pixhawk PX4 2.4.8 fight controller.

Arduino Nano R3: The Arduino Nano R3 microcontroller is the interface for connecting multiple sensors and is connected to the NodeMCU for data processing. It demonstrates excellent efficiency in processing data signals. Based on the Atmega328, it shares many capabilities with the Arduino Uno, including support for analog and digital inputs and outputs and PWM functionality.

NodeMCU ESP8266: The Arduino Nano is connected to the controller, specifically the NodeMCU ESP8266, through the serial transmission to receive signals for further computational processing. The NodeMCU ESP8266 is an open-source chip that provides analog and digital input and output pins, enabling computations and connection to a computer to display results.

S500 Quadcopter Frame: The S500 Quadcopter Frame is designed with the primary objective of establishing a well-balanced system capable of safely transporting components during fire circumstances. It is specifically engineered for drones to ensure they maintain a stable and suitable shape while in flight, enabling them to carry payloads effectively.

DJI 2212 Motor: In this work, four DJI 2212 motors are employed. These motors are DC synchronous motors that receive power from a DC electric source through a built-in inverter switching power supply. This power supply converts the input to an AC electric signal to operate the motor. Table 1 demonstrates that these motors are capable of supporting the weight of the drone and keeping it airborne during firefighting operations.

Table 1.

Features of DJI 2212 920KV brushless motor.

Voltage	No load		On load				Load type
	Current	Speed	Current	Pull	Power	EEP	Battery
V	A	rpm	A	g	W	%	Battery
11.1	0.3	10,200	8.5	600	94.4	6.4	3s Lipo/8045
14.8	0.3	13,610	13	940	192.4	4.9	4s Lipo/8045
18.5	0.4	17,000	17.2	1270	318.2	4.0	5s Lipo/8045

35A Esc: The 35A ESC (Electronic Speed Controller) acts as an essential component that utilizes signals from the Arduino Nano microcontroller to provide precise instructions to motors, enabling them to rotate, accelerate, or change direction accordingly.

First-person view (FPV) Camera: An FPV camera is employed in this work to receive and display data on an FPV monitor. In this setup, a video transmitter device is utilized, serving as both the receiver and broadcaster of the camera’s feed. This device effectively sends the captured information to the display. The camera features a conventional 720P/60FPS lens, accurately locating the fire’s exact position.

Pixhawk Radio Telemetry: The Pixhawk Radio Telemetry establishes a reliable communication link with the employed flight controller. It stands out as one of the most cost-effective design controllers, providing comprehensive GPS control capabilities. The Pixhawk Radio Telemetry comprises two key components: the transmitter and the receiver. We gain control over the drone by connecting the transmitter section to the microprocessor and the receiver section to the computer. The radio signal transmitted by this module furnishes real-time data on our position, altitude, angle, airspeed, vertical speed, battery life, and height. With the aid of this module, we can successfully launch and land the drone utilizing the Mission Planner framework, as depicted in Figure 3.

Figure 3.

Controlled drone flights in the mission planner framework.

MQ3: The MQ3 sensor is capable of identifying substances such as alcohol, benzine, CH4, hexane, LPG, and CO. This sensor utilizes an evaporator to heat a layer of conducting material, and the resulting resistance is measured.

MQ4: The MQ4 gas sensor modules are employed in gas leak detection devices for buildings and homes. These sensors are capable of detecting methane, butane, propane, and other hydrocarbon gases. The analog signal that indicates the amount of gas in the surrounding environment is continuously monitored by the gas instrument. It functions in the 500–1100 ppm LPG concentration range and has an internal resistance value of about 25 kΩ (15–50 kΩ).

MQ9: The MQ9 sensor is an electrical device that is used to measure the amount and presence of specific compounds in the atmosphere. It works especially well as a detector of dangerous gas leaks in homes and businesses. The LPG, CO, and CH4 gases are highly sensitive to the MQ9 sensor.

MQ135: The MQ135 sensor can identify distinct substances, including alcohol, benzene, smoke, CO2, NH3, and NOx. The steel exoskeleton that houses the gas-sensing element provides a broad detection range, excellent stability, and a quick response time.²⁷

DHT22: The DHT22 sensor is utilized to measure relative humidity and temperature. It operates by sensing the ambient air using a humidity-sensitive element and a thermistor, and it outputs a digital signal through the data pin.

Flame Sensor Fire Detection module: When the drone is in flight, the flame sensor is utilized to find fire sources. It sends data to the microcontroller after detecting light with a wavelength ranging from 720 to 1150 nm released by the fire flame.

Servo: A DC servo motor is utilized in this prototype to launch the fire-extinguishing ball toward the detected fire precisely. The servo motor has a 180-degree range of motion and mechanically opens the lid of the box.

Battery: The proposed firefighting drone incorporates a Li-Po battery with a current capacity of 16,000 mAh, a voltage of 44.4 V, and a charge-discharge rate of 25 C to power the drone frame. Different systems on the multi-rotor quadcopter require varying voltages and amperages for operation. The selection of the battery capacity needs to consider the weight limitations of the drone and the power requirements of its various systems. Table 2 displays the various systems on the drone and their operating voltage and current levels.

Table 2.

Operating voltage and current levels of various components used in this work.

System	Voltage (V)	Ampere (A)
3DR-transmitter	6–12	0.855
3DR-receiver	6–12	0.155
Pixhawk	5	0.500
Arduino	5–10	0.05
NodeMCU	5–10	0.05
Sensor and servo	12	5
FPV camera	12 ± 10%	0.05
Multirotor drone (S500)	22.2	40

Fly sky 16X controller: In this research, the proposed firefighting drone has been operated using a wireless controller known as a 2.4 GHz 6-10CH Channel Transmitter, which enables data transmission to an X6B Receiver. The X6B Receiver is installed within the drone frame. The bandwidth of the wireless controller spans from 2.408 to 2.475 GHz, and it has 135 bands.

Software tools

In this work, we utilized two software tools to create the proposed firefighting drone. The first tool employed is Mission Planner 1.3.77, which enables us to configure, calibrate, update firmware, and simulate flight on the Pixhawk PX4 2.4.8 Fight controller microcontroller. This software serves as a comprehensive platform for sending appropriate signals to the controller and receiver components. We primarily use Mission Planner to receive instructions, adjust motor settings such as direction and position, access live data, and store commands. Additionally, Arduino IDE 1.8.15 is utilized to edit and upload the code for the Arduino Nano R3 and NodeMCU ESP8266 microcontrollers.

Figures 4 and 5 show the 3D design of the prototype drone designed in Adobe Illustrator Editor application. The fundamental goal of this step is to develop and build an effective system capable of carrying elements and loads in the air, detecting gas disruption, and allowing drones to function safely around a fire.

Figure 4.

3D front view of the drone.

Figure 5.

3D side view of the drone.

The operational flowchart of the proposed drone is illustrated in Figure 6. Initially, the sensors detect the presence of fire and the corresponding data is transmitted via the NodeMCU platform. Subsequently, the microcontroller determines if the drone is in close proximity to deliver the extinguishing ball. This drone incorporates advanced technology and can carry payloads weighing up to 5 kg. The take-off weight of the drone ranges between 2 and 3 kg. A dispenser is utilized to ensure stability during payload release, designed to maintain the drone’s center of gravity as it loses weight. The operation of this dispenser is controlled by a servo motor, which is managed by the drone’s Arduino Nano microcontroller.

Figure 6.

Flowchart of the operation of the drone.

The proposed fire extinguishing drone is powered by brushless motors controlled by the Fight controller microcontroller. The four motors are connected to an electronic speed controller, which ensures the drone’s stability and efficient flight. Propellers are securely attached to the motors, enabling them to spin and generate propulsion. Figures 7 to 9 illustrate the top, front, and side views of the proposed firefighting drone, respectively.

Figure 7.

Top view of the designed firefighting drone.

Figure 8.

Front view of the proposed firefighting drone.

Figure 9.

Side view of the proposed firefighting drone.

The GPS system accurately determines the UAV’s precise location, enabling direct control through radio telemetry. Necessary instructions are provided to the processor, which will retain and execute them using GPS data to navigate to specified destinations. Additionally, a camera has been utilized to monitor the fire’s progression and capture early instantaneous local images from the drone. The camera’s signal can be tested to ensure proper functionality. An on-screen display (OSD) element is incorporated into the camera, overlaying Pixhawk tracking data. This operation provides the operator with additional information about the drone’s orientation and other flight controller data. The video transmitter is responsible for sending data to the monitor. The communication network includes the Fly Sky 16X controller, a six-channel radio transmitter, and a receiver. The controller commands the drone, while the radio telemetry receiver module plays a crucial role in directing the drone to specific areas and returning it to the operator.

Figure 10 displays the external components of the proposed firefighting drone. In this system, a total of six sensors have been employed to transmit important air quality data to the ground station, ensuring the safe and effective operation of the drone. These sensors enable the detection of fires and chemical substances in specific areas. While some of the data is collected using the current Pixhawk and UAV monitoring tools, additional sensors and systems can be added as optional extras.

Figure 10.

External components after connection.

The circuit diagram for the receiver network of the firefighting drone is presented in Figure 11. This drone is specifically designed for firefighting purposes, with automatic control capabilities. A camera is utilized to observe the situation and display the visuals on a monitor. The proposed drone monitors the air quality data employing various sensors and detects primary sources of fire using the flame sensor. In the event of any fire incidents, non-toxic fire extinguisher chemicals (balls) are distributed by the drone.

Figure 11.

Circuit diagram of the receiver network.

In this stage, a variety of sensors are utilized for fire detection and monitoring, as illustrated in Figure 12. In this research, two microcontrollers are employed to handle the collected data. The Arduino Nano R3 is connected to gas sensors, including flame, MQ3, MQ4, MQ9, and MQ135, as well as a DHT22 temperature sensor and a servo motor. The Arduino Nano R3 transfers the data to the Node MCU, which processes the information and displays the final output of the gas readings. The gas sensors are installed on the drone, enabling real-time data collection from any location. A servo motor, controlled by a transmitter, is also responsible for opening the structure that carries the fire extinguisher ball.

Figure 12.

Circuit diagram of the transmitter network.

In this study, the desired position and altitude of the designed drone are regulated through a fuzzy-based backstepping control (FBSC) adaptive optimization technique, illustrated in Figure 13. The fuzzy logic control system is operationalized using a vectoral distance-based rule base algorithm. This fuzzy network adeptly receives and processes feedback, subsequently adjusting and issuing the manipulated input essential for the backstepping control design. The deployment of the FBSC technique ensures robust control and expedites convergence for the proposed quadcopter, even in the presence of external disturbances, maintaining stability, and precision in trajectory tracking under varying conditions.

Figure 13.

Fuzzy-based backstepping control of the proposed quadcopter.

Results and discussion

Real-time air quality data is collected by the various sensors integrated into the proposed device, and the data is presented in this section along with corresponding figures and tables. The sensors generate string-based inputs, which are transmitted to a server using a Wi-Fi module connected to the device. Subsequently, the data can be accessed and analyzed through an IoT application.

In this work, real experiments have been conducted in an open field of the Mirpur area of the capital, Dhaka of Bangladesh. Various investigations have been performed to check whether individual sensors are collecting instantaneous air quality data before initiating aerial navigation of the drone. Figure 14 depicts the real-time positioning of the designed firefighting drone achieved through Wi-Fi using ArduPilot. The duration of the flight was approximately 1 h, during which the drone maintained a flight altitude of around 10 m, with a specific altitude reading of 2.21 m.

Figure 14.

Drone’s position in ArduPilot.

The quadcopter’s position and altitude robustness have been enhanced using a fuzzy-based backstepping control optimization technique. Upon takeoff, the drone ascends to an altitude of 3 m and maintains this level for 20 s before initiating its descent. With the FBSC controller, the drone demonstrates efficient adherence to the altitude reference setpoint, as depicted in Figure 15. Notably, the altitude stabilizes swiftly within 15 s during ascent. In contrast, the drone achieves a steady altitude within 25 s during the descent phase. Notably, the Mission Planner framework governs the drone’s altitude settings and landing operations.

Figure 15.

Altitude versus time of the designed drone with FBSC controller.

The developed fire-mitigant UAV system was field tested in the controlled experiment site in Mirpur on April 10, 2023, at 3 pm, as depicted in Figure 16. The investigation location recorded a temperature of 35°C with 30% humidity. The air quality and fire data for this work were collected under controlled conditions at a secure outdoor open field location. Figure 17 illustrates the concentrations of different air contaminants (PPM) with the change of time. During the calibration process, the graphs were plotted using a serial plotter. The serial plotter accepts input from Arduino and visualizes it as waveforms. It can display data from one or more sensors on a single graph simultaneously. The unit of measure used in the graphs is parts per million (PPM), which is a standard fractional unit for concentration. For example, two PPM of methane indicates that two out of every million air molecules are methane. Various contaminants (alcohol, clothes, plastic, paper and leaves) were intentionally burned, creating the desired environment to generate gases detectable by the installed sensors. The proposed system effectively measured and accurately determined the values using a variety of sensors, and the collected data was transmitted to the local server.

Figure 16.

Controlled experimental site at Mirpur, Dhaka.

Figure 17.

Concentrations of different air contaminants (PPM).

It is noteworthy that real-time data obtained from the continuous burning of different materials, such as alcohol swab, paper, plastic, wood, and leaves, exhibited varying levels of air pollutants. Burning paper produces CO2, and wood results in carbon dioxide, water vapor, oxygen, and nitrogen. Plastic emits carbon dioxide and carbon monoxide, while leaves generate carbon monoxide. Consequently, the developed firefighting drone measured different air pollutant materials and activated a servo motor to mitigate the presence of fire.

Figure 18 demonstrates that as the time scale increases, the sensor graphs exhibit increased fluctuations, responding to the sensitivity of gases. As the concentration of gases increases due to the burning of various components, the wave amplitudes increase. When an alcohol pad was burned near the sensors, the graph indicated a 3× increase in alcohol content, with a corresponding rise in the sensor’s output voltage. The MQ3 alcohol sensor can detect alcohol concentrations ranging from as low as 25 ppm to as high as 500 ppm. The MQ135 air quality sensor is frequently employed for detecting smoke and hazardous substances in outdoor air. It operates on a 5 V supply and consumes 150 mA. During the testing, it was observed that the MQ135 sensor required a 20-s preheating period before operation. After calibrating the MQ135 sensing module for over 300 units, the results began to exhibit a sharp increment by a factor of approximately 2.18. For the MQ9 sensor, the output gas concentrations are escalated by a factor of roughly 1.45.

Figure 18.

Changes of gas contaminants with the burning of various materials.

Table 3 shows the proposed firefighting drone’s component details with the corresponding estimated cost, dimension, and weight. According to this table, the developed device costs approximately $468 USD and comprises a total weight of 990 g.

Table 3.

Estimated cost, dimension, and weight of the entire system.

Component	Unit price (BDT)	Quantity	Total cost (BDT)	Dimension (mm)	Weight (g)
Pixhawk PX4 2.4.8 flight controller	12,000	1	12,000	82 × 50 × 16	40
Pixhawk radio telemetry kit	4000	1	4000	26 × 26 × 9	36
Quadcopter frame	1700	1	1700	–	400
GPS module with compass	2450	1	2450	26 × 36 × 4	30
Flysky 16X controller	7000	1	7000	183 × 193 × 100	880
DJI 2212 motor	1100	4	4400	144.018 × 118.11 ×34.036	55
NodeMCU	410		410	24 × 16 × 3	60
ESC	1050	1	1050	–	6.6
Monitor	3200	1	3200	184 × 126 × 28	450
Camera	1990	1	1.990	12.5 × 12.5 ×17	5
Flame sensor	50	4	200	15.25 × 33 × 12.7	5
Battery	2200	1	2200	3.5 × 8.0 × 20	20
Servo motor	150	2	300	39.5 × 20.5 × 40.7	14
B3 pro compact charger	500	1	500	95 × 55 × 35	180
VTX transmitter	3660	1	3660	36 × 36 × 4.5	6.8
Video transmitter	3000		3000	73 × 41 × 14	80
MQ4 sensor	150	1	150	32 × 22 × 27	12
MQ9 CO combustible gas sensor	130	1	130	32 × 20 × 22	10
MQ3 sensor	170	1	170	32 × 22 × 16	10
DHT22 sensor	350	1	350	15.3 × 7.8 × 25.3	10
MQ135 sensor	150	1	150	32 × 22 × 23	10
Arduino nano	600	1	600	–	18
Arduino nano expansion board	350	1	350	72 × 58 × 15	29
Subtotal = 49,990 BDT ($468 USD)				Full system dimension≈ 1180 × 1250 × 860	Total weight ≈ 990 g

A summary of various components to design the prototype of the proposed drone is listed in Table 4.

Table 4.

Various components of the proposed firefighting drone.

Component	Description
Flight controller	Pixhawk PX4 2.4.8
Radio telemetry	Pixhawk Radio Telemetry Kit 433 Mhz 100 mW
GPS module with compass	Compass Stand Holder for APM ArduPilot Mega 2.6 Pixhawk Flight Control
Quadcopter frame	S500 S500 480 mm Quadcopter Drone Frame Integrated PDB with High Landing Gear
Controller	Flysky FS-i6X 2.4GHz 6CH/10CH AFHDS 2A RC Transmitter Remote Controller with FS-X6B Receiver
Motor	DJI 2212 920 kV Brushless Motor 1CW and 1CCW
NodeMCU	ESP8266 CH340 NodeMCU Wi-Fi Module Lua V3 (Power input: 4.5 ~ 10 V, USB-powered)
ESC	LANRC 35A BLHeli_S 2-6S ESC With RGB Led
Monitor	7-inch LCD TFT FPV 1024 × 600 Monitor with T plug Screen No blue FPV Monitor Photography for Ground Station DJI Phantom
Camera	New Foxeer Predator V4 Micro FPV Camera 16:9/4:3 PAL/NTSC switchable Super WDR OSD 4 ms Latency Upgraded Foxeer Predator V3
Flame sensor	detect flames in the 760–1100 nanometer wavelength range (Power Supply: 3.3/5 V)
Battery	Wild Scorpion 11.1 V 2200 mAh Li-Po Battery
Servo motor	Mini Servo SG90 (Operating voltage: 3.0 ~ 7.2 V)
VTX transmitter	High-quality 5.8 GHz VTX that comes with four adjustable levels of transmitter power
Video transmitter	FPV 1.2 Ghz 1.2G 8CH 1500 mw Wireless AV Sender TV Audio Video Transmitter Receiver for QAV250 250 FPV Quadcopter
MQ9 CO combustible gas sensor	Carbon Monoxide, Methane, and LPG Gas Sensor can be used to sense Carbon Monoxide and Methane Gas
MQ3 alcohol sensor	suitable for detecting Alcohol, Benzine, CH₄, Hexane, LPG, and CO. It provides an analogue resistive output based on alcohol concentration.
DHT22 digital temperature humidity sensor	Temperature and Humidity Sensor Module AM2302 is a basic, low-cost digital temperature and humidity sensor.
MQ135 air quality sensor	Ammonia, Sulfide, and Benzene steam, and sensitive to smoke and other harmful gases. When the target combustible gas exists, the conductivity of the sensor is higher along with the gas concentration rising
Arduino nano expansion board	Make it easy to connect a variety of sensors and other components altogether. It expands Arduino’s Digital I/O and Analog Input Pins with Power and GND.

A comparison between the proposed firefighting drone and existing systems is presented in Table 5. In contrast to other systems that remain primarily theoretical, this study employs a diverse range of gas sensors to measure various air components. The research provides a comprehensive demonstration of the designed prototype and conducts real-world experiments on different fire locations with extensive controlled burns to validate its performance. The proposed firefighting system provides real-time data on gas concentrations and the precise site of the incident.

Table 5.

Comparison of the proposed device with other works.

Ref.	Main features	Primary microcontroller	Employed sensors	Demonstrated experimental results	Cost (USD)
Aydin et al.⁵	Extinguish forest fire	Raspberry Pi Zero	RC TGY-16, Ultrasonic Sensor	Activation time of wildfire extinguisher	N/A
Kinaneva et al.⁶	Early fire detection	Raspberry Pi 3	Temperature, humidity, and atmospheric pressure	No experimental results	N/A
Jayapandian¹⁰	Firefighting drone design	Cloud controller unit	Fire, smoke, thermal, collisions detecting sensor	No experimental results	N/A
Huiwhdehfirj et al.¹⁴	Real-time forest fire monitoring	Arduino Mega	Temperature sensors and other sensors	No experimental results	N/A
Wang et al.¹⁶	Fire extinguishing drone for skyscrapers	Built-in	Flame sensor	Water and nozzle pressure, angle, water flow rate, time	N/A
Dampage et al.¹⁷	Detection of fire	Arduino Nano	DHT22, LDR, MQ9	Temperature, humidity and light intensity monitor during forest fire	N/A
Raafeek et al.²²	IoT-based guided firefighting vehicle	Arduino Uno	Flame sensor	No experimental results	N/A
This work	Automatic Firefighting Drone using IoT	Pixhawk PX4, Arduino Nano, NodeMCU	Flame, MQ3, MQ4, MQ9, MQ135, DHT22	Temperature, humidity, flame and various gas emissions monitor during burning of different materials	$468

Conclusions

In this paper, the development and implementation of a cost-effective firefighting drone has been presented. The S500 Quadcopter frame-built prototype incorporates various components, including the flight controller, GPS module, camera, radio telemetry, drone controller, MQ3, MQ9, MQ135, DHT22, and flame sensor. The flight controller performs multiple functions and is connected to the motors. The GPS module is connected to the flight controller, and the radio telemetry receives data. The sensors integrated into the prototype facilitate measuring different types of gases during the ignition of various materials. The Arduino Nano microcontroller serves as the interface for connecting these sensors and is linked to the NodeMCU for data processing. Actual field tests with the burning of various components have been performed by the developed system to measure different air pollutants.

In the future, the utilization of advanced sensors could enhance the firefighting drone’s capabilities by providing more comprehensive and accurate information about the fire. Incorporating thermal imaging cameras, radiation sensors, or other specialized sensing modules would enable a deeper understanding of fire dynamics. Furthermore, potential upgrades to the drone could involve increasing its payload capacity to carry cylinders or fire-controlling foam, thus enhancing its effectiveness in fire suppression operations. Extending the drone’s flight time would also be beneficial, as it would allow for more extended surveillance and increased operational duration. There is considerable room for improvement and innovation in firefighter drones, and as technology continues to progress, we anticipate the development of more advanced features and functionalities. Deep learning-based artificial intelligence techniques can be employed to detect fires automatically.

Footnotes

Not applicable.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

Grant numbers

Not applicable.

Ethical committee

Not applicable.

ORCID iD

Riasat Khan

Data availability statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

Caton

Hakes

RSP

Gorham

, et al. Review of pathways for building fire spread in the wildland urban interface part I: exposure conditions. Fire Technol 2017; 53: 429–473.

Michael

Lensky

Brenner

, et al. Economic assessment of fire damage to urban forest in the wildland–urban interface using planet satellites constellation images. Remote Sens 2018; 10: 1479.

Sahebi

Rahman

. Fire risk situation analysis in the Nimtoli area of Old Dhaka. J Asiat Soc Bangladesh Sci 2021; 46: 91–102.

Haque

Marma

HUM

Chowdhury

. Prediction of burn incidents due to faulty installations in RMG factories in Bangladesh. In: International conference on electrical, computer and communication engineering, Cox’s Bazar, Bangladesh, 16–18 February 2017, pp.733–737. New York, NY: IEEE.

Aydin

Selvi

Tao

, et al. Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire Fighting. Drones 2017; 3: 17.

Kinaneva

Hristov

Raychev

, et al. Early forest fire detection using drones and artificial intelligence. In: International Convention on Information and Communication Technology, Electronics and Microelectronics, Opatija, Croatia, 20–24 May 2019, pp.1060–1065. New York, NY: IEEE.

Cervantes

Garcia

Herrera

, et al. A conceptual design of a firefighter drone. In: International conference on electrical engineering, computing science and automatic control, Mexico City, Mexico, 5–7 September 2018, pp.1–5. New York, NY: IEEE.

Hristov

Raychev

Kinaneva

, et al. Emerging methods for early detection of forest fires using unmanned aerial vehicles and lorawan sensor networks. In: EAEEIE annual conference, Hafnarfjordur, Iceland, 26–28 September 2018, pp. 1–9. New York, NY: IEEE.

Hariveena

Anitha

Ramesh

. IoT-based fire detection and prevention system. International conference on recent advancements in engineering and management, Warangal, India, 9–10 October 2020, pp.1–6. Bristol: IOP Publishing

10.

Jayapandian

. Cloud enabled smart firefighting drone using internet of things. In: International conference on smart systems and inventive technology, Tirunelveli, India, 27–29 November 2019, pp.1079–1083. New York, NY: IEEE.

11.

Kanwar

Agilandeeswari

. IOT based fire fighting robot. In: International conference on reliability, infocom technologies and optimisation, Noida, India, 29–31 August 2018, pp.718–723. New York, NY: IEEE.

12.

Chien

Guo

, et al. Develop a multiple interface based fire fighting robot. In: International conference on mechatronics, Kumamoto, Japan, 8–10 May 2007, pp.1–6. New York, NY: IEEE.

13.

Viegas

Chehreh

Andrade

, et al. Tethered UAV with combined multi-rotor and water jet propulsion for forest fire fighting. J Intell Robot Syst 2022; 104: 21.

14.

Huiwhdehfirj

Wardihani

Ramdhani

, et al. System for real-time forest fire monitoring using unmanned aircraft. J Eng Sci Technol 2018; 13: 1587–1594.

15.

Pavol

Paulína

Martin

. Unnamed Aerial Vehicle technology in the process of early fire localization of buildings. In International scientific conference on sustainable, modern and safe transport, Slovak Republic, 29–31 May 2019. Amsterdam, Netherlands: Elsevier.

16.

Wang

Yuan

Chen

, et al. A study of fire drone extinguishing system in high-rise buildings. Fire 2022; 5: e75.

17.

Dampage

Bandaranayake

Wanasinghe

, et al. Forest fire detection system using wireless sensor networks and machine learning. Sci Rep 2022; 12: 46.

18.

Rakib

Sarkar

MAR

. Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller. In: International conference on informatics, electronics and vision, Dhaka, Bangladesh, 13–14 May 2016, pp.909–914. New York, NY: IEEE.

19.

Hassanein

Elhawary

Jaber

, et al. An autonomous firefighting robot. In: International conference on advanced robotics, Istanbul, Turkey, 27–31 July 2015, pp.530–535. New York, NY: IEEE.

20.

Shah

Mamtora

, et al. Fire fighting robot. Int J Emerg Trends Technol Comput Sci 2013; 2: 232–234.

21.

Zhang

, et al. Intelligent firefighting robot based on STM32. In: Chinese automation congress, Hangzhou, China, 22–24 November 2019, pp.3369–3373. New York, NY: IEEE.

22.

Raafeek

ARM

Satheeskanth

Mathavan

, et al. IoT based guided fire fighting robot. In: International conference on materials science and manufacturing technology, Coimbatore, India, 8–9 April 2022. Bristol: IOP Publishing.

23.

Abro

GEM

Zulkifli

SABM

Asirvadam

, et al. Model-free-based single-dimension fuzzy SMC design for underactuated quadrotor UAV. Actuators 2021; 10: 191.

24.

Abro

GEM

Zulkifli

SABM

Asirvadam

, et al. Effects of unmodelled dynamic factors on an under-actuated quadrotor: a review of hybrid observer design methods. Meas Control 2020; 53: 1978–1987.

25.

Abro

GEM

Ali

Zulkifli

SABM

, et al. Performance evaluation of different control methods for an underactuated quadrotor unmanned aerial vehicle (QUAV) with position estimator and disturbance observer. Math Problems Eng 2021; 2021: 1–22.

26.

Abro

GEM

Zulkifli

SABM

Asirvadam

. Dual-loop single dimension fuzzy-based sliding mode control design for robust tracking of an underactuated quadrotor craft. Asian J Control 2023; 25: 144–169.

27.

Kosim

NAM

Hassan

SLM

Halim

ISA

, et al. Air quality monitoring system using Arduino MATLAB analysis. In: Control and system graduate research colloquium, Shah Alam, Malaysia, 23–23 July 2022, pp.225–230. New York, NY: IEEE.

Development of an IoT-based firefighting drone for enhanced safety and efficiency in fire suppression

Abstract

Keywords

Introduction

Proposed system

Hardware tools/components

Software tools

Results and discussion

Conclusions

Footnotes

Declaration of conflicting interests

Funding

Grant numbers

Ethical committee

ORCID iD

Data availability statement

References