Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely used for tasks involving surveillance and reconnaissance. A version of the pursuit-evasion problems centered around UAVs and its variants has been extensively studied in recent years due to numerous breakthroughs in AI. We present an approach to …
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37 results for “UAVs”
Paper proposes efficient UAV placement for aerial base stations.
problem Efficient placement of UAVs as aerial base stations to serve varying traffic demand.
method Modeling UAV deployment as a non-cooperative game and using a learning-based algorithm to update UAV locations.
result Significant performance gains up to 52% and 74% in terms of throughput and dropped users compared to an optimized baseline algorithm.
Current UAV-recorded datasets are mostly limited to action recognition and object tracking, whereas the gesture signals datasets were mostly recorded in indoor spaces. Currently, there is no outdoor recorded public video dataset for UAV commanding signals. Gesture signals can be effectively used with UAVs by leveraging…
Paper proposes a federated learning framework for UAV swarms, optimizing convergence rate and energy consumption.
problem Challenges of centralized ML in UAV swarms due to limited connections and large data volumes.
method Distributed federated learning within UAV swarm, joint power allocation and scheduling design.
result Joint design strategy reduces communication rounds by up to 35%.
A scheme for UAVs to borrow spectrum from terrestrial networks for disaster relief.
problem Spectrum shortage in UAV networks for critical missions.
method Hierarchical spectrum sharing model with relaying UAVs and reinforcement learning.
result Autonomous spectrum management improves QoS and prolongs UAV lifetime.
A Residual Variance Matching Recursive Least Squares Filter for Real-time UAV Terrain Followingeess.SP
Improved real-time UAV terrain following with RVM-RLS filter.
problem Accurate real-time waypoints estimation under measurement noise in nonlinear, time-varying systems.
method Residual Variance Matching Recursive Least Squares (RVM-RLS) filter guided by RVME criterion.
result Improved waypoints estimation accuracy by approximately 88% compared to benchmarks.
SLIC-UAV monitors forest recovery using UAVs and machine learning.
problem Challenges in monitoring forest recovery, especially in logged tropical forests.
method Novel pipeline for UAV imagery analysis, combining crown labelling, species classification, and superpixel segmentation.
result SLIC-UAV achieves high accuracy in species mapping, from 79.3% to 90.5%.
Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approacheess.SY
This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting c…
A novel UNet detector detects sheep in UAV imagery.
problem Detecting small objects (sheep) in UAV imagery.
method Developed a novel dataset, used various object detectors, and evaluated their performance.
result UNet detector with weighted Hausdorff distance is best for sheep detection.
Paper optimizes UAV navigation for IoT data freshness and energy efficiency.
problem Improving data freshness and connectivity for IoT devices with UAVs.
method Deep reinforcement learning model with experience replay for energy-efficient UAV trajectory optimization.
result The proposed approach is 3.6% and 3.13% more energy efficient than greedy and baseline methods.
Gated Recurrent Units Learning for Optimal Deployment of Visible Light Communications Enabled UAVseess.SP
In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interf…
Paper uses machine learning to optimize UAV deployment for traffic offloading.
problem Optimizing UAV deployment for efficient traffic offloading from ground BSs.
method LSTM for traffic prediction, KEG algorithm for service area determination, multi-access techniques comparison.
result RSMA reduces up to 24% total power consumption compared to conventional methods.
UAV uses RL to navigate, map, and detect targets in unknown environments.
problem Optimizing UAV trajectory for accurate mapping and target detection in unknown environments.
method Formulated as an MDP, UAV uses RL to infer navigation policy.
result UAV autonomously explores high target detection areas while reconstructing the environment.
Recent advances in unmanned aerial vehicle (UAV) technology have revolutionized a broad class of civil and military applications. However, the designs of wireless technologies that enable real-time streaming of high-definition video between UAVs and ground clients present a conundrum. Most existing adaptive bitrate (AB…
UAVs learn to collect data from IoT sensors efficiently.
problem Efficient UAV path planning for wireless data collection.
method Deep reinforcement learning (DDQN) with experience replay and convolutional layers.
result UAV control policy generalizes over changing scenario parameters.
The paper optimizes UAV path and power for QoS in cellular networks.
problem Optimizing UAV path and power for QoS in cellular networks.
method Apprenticeship learning via deep inverse reinforcement learning (IRL) combined with Q-learning and DRL.
result The proposed method achieves expert-level performance and maintains performance in unseen situations.
This study improves UAV identification using RF signals with one-shot generative methods.
problem Limited RF environments and signal variability make traditional RF identification ineffective.
method Introduces one-shot generative methods to augment RF signals for UAV identification.
result One-shot generative methods outperform traditional methods in low-data regimes.
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoringstat.ML
Novel path planning improves UAV detection of extreme anomalies.
problem Improper criteria for UAV anomaly detection.
method Mathematical criteria guiding UAV towards strong anomalies.
result Proposed approach outperforms traditional methods in various applications.
Unmanned aerial vehicles (UAVs) are envisioned to complement the 5G communication infrastructure in future smart cities. Hot spots easily appear in road intersections, where effective communication among vehicles is challenging. UAVs may serve as relays with the advantages of low price, easy deployment, line-of-sight l…
Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning…
This paper optimizes UAV and FeICIC locations in a three-tier LTE-Advanced network.
problem Optimizing UAV and FeICIC locations in a three-tier LTE-Advanced network.
method Integrates UAVs as both UE and BS in LTE-Advanced HetNet, uses CRE, ICIC, 3D beamforming, and genetic algorithms for optimization.
result Heuristic algorithms outperform brute-force techniques in achieving better 5pSE and coverage probability.
Learning Pose Estimation for UAV Autonomous Navigation andLanding Using Visual-Inertial Sensor Datacs.LG
Improved UAV navigation and landing using deep learning.
problem Autonomous navigation and landing of UAVs with high accuracy.
method Multimodal fusion of visual and inertial sensor data using deep neural networks.
result 25% improvement in pose estimation accuracy compared to traditional methods.
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingeess.SP
In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UAVs) serve as equipment providing computation resource, and they enable task offloading from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, reso…
New method uses UAV imagery and ML to map crops and weeds.
problem Mapping crops and weeds at high resolution.
method Machine Learning algorithms trained on expert-masked images.
result Maps with >90% identification efficiency at 5m altitude.
Bayesian fusion improves radar target recognition for UAVs.
problem Improving radar target recognition for UAVs using multistatic radar configurations.
method Proposes a fully Bayesian RATR framework using Optimal Bayesian Fusion (OBF) to aggregate classification probability vectors from multiple radars.
result Empirical results show that the OBF method significantly enhances classification accuracy compared to other fusion methods and single radar configurations.
Researchers develop multi-agent systems for quadcopters to collaborate in missions.
problem Enable multiple quadcopters to work together in remote sensing tasks.
method Agent dynamics, network topologies, collective behaviors, agreement protocol, equations of motion for quadcopters.
result Multi-agent systems can successfully collaborate in remote sensing missions.
This work enables UAVs to autonomously form desired trajectories without needing a central plan.
problem Autonomous formation of complex trajectories in UAVs.
method Decentralized control system using geometric embeddings.
result Quadcopters self-organize into desired trajectories while maintaining separation.
Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest. A modern technique for recognition of events based on aerial pho…
The paper detects amateur drones using acoustic signals, overcoming interference.
problem Detection of unmonitored UAVs (amateur drones) for security and safety.
method Machine learning approach using Independent Component Analysis (ICA) for acoustic signal detection.
result The RMS values of PSD with KNN outperform other methods in detecting AmDr.
A new algorithm finds optimal solutions for constrained decision processes.
problem Optimizing state-value functions with constraints in CMDPs.
method Gradient-Aware Search (GAS) exploiting PWLC structure.
result GAS converges faster and more reliably than existing methods.
PRISM provides real-time SLAM with uncertainty estimates for agent and map states.
problem Lack of uncertainty estimates and real-time capability in SLAM.
method Combines differentiable rendering and 6-DoF dynamics, uses approximations for Bayesian inference.
result Runs at 10Hz real-time with similar accuracy to state-of-the-art SLAM.
A Distributed Approach for Networked Flying Platform Association with Small Cells in 5G+ Networkscs.NI
The densification of small-cell base stations in a 5G architecture is a promising approach to enhance the coverage area and facilitate the ever increasing capacity demand of end users. However, the bottleneck is an intelligent management of a backhaul/fronthaul network for these small-cell base stations. This involves …
Meta-learning improves drone trajectory design for dynamic wireless networks.
problem Designing optimal trajectories for energy-constrained drones in dynamic network environments.
method Proposes a meta-learning algorithm to adaptively tune a reinforcement learning solution for trajectory design.
result Meta-tuned RL yields faster convergence and improved communication performance compared to baseline algorithms.
Batch-normalization (BN) layers are thought to be an integrally important layer type in today's state-of-the-art deep convolutional neural networks for computer vision tasks such as classification and detection. However, BN layers introduce complexity and computational overheads that are highly undesirable for training…
Optimizes UUV hull design with a two-orders-of-magnitude speedup.
problem Designing efficient underwater vehicle hulls using CFD simulations.
method Bayesian Optimization-LCB algorithm and DNN-based surrogate model.
result Two-orders-of-magnitude speedup in design optimization process.
Hybrid neural network infers states from black-box systems.
problem Inference of state models in black-box systems without source code instrumentation.
method Convolutional and recurrent layers learn correlations between time series signals.
result Improves state change point detection by up to 102%.
The LG Fibrationmath.DG
Paper presents a nearly invertible mapping between high-dimensional and lower-dimensional spheres.
problem Lack of rigorous mathematical foundation in deep learning algorithms.
method Utilizes Multicomplex rotation groups and polyspherical coordinates to define two maps and a composite LG Fibration.
result Derives a distance difference function to determine invariant inner products under the transformation.