Innovations in Drone Swarm Technology

Multi-UAV cooperative coverage path planning using an improved ant colony algorithm with Q-learning adaptive strategy to improve efficiency and robustness. The algorithm has stages to optimize path planning for collaborative coverage by UAVs in complex environments. It uses an adaptive ant colony algorithm with uneven pheromone and Q-learning to balance exploration and convergence. The algorithm also determines optimal sampling point count based on UAV characteristics, environment, and scenario. This considers UAV capabilities and environmental factors to accurately plan paths for cooperative coverage.

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Automated system for grouping and coordinating semi-autonomous drones to perform tasks like surveillance, delivery, mapping, etc. The system enables dynamically assigning drones to tasks based on capabilities, rather than manual assignment. It involves a server receiving task requests, determining drone assignments, and communicating plans back to the drones. If a drone fails, another is assigned. The drones then follow the plans to complete the tasks. This allows flexible, scalable drone coordination without central control.

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Optimizing multi-UAV cooperative tracking of a moving ground target using a dynamic optimization algorithm. The method involves using a processor to generate UAV commands and track the ground target. The processor builds a 3D map of the flight area, predicts future UAV trajectories, and optimizes them using a customized whale optimization algorithm. The algorithm adjusts weight coefficients based on priority, uses guidance functions for UAV input selection, and adapts parameters to improve convergence and avoid local optima. The UAVs communicate status to the processor which coordinates tracking and obstacle avoidance.

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Dual-mode radar and communications devices for use in swarms of autonomous drones. The devices combine radar sensing and high-speed data communication capabilities in a single module with low size, weight, power, and cost. The modules leverage integrated circuits, phased arrays, and software defined radio techniques to provide radar sensing at medium range and data communication at high rates. This enables swarms of drones to autonomously navigate, inspect objects, and share sensor data using a mesh network with focused radar beams to reduce interference.

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Deploying small unmanned aircraft systems (sUAS) and loitering munitions (LM) simultaneously from an attritable small tactical unmanned aircraft system (STUAS) while airborne. The STUAS acts as a multi-role precision strike, decoy, and intelligence, surveillance, and reconnaissance (ISR) platform with the ability to perform as a single agent or a member of a swarm team in offensive operations and missions including SEAD/DEAD, HVT, MDO, and CAS. It is also configured to act as a single agent or a swarm team in defensive operations against small, low, slow, and autonomous UAS threats. The STUAS mothership carries and launches sUAS and LMs simultaneously, allowing cooperative strike operations between land and air forces in contested environments. The STUAS can be operated by joint forces with cross-domain command

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Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within UAV cluster, is responsible for coordinating managing various tasks cluster. When ACA undergoes fault recovery, a handover operation required, during which must re-authenticate its identity with cluster re-establish secure communication. However, traditional, centralized authentication mechanisms face security risks such single points of failure man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading reduced efficiency increased latency. To address these challenges, this paper proposes mathematically structured dual-chain framework that utilizes distributed ledger decouple management information. We formalize process using cryptographic primitives accumulator functions validate through BAN logic. Furthermore, we conduct quantitative analyses key performance metr... Read More

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A system for forming 3D shapes using swarms of programmable units like drones or robots that can autonomously assemble into complex shapes without collisions or central guidance. The system allows swarms of simple, replaceable units to form shapes in response to imperfect instructions passed between units using a gossip protocol. The instructions are designed to require minimal processing by the units. This allows large, complex shapes to be formed using swarms of basic units without the need for central guidance or complex instructions.

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Robot swarm that can switch from electromagnetic to acoustic communication when electromagnetic communication is disrupted. The robots have microphones, speakers, and terminals for electromagnetic communication. When electromagnetic communication fails, a robot switches to acoustic communication by exchanging signals with a partner robot using their microphones and speakers. This allows the robots to continue communicating when electromagnetic signals are disrupted. The robots can also randomly assign a leader using a token to coordinate the swarm.

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A drone system with physically connected drones for improved efficiency, durability, and swarming capabilities. The drones in this system have multiple propellers enclosed within a central core with arms extending outwards. This allows the propellers to be oriented independently from the drone body. The drones also have mechanisms to connect and disconnect from each other, forming a chain. This allows the connected drones to share thrust and power, improving efficiency. It also provides physical connections between the drones, enabling cooperative maneuvers like formations and swarming. The enclosed propellers also protect them from debris and damage.

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Swarm management for coordinating multiple autonomous devices to efficiently complete tasks. The system uses swarm intelligence to optimize task assignment and resource utilization when multiple devices are performing the same task. By analyzing factors like device capabilities, battery levels, and location, the system can determine the optimal device configuration for each task segment to minimize redundancy and ensure complete coverage. This allows coordinated, efficient swarm cleaning where multiple devices collaborate to clean a space in parallel and avoid overlaps.

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Abstract Existing collective navigation systems for drone swarms typically rely on the communication between drones, which limits application in specific mission scenarios and reduces robustness against interference. To address this challenge, a communication-free method enhanced by relative state estimation is proposed study. It consists of three key components: visual perception localization, estimation, swarm motion decision. First, sensors are employed to detect nearby drones real time calculate their positions. Second, an optimized model set adaptive interacting multiple (OMSA-IMM) filtering algorithm fuse predicted states from with measurements achieve continuous high-precision positioning. The fused then fed back into decision high-level control. Finally, effectiveness validated through numerical simulations real-world flight experiments. results demonstrate that significantly enhances accuracy localization improves performance algorithm, enabling cohesive collision-free communication-denied environments.

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Scalable multi-agent reinforcement learning (MARL) for dynamic environments with large numbers of agents by enabling independent learning of cooperative behaviors instead of centralized training. The approach involves training each agent individually using regular MARL algorithms, but allowing them to learn what, when, and how to communicate with each other and the environment. This enables rapid re-planning in dynamic situations where the environment changes, compared to combinatorial explosion in centralized MARL. The independent learners coordinate using a graph of decision waypoints representing goals, and motor waypoints between them.

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With the increasing adoption of cooperative multi-UAV systems in applications such as cargo delivery and ground reconnaissance, demand for scalable efficient path planning methods has grown substantially. However, traditional heuristic algorithms are frequently trapped local optima, require task-specific manual tuning, exhibit limited generalization capabilities. Furthermore, their dependence on iterative optimization renders them unsuitable large-scale real-time applications. To address these challenges, this paper introduces an end-to-end deep reinforcement learning framework that bypasses reliance handcrafted rules. The proposed method leverages encoderdecoder architecture with multi-head attention (MHA), where encoder generates embeddings UAVs task parameters, while decoder dynamically selects actions based contextual enforces feasibility through a masking mechanism. MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. Additionally, we integrate Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via paralleli... Read More

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This research introduces a novel framework for autonomous drone swarms, addressing critical challenges in physical-cyber security by integrating advanced computational models, decentralized swarm intelligence, and robust cryptographic protocols. The work is motivated the increasing reliance on swarms securing infrastructure, disaster response, surveillance, where hybrid physical cyber threats present significant risks. study proposes bio-inspired algorithms adaptive coordination, physics-informed neural networks real-time collision avoidance, quantum-inspired optimization models resource-aware task allocation, further fortified lattice-based protocols to counter quantum-era adversarial threats. experimental evaluation, conducted through high-fidelity simulations deployments, demonstrates systems robustness mitigating threats, achieving high avoidance accuracy, maintaining communication integrity diverse scenarios. Results show scalability with up 100 drones 80 tests, highlighting bandwidth as key area refinement. findings advance field offering multi-layered coordination applicabl... Read More

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Pursuitevasion games are a fundamental framework for advancing autonomous decision-making and cooperative control in multi-UAV systems. However, the application of reinforcement learning to pursuitevasion involving fixed-wing UAVs remains challenging due constraints, such as minimum velocity, limited turning radius, high-dimensional continuous action spaces. To address these issues, this paper proposes method that integrates automatic curriculum with multi-agent proximal policy optimization. A self-play mechanism is introduced simultaneously train both pursuers evaders, enabling dynamic adaptive encirclement strategies. In addition, reward structure specifically tailored task was designed guide gradually achieving evader while ensuring their own safety. further improve training efficiency convergence, develops subgame progressively exposes agents increasingly complex scenarios, facilitating experience accumulation skill transfer. The simulation results demonstrate proposed approach improves pursuit performance under realistic UAV dynamics. This work provides practical scalable ... Read More

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Dynamic collision avoidance for multiple drones operating in a shared airspace. The system employs a multi-stage approach to prevent collisions between drones, where each drone continuously monitors its surroundings and adjusts its behavior based on the presence of other drones. The system uses a combination of real-time imaging, collision detection, and path management to dynamically reorient and change drone paths to avoid collisions. This approach enables efficient collision avoidance while maintaining operational flexibility and adaptability among multiple drones.

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Managing a group of UAVs (drones) using AI to predict and respond to unexpected events during flight. The system uses AI to analyze UAV flight paths and predict potential issues. If an issue is identified, it takes action to mitigate it. For example, if a UAV is predicted to collide with another, it could divert that UAV's path. If a UAV loses communication, it could transfer control to a nearby UAV. The AI also helps allocate UAVs to handle unexpected events based on real-time parameters. This allows managing multiple UAVs in complex scenarios with reduced risk.

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This paper utilizes the distributed model predictive control (DMPC) method to investigate formation problem of unmanned aerial vehicles (UAVs) in obstacle environment and establishes cooperative capability evaluation metrics swarm. Based on DMPC approach, cost function is constructed adjust relative positions velocities UAVs, ensuring desired formation. Additionally, address avoidance formation, designed provide safe environment. To evaluate we design from multiple dimensions reflect swarms capability. Finally, simulation results show effectiveness with applicability metrics.

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Unmanned Aerial Vehicles (UAVs) have traditionally served as network processors in mobile networks, and more recently, servers Mobile Edge Computing (MEC). However, deploying UAVs dynamic obstacle-rich environments while ensuring efficient coordination among multiple presents significant challenges. To tackle these challenges, we propose a unified reinforcement learning framework for multi-UAV MEC platform that enhances Quality-of-Service (QoS) optimizes path planning. Specifically, our contributions include: (1) Integrating QoS optimization UAV planning into framework; (2) Modeling terminal users' demands with sigmoid-like function to enhance service quality; (3) Incorporating demand, risk factors, geometric distance the reward matrix balancing quality, mitigation, cost efficiency. Experimental results demonstrate frameworks effectiveness: simulations 3D show 2035% reduction collision incidents compared greedy algorithms 1525% improvement demand fulfillment rates under user distributions. Further analysis highlights impact of sigmoid parameter tuning on risk-QoS trade-offs,... Read More

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ABSTRACT As autonomous drone swarms become increasingly important for complex missions, there remains a critical need integrated approaches that can simultaneously handle task allocation and safe navigation in dynamic environments. This paper addresses the challenge of optimally allocating tasks generating collisionfree trajectories operating obstaclerich settings. Our proposed Swarm Allocation Route Generation (SARG) framework integrates optimal assignment with dynamically feasible trajectory planning, enabling efficient mission completion while ensuring through 3D workspaces. Using quadrotors as our experimental platform, incorporates both DronetoObstacle DronetoDrone collision avoidance algorithms, alongside modified path planning algorithm enhances simultaneous graph search efficiency. extensive experiments demonstrate SARG significantly improves performance over existing approaches. The framework, maintaining 100% rate dense environments, achieves 21.6% reduction computation time searching phase compared to conventional methods, contributing overall system Thes... Read More

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Dynamic grouping of semi-autonomous drones to efficiently perform tasks like surveillance, delivery, mapping, etc. The drones can self-organize into teams based on capability matching to execute complex operations. A server determines tasks and assigns drones based on capabilities. Lead drones decompose tasks and create plans. Followers execute tasks. If a leader fails, another drone takes over. This allows flexible and scalable drone swarms without centralized control.

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The growing use of unmanned aerial vehicles (UAVs) in diverse fields such as disaster recovery, rural regions, and smart cities necessitates effective dynamic drone network establishment techniques. Conventional optimization techniques like genetic algorithms (GAs) particle swarm (PSO) are weak when it comes to real-time adjustment the environment multi-objective constraints. This paper proposes a hybrid framework combining reinforcement learning (RL) improve deployment networks. We integrate Q-learning into GA mutation process allow drones adaptively adjust locations real time under coverage, connectivity, energy In scenario large-scale simulations for wildfire tracking, response, urban monitoring tasks, approach performs better than PSO. greatest enhancements 6.7% greater 7.5% less average link distance, faster convergence optimal deployment. proposed allows establish strong stable networks that nature adapt mission demands with efficient coordination. research has important applications autonomous UAV systems mission-critical where adaptability robustness essential.

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UAV collision avoidance method that allows multiple UAVs to fly safely in proximity without constant communication. UAVs exchange flight information with nearby UAVs. They calculate intersection times based on the received flight info. If an intersection is imminent, they adjust their flight paths to avoid collision. This allows UAVs to dynamically adapt their paths to prevent mid-air collisions without constant communication.

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<title>Abstract</title> A wireless network composed of UAVs collaborating to upload data the inter-net is known as a swarm unmanned aerial vehicles (S-UAVs). are utilized extensively convey important during emergency situations like earthquakes or floods. Clustering, which divides into clusters with cluster head (CH) and members (CM), one most dependable routing strategies in S-UAVs. disconnected loss result damaged non-functional CH, topic that rarely explored. This work proposes an improved multiple redundant strategy based on three weighted parameters: energy, rewarding, propagation delay for reliable transmission through ensuring functional CH. paper exclusively uses coefficient variation set weights used calculation generates new parameter denoted index. Based index, nodes selected CH CMs, thus forming clusters. To decrease probability we propose cooperation different types 1 agents, our case. Having agents will increase systems reliability, risk non-functionality crisis scenario depends highly agents type. ensure take over duties connection stability. Every node has ensur... Read More

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Optimizing communication in swarms of drones to enable efficient and reliable control of large numbers of drones. The optimization involves clustering the drones into groups with a master drone that communicates with a central server, and slave drones that relay messages from the master. This reduces the number of required communication channels compared to each drone directly connecting. Clustering also allows faster area coverage, obstacle avoidance, and resource sharing. If a master fails, another slave can be promoted. This enables robust swarm operation by minimizing communication breakdowns.

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Coordinated control of multiple distributed devices like robots using an environment manager that monitors device operations and updates environment information. The devices monitor the environment info and if it meets a condition, they execute a specific operation. This allows devices to coordinate actions based on shared environment data without central control. The environment manager updates the environment info as devices operate, allowing coordination across devices.

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Formation flying of multiple unmanned aerial vehicles (UAVs) has attracted much attention for its versatility in cooperative tasks. In this paper, a distributed formation planning method is proposed UAVs. First, we design path searching algorithm, swarm-A*, which can enhance the cohesion swarm, i.e., preventing disintegration swarm when it encounters an obstacle. Then, after waypoint reallocation, trajectory optimization framework formulated. Smooth trajectories UAVs to travel safely obstacle-laden environments be obtained by solving problem. Next, tracking controller based on sliding mode control designed, ensuring that follow planned under dynamic constraints. Finally, numerical simulations and experiments are conducted validate effectiveness method.

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Multi-agent path finding (MAPF) is a safety-critical scenario where the goal to secure collision-free trajectories from initial desired locations. However, due system complexity and uncertainty, integrating learning-based controllers with MAPF challenging cannot theoretically guarantee safety of learned controllers. In response, our study proposes verified safe multi-agent neural control (VSMANC) approach for MAPF, focusing on unified training Decentralized Control Barrier Functions (DCBF) enhence safety. VSMANC enables all agents concurrently learn DCBFs using loss function designed maximize safety, adhere standard policies, incorporate path-finding-related heuristics. We also propose formal verification-guided retraining process both verify properties generate counterexamples retraining, thereby providing guarantee. validate through shape formation experiments UAV simulations, demonstrating significant improvements in effectiveness complex environments.

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An unmanned vehicle system that enables autonomous launch and recovery of payloads like vessels, equipment, and people in marine and submarine environments. The system uses a host vehicle like a submersible drone to carry and deploy guest vehicles like mini-subs, surface drones, and payload carriers. The host and guest vehicles can operate independently in different modes like air, surface, and sub-surface. This allows covert missions involving multiple environments and tasks. The host vehicle provides mobility, maneuverability, and launch capabilities while the guest vehicles perform specialized missions. The system enables multi-environment unmanned vehicle swarms that can be coordinated in time and space for complex missions.

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Using a team of drones equipped with inspection sensors to simultaneously and autonomously inspect a gas turbine engine. The drones are choreographed to move along specific paths to cover the entire engine area. If an abnormality is detected, a drone can retrace the path to confirm. If a drone takes too long, another drone is summoned to finish. This allows thorough engine inspection without engine removal or downtime.

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Managing flights of mobile terminals like drones that use multiple cellular networks. A central management apparatus allocates flight zones across cellular networks based on shared 3D space. This involves dividing airspace into zones identified by latitude, longitude, and altitude. The management apparatus distributes this zone allocation data to the cellular networks. Each network then uses it to coordinate flights of devices connected to their network. This ensures consistent flight management across networks when multiple devices fly in proximity.

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Collaborative control method for unmanned cluster systems that improves resilience against network attacks. The method takes into account parameter uncertainty and network attack impacts. It involves constructing a dynamic model, uncertainty boundary function, and adaptive robust controllers for each unmanned system. The controllers adaptively adjust parameters to compensate for uncertainty and attack effects. This allows the cluster to quickly and stably meet performance requirements in the face of parameter uncertainty and network attacks.

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Efficient path planning for swarms of unmanned aerial vehicles (UAVs) that balances steering the swarms towards their destinations while avoiding collisions. The approach breaks path planning into two levels: swarm-level planning for the entire swarm and individual-level planning for each UAV within a swarm. Swarm-level planning steers the swarm as a whole, avoiding conflicts with other swarms. Individual-level planning steers UAVs within a swarm towards the swarm leader and avoids conflicts between UAVs.

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Learning system for multi-agent applications that enables robust, scalable, and generalizable autonomous behaviors in complex environments like air-to-air engagements. The system uses population-based curriculum learning, hierarchical temporal learning, and behavior adaptation learning techniques to improve performance. It trains a diverse population of agents in sequential mini-games, learns high-level behaviors from low-level actions, and adapts behaviors to new tasks. This enables robustness, scalability, and generalizability of autonomous behaviors in multi-agent applications.

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Unicast air-to-everything (A2X) communications for drones that enables direct communication between drones using unicast links instead of broadcast. The drones initiate unicast links by exchanging messages over broadcast channels. The source drone sends a direct communication request (DCR) with its unique ID and application-layer ID. The target drone responds with a link establishment message. This establishes security and allows the drones to exchange unicast messages using the application-layer ID. The DCR can include proximity parameters for link setup based on drone positions.

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Coordinated trajectory planning for devices with multiple output components to improve control of coordinated movements. The technique involves a central component detecting trajectory errors for one component, then calculating corrected trajectories for other components to compensate. It provides look-ahead trajectories to components to reduce communication burden. The technique improves coordination efficiency and reduces network traffic in scenarios with many coordinated devices.

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Efficiently coordinating movement of multiple output components in a system using a centralized trajectory planning component. The method involves generating encoded output trajectories for the components, decoding them locally, and executing the decoded trajectories. This reduces communication overhead compared to streaming individual commands to each component. The centralized component calculates the trajectories based on triggers, then sends encoded versions to the local controllers for decoding and execution. This allows real-time feedback and adaptation to optimize coordination.

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An operation management system for efficiently and safely managing multiple vertical takeoff and landing aircraft in a shared airspace. The system uses 4D route planning to optimize flight paths considering uncertainty and external factors. It designs appropriable spaces around each aircraft that prevent collisions. The system re-plans routes during flight based on the moving and fixed appropriable spaces. This allows automated, coordinated takeoff and landing of multiple aircraft with collision avoidance and efficient use of airspace.

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A method for continuous control of multi-agent systems with complex, non-Markovian specifications using temporal equilibrium analysis and reinforcement learning. The method involves breaking down complex multi-agent tasks with LTL specifications into simpler subtasks that can be learned using reinforcement learning. The subtasks are derived through temporal equilibrium analysis, which generates abstract top-level policies. These policies are then applied to the low-level continuous control of the agents using reinforcement learning algorithms. The method improves scalability and interpretability of multi-agent systems by leveraging the strengths of both temporal equilibrium analysis and reinforcement learning.

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Intelligent swarm is a powerful tool for targeting high-value objectives. Within the Anti-Access/Area Denial (A2/AD) context, an unmanned aerial vehicle (UAV) must leverage its autonomous decision-making capability to execute tasks with independence. This paper focuses on Suppression of Enemy Air Defenses (SEAD) mission intelligent stealth UAV swarms. The current research field mainly faces challenges in fully simulating complexity real-world scenarios and insufficient task planning capabilities. To address these issues, this develops representative problem model, establishes six-tier standardized simulation environment, selects Deep Deterministic Policy Gradient (DDPG) algorithm as core enhance capabilities At level, designs reward functions corresponding behaviors, aiming motivate swarms adopt more effective action strategies, thereby achieving planning. Simulation results demonstrate that scenario architectural design are feasible artificial intelligence algorithms can enable show higher level intelligence.

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A control system for coordinating multiple robots that reduces communication bandwidth and storage requirements compared to traditional centralized multi-robot systems. The system uses a central control apparatus to manage the robots instead of having each robot communicate with all others. The control apparatus assigns tasks to individual robots based on their observed environments. This decentralized task allocation reduces the need for inter-robot communication and avoids propagating all sensor data. The control apparatus can also store and process the raw sensor data, further reducing robot memory requirements.

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Reinforcement learning approach for training collaborative multi-robot systems using pre-learned action primitives. The method involves breaking down complex collaborative tasks into basic actions and interactions, storing them as primitives in a library, and training a model to combine and coordinate primitives to complete tasks. This allows leveraging pre-learned primitives to efficiently train collaborative robot systems instead of learning entire tasks from scratch.

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Autonomous aerial delivery system with multiple drones that can operate fully autonomously to deliver items without human intervention. The system has a central management server that schedules flight plans for the drones. The drones can rotate 360 degrees and transition between vertical takeoff/landing and horizontal flight. They communicate with the server over a wireless network to receive flight plans and check for updates. This allows coordinated, autonomous delivery of multiple items using a fleet of drones.

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This paper presents the development of new decentralized control algorithms for coordinating robot swarms in emergency response scenarios. The key distinguishing feature proposed approach is use adaptive weight coefficients dynamically adjusting balance between separation, alignment, cohesion, and repulsion forces. A software simulation platform has been developed that implements enables systematic computational experiments. For quantitative evaluation swarm dispersion effectiveness, metrics based on graph theory have introduced, characterizing connectivity agent interaction graph. series experiments involving 100 independent model runs each parameter combination analyzes influence size, radius, force dynamics. Statistical analysis with 95% confidence intervals demonstrates significance obtained results. comparison modern approaches to deep reinforcement learning evolutionary conducted. It shown provides comparable quality significantly lower costs. results advance intelligence methods contribute effective groups situations.

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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 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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