An unmanned aerial vehicle for search and rescue applications

Unmanned aerial vehicles (UAVs) have emerged as potential tools for search and rescue. This study evaluates the feasibility of developing UAVs for search and rescue from multiple perspectives, including positioning and environmental monitoring considerations. The system architecture incorporates a UAV equipped with sensors for particulate matter, temperature/humidity, and carbon monoxide (CO) detection. Data collected by these sensors were compared with official data from the Environmental Protection Administration (EPA) in Taiwan to assess the reliability of the obtained results. The findings indicate that the data collected by the UAV closely align with the official data, suggesting their reliability and values as references. Furthermore, the study simulates the use of the UAV for automated flight route planning during disaster scenarios. The UAV efficiently navigates predetermined paths around disaster sites while monitoring environmental factors. The results highlight the importance of developing UAVs for search and rescue, as they integrate Internet of things (IoT) technology and provide geolocation and environmental information. The developed UAV enables the rapid acquisition of critical data, supporting decision-making and execution during rescue operations. This research provides a substantial demonstration of the UAV for search and rescue applications.


Introduction
In the era of technological advancements, natural disasters are unpredictable and unavoidable.Traditional rescue methods usually involve post-disaster response and relief efforts.However, in certain disaster scenarios, such as the Valparaíso wildfires in Chile [1], these disasters are often caused by human activities, resulting in severe social issues including loss of lives and property.Past major fires in Valparaíso have required significant manpower for rescue operations.However, conducting human rescue operations in hazardous environments can pose risks to the rescuers themselves, and the more personnel involved, the higher the potential danger.With the changing societal landscape, there is a growing trend towards achieving optimal efficiency with minimal personnel.Therefore, utilizing unmanned aerial vehicles (UAVs) [2][3][4][5][6] and Internet of things (IoT) [7][8][9][10][11][12][13][14][15][16][17][18] technology to achieve costeffective disaster rescue operations has become a viable solution.With the continuous research and development of UAV technology, there is an increasing demand for UAV assistance in various fields.This includes search and rescue drones [2,3], transport drones [4], security drones [5], and service drones [6], among others.The concept of the IoT refers to the interconnection of physical devices and objects through the internet, enabling information sharing and intelligent interaction.The integration of the IoT with UAVs allows for real-time monitoring, remote control, and data collection capabilities.This integration is particularly advantageous in disaster search and rescue missions, as it provides more accurate disaster area information, real-time rescue instructions, and efficient resource allocation.The combination of IoT technology and UAVs holds the promise of delivering more efficient and safer solutions for disaster rescue.With respect to search and rescue applications, Riveral et al. proposed a UAV equipped with global positioning system (GPS), thermal imaging, and optical imaging capabilities for human detection and geolocation [2].It primarily serves as a tool for search and rescue operations.However, environmental monitoring functionalities are not included in the UAV.Understanding the air quality and other environmental factors in disaster-stricken areas is crucial for decision-makers to develop more effective rescue plans.An IoT-based UAV system for natural environment monitoring and disaster prevention was reported [3], incorporating real-time image inspection and environmental information detection.Nevertheless, it does not include GPS functionality, which may limit its comprehensiveness in search and rescue operations.In this paper, a rescue UAV with a web camera and Wi-Fi module is proposed, enabling real-time image acquisition.It is equipped with carbon monoxide (CO) sensors, temperature and humidity sensors, as well as PM2.5/PM10 sensors, enabling effective monitoring of the air conditions in disaster areas.Additionally, it features a GPS module and an automatic controller, facilitating stable autonomous flight of the UAV.The integration of these functionalities allows the rescue UAV to provide comprehensive support in disaster areas.Through IoT technology, UAVs can seamlessly connect with other devices, sensors, and systems, forming an intelligent rescue network.This integration not only enhances the efficiency of disaster search and rescue but also better protects the safety of rescue personnel.

System design
The system block diagram depicting the components of the quadcopter UAV is illustrated in Figure 1.This UAV configuration encompasses various essential elements, including a battery, a web camera, a GPS module, a receiver, embedded system, an auto pilot controller, a Wi-Fi module, a CO sensor, temperature-humidity and PM2.5/PM10 sensors, an electronic speed controller, and brushless motors.Through the utilization of sensors integrated within the quadcopter UAV, atmospheric conditions can be effectively detected.These sensors play a pivotal role in collecting fire-related information, which can then be promptly transmitted to the disaster relief center.Consequently, this data facilitates immediate decision-making by the relief center personnel, aiding in the assessment of the fire's severity.The quadcopter UAV combines IoT technology with the PM2.5 and PM10 particulate matter sensors and other components to enable real-time acquisition of images and understanding of the air quality in disaster-stricken areas, with a focus on assessing threats to human life.The system provides up-to-date situational information to enable rescue personnel to be well-prepared and avoid excessive exposure to harmful gases.A designed air quality monitoring system is integrated into an UAV using autonomous navigation technology to perform airborne air quality monitoring tasks.The UAV utilizes a microcontroller to control stable autonomous navigation capabilities.A 5200 mAh lithium polymer battery is chosen to enhance stability and safety.Brushless motors and electronic speed controllers are utilized for motor speed control.By integrating all the components, the goal of stable autonomous flight for the UAV is achieved.The PM2.5 and PM10 particulate matter sensors operate on the basis of laser scattering principles, as shown in Figure 2. By illuminating lasers onto suspended particles in the air, the sensor collects intensity variations of scattered light at specific angles.A microprocessor performs calculations on the scattered light to determine the number of suspended particles with different diameters.The sensor achieves a particle counting efficiency of 50% for 0.3 μm particle size and 98% efficiency for particles ≥0.5 μm.Furthermore, it effectively measures particulate matter mass concentrations in the range of 0 to 500 μg/m³.The temperature-humidity sensor has a wide operating range, with a temperature range of -10℃ to 50℃, humidity range of 0% to 99%, temperature measurement accuracy of ±0.5℃, and humidity measurement accuracy of ±2%.To ensure the safety of rescue personnel in potentially hazardous environments, the UAV utilizes a CO sensor to detect excessive levels of CO in disaster-stricken areas and prevent potential harm in advance.
By obtaining real-time information, rescue personnel can make timely judgments and choose appropriate rescue equipment for their operations.The CO sensor measures concentrations ranging from 10 to 1000 ppm, operates at a voltage between 4.9 and 5.1 V, and consumes power within the range of 0.5 to 350 mW.Through these measured values, the degree of CO concentration can be accurately determined, allowing corresponding measures to be taken at the disaster site.The application of GPS positioning technology allows the UAV to accurately determine its position and navigate according to pre-programmed target point.Additionally, through execution commands such as the camera shutter command, specific mission tasks can be carried out during flight without compromising the stability of the UAV.

Results and discussion
The developed UAV is shown in Figure 3.In this work, data collection was conducted using particulate matter and temperature-humidity sensors, and the obtained data was compared with the data from the Environmental Protection Administration (EPA).The suspended particles, temperature, and humidity measurement data are listed in Table 1.The measurement data from both the EPA and this work exhibited a high degree of coincidence.2. In this study, a CO sensor is employed to collect CO data, which are compared with the data provided by the EPA.The flight path planning of UAVs during disaster scenarios was conducted.Figure 4 shows the flight path planning diagram.Firstly, the disaster relief center for emergency personnel was designated.Subsequently, three simulated disaster sites A, B, and C were established.The UAV was programmed to fly in a predetermined pattern, circling each disaster site five times while utilizing the onboard monitoring system of the UAV to assess environmental factors.The autonomous navigation path was then planned as follows: starting from the designated disaster relief center, the UAV flew to the simulated disaster site A, performed five loops upon reaching the first target, proceeded directly to the simulated disaster site B, completed five loops at that location, and finally returned to the simulated disaster site C. Upon completing five additional loops, the UAV returned to the starting point for landing.Overall, the results demonstrate the successful integration of suspended particle, temperature-humidity, and CO sensors for data collection and the efficacy of autonomous drone navigation in disaster scenarios.These findings contribute to the advancement of environmental monitoring for search and rescue.

Conclusion
The UAV equipped with particulate matter sensors, temperature/humidity sensors, and CO sensors has been developed.The flight controller board is used in conjunction with a Wi-Fi module, particulate matter sensors, temperature and humidity sensors, a CO sensor, and electronic speed controllers.The electronic speed controllers utilize the signals provided by the flight controller board to control the current in the battery section while also controlling the web camera and embedded system on the UAV.The UAV can rapidly reach the destination sites and accurately transmit on-site data to the command center.This enables rescue personnel to equip themselves appropriately and be well-prepared to provide the best possible care to affected individuals in the shortest possible time.The application of these sensors provides real-time and accurate information about the disaster scene, facilitating the command center in formulating more effective rescue strategies and improving the efficiency of disaster response operations.The UAV developed can enhance disaster response efficiency, ensure the safety of rescue personnel, and provide accurate geographic and environmental information.Furthermore, the application of the UAV equipped with sensors allows for the rapid acquisition of critical data during disasters, supporting decision-making and execution for search and rescue.

Figure 2 .
Figure 2. Principle of operation of suspended particle detector.

Table 1 .
Suspended particles, temperature, and humidity measurement data.
5CO measurement data are listed in Table

Table 2 .
CO measurement data.
Figure 4. Flight path planning diagram.