A disaster relief system using smart antennas in AI-powered drone swarms

Abstract

A disaster relief system using AI-powered intelligent antennas in drone swarms, consisting of: a plurality of drones configured to collect environmental data in real time, with the drones being deployed in a disaster area environment; a smart antenna system mounted on each drone, each smart antenna system configured to perform adaptive beamforming to maintain communication links between drones; an artificial intelligence (AI) processing unit configured to receive real-time signal data, including obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the intelligent antenna systems; a swarm communication protocol module configured to establish communication links between the plurality of drones using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation; and a central control system configured to monitor all swarm operations, process environmental data, and adapt swarm behavior based on disaster relief requirements.

Description

FIELD OF THE INVENTION

[0001] The present disclosure relates to a disaster relief system using AI-powered smart antennas in drone swarms. More specifically, the present invention relates to an AI-powered smart antenna system integrated to improve communication and coordination between drones in swarm-based disaster relief scenarios. BACKGROUND OF THE INVENTION

[0002] Natural disasters such as earthquakes pose enormous challenges for emergency response teams, particularly in urban environments where conventional communications infrastructure often fails. Traditional rescue operations face significant limitations in coordinating operations in devastated areas and accessing areas where survivors may be trapped. While drones are increasingly being used in disaster relief, their effectiveness is hampered by communication problems in complex urban environments.

[0003] Existing drone communication systems rely primarily on direct line-of-sight or traditional wireless networks, which become unreliable in disaster areas due to physical obstacles, interference, and damage to network infrastructure. Furthermore, current drone swarm technologies lack the sophisticated coordination required for large-scale disaster relief operations, especially in environments with dynamic obstacles and changing conditions.

[0004] Previous attempts to address these challenges included mesh networks and simple antenna arrays, but these solutions often suffer from limited range, poor signal quality in cluttered environments, and an inability to adapt to rapidly changing conditions. The need for reliable, adaptive communications systems that can maintain robust connections in disaster scenarios while enabling precise coordination among multiple drones has largely remained unmet.

[0005] The integration of artificial intelligence into smart antenna technology represents a significant advance in overcoming these limitations. The present invention aims to combine AI-driven adaptive beamforming, reinforcement learning, and swarm intelligence to create a system capable of maintaining reliable communications links in challenging environments while optimizing resource utilization and mission effectiveness. By enabling drones to dynamically adapt their communication patterns and coordinate their actions autonomously, this system overcomes the key obstacles that have limited the effectiveness of drone-based disaster relief operations in the past. SUMMARY OF THE INVENTION

[0006] The present disclosure relates to a disaster relief system using AI-powered smart antennas in drone swarms. The present invention provides an AI-powered smart antenna system for precise drone swarms in disaster relief scenarios. The system integrates advanced beamforming technology with artificial intelligence to enable robust communication and coordination between drone swarms operating in challenging disaster environments. The invention includes smart antennas attached to each drone, an AI processing unit utilizing reinforcement learning, a swarm communication protocol module, and a central control system. The smart antennas dynamically adjust their beam patterns based on real-time environmental data and drone positions, while the AI ​​system continuously optimizes communication parameters for maximum efficiency and reliability.This integration enables unprecedented coordination in disaster relief tasks, including search and rescue, resource delivery, and environmental mapping.

[0007] The present disclosure aims to provide a disaster relief system using AI-powered smart antennas in drone swarms. The system includes: a plurality of drones configured to collect real-time environmental data, wherein the drones are deployed in a disaster zone environment; a smart antenna system mounted on each drone, wherein each smart antenna system is configured to perform adaptive beamforming to maintain communication links between drones;An artificial intelligence (AI) processing unit configured to receive real-time signal data including obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the intelligent antenna systems; a swarm communication protocol module configured to establish communication links between the plurality of drones using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation;and a central control system configured to monitor the entire swarm operation, process environmental data, and adapt swarm behavior based on disaster relief requirements.;

[0008] One objective of the present disclosure is to provide a disaster relief system using AI-powered smart antennas in drone swarms.

[0009] Another objective of the present disclosure is to provide a robust and adaptive communication system for drone swarms operating in disaster environments by implementing AI-enabled smart antenna technology.

[0010] Another objective of the present disclosure is to improve the efficiency and effectiveness of disaster response through improved drone coordination and real-time adaptation to changing environmental conditions.

[0011] Another objective of this disclosure is to minimize communication errors and extend operating time through optimized power management and intelligent resource allocation.

[0012] To further clarify the advantages and features of the present disclosure, a more detailed description of the invention will be given with reference to specific embodiments thereof illustrated in the accompanying drawings. It should be noted that these drawings represent only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained in additional detail and in greater detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE CHARACTERS

[0013] These and other features, aspects, and advantages of the present disclosure will be better understood when the following detailed description is read with reference to the accompanying drawings, in which like characters represent like parts throughout. Fig. 1 shows a block diagram of a disaster relief system using AI-powered smart antennas in drone swarms according to an embodiment of the present disclosure. Fig. 2 shows a block diagram for the AI-powered smart antennas for precise drone swarms in disaster relief according to an embodiment of the present disclosure.

[0014] Furthermore, those skilled in the art will appreciate that elements in the drawings are shown for convenience and may not necessarily be drawn to scale. For example, the flowcharts illustrate the method by key steps to enhance understanding of aspects of the present disclosure. Furthermore, with respect to device construction, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only the specific details relevant to understanding embodiments of the present disclosure in order not to clutter the drawings with details that would be readily apparent to those skilled in the art who would benefit from the description herein. DETAILED DESCRIPTION:

[0015] To promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and described in specific language. It is to be understood, however, that no limitation upon the scope of the invention is intended thereby, since such changes and further modifications of the illustrated system, and such further applications of the principles of the invention as illustrated therein, are contemplated as would normally occur to one skilled in the art to which the invention pertains.

[0016] It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.

[0017] References in this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, the occurrences of the phrase "in one embodiment," "in another embodiment," and similar language throughout this specification may or may not all refer to the same embodiment.

[0018] The terms "comprises," "comprising," or other variations thereof are intended to cover non-exclusive inclusion, such that a process or method comprising a list of steps not only includes those steps, but may also include other steps not expressly listed or inherent in such process or method. Likewise, one or more devices or subsystems or elements or structures or components preceded by "comprises...a" does not preclude, without further limitation, the existence of other devices or other subsystems or other elements or other structures or other components or additional devices or additional subsystems or additional elements or additional structures or additional components.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are for illustrative purposes only and should not be considered limiting.

[0020] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0021] The functional units described in this specification have been referred to as devices. A device may be implemented in programmable hardware devices such as processors, digital signal processors, central processing units, field-programmable gate arrays, programmable array logic, programmable logic devices, cloud processing systems, or the like. The devices may also be implemented in software for execution by various types of processors. An identified device may contain executable code and may, for example, comprise one or more physical or logical blocks of computer instructions, which may be organized, for example, as an object, procedure, function, or other construct.However, the executable file of an identified device need not be physically located together, but may comprise different instructions stored in different locations which, when logically linked together, constitute the device and fulfill the stated purpose of the device.

[0022] Indeed, executable code of a device or module may be a single instruction or multiple instructions, and may even be distributed across several different code segments, among different applications, and across multiple storage devices. Similarly, operational data may be identified and represented herein within the device and embodied in any suitable form and organized in any suitable type of data structure. The operational data may be captured as a single set of data or distributed across different locations, including different storage devices, and may exist, at least in part, as electronic signals in a system or network.

[0023] References in this specification to "a selected embodiment," "an embodiment," or "an embodiment" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the disclosed subject matter. Therefore, the appearances of the phrases "a selected embodiment," "in an embodiment," or "in an embodiment" in various places in this specification do not necessarily refer to the same embodiment.

[0024] Furthermore, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the disclosed subject matter. However, one of ordinary skill in the art will recognize that the disclosed subject matter may be practiced without one or more of the specific details, or with different methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosed subject matter.

[0025] According to the example embodiments, the disclosed computer programs or modules may be executed in many example ways, for example, as an application stored in the memory of a device or as a hosted application running on a server and communicating with the device application or browser via a variety of standard protocols such as TCP / IP, HTTP, XML, SOAP, REST, JSON, and other suitable protocols. The disclosed computer programs may be written in example programming languages ​​that execute from memory on the device or from a hosted server, for example, BASIC, COBOL, C, C++, Java, Pascal, or scripting languages ​​such as JavaScript, Python, Ruby, PHP, Perl, or other suitable programming languages.

[0026] Some of the disclosed embodiments comprise or otherwise involve data transmission over a network, such as communicating various inputs or files over the network. The network may comprise, for example, one or more of the following: the Internet, wide area networks (WANs), local area networks (LANs), analog or digital wired and wireless telephone networks (e.g., a PSTN, Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (xDSL)), radio, television, cable, satellite, and / or any other delivery or tunneling mechanism for transmitting data. The network may comprise multiple networks or subnetworks, each of which may comprise, for example, a wired or wireless data path. The network may comprise a circuit-switched voice network, a packet-switched data network, or any other network capable of transmitting electronic communications.For example, the network may include networks based on Internet Protocol (IP) or Asynchronous Transfer Mode (ATM), and may support voice using, for example, VoIP, Voice over ATM, or other comparable protocols used for voice data communications. In one implementation, the network includes a cellular network configured for the exchange of text or SMS messages.

[0027] Examples of the network include, but are not limited to, a Personal Area Network (PAN), a Storage Area Network (SAN), a Home Area Network (HAN), a Campus Area Network (CAN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a Virtual Private Network (VPN), an Enterprise Private Network (EPN), the Internet, a Global Area Network (GAN), and so on.

[0028] Fig. 1 shows a block diagram of a disaster relief system (100) using AI-powered smart antennas in drone swarms according to an embodiment of the present disclosure.

[0029] With reference to Fig. 1, the system (100) comprises: a plurality of drones (102) configured to collect environmental data in real time, wherein the drones (102) are deployed in a disaster area environment; a smart antenna system (104) mounted on each drone (102), each smart antenna system (104) configured to perform adaptive beamforming to maintain communication links between the drones (102); an artificial intelligence (AI) processing unit (106) configured to receive real-time signal data comprising obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the smart antenna systems (104);a swarm communication protocol module (108) configured to establish communication links between the plurality of drones (102) using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation; and a central control system (110) configured to monitor overall swarm operations, process environmental data, and adapt swarm behavior based on disaster relief requirements.

[0030] In one embodiment, each smart antenna system (104) comprises: a phased array antenna; a signal processing unit; and a beamforming control module, wherein the beamforming control module is configured to dynamically adjust beam patterns based on the optimized beamforming parameters.

[0031] In one embodiment, the AI ​​processing unit (106) further comprises a neural network module (106a) configured to predict optimal beam patterns based on historical performance data, adapt to changing environmental conditions, and optimize power consumption across the entire drone swarm.

[0032] In one embodiment, each drone (102) further comprises: thermal imaging sensors, acoustic sensors, and payload delivery mechanisms, wherein the sensors are configured to detect survivors and environmental hazards in the disaster area.

[0033] In one embodiment, the swarm communication protocol module (108) is configured to: implement a mesh network between the drone swarm, provide automatic failover mechanisms to maintain communication links, and optimize bandwidth allocation based on mission priorities.

[0034] In one embodiment, the central control system (110) further comprises a 3D mapping module (110a) configured to generate topographic maps of the disaster area in real time, to identify optimal flight paths for the drone swarm, and to mark locations of survivors and hazards.

[0035] In one embodiment, each intelligent antenna system (104) of each drone (102) is further configured to operate in multiple frequency bands, suppress interference from external sources, and maintain minimum signal-to-noise ratio thresholds for reliable communication.

[0036] In one embodiment, the AI ​​processing unit (106) implements: a multi-agent reinforcement learning algorithm for coordinated decision making; predictive modeling to anticipate communication failures; and adaptive power management strategies.

[0037] In one embodiment, the system (100) further comprises an emergency failover module (112) configured to detect critical system failures, initiate autonomous return-to-base procedures, and maintain minimal communication links during emergencies.

[0038] In one embodiment, the central control system (110) further comprises a mission planning module (110b) configured to prioritize rescue missions based on survivor detection, optimize resource delivery routes, and coordinate with ground rescue teams.

[0039] The present invention relates to a disaster relief system using AI-powered smart antennas in drone swarms. The proposed system operates through a sophisticated integration of hardware and software components that work together to enable precise drone swarm operations in disaster scenarios. Each drone in the swarm is equipped with a smart antenna system enabling adaptive beamforming and is controlled by an AI processing unit that continuously analyzes environmental data and optimizes communication parameters. The system begins its operation by collecting real-time signal data, including obstacle information, drone positions, and interference patterns, from the disaster environment. This data is processed by the AI ​​unit using reinforcement learning algorithms to generate optimized beamforming parameters.The intelligent antennas then dynamically adjust their beam patterns based on these parameters, maintaining robust communication links between drones despite challenging conditions. The swarm communication protocol module coordinates task assignments and maintains network integrity, while the central control system monitors overall operations and adjusts swarm behavior based on mission requirements. The system incorporates multiple fail-safe mechanisms and can adapt to changing conditions in real time, enabling continuous operation even in highly dynamic environments. Thanks to its advanced thermal imaging, acoustic sensing, and 3D mapping capabilities, the system provides comprehensive situational awareness while facilitating critical tasks such as survivor detection and resource deployment.The integration of mesh networks and multi-agent reinforcement learning ensures reliable communication and coordinated decision-making across the swarm, making it an effective solution for complex disaster response scenarios.

[0040] Fig. 2 shows a block diagram for the AI-powered smart antennas for precise drone swarms in disaster relief according to an embodiment of the present disclosure.

[0041] In relation to Fig.2, the present invention relates to an AI-driven smart antenna system for improving communication and coordination between drones in swarm-based disaster response scenarios. Smart antennas improve the reliability of communication links between drones by dynamically adjusting beam patterns to maintain line of sight and reduce interference in complex environments. By integrating AI algorithms, the system enables real-time decision-making, ensuring robust communications even in challenging disaster areas.

[0042] The system consists of several key components, each playing a critical role in improving drone swarm coordination for disaster management. The disaster environment serves as a source of dynamic obstacles and signal data. This constantly changing landscape presents challenges that require real-time adaptability and precision in communications. The AI-powered smart antenna is mounted on the drones, a crucial element that enables real-time beamforming. By dynamically adjusting signal direction and strength, the smart antenna improves communication reliability and ensures that the drones remain connected even under challenging conditions. The Swarm Communication Protocol is used to enable seamless coordination between drones.This protocol governs the exchange of information between drones, enabling them to share critical data, avoid collisions, and optimize their joint response to disaster scenarios. At the heart of decision-making is the AI ​​model, which uses reinforcement learning to adapt and respond to dynamic environments. This model continuously learns from real-time data and refines its decision-making processes to optimize drone movements and communication strategies. The combination of these components leads to improved drone swarm coordination, the ultimate goal of the system. By integrating AI-powered smart antennas, reinforcement learning, and swarm communication, the system ensures precise, efficient, and adaptive drone deployments in disaster relief scenarios.The block diagram provides an overview of how these technologies interact and highlights their critical role in achieving seamless coordination and improved outcomes in disaster management.

[0043] Disaster situations are highly unpredictable and often characterized by dynamic obstacles such as collapsed buildings, dense vegetation, or smoke. These factors pose significant challenges for drone operations, particularly in maintaining stable communication signals. The ability of drones to navigate such conditions and adapt to rapidly changing scenarios is critical for effective disaster management. By providing real-time data on environmental conditions, drones must overcome obstacles such as moving debris or the changing locations of survivors, ensuring their continued functionality in challenging terrain.

[0044] A crucial aspect of drone communications in disaster areas is signal data collection. This includes identifying obstacles that may weaken or block signals, tracking drone positions in real time to coordinate swarm movements, and detecting interference from other wireless communication systems operating in the area. Understanding these factors allows the system to adapt to the communications challenges posed by the disaster environment. The collected data forms the basis for AI-driven adjustments that enable the system to maintain robust connectivity between drones despite unpredictable obstacles.

[0045] The heart of the system is the AI-controlled smart antenna, which is attached to each drone to dynamically adjust its beamforming pattern. Using advanced signal processing techniques, these smart antennas can focus communication beams on specific targets, such as other drones in the swarm, while simultaneously suppressing interference and noise by directing nulls toward sources of interference. The antenna continuously adapts to drone movements and environmental changes, ensuring uninterrupted, high-quality communication even in congested environments. This capability significantly reduces signal loss and improves the overall efficiency of drone operations, allowing them to function effectively even in complex disaster zones.

[0046] The intelligent communications system is powered by an AI model based on reinforcement learning. This model continuously analyzes signal data to determine optimal beamforming parameters. It autonomously adapts to changing conditions, such as drone movement or the appearance of new obstacles, and optimizes communication processes in real time. By minimizing latency and maximizing signal strength, the AI ​​model enables the system to function without human intervention. This autonomy increases the efficiency and reliability of drone swarm operations, making them well-suited for unpredictable disaster areas where rapid adaptability is essential.

[0047] Effective coordination between drones is ensured by the Swarm Communication Protocol, which governs how drones exchange critical data and synchronize their actions. By integrating the optimized beamforming outputs of the smart antennas, the protocol establishes reliable connections that facilitate the exchange of critical information such as survivor locations and resource requirements. Furthermore, it ensures seamless coordination of swarm movements and task assignments. This capability enables drones to function as a cohesive unit and accurately conduct search and rescue, resource delivery, and reconnaissance missions. By minimizing miscommunication or delays, the Swarm Communication Protocol plays a critical role in disaster relief efforts.

[0048] The AI-controlled smart antennas generate adaptive beamforming outputs that optimize signal strength and direction while reducing interference and power consumption. These outputs improve overall communication efficiency and ensure that the drones remain connected even in highly obstructed or noisy environments. Reliable communication links enable drones to conduct disaster relief effectively regardless of environmental challenges.

[0049] Ultimately, the combination of these advanced technologies leads to improved coordination of drone swarms. The optimally synchronized drone swarm can conduct disaster relief operations with high precision and reliability. This includes locating survivors with minimal delays, delivering critical resources to affected areas, and providing real-time monitoring and updates to rescue teams. By enabling faster and more precise disaster relief efforts, this system significantly reduces the workload of human operators while increasing the chances of saving lives and mitigating damage. The integration of AI-driven intelligent communication and swarm coordination transforms drones into highly efficient tools in disaster management and revolutionizes the way emergency operations are conducted.

[0050] The AI-controlled smart antennas play a critical role in maintaining strong and reliable communication links between drones, even in environments where signal blockages or interference are significant. This ensures that drones can operate effectively in disaster areas where traditional communication networks may be compromised or unavailable. By leveraging artificial intelligence, the system continuously adapts to the dynamic nature of disaster zones, ensuring uninterrupted operations regardless of environmental changes such as moving debris, harsh weather conditions, or fluctuating signal strengths. Drones equipped with smart antennas and AI capabilities can cover larger areas, respond faster, and perform more complex tasks with minimal human intervention.This increases the efficiency of disaster relief efforts, enabling rapid assessments of affected regions and rapid deployment of aid where it is most needed. Furthermore, the system minimizes energy consumption by optimizing beamforming and communication protocols. This extends drone operating time, a crucial factor in disaster relief operations that require continuous monitoring and assistance over extended periods. The swarm communication protocol ensures precise coordination between drones, allowing them to perform tasks such as locating survivors or ensuring the smooth delivery of relief supplies. This level of coordination improves the overall effectiveness of rescue operations, reduces delays, and increases the chances of mission success.Furthermore, the system is highly scalable, allowing drone swarms to be expanded or integrated with other technologies such as ground robots or satellites. This adaptability enables a more comprehensive disaster management solution and ensures that different response mechanisms work together efficiently to mitigate the impacts of disasters.

[0051] During a disaster situation, the drones are launched in coordinated swarms, each equipped with AI-driven smart antennas that enable adaptive beamforming. This technology ensures robust and uninterrupted communications in the disaster zone, overcoming challenges posed by physical obstacles and environmental noise. The real-time adaptability of the swarm communication network allows drones to efficiently share data, enabling precise coordination and situational awareness. Thermal imaging and acoustic sensors onboard the drones detect survivors, while AI algorithms process this data to prioritize rescue sites. The delivery of medical supplies and food is handled by drones with payloads that autonomously navigate complex routes to reach survivors.The 3D mapping function provides a detailed overview of the disaster area and supports rescue teams in strategic planning and navigation. If communications are interrupted, for example during aftershocks, the intelligent antennas dynamically adapt to maintain network integrity and ensure continuous operations. After deployment, the drones' ability to act as temporary communications relays restores critical connectivity for rescue teams and affected civilians. The use of this system offers transformative benefits in disaster management. By reducing response times and ensuring precise coordination, the drones significantly increase the efficiency of search and rescue operations. The ability to maintain reliable communications in challenging environments improves the safety and effectiveness of rescue teams.The system's scalability and adaptability make it a versatile tool for diverse disaster scenarios, providing unprecedented support in times of crisis. This innovative integration of AI-driven smart antennas with drone swarms represents a critical advance in disaster response technology. It not only addresses immediate challenges such as locating survivors and delivering resources, but also creates a robust framework for future disaster preparedness and management. This AI-driven smart antenna system transforms drone swarm operations by providing a robust and adaptive communications framework. Its ability to operate in challenging disaster environments ensures that rescue missions are conducted quickly, accurately, and reliably.By automating critical aspects of communication and coordination, the system improves the overall effectiveness of disaster response, saves lives, and reduces the impact of catastrophic events.

[0052] The drawings and the foregoing description provide examples of embodiments. Those skilled in the art will recognize that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be separated into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of the processes described herein may be changed and are not limited to the manner described herein. Furthermore, the actions of any flowchart need not be implemented in the order shown; nor do all actions necessarily need to be performed. Also, those actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is in no way limited by these specific examples.Numerous variations, whether explicitly stated in the specification or not, such as differences in structure, dimensions, and use of materials, are possible. The scope of the embodiments is at least as broad as indicated in the following claims.

[0053] Advantages, other benefits, and solutions to problems have been described above with respect to specific embodiments. However, the advantages, benefits, solutions to problems, and any components that may cause an advantage or solution to occur or become more apparent are not to be construed as a critical, required, or essential feature or component of any or all of the claims. REFERENCES 100 A disaster relief system with AI-controlled intelligent antennas in drone swarms. 102 Variety of Drones 104 Intelligent antenna system 106 Processing Unit For Artificial Intelligence (AI) 106a Neural network module 108 Swarm communication protocol module 110 Central Control System 110a 3D mapping module 110b Mission Planning Module 112 Emergency Failsafe Module 202 Disaster Environment 204 AI-supported smart antenna 206 Swarm communication protocol 208 Adaptive Beamforming Output 210 AI model 212 signal data 214 Improved drone swarm coordination

Claims

[1] A disaster relief system using AI-based intelligent antennas in drone swarms, consisting of: a plurality of drones configured to collect environmental data in real time, the drones being deployed in a disaster area environment; a smart antenna system mounted on each drone, each smart antenna system configured to perform adaptive beamforming to maintain communication links between drones; an artificial intelligence (AI) processing unit configured to receive real-time signal data, including obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the intelligent antenna systems; a swarm communication protocol module configured to establish communication links between the plurality of drones using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation; and a central control system configured to monitor all swarm operations, process environmental data, and adapt swarm behavior based on disaster relief requirements. [2] The system of claim 1, wherein each intelligent antenna system comprises: a phased array antenna; a signal processing unit; and a beamforming control module, wherein the beamforming control module is configured to dynamically adjust beam patterns based on the optimized beamforming parameters. [3] The system of claim 1, wherein the AI processing unit further comprises a neural network module configured to predict optimal beam patterns based on historical performance data, adapt to changing environmental conditions, and optimize power consumption across the drone swarm. [4] The system of claim 1, wherein each drone further comprises: Thermal imaging sensors, acoustic sensors, and payload delivery mechanisms, with the sensors configured to detect survivors and environmental hazards in the disaster area. [5] The system of claim 1, wherein the swarm communication protocol module is configured to: implement mesh networking within the drone swarm, provide automatic failover mechanisms to maintain communication links, and optimize bandwidth allocation based on mission priorities. [6] The system of claim 1, wherein the central control system further comprises a 3D mapping module configured to generate topographic maps of the disaster area in real time, identify optimal flight paths for the drone swarm, and mark locations of survivors and hazards. [7] The system of claim 1, wherein each intelligent antenna system is further configured to operate in multiple frequency bands, reject interference from external sources, and maintain minimum signal-to-noise ratio thresholds for reliable communication. [8] The system of claim 1, wherein the AI processing unit implements: a multi-agent reinforcement learning algorithm for coordinated decision-making; predictive modeling to predict communication failures; and adaptive energy management strategies. [9] The system of claim 1, wherein the system further comprises an emergency failover module configured to detect critical system failures, initiate autonomous return-to-base procedures, and maintain minimal communication links during emergencies. [10] The system of claim 1, wherein the central control system further comprises a mission planning module configured to prioritize rescue missions based on survivor detection, optimize resource delivery routes, and coordinate with ground rescue teams.

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