Innovations in Drone Swarm Technology

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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