Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang, G. F.; Gu, Chunyang; Li, J.; Wang, Jiqiang; Chen, X.; Zhang, He

doi:10.3390/drones7060380

Cited by 3 publications

(2 citation statements)

References 155 publications

(162 reference statements)

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“…While the neck network can extract relatively complex feature information, it might overlook the more prominent characteristics of motion keypoints layer features. Therefore, to further enhance the algorithm's feature extraction capabilities for small target human keypoints and prevent the loss of essential information during information transmission, we introduce cross-layer feature fusion [21].…”

Section: Cross-layer Cascaded Feature Fusionmentioning

confidence: 99%

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Wang,

2024

Electronics

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In the field of multimodal robotics, achieving comprehensive and accurate perception of the surrounding environment is a highly sought-after objective. However, current methods still have limitations in motion keypoint detection, especially in scenarios involving small target detection and complex scenes. To address these challenges, we propose an innovative approach known as YOLOv8-PoseBoost. This method introduces the Channel Attention Module (CBAM) to enhance the network’s focus on small targets, thereby increasing sensitivity to small target individuals. Additionally, we employ multiple scale detection heads, enabling the algorithm to comprehensively detect individuals of varying sizes in images. The incorporation of cross-level connectivity channels further enhances the fusion of features between shallow and deep networks, reducing the rate of missed detections for small target individuals. We also introduce a Scale Invariant Intersection over Union (SIoU) redefined bounding box regression localization loss function, which accelerates model training convergence and improves detection accuracy. Through a series of experiments, we validate YOLOv8-PoseBoost’s outstanding performance in motion keypoint detection for small targets and complex scenes. This innovative approach provides an effective solution for enhancing the perception and execution capabilities of multimodal robots. It has the potential to drive the development of multimodal robots across various application domains, holding both theoretical and practical significance.

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Section: Cross-layer Cascaded Feature Fusionmentioning

confidence: 99%

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Wang,

2024

Electronics

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“…The methodologies mentioned above demonstrate the efficacy of GNNs in handling homogeneous multi-sensor data. Faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, Wang et al [ 19 ] noted the importance of heterogeneous information in aircraft systems. However, research on leveraging GNNs to learn the hidden topological relationships of heterogeneous mechanical equipment is still ongoing [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Conditional Enhanced Variational Autoencoder-Heterogeneous Graph Attention Neural Network: A Novel Fault Diagnosis Method for Electric Rudders Based on Heterogeneous Information

Cao,

Yang,

Guo

et al. 2024

Sensors

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In machine fault diagnosis, despite the wealth of information multi-sensor data provide for constructing high-quality graphs, existing graph data-driven diagnostic methods face challenges posed by handling these heterogeneous multi-sensor data. To address this issue, we propose CEVAE-HGANN, an innovative model for fault diagnosis based on the electric rudder, which can process heterogeneous data efficiently. Initially, we facilitate interaction between conditional information and the original features, followed by dimensional reduction via a conditional enhanced variational autoencoder, thereby achieving a more robust state representation. Subsequently, we define two meta-paths and employ both the Euclidean distance and Pearson coefficient in crafting an effective adjacency matrix to delineate the relationships among edges within the graph, thereby effectively representing the complex interrelations among these subsystems. Ultimately, we incorporate heterogeneous graph attention neural networks for classification, which emphasizes the connections among different subsystems, moving beyond the reliance on node-level fault identification and effectively capturing the complex interactions between subsystems. The experimental outcomes substantiate the superiority of the electric rudder-based CEVAE-HGANN model fault diagnosis.

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Traffic Management in Smart Cities Using Unmanned Aerial Vehicles

Raj,

Mahadevan,

Pushparaj

et al. 2024

Drone Applications for Industry 5.0

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Traffic management in smart cities using unmanned aerial vehicles (UAVs), commonly known as drones, is an innovative and promising approach to address the growing challenges associated with urban transportation. UAVs can be deployed in various ways to monitor, analyze, and improve traffic flow and safety in smart cities. Traffic surveillance and monitoring, traffic flow optimization, emergency response, public safety, parking management, environmental monitoring, traffic surveys, event and congestion management, infrastructure inspection, and public transport support are some ways in which UAVs can be used for traffic management in smart cities. Collaborations between government agencies, law enforcement, transportation departments, and drone operators are essential for the successful integration of UAVs into the smart city infrastructure. Additionally, advanced technologies like artificial intelligence and machine learning can be used to process and analyze the vast amounts of data collected by drones, allowing for more proactive and efficient traffic management in smart cities.

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Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Cited by 3 publications

References 155 publications

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

YOLOv8-PoseBoost: Advancements in Multimodal Robot Pose Keypoint Detection

Conditional Enhanced Variational Autoencoder-Heterogeneous Graph Attention Neural Network: A Novel Fault Diagnosis Method for Electric Rudders Based on Heterogeneous Information

Traffic Management in Smart Cities Using Unmanned Aerial Vehicles

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