Low-power deep learning edge computing platform for resource constrained lightweight compact UAVs

Albanese, Andrea; Nardello, Matteo; Brunelli, Davide

doi:10.1016/j.suscom.2022.100725

Cited by 14 publications

(13 citation statements)

References 44 publications

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“…In [98], Palossi et al demonstrated a navigation engine for autonomous nano-drones endowed with end-to-end DNN-based visual navigation based on a novel parallel ultra-low-power computing platform. In [99], Albanese et al presented a modular system with landing pad detection and facial recognition ML algorithms on a resource-constrained UAV in real-time.…”

Section: Visual-guided Landingmentioning

confidence: 99%

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

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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Section: Visual-guided Landingmentioning

confidence: 99%

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

Wang

et al. 2023

Drones

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“…Convolutional deep neural network-based target detection methods have also been rapidly developed, and the single-board devices that can be carried on UAVs are becoming smaller and lighter, while being able to achieve larger computational volumes with lower power consumption [15], so that applying UAVs and deploying deep learning-based target detection models in open-pit mines can be used for real-time monitoring of mining trucks.…”

Section: Related Workmentioning

confidence: 99%

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

Wu¹,

Wang²,

Ni³

2023

OJAppS

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In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV. The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment. A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed. Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.

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“…The KNN algorithm is used to classify human actions and estimate the full-body position of the human operator. The swarm bases its objective direction on the Policy-based Reinforcement Learning [57], [58] Address the limitations of PID control Value-based Reinforcement Learning [59], [60] Long-term resource allocation [61] Inefficiency of existing route planning algorithms [62] UAV navigation and obstacle avoidance Imitation RL learning [63] To detect the precise motions necessary to lead the UAV along the trajectories [64] Enhancing UAV tracking performance [65] UAV deployment strategy to maximize UAV owner profit and on-ground user benefits Inverse reinforcement learning [117], [67] To track a multirotor UAV's path Model-ensemble based Hybrid RL algorithms [68] To predict wheat output using UAVs in the winter [69] To evaluate total nitrogen concentration in water [70] Coordination among several UAVs traveling over a vast region LeNet Shallow Convolutional Neural Networks [72], [73] For spreading deep neural networks (DNNs) within unmanned aerial vehicles and to adjust the system to the UAV's dynamic movement and network fluctuation (UAVs) [118] Incorporating machine learning (ML) capabilities into small UAVs AlexNet Shallow Convolutional Neural Networks [80] To automatically detect damage to wind turbine blade surfaces Deep Residual Learning Network (ResNet) [83] For real-time UAV identification InceptionNet [85] To aid UAV-based surveillance operations that include the collection of movies using a mobile camera Deep Convolutional GANs (DCGANs) [92] To enable 5G-enabled maritime UAV communication employing millimeter wave (mmWave) for the air-to-surface link Wasserstein GANs (WGANs) [95] To enhance wireless signal-based detection of unauthorized UAVs StarGANs [119] Transmitting emotions using 3D hand and full-body motion CycleGANs [98] To reduce wildfire damage Long Short-Term Memory (LSTM) in recurrent neural networks (RNNs) [104] Resource allocation issue for UAVs [105] UAV anomaly detection [106] UAV communication for future wireless networks Gated Rec...…”

Section: E Classical Machine Learning Algorithmsmentioning

confidence: 99%

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

Sai,

Garg,

Jhawar

et al. 2023

IEEE Open J. Veh. Technol.

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Artificial Intelligence(AI) is an emerging technology that finds its application in various industries. Integration of AI in Unmanned Aerial Vehicles(UAVs) can lead to tremendous growth in the field of UAVs by improving flight safety and efficiency. Machine learning algorithms can enable UAVs to make real-time decisions in complex environments and reach the optimal solution that aims to fulfill a mission's requirements within the hardware constraints such as battery and payload. Several recent works in UAVs employed a variety of machine learning algorithms to enhance the capabilities of UAVs and assist them. Although several reviews have been published examining the various aspects of AI for UAVs, they are all pertaining to particular applications or technologies. Addressing this research gap, we present a comprehensive and diversified review to enable researchers to analyze the current and future requirements and develop the latest solutions utilizing AI. We have classified the reviewed works based on three different classification schemes: 1) application scenario-based, 2) AI algorithm-based, and 3) AI training paradigm-based. We have also presented a compilation of frameworks, tools, and libraries used in AI-integrated UAV systems. We identified that the integration of AI in UAVs has a wide array of applications ranging from path planning to resource allocation. We have observed that Reinforcement Learning based algorithms are more often used in AI-integrated UAV systems than other AI algorithms. Further, our findings reveal that UAV frameworks employing federated learning and other distributed machine learning paradigms are quickly emerging. Furthermore, we also have put forth several challenges and potential applications of AI-integrated UAV systems.

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Low-power deep learning edge computing platform for resource constrained lightweight compact UAVs

Cited by 14 publications

References 44 publications

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

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

Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine

A Comprehensive Survey on Artificial Intelligence for Unmanned Aerial Vehicles

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