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…
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48 results for “unmanned aerial vehicle”
Geometric families of low-rank covariances improve flexibility and tractability in high dimensions.
problem Interpolating and identifying covariance matrices in high dimensions with limited data.
method Differential geometric construction of low-rank covariance families, interpolation on manifolds, and distance minimization for identification.
result Differential geometric covariance families offer significant flexibility and computational tractability.
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.
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…
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.
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.
Predicting the outcomes of integrating Unmanned Aerial Systems (UAS) into the National Aerospace (NAS) is a complex problem which is required to be addressed by simulation studies before allowing the routine access of UAS into the NAS. This thesis focuses on providing 2D and 3D simulation frameworks using a game theore…
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 …
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.
UAVs use RL to outperform a greedy shooter in 2D engagements.
problem Optimizing UAV pursuit-evasion strategies in 2D environments.
method Reinforcement learning applied to a greedy shooter strategy in a 2D aerial engagement.
result Deep Q-learning UAV outperforms greedy shooter UAV in simulations.
The paper provides guarantees for feedback control with sensor errors.
problem Certifying performance and safety in feedback control systems with sensor errors.
method Solving a supervised learning problem to characterize sensor errors and providing uniform error bounds.
result Finite-time convergence rate on sub-optimality of using a regressor in closed-loop for waypoint tracking.
We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable reward function is designed keeping in mind the cost and weight constraints for micro drone with minimum number of sensing modalities. Collection of small number of…
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.
Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challenge for the deployment of ML-based systems in safety- and security-critical environments like autonomous driving, disease detection or unmann…
We solve 6-DoF localisation and 3D reconstruction using deep state-space models.
problem 6-DoF localisation and dense 3D reconstruction in spatial environments.
method Approximate Bayesian inference in a deep state-space model combining learning and domain knowledge.
result Near state-of-the-art performance on UAV flight data.
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.
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.
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 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.
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.
Many DNN-enabled vision applications constantly operate under severe energy constraints such as unmanned aerial vehicles, Augmented Reality headsets, and smartphones. Designing DNNs that can meet a stringent energy budget is becoming increasingly important. This paper proposes ECC, a framework that compresses DNNs to m…
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.
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…
This work reveals a new scaling law for optimal design of multirotor aerial vehicles.
problem Designing optimal configurations for fully-actuated multirotor aerial vehicles.
method Formulated on the product manifold of Projective Lines \RP^2^N, minimizing a coordinate-invariant Log-Volume isotropy metric.
result The topology of the global optima is governed by the symmetry of the chassis, leading to a N-5 Scaling Law.
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.
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.
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.
Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computingeess.SP
Paper optimizes UAV-assisted mobile edge computing for energy efficiency.
problem Minimizing energy consumption in UAV-assisted mobile edge computing.
method Proposes CAT and RAT algorithms combining convex optimization and deep reinforcement learning.
result RAT achieves similar performance and outperforms traditional algorithms.
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.
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…
Advances in deep learning and reinforcement learning enable autonomous UAS.
problem Current UAS lack autonomy, limiting their applications.
method Discussing deep learning and reinforcement learning techniques for UAS autonomy.
result Machine learning can enhance UAS autonomy and expand their use.
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator c…
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 presents a new port-Hamiltonian model for vehicle manipulators.
problem Complex mechanical systems' energy flow and conservation.
method Derives port-Hamiltonian dynamics from Hamiltonian reduction theory.
result Establishes mathematical equivalence with existing formulations.
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%.
Scenario-based testing for the safety validation of highly automated vehicles is a promising approach that is being examined in research and industry. This approach heavily relies on data from real-world scenarios to derive the necessary scenario information for testing. Measurement data should be collected at a reason…
Deep reinforcement learning provides a promising approach for vision-based control of real-world robots. However, the generalization of such models depends critically on the quantity and variety of data available for training. This data can be difficult to obtain for some types of robotic systems, such as fragile, smal…
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%.
DALES offers a large annotated aerial LiDAR dataset for 3D deep learning.
problem Lack of large-scale annotated aerial LiDAR datasets for deep learning.
method Collection and annotation of over half a billion hand-labeled points from an ALS scanner.
result DALES is the most extensive publicly available ALS data set with improved resolution and coverage.
In this paper, we use an aerial base station (aerial-BS) to enhance fairness in a dynamic environment with user mobility. The problem of optimally placing the aerial-BS is a non-deterministic polynomial-time hard (NP-hard) problem. Moreover, the network topology is subject to continuous changes due to the user mobility…
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.
In spite of the many advantages of aerial imagery for crowd monitoring and management at mass events, datasets of aerial images of crowds are still lacking in the field. As a remedy, in this work we introduce a novel crowd dataset, the DLR Aerial Crowd Dataset (DLR-ACD), which is composed of 33 large aerial images acqu…
New method improves generative model performance by fully conditioning variational posteriors.
problem Inaccurate inference due to partial conditioning of variational posteriors in sequential LVMs.
method Introduces fully-conditioned approximate posteriors to improve generative model performance.
result Improves generative modelling and multi-step prediction performance.
Study analyzes cost-benefit of CBM for unmanned systems.
problem Determining ROI for CBM strategies in unmanned systems.
method MDFTA with MCS for assessing maintenance requirements.
result Different CBM strategies can significantly impact ROI.
Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Featurecs.CV
This paper classifies typhoon damage features using aerial photography.
problem Typhoon damages and slow recovery times.
method Aerial photography for classification of eight classes including land covers and disaster areas.
result Visualize and explain typhoon disaster features using convolutional activation maps.
Paper proposes a method to improve building extraction from aerial images by adapting CNN models.
problem Limited generalization of CNN-based segmentation models for unseen images.
method Combines domain transfer and adversarial attack concepts to adapt input images to target images.
result Improves overall IoU and outperforms other methods in cross-dataset experiments.
Review of recent advances in decentralized MARL with networked agents.
problem Sequential decision-making by networked agents without central coordination.
method Analysis of recent algorithms and their theoretical foundations.
result Inspiring more research in this challenging area.
Deep Reinforcement Learning (deep RL) has made several breakthroughs in recent years in applications ranging from complex control tasks in unmanned vehicles to game playing. Despite their success, deep RL still lacks several important capacities of human intelligence, such as transfer learning, abstraction and interpre…