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1. Architecture, Classification, and Applications of Contemporary Unmanned Aerial Vehicles

   https://doi.org/10.1109/MCE.2021.3063945  

   Alghamdi, Yousef; Munir, Arslan; La, Hung Manh (March 2021, IEEE Consumer Electronics Magazine)

   null (Ed.)

   Unmanned aerial vehicles (UAV) have been gaining significant attention in recent times as they are becoming increasingly accessible and easier to use. The advancements in flight controller technology have enabled users to fly a recreational UAV without any previous flight experience. UAVs are used in a variety of applications, ranging from civilian tasks, law enforcement, and rescue applications, to military reconnaissance and air strike missions. This article serves as an introduction to UAV systems' architecture, classification, and applications to help researchers and practitioners starting in this field get adequate information to understand the current state of UAV technologies. The article starts by inspecting the UAVs' body configuration styles and explains the physical components and sensors that are necessary to operate and fly a UAV system. The article also provides a comparison of several components for state-of-the-art UAVs. The article further discusses different propulsion methods and various payloads that could be mounted on the UAV. The article then explores the classification of UAVs followed by the application of UAVs in different domains, such as recreational, commercial, and military. Finally, the article provides a discussion of futuristic technologies and applications of UAVs along with their associated challenges. 

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2. Unmanned Aerial Vehicles‐Based Mission Reliability in Collaborative, Heterogeneous, and Dynamic Environments

   https://doi.org/10.1002/qre.70068  

   Ren, Junxing; Xing, Liudong; Lin, Yi‐Kuei (September 2025, Quality and Reliability Engineering International)

   ABSTRACT Unmanned aerial vehicles (UAVs) are revolutionizing a wide range of military and civilian applications. Since mission failures caused by malfunctions of UAVs can incur significant economic losses, modeling and ensuring the reliability of UAV‐based mission systems is a crucial area of research with challenges posed by multiple dependent phases of operations and collaborations among heterogeneous UAVs. The existing reliability models are mostly applicable to single‐UAV or homogeneous multi‐UAV systems. This paper advances the state of the art by proposing a new analytical modeling method to assess the reliability of a multi‐phased mission performed by heterogeneous collaborative UAVs. The proposed method systematically integrates an integral‐based Markov approach with a binary decision diagram‐based combinatorial method, addressing inter‐ and intraphase collaborations as well as phase‐dependent configurations of heterogeneous UAVs for accomplishing different tasks. As demonstrated by a detailed analysis of a two‐phase rescue mission performed by six UAVs, the proposed method has no limitations on UAV's time‐to‐failure and time‐to‐detection distributions. Another contribution is to formulate and solve UAV allocation problems, achieving a balance between mission success probability and total cost. Given the uncertainties inherent in the mission scenario, the random phase duration problem is also examined. 

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3. BERRY: Bit Error Robustness for Energy-Efficient Reinforcement Learning-Based Autonomous Systems

   https://doi.org/10.1109/DAC56929.2023.10247999  

   Wan, Zishen; Chandramoorthy, Nandhini; Swaminathan, Karthik; Chen, Pin-Yu; Reddi, Vijay Janapa; Raychowdhury, Arijit (July 2023, In 2023 60th ACM/IEEE Design Automation Conference (DAC()

   Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to run complex reinforcement learning (RL) models to execute fully autonomous positionnavigation-time tasks within stringent onboard weight and power constraints. We observe that reducing onboard operating voltage can benefit the energy efficiency of both the computation and flight mission, however, it can also result in on-chip bit failures that are detrimental to mission safety and performance. To this end, we propose BERRY, a robust learning framework to improve bit error robustness and energy efficiency for RL-enabled autonomous systems. BERRY supports robust learning, both offline and on-board the UAV, and for the first time, demonstrates the practicality of robust low-voltage operation on UAVs that leads to high energy savings in both compute-level operation and systemlevel quality-of-flight. We perform extensive experiments on 72 autonomous navigation scenarios and demonstrate that BERRY generalizes well across environments, UAVs, autonomy policies, operating voltages and fault patterns, and consistently improves robustness, efficiency and mission performance, achieving up to 15.62% reduction in flight energy, 18.51% increase in the number of successful missions, and 3.43× processing energy reduction. 

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4. UAV-based Networked Airborne Computing Simulator and Testbed Design and Implementation

   https://doi.org/10.1109/ICUAS57906.2023.10156100  

   Wang, Baoqian; Xie, Junfei; Ma, Ke; Wan, Yan (June 2023, 2023 International Conference on Unmanned Aircraft Systems (ICUAS))

   The integration of onboard computing capabilities with unmanned aerial vehicles (UAV) has gained significant attention in recent years as part of mobile computing paradigms such as mobile edge computing (MEC), fog computing, and mobile cloud computing. To enhance the performance of airborne computing, networked airborne computing (NAC) aims to interconnect UAVs through direct flight-to-flight links, with UAVs sharing resources with each other. However, despite the growing interest in NAC and UAV-based computing, existing studies rely heavily on numerical simulations for performance evaluation and lack realistic simulators and hardware testbeds. To fill this gap, this paper presents the development of two NAC platforms: a realistic simulator based on ROS and Gazebo, and a hardware testbed with multiple UAVs communicating and sharing computing resources. Through simulation and real flight tests with two computation applications, we evaluate the platforms and examine the impact of mobility on NAC performance. Our findings offer valuable insights into NAC and provide guidance for future advancements. 

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5. Trajectory Design for Unmanned Aerial Vehicles via Meta-Reinforcement Learning

   https://doi.org/10.1109/INFOCOMWKSHPS57453.2023.10226090  

   Lu, Ziyang; Wang, Xueyuan; Gursoy, M. Cenk (May 2023, IEEE INFOCOM 2023 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

   This paper considers the trajectory design problem for unmanned aerial vehicles (UAVs) via meta-reinforcement learning. It is assumed that the UAV can move in different directions to explore a specific area and collect data from the ground nodes (GNs) located in the area. The goal of the UAV is to reach the destination and maximize the total data collected during the flight on the trajectory while avoiding collisions with other UAVs. In the literature on UAV trajectory designs, vanilla learning algorithms are typically used to train a task-specific model, and provide near-optimal solutions for a specific spatial distribution of the GNs. However, this approach requires retraining from scratch when the locations of the GNs vary. In this work, we propose a meta reinforcement learning framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Instead of training task-specific models, we train a common initialization for different distributions of GNs and different channel conditions. From the initialization, only a few gradient descents are required for adapting to different tasks with different GN distributions and channel conditions. Additionally, we also explore when the proposed MAML framework is preferred and can outperform the compared algorithms. 

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