UAVNet: An Efficient Obstacel Detection Model for UAV with Autonomous Flight

Chen, Po-Heng; Lee, Chen‐Yi

doi:10.1109/icoias.2018.8494201

Cited by 11 publications

(8 citation statements)

References 1 publication

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“…Due to the limitation of payloads, it is infeasible to carry sophisticated heavy sensors, which will lead to increase the power consumption and drastically decrease the flight time [96]. Moreover, due to the limited power available onboard, UAVs must make careful decisions about how to best utilize power for communication [97] and processing where the communications requirements [71] and data processing consume much energy.…”

Section: ) Fauav Infrastructure Design Within Scmentioning

confidence: 99%

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Kuru

2021

IEEE Access

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Section: ) Fauav Infrastructure Design Within Scmentioning

confidence: 99%

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Kuru

2021

IEEE Access

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“…Another proposal in forest environments is presented in the work by the authors of [71], which uses a pretrained AlexNet [45] for tree detection and for the prediction of the direction for collision avoidance. Other outdoor UAV solutions are trained for urban spaces, such as CNN-based models that detect all the potential obstacles [72] or that control a UAV through the streets of a city environment [62]. [70] CNN The network detects the gate center.…”

Section: On the Application Of DL To Uavsmentioning

confidence: 99%

“…Outdoors The distance is learned as three classes. [72] CNN Object detection. Outdoors [73] CNN The network computes feature extraction Outdoors for learning safe trajectories.…”

Section: On the Application Of DL To Uavsmentioning

confidence: 99%

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

et al. 2019

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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remote sensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recent developments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.

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“…Chen and Lee [17] focused on proposing a novel and memory efficient deep network architecture named UAVNet for small UAVs to achieve obstacle detection in urban environments. The proposal shows that UAVNet can detect obstacles at a rate of 15 fps, meeting real-time application requirements.…”

Section: Related Workmentioning

confidence: 99%

A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions

Fabra

Zamora

Sanguesa

et al. 2019

Sensors

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As the number of potential applications for Unmanned Aerial Vehicles (UAVs) keeps rising steadily, the chances that these devices get close to each other during their flights also increases, causing concerns regarding potential collisions. This paper proposed the Mission Based Collision Avoidance Protocol (MBCAP), a novel UAV collision avoidance protocol applicable to all types of multicopters flying autonomously. It relies on wireless communications in order to detect nearby UAVs, and to negotiate the procedure to avoid any potential collision. Experimental and simulation results demonstrated the validity and effectiveness of the proposed solution, which typically introduces a small overhead in the range of 15 to 42 s for each risky situation successfully handled.

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UAVNet: An Efficient Obstacel Detection Model for UAV with Autonomous Flight

Cited by 11 publications

References 1 publication

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

Planning the Future of Smart Cities With Swarms of Fully Autonomous Unmanned Aerial Vehicles Using a Novel Framework

A Review on IoT Deep Learning UAV Systems for Autonomous Obstacle Detection and Collision Avoidance

A Distributed Approach for Collision Avoidance between Multirotor UAVs Following Planned Missions

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