AN AUTONOMOUS DRONE SWARMS FOR DISASTER MANAGEMENT

Abstract

The present invention discloses an autonomous drone swarm system configured for disaster management applications. It comprises multiple autonomous drones (101) equipped with onboard sensors (102), communication modules (103), cameras (106), GPS systems (107), and obstacle detection systems (109). The drones operate collaboratively using IoT-enabled communication (104) and swarm intelligence algorithms (201) for tasks such as search and rescue, resource delivery, and environmental monitoring. Equipped with payload management systems (501) and advanced power modules (503), the system ensures precise operations, real-time data sharing, and uninterrupted functionality in disaster scenarios.

Specification

Description:FIELD OF THE INVENTION:
The present invention relates to autonomous drone swarms configured for disaster management applications. It integrates advanced robotics and communication technologies to enable coordinated operations such as search and rescue, damage assessment, resource delivery, and environmental monitoring in disaster scenarios, enhancing response efficiency and minimizing risks to human responders.

BACKGROUD OF THE INVENTION:
Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
Natural and man-made disasters have increasingly become a global concern, with their frequency and intensity growing over the past decades. Events such as earthquakes, floods, wildfires, industrial accidents, and other calamities often lead to significant loss of life, property damage, and disruption of communities. Timely and efficient disaster management is critical to minimize the impact of such events. However, traditional disaster response methods have faced limitations in terms of speed, coverage, and the safety of human responders. These conventional approaches often involve delayed actions, restricted access to critical information, and exposure of rescue teams to hazardous environments. The need for innovative, efficient, and safer solutions has become more apparent than ever.
The use of drones in disaster management has emerged as a promising technological advancement. Drones have been employed for aerial surveillance, resource delivery, and environmental monitoring in challenging conditions. However, individual drones have limited capabilities, such as constrained operational range, payload capacity, and data-processing power. They are also limited in their ability to coordinate complex tasks in dynamic and expansive disaster scenarios. The concept of autonomous drone swarms offers a transformative solution to overcome these challenges. By utilizing multiple drones working collectively as a cohesive unit, the efficiency and effectiveness of disaster response can be significantly enhanced.
To enable such swarms, advanced robotics, and communication technologies are essential. Autonomous drones equipped with sensors, cameras, and real-time communication systems can collaborate to perform critical tasks with precision and speed. Each drone in the swarm operates autonomously while maintaining continuous communication with others, enabling the group to make collective decisions. This eliminates the need for constant human intervention, allowing disaster management teams to focus on strategic operations rather than micromanaging individual drones. These features make autonomous drone swarms particularly suited for high-risk and time-sensitive disaster scenarios.
The integration of IoT-enabled technologies into autonomous drone swarms further revolutionizes their capabilities. By leveraging IoT protocols, the drones can share data in real time with a central control system as well as with each other. This real-time data exchange ensures that decisions are made based on the most current information available, such as the location of survivors, the extent of structural damage, or the changing environmental conditions in the disaster zone. IoT-enabled communication also facilitates seamless synchronization of the swarm's activities, enabling wide-area coverage and the ability to adapt to unexpected challenges during operations.
One of the critical applications of autonomous drone swarms is search and rescue. In the aftermath of a disaster, locating survivors is often the most urgent task. Traditional search and rescue operations are limited by the availability of personnel and their ability to safely navigate dangerous environments. Autonomous drone swarms equipped with thermal cameras and advanced navigation systems can efficiently scan large areas, identify survivors, and communicate their locations to rescue teams. The collective intelligence of the swarm ensures comprehensive coverage, even in complex terrains or debris-filled environments. Additionally, the drones' ability to operate in hazardous conditions such as extreme heat, smoke, or toxic environments makes them invaluable tools for search and rescue missions.
Another important application is damage assessment. Accurate and timely assessment of damage is crucial for planning relief and recovery efforts. Autonomous drone swarms can capture high-resolution images and environmental data, which are analyzed to evaluate the extent of destruction. This information is critical for prioritizing resource allocation and developing effective recovery strategies. Unlike traditional methods, which may take days or weeks, drone swarms can provide detailed assessments within hours, enabling faster decision-making and response.
Resource delivery is another area where autonomous drone swarms prove their value. Disasters often disrupt transportation infrastructure, making it difficult to deliver essential supplies such as food, water, and medical aid to affected areas. Autonomous drones with payload capabilities can deliver these resources quickly and accurately, ensuring that help reaches those in need. The ability of the swarm to coordinate and distribute resources efficiently minimizes delays and ensures equitable distribution, even in remote or inaccessible locations.
Environmental monitoring during and after disasters is also critical. Autonomous drones equipped with environmental sensors can monitor parameters such as temperature, humidity, air quality, and water contamination levels. This information is essential for assessing ongoing risks and planning mitigation measures. For instance, during a wildfire, drones can monitor the spread of the fire in real-time, providing valuable data to firefighting teams. Similarly, in the case of floods, drones can track water levels and predict further flooding, helping authorities take preventive measures.
Despite the significant potential of autonomous drone swarms, several challenges remain in their development and deployment. Power management is a critical concern, as drones rely on batteries with limited operational life. Efficient power systems, including solar recharging capabilities and automated charging stations, are necessary to ensure uninterrupted operation. Robust communication protocols are also essential to maintain connectivity in challenging environments, where traditional networks may be disrupted. Furthermore, the development of advanced algorithms for autonomous decision-making and swarm intelligence is crucial to enable effective coordination and adaptability in dynamic disaster scenarios.
The present invention addresses these challenges by providing a comprehensive system that integrates autonomous drones with IoT-enabled communication and advanced robotics. By combining cutting-edge technologies, this invention offers a scalable, efficient, and reliable solution for disaster management. It not only enhances the speed and effectiveness of response operations but also reduces the risks faced by human responders. The deployment of autonomous drone swarms represents a significant step forward in disaster management, with the potential to save lives, reduce damage, and support recovery efforts in a wide range of scenarios.
Therefore, autonomous drone swarms represent a paradigm shift in disaster management. By leveraging advanced robotics, IoT-enabled communication, and swarm intelligence, these systems offer unparalleled capabilities for search and rescue, damage assessment, resource delivery, and environmental monitoring. The present invention builds on these concepts to deliver a robust and adaptable solution that meets the demands of modern disaster scenarios. Through its innovative design and integration of state-of-the-art technologies, this invention sets a new standard for disaster response and recovery efforts.

OBJECTS OF THE INVENTION:
The prime object of the invention is to provide an autonomous drone swarm system configured for disaster management applications, capable of performing tasks such as search and rescue, damage assessment, resource delivery, and environmental monitoring. The system ensures efficient and timely response to disasters while minimizing risks to human responders.
Another object of the invention is to enable coordinated operations of multiple drones using advanced swarm intelligence algorithms. The drones work collaboratively, sharing real-time data and adapting dynamically to changing scenarios, ensuring comprehensive coverage of disaster-stricken areas and efficient utilization of resources.
Yet another object of the invention is to integrate IoT-enabled communication protocols into the drone swarm system to facilitate seamless real-time data exchange between the drones and a central control station. This ensures that disaster management teams receive continuous updates on drone activities, environmental conditions, and the status of ongoing operations.
Still another object of the invention is to enhance the capability of drones to operate autonomously in hazardous and complex environments. The drones are equipped with advanced sensors such as cameras, thermal imaging devices, and obstacle detection systems, allowing them to navigate safely and perform tasks effectively even in debris-laden or high-risk areas.
A further object of the invention is to optimize resource delivery operations during disasters. The drones are equipped with payload management systems that ensure accurate and efficient delivery of essential supplies such as food, water, and medical aid to affected areas, particularly in remote or inaccessible locations.
Another object of the invention is to provide a reliable power management system for the drone swarm. This includes high-capacity rechargeable batteries, automated charging stations, and optional solar recharging capabilities to ensure uninterrupted operation, even in extended disaster response scenarios.
Yet another object of the invention is to enhance the scalability and adaptability of the system for a wide range of disaster scenarios. The drone swarm can be deployed for various applications, including natural disasters such as floods, earthquakes, and wildfires, as well as man-made disasters like industrial accidents and urban emergencies.
Still another object of the invention is to enable precise and efficient environmental monitoring during and after disasters. The drones are configured to collect and relay data on parameters such as temperature, humidity, air quality, and water contamination levels, providing valuable information for risk assessment and mitigation planning.
A further object of the invention is to reduce the dependence on human responders in high-risk disaster environments. By deploying autonomous drones for critical tasks, the invention aims to safeguard human lives while ensuring effective disaster response and recovery operations.
Another object of the invention is to provide a scalable and modular design for the drone swarm system, allowing easy customization and integration with existing disaster management frameworks. This ensures that the system can be tailored to meet the specific needs and challenges of different disaster scenarios.

SUMMARY OF THE INVENTION:
The invention, titled An Autonomous Drone Swarms for Disaster Management, introduces a highly advanced and efficient system for addressing the challenges of modern disaster scenarios. It integrates autonomous drones with IoT-enabled communication, swarm intelligence, and advanced robotics to perform critical tasks such as search and rescue, damage assessment, resource delivery, and environmental monitoring. The system enhances the speed, accuracy, and safety of disaster management operations, reducing risks to human responders while ensuring timely and effective interventions.
An inventive aspect of the invention is to provide a coordinated swarm of autonomous drones configured with advanced sensors, cameras, and communication modules. These drones collectively operate to cover large disaster-affected areas, performing multiple tasks simultaneously while adapting to changing conditions in real time.
Another inventive aspect of the invention is to provide a swarm intelligence algorithm that enables the drones to function collaboratively as a cohesive unit. Each drone shares data with others in the swarm, allowing collective decision-making based on real-time information. This ensures optimized operations, including dynamic reallocation of tasks based on evolving disaster scenarios.
Yet another inventive aspect of the invention is to provide IoT-enabled communication protocols for seamless data exchange between the drones and a central control system. The system leverages low-latency, high-bandwidth communication technologies to transmit real-time updates, such as environmental metrics, drone positions, and survivor locations, ensuring that disaster management teams are continuously informed.
Still another inventive aspect of the invention is to equip the drones with autonomous navigation capabilities. The drones are configured with sensors such as GPS, LIDAR, and obstacle detection systems that enable precise and safe navigation through complex terrains, including debris-filled and hazardous environments. These features enhance the drones' ability to operate independently and effectively in challenging conditions.
An inventive aspect of the invention is to provide drones with resource delivery capabilities optimized for disaster relief. The drones are equipped with payload management systems that ensure accurate delivery of essential supplies such as food, water, and medical aid to affected areas, particularly in remote or inaccessible locations.
Another inventive aspect of the invention is to integrate power management systems into the drone swarm, including high-capacity rechargeable batteries, solar recharging capabilities, and automated charging stations. These systems ensure uninterrupted operation, allowing the drones to function efficiently even during extended disaster response efforts.
Yet another inventive aspect of the invention is to enable real-time environmental monitoring using advanced sensors. The drones are configured to measure and report critical parameters such as temperature, humidity, air quality, and water contamination levels. This information is invaluable for assessing ongoing risks and planning effective mitigation strategies.
Still another inventive aspect of the invention is to provide a modular and scalable system design. The drone swarm can be easily customized and expanded to suit specific disaster scenarios, making it adaptable for various applications such as natural disasters, industrial accidents, and urban emergencies.
An inventive aspect of the invention is to enhance the safety and efficiency of disaster management operations by reducing the dependence on human responders in high-risk environments. By delegating critical tasks to autonomous drones, the invention minimizes the exposure of rescue teams to hazardous conditions while ensuring effective response and recovery efforts.
Another inventive aspect of the invention is to incorporate advanced decision-making algorithms that enable the drones to autonomously adapt to unforeseen challenges. For example, if one drone encounters a low battery, it can communicate with nearby drones and the central control system to initiate a replacement or recharging sequence without disrupting the swarm's operations.
In summary, the invention provides a comprehensive and innovative solution to modern disaster management challenges. By integrating autonomous drones with IoT-enabled communication, swarm intelligence, and advanced robotics, the system offers unparalleled capabilities for search and rescue, damage assessment, resource delivery, and environmental monitoring. Through its inventive features, the invention sets a new benchmark for disaster response and recovery technologies, ensuring faster, safer, and more effective interventions in a wide range of scenarios.

BRIEF DESCRIPTION OF DRAWINGS:
The accompanying drawings illustrate various embodiments of An Autonomous Drone Swarms for Disaster Management, highlighting key aspects of its configuration and operational methodology. These figures are intended for illustrative purposes to aid in understanding the invention and are not meant to limit its scope.
FIG. 1 depicts a block diagram of an autonomous drone swarm system, showing its components and their interconnections, according to an embodiment of the present invention.
The drawings provided will be further described in detail in the following sections. They offer a visual representation of the autonomous drone swarm system's configuration, communication flow, and operational capabilities, helping to clarify and support the detailed description of the invention.

DETAILED DESCRIPTION OF THE INVENTION:
In the following detailed description, reference is made to the accompanying drawings which form a part hereof, and in which is shown by way of illustration specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that the embodiments may be combined, or that other embodiments may be utilized and that structural and logical changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents.
The present invention is described in brief with reference to the accompanying drawings. Now, refer in more detail to the exemplary drawings for the purposes of illustrating non-limiting embodiments of the present invention.
As used herein, the term "comprising" and its derivatives including "comprises" and "comprise" include each of the stated integers or elements but does not exclude the inclusion of one or more further integers or elements.
As used herein, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. For example, reference to "a device" encompasses a single device as well as two or more devices, and the like.
As used herein, the terms "for example", "like", "such as", or "including" are meant to introduce examples that further clarify more general subject matter. Unless otherwise specified, these examples are provided only as an aid for understanding the applications illustrated in the present disclosure, and are not meant to be limiting in any fashion.
As used herein, the terms ""may", "can", "could", or "might" be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.
Various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.
Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases, it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., "such as") provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition and persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.
With reference to FIG. 1, in an embodiment of the present invention, the autonomous drone swarm system is a highly advanced and efficient technology configured for disaster management applications. It comprises multiple autonomous drones (101) that are equipped with various onboard sensors (102), communication modules (103), cameras (106), GPS systems (107), and obstacle detection systems (109). These drones operate collectively as a swarm to perform critical tasks such as search and rescue, damage assessment, resource delivery, and environmental monitoring in disaster-affected areas. By leveraging advanced technologies and algorithms, the system ensures rapid, precise, and coordinated responses to disasters, minimizing human risks and enhancing overall operational efficiency.
The drones are fitted with a range of onboard sensors (102) that include temperature sensors, humidity sensors, and LIDAR systems. These sensors enable the drones to collect real-time environmental data with high precision, even in challenging and complex disaster zones. The temperature and humidity sensors provide vital information about the environmental conditions, while the LIDAR systems are crucial for creating detailed 3D maps of the terrain and detecting obstacles. This advanced sensory capability allows the drones to navigate safely through debris-filled or hazardous areas, ensuring that operations are carried out effectively without the need for manual intervention.
An IoT-enabled communication network (104) is a fundamental component of the system, facilitating real-time data exchange between the drones and a central control station (105). This network allows the drones to communicate with each other and share critical information such as video feeds, environmental readings, and GPS coordinates. By using advanced communication protocols like MQTT or CoAP (302), the network ensures efficient and low-latency data transmission, even in environments where traditional communication infrastructure may be compromised. The continuous data flow enables disaster management teams to monitor operations in real time and make informed decisions based on the latest information.
The system incorporates robust power management modules (503) in each drone to ensure uninterrupted operation during extended disaster response scenarios. These modules include high-capacity rechargeable batteries that provide long flight times, solar recharging capabilities to harness renewable energy in remote or resource-constrained environments, and automated charging stations (504) for seamless recharging during operations. This comprehensive power management system ensures that the drones remain operational throughout the mission, reducing downtime and enhancing the overall efficiency of disaster management efforts.
The drones are also equipped with payload management systems (501) that enable them to deliver resources such as food, water, and medical supplies accurately and safely. These systems are designed to handle various payload types and ensure precise delivery to the intended locations. This capability is particularly valuable in disaster zones where traditional transportation infrastructure may be severely damaged or inaccessible. By autonomously navigating to remote or hard-to-reach areas, the drones can provide timely assistance to affected populations, ensuring that critical supplies reach those in need without unnecessary delays.
Search and rescue operations are a critical application of the system. Each drone is equipped with thermal cameras (402) and advanced navigation systems that enable it to locate survivors in disaster zones. The thermal cameras can detect heat signatures of individuals trapped under debris or in inaccessible areas, providing accurate location data to rescue teams. The drones operate autonomously, adjusting their flight paths in real time based on sensor data (202) to optimize search patterns and avoid obstacles. This capability significantly enhances the speed and effectiveness of search and rescue missions, increasing the likelihood of saving lives in time-sensitive situations.
Environmental monitoring is another key function of the autonomous drone swarm system. The drones are configured to collect and relay real-time data on parameters such as temperature, humidity, air quality, and water contamination levels through the IoT-enabled communication network (104). This information is critical for assessing ongoing risks, identifying potential hazards, and planning effective mitigation strategies. The drones' ability to operate in hazardous environments, such as areas affected by toxic gases or contaminated water, ensures comprehensive monitoring without exposing human responders to danger.
Swarm intelligence algorithms (203) play a central role in the system, enabling the drones to operate collaboratively as a cohesive unit. These algorithms allow the drones to share data, dynamically reallocate tasks, and make collective decisions in real time. For example, if one drone detects a low battery (204) or encounters an obstacle, nearby drones can adjust their operations to maintain uninterrupted coverage of the disaster zone. This collective decision-making capability ensures that the swarm operates efficiently and adapts dynamically to changing conditions, even in complex and unpredictable environments.
The central control station (105) serves as the command hub for the entire system. It provides manual overrides (303) that allow disaster management teams to intervene and direct the drones as needed. The control station also features a visual interface that displays real-time information on drone positions, environmental conditions, and ongoing operations. This centralized control and monitoring capability ensures that disaster management teams have complete situational awareness and can coordinate the swarm's activities effectively.
The drones are configured to autonomously adjust their flight paths based on real-time sensor data (202). This capability enables them to avoid obstacles, optimize search patterns, and adapt to dynamic conditions in disaster zones. By continuously analyzing data from their onboard sensors, the drones can navigate safely and efficiently through complex terrains, ensuring that missions are carried out with precision and minimal risk.
Overall, the autonomous drone swarm system represents a transformative solution for modern disaster management challenges. By integrating advanced sensors, IoT-enabled communication, robust power management, and intelligent algorithms, the system offers unparalleled capabilities for responding to disasters. Its ability to perform search and rescue, resource delivery, environmental monitoring, and other critical tasks autonomously and collaboratively sets a new standard for disaster response technologies. The system not only enhances the speed and effectiveness of disaster management operations but also reduces the risks to human responders, making it an invaluable tool for addressing the challenges of natural and man-made disasters.

Algorithm for Autonomous Drone Swarm System for Disaster Management
Step 1: Initialization
1.1 Power on drones (101) and initiate onboard systems including sensors (102), communication modules (103), cameras (106), GPS (107), and obstacle detection systems (109).
1.2 Establish connectivity with the IoT-enabled communication network (104) using protocols such as MQTT or CoAP (302).
1.3 Sync all drones with the central control station (105) for mission parameters and operational settings.
Step 2: Deployment
2.1 Assign drones to pre-defined regions or flight patterns using autonomous navigation algorithms (201).
2.2 Check environmental and operational readiness using onboard sensors (102).
Step 3: Real-Time Data Collection and Processing
3.1 Each drone collects real-time data using:
• Cameras (106) for video feeds.
• GPS (107) for location tracking.
• Temperature and humidity sensors for environmental monitoring.
• LIDAR systems for obstacle detection and terrain mapping.
3.2 Transmit collected data to neighboring drones and the central control station (105) via the IoT network (104).
Step 4: Autonomous Navigation and Swarm Intelligence
4.1 Process sensor data (202) locally to avoid obstacles and adjust flight paths dynamically.
4.2 Share critical data with the swarm using swarm intelligence algorithms (203) to optimize collective operations.
4.3 Reallocate tasks dynamically based on real-time conditions, such as:
• Redirecting drones for search and rescue if survivors are detected using thermal cameras (402).
• Assigning resource delivery tasks (501) based on updated demands.
Step 5: Search and Rescue Operations
5.1 Use thermal cameras (402) to detect survivors' heat signatures.
5.2 Relay survivors' locations to the central control station (105).
5.3 Nearby drones provide support by creating pathways or dropping emergency supplies.
Step 6: Resource Delivery
6.1 Drones equipped with payload management systems (501) autonomously deliver resources such as food, water, and medical supplies to designated locations.
6.2 Optimize delivery paths based on GPS coordinates and environmental conditions.
Step 7: Environmental Monitoring
7.1 Collect data on parameters such as temperature, humidity, air quality, and water contamination.
7.2 Transmit data to the central control station (105) for risk assessment and decision-making.
Step 8: Power Management
8.1 Monitor battery levels using power management modules (503).
8.2 Drones with low battery levels (204) autonomously navigate to automated charging stations (504) or switch to solar recharging mode if available.
8.3 Re-integrate recharged drones into the swarm seamlessly.
Step 9: Central Control and Override
9.1 Central control station (105) processes real-time data from drones and provides situational updates.
9.2 Operators can manually override autonomous operations (303) if necessary, to direct specific drones or respond to unexpected scenarios.
Step 10: Mission Completion and Return
10.1 Upon mission completion or depletion of primary objectives, drones autonomously return to the base.
10.2 Upload all collected data to the central system for post-mission analysis.
Step 11: Post-Mission Analysis
11.1 Analyze data collected during the operation for performance evaluation and improvement.
11.2 Update drone swarm algorithms (203) based on insights for enhanced future deployments.

Below is the Arduino-based example code for controlling an autonomous drone swarm system for disaster management. This code includes functionality for IoT-based communication, sensor data collection, autonomous navigation, and real-time data sharing.
Libraries Required
1. ESP8266WiFi or WiFi.h (for ESP32 communication)
2. PubSubClient (for MQTT-based IoT communication)
3. Adafruit_Sensor (for environmental sensors such as temperature and humidity)
In an embodiment of present invention, Arduino code for example:
#include <ESP8266WiFi.h> // Use WiFi.h for ESP32
#include <PubSubClient.h> // MQTT communication
#include <Wire.h> // I2C communication for sensors
#include <Adafruit_Sensor.h>
#include <Adafruit_BMP280.h> // Example for a temperature sensor

// WiFi credentials
const char* ssid = "your_SSID";
const char* password = "your_PASSWORD";

// MQTT broker details
const char* mqtt_server = "broker.hivemq.com"; // Public MQTT broker
const char* topic = "drone/swarm/data";

// Create WiFi and MQTT clients
WiFiClient espClient;
PubSubClient client(espClient);

// Sensor and GPS setup
Adafruit_BMP280 bmp; // Initialize the BMP280 temperature sensor

// Drone parameters
float latitude = 0.0;
float longitude = 0.0;
float altitude = 0.0;

// Function to connect to WiFi
void setup_wifi() {
delay(10);
Serial.println("Connecting to WiFi...");
WiFi.begin(ssid, password);
while (WiFi.status() != WL_CONNECTED) {
delay(500);
Serial.print(".");
}
Serial.println("");
Serial.println("WiFi connected");
}

// Function to reconnect to MQTT broker
void reconnect() {
while (!client.connected()) {
Serial.print("Connecting to MQTT...");
if (client.connect("DroneSwarmClient")) {
Serial.println("connected");
client.subscribe(topic);
} else {
Serial.print("failed, rc=");
Serial.print(client.state());
delay(5000);
}
}
}

// Setup function
void setup() {
Serial.begin(115200);
// Setup WiFi and MQTT
setup_wifi();
client.setServer(mqtt_server, 1883);

// Initialize sensors
if (!bmp.begin()) {
Serial.println("BMP280 sensor not detected.");
while (1);
}
Serial.println("Drone Ready");
}

// Main loop
void loop() {
if (!client.connected()) {
reconnect();
}
client.loop();

// Read sensor data (temperature and altitude)
float temperature = bmp.readTemperature();
altitude = bmp.readAltitude();

// Example GPS coordinates for testing
latitude = 37.7749; // Replace with actual GPS latitude data
longitude = -122.4194; // Replace with actual GPS longitude data

// Prepare MQTT message
String payload = "{\"temperature\":";
payload += String(temperature);
payload += ", \"altitude\":";
payload += String(altitude);
payload += ", \"latitude\":";
payload += String(latitude);
payload += ", \"longitude\":";
payload += String(longitude);
payload += "}";

// Publish sensor data to the central control system
client.publish(topic, payload.c_str());
Serial.println("Data sent: " + payload);

// Autonomous navigation logic
if (altitude < 50.0) {
Serial.println("Drone ascending...");
// Add drone control logic for ascension
}

delay(2000); // Send data every 2 seconds
}
Code Description:
1. WiFi Connection: The drone connects to a WiFi network using ESP8266WiFi or WiFi.h (for ESP32). The credentials need to be replaced with the actual network details.
2. MQTT Communication: Data from sensors is sent to a public MQTT broker (e.g., HiveMQ) to simulate IoT-enabled communication.
3. Sensor Integration: The BMP280 sensor measures temperature and altitude, with placeholders for GPS coordinates.
4. Real-Time Data Sharing: The drone transmits sensor data in JSON format to the central control system through MQTT.
5. Autonomous Navigation Logic: Simple logic for altitude adjustment is included as an example of dynamic navigation.
This code is modular and can be expanded to include additional functionalities such as:
a) Obstacle detection using LIDAR or ultrasonic sensors.
b) Dynamic task allocation within the swarm using real-time updates.
c) Advanced autonomous decision-making algorithms for optimized swarm operations.

Working of the invention: The autonomous drone swarm system operates collaboratively to address disaster management challenges. Multiple drones equipped with sensors, communication modules, cameras, GPS, and obstacle detection systems gather real-time data, navigate disaster zones, and perform tasks such as search and rescue, environmental monitoring, damage assessment, and resource delivery. Using IoT-enabled communication, drones exchange data with each other and the central control station, enabling dynamic task allocation and synchronized operations.
Swarm intelligence algorithms allow drones to work collectively, optimizing task execution and coverage. Thermal cameras detect survivors, while payload management systems ensure accurate resource delivery in inaccessible areas. The drones autonomously adjust flight paths based on sensor feedback and reallocate tasks based on real-time priorities.
Power management includes rechargeable batteries, solar recharging, and automated charging stations for uninterrupted operation. The central control station provides real-time monitoring and manual override capabilities, ensuring adaptability and efficiency during disaster response missions.

ADVANTAGES OF THE INVENTION:
The prime advantage of the invention is to provide an efficient disaster management system that minimizes risks to human responders by deploying autonomous drones for critical tasks such as search and rescue, resource delivery, and environmental monitoring.
Another advantage of the invention is its ability to operate collaboratively using swarm intelligence, enabling drones to share data, make collective decisions, and dynamically reallocate tasks for comprehensive coverage and optimized disaster response operations.
Yet another advantage of the invention is its IoT-enabled communication network, which ensures seamless real-time data exchange between drones and the central control station, enabling informed decision-making and coordinated operations in disaster scenarios.
Still another advantage of the invention is the autonomous navigation capability, allowing drones to safely navigate complex terrains, avoid obstacles, and adapt to changing conditions, ensuring efficient operation in hazardous and unpredictable environments.
A further advantage of the invention is its advanced power management system, including rechargeable batteries, solar recharging, and automated charging stations, ensuring uninterrupted operation during extended disaster response missions.
, Claims:CLAIM(S):
We Claim:
1. An autonomous drone swarm system configured for disaster management applications, comprising:
a) multiple autonomous drones (101) equipped with onboard sensors (102), communication modules (103), cameras (106), GPS systems (107), and obstacle detection systems (109);
b) an IoT-enabled communication network (104) facilitating real-time data exchange between the drones and a central control station (105); and
c) autonomous navigation and swarm intelligence algorithms (201) enabling collective decision-making and dynamic task allocation.
2. The autonomous drone swarm system of claim 1, wherein the onboard sensors (102) include temperature sensors, humidity sensors, and LIDAR systems for precise navigation and environmental monitoring in disaster zones.
3. The autonomous drone swarm system of claim 1, wherein the IoT-enabled communication network (104) utilizes MQTT or CoAP protocols (302) to transmit real-time data, including video feeds, environmental readings, and GPS coordinates.
4. The autonomous drone swarm system of claim 1, further comprising power management modules (503) in each drone, including:
a) high-capacity rechargeable batteries;
b) solar recharging capabilities; and
c) automated charging stations (504) for seamless operation in extended disaster response scenarios.
5. The autonomous drone swarm system of claim 1, wherein the drones are equipped with payload management systems (501) configured for accurate and safe delivery of resources, including food, water, and medical supplies.
6. The autonomous drone swarm system of claim 1, further configured to perform search and rescue operations using thermal cameras (402) and onboard navigation systems to locate and relay survivor locations to the central control station (105).
7. The autonomous drone swarm system of claim 1, wherein environmental monitoring tasks include real-time data collection on temperature, humidity, air quality, and water contamination levels, relayed through IoT-enabled communication (104).
8. The autonomous drone swarm system of claim 1, wherein swarm intelligence algorithms (203) enable collaborative operation of the drones, dynamically reallocating tasks and ensuring uninterrupted coverage during battery replacements (204) or low power conditions.
9. The autonomous drone swarm system of claim 1, wherein the central control station (105) provides manual overrides (303) and a visual interface displaying drone positions, environmental conditions, and real-time operation status for disaster management teams.
10. The autonomous drone swarm system of claim 1, wherein the drones autonomously adjust their flight paths based on real-time sensor data (202) to avoid obstacles and optimize search patterns in debris-filled or hazardous environments.

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