Drone Swarm Coordination

A multi-drone coordination system for efficient, intelligent, and reliable cooperative flight and task execution of multiple drones. The system uses optimization algorithms to coordinate and optimize flight missions for multiple drones. It assigns tasks and generates optimal flight paths based on mission requirements and drone status. The drones have flight control units with modules for attitude control, navigation, path planning, and obstacle avoidance. They also have sensors for positioning, environment perception, and collision detection. The system uses wireless networks with high-speed, parallel, reliable, encrypted data transmission for efficient communication.

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Multi-rotor unmanned aerial vehicle (UAV) cluster system that enables coordinated flight and collaborative tasks between multiple UAVs. The system has a ground control station, UAV cluster, communication system, and load equipment. The UAV cluster and ground control station are connected through the communication system. Each UAV has an electronic control system with sensors, actuators, and a controller. The UAVs can autonomously navigate and adjust flight using onboard sensors and logic. The communication system enables real-time data exchange between UAVs and ground station. Load equipment like cameras, sensors, and weapons can be added to UAVs. The cluster allows multiple UAVs to work together on tasks like search and rescue, surveillance, or cargo transport.

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UAV task scheduling and fault handling system using Kubernetes to improve efficiency and reliability of unmanned aerial vehicle (UAV) systems. The system has a distributed cloud control center, task UAVs, and backup UAVs. The control center schedules tasks, manages resources, and detects faults. Task UAVs execute missions, report faults, and transmit data. Backup UAVs take over failed tasks. Kubernetes security, authorization, and network isolation protect the system. Task segmentation and moving windows optimize distribution. Fault replacement and task takeover by backup UAVs ensure continuity.

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Collaborative intelligent control and optimization system for drones that enables multiple drones to work together in complex environments to complete tasks efficiently. The system uses real-time communication, sensor data, and machine learning to enable stable, coordinated flight, task allocation, and path planning among multiple drones. The system addresses challenges like network bandwidth limitations, sensor reliability, and computational resources by encoding/decoding instructions, using hotspot networking, and optimized algorithms.

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Multi-drone communication control system that enables flexible and coordinated operation of multiple drones for collaborative tasks. The system allows seamlessly switching between single drone and multi-drone control. It uses a ground station with a monitoring system and data communication. Each drone has a wireless module, computer, flight controller, positioning, driving, sensors, power. The ground station provides commands and receives drone feedback. The drones communicate with each other via the ground station. This allows group control, task delegation, and dynamic reconfiguration of drone teams. The ground station also provides long-range mobile communication via 3G/4G.

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A vehicle-mounted drone control method that improves task execution efficiency by leveraging the computing resources of the vehicle's onboard equipment. The method involves dividing tasks into real-time and non-real-time components. Real-time tasks are executed locally on the drone, while non-real-time tasks are offloaded to the vehicle's equipment. This makes better use of the vehicle's idle computing resources, alleviating drone's resource constraints. When multiple drone systems are clustered, they share information and resources. This improves overall task efficiency by utilizing the vehicle's computing power.

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A unified scheduling method for coordinated flight of multiple drones to reduce complexity and central server pressure compared to traditional centralized drone scheduling. The method involves distributed task allocation and dynamic path planning where drones negotiate and compete for tasks, hold locks, and dynamically adjust routes to adapt to new tasks and environments. This allows decentralized coordination without constant central server involvement. Conflict resolution, locking, and status updates enable distributed task allocation and execution without requiring centralized scheduling.

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UAV fleet management system for coordinated operation of multiple drones that allows faulty drones to be replaced by backup drones to maintain mission continuity. The system has a central management center with a UAV control system and a UAV dispatching system. The control system manages the drones' work while the dispatching system swaps faulty drones with backups to replace them. The systems communicate to detect drone faults and transfer work to backups.

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UAV Bluetooth communication system and method for efficient low-altitude UAV data collection and coordination using IoT techniques. The system involves converting complex frequency domain UAV data into time domain using Bluetooth communication and the Fast Fourier Transform. This reduces noise and improves accuracy compared to using wireless like WiFi or cellular for UAV data exchange. The system has modules for coordination, task allocation, trajectory planning, and energy monitoring on the cloud and UAVs. By using Bluetooth and FFT for UAV-UAV and UAV-cloud communication, it enables real-time UAV fleet management and data sharing over shorter distances with lower interference.

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Controlling unmanned vehicles to prevent collisions and reduce user burden when multiple vehicles are operated. It provides autonomous control for commercial off-the-shelf unmanned vehicles via an add-on controller that receives user inputs and generates modified control signals to instruct the vehicles to follow pre-determined routes. The controller analyzes the user inputs and extracts the intended maneuvering commands while discarding velocity changes. To avoid collisions, the routes are generated by a server based on sensor data and deconflicting with other vehicles.

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Centralized management of a plurality of unmanned aerial vehicles (UAVs) of different types for preventing complication of processing related to information acquisition from each UAV, even in a case where flight management of a plurality of types of UAVs different from each other is performed. The technique involves using an information communication device attached to the UAVs that acquires flight status information and transmits it wirelessly in a predetermined data format. A centralized management device receives and monitors the UAVs using the flight status information transmitted from the information communication device. This standardized format allows uniform acquisition of flight status from different UAV types.

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Using reinforcement learning to generate optimal operation plans for multi-drone networks performing cooperative tasks like data sensing and communication relay. The approach trains actor neural networks for each drone to learn policies for state-based decision making. These policies are then used to generate a plan by simulating the game state, obtaining observations, inferring actions, and recording the history. The resulting plan provides coordinated task execution that maximizes efficiency while maintaining network connections.

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Detecting, quantifying and GPS-locating trace gases using unmanned aerial vehicles (UAVs) to monitor the presence of gases like methane, propane, carbon dioxide, and volatile organic compounds in real time. The UAV carries gas sensors along with environmental sensors like temperature, humidity, and barometric pressure sensors. The UAVs are flown in coordinated patterns to simultaneously detect gases at different locations in an area of interest. This enables efficient, periodic monitoring of ambient air quality as well as detecting point source pollution events. The system can detect both anthropogenic and biogenic sources of pollution.

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Autonomous vehicle (AV) team coordination system that allows AVs to continue functioning as a team even when communication is disrupted. The system uses controllers on each AV to store mission data including objectives and default trajectories. If communication is lost, AVs can estimate teammate trajectories based on the stored mission data.

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An autonomous vehicle team coordination system that facilitates efficient search operations with multiple autonomous vehicles. The system involves capturing measurement data from sensors on each vehicle, matching time slices of data between vehicles, and using that to build a synchronized occupancy map of the environment. This allows the vehicles to share what areas have been searched or not, even when communication is intermittent due to the changing environment.

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A system and method for managing unmanned aerial vehicle (UAV) data transmission in a smart city using the Internet of Things (IoT). The system involves a management platform that coordinates multiple UAVs performing different missions by assigning them to specific time domains and airspaces. This allows efficient data collection without interference. The platform also uses techniques like relay transmission, split transmission, and merging transmission to improve transmission efficiency based on the data requirements and features.

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Automated modular multi-vehicle teaming for unmanned systems with payloads like sensors, weapons, and cargo. It enables efficient and survivable missions with a team of unmanned vehicles. The team is led by a primary asset with critical payload, surrounded by secondary assets. They fly in a formation that provides surveillance and protection. If secondary assets are lost, the primary can continue. The secondary assets autonomously reposition based on mission priorities or losses. The team uses algorithms to navigate airspace safely against threats. The guidance commands are computed in real-time to enable complex missions with diverse payloads.

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Intelligent scheduling of unmanned aerial vehicles (UAVs) to optimize task allocation and control. The method involves dynamically determining flight tasks based on real-time UAV states and environmental conditions. It analyzes flight data from multiple UAVs to evaluate their flight status. It also collects environmental data where UAVs are located. Using this data, it intelligently assigns tasks considering mission requirements, UAV capabilities, and environmental factors. This enables efficient, adaptive tasking for UAV fleets that can change tasks mid-flight based on conditions.

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A method for controlling multiple drones to coordinate their flights and communications in a shared area to improve data collection accuracy and transmission timeliness. The drones initially plan their flight paths separately in the area. They then interactively adjust their paths to meet at predetermined points to exchange data and power. By balancing interaction points with flight efficiency, it avoids drones diverging far from the area or colliding. This allows simultaneous data acquisition and transmission without affecting each other.

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Coordinated swarms of UAVs that can autonomously detect, intercept and capture rogue drones to neutralize threats from unauthorized or dangerous drones. The system uses multiple counter-attack UAVs equipped with nets that can be deployed to capture target drones. An external drone detection system provides target tracking data to the counter-attack UAVs. The coordinated swarm intercepts the target drone and captures it in the net, effectively neutralizing the threat. The counter-attack UAVs autonomously coordinate their flight to surround and capture the target drone in the net.

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