Detect and avoid system based on multi sensor fusion for UAV

Ki, Myungseok; Cha, Jaehyuk; Lyu, Hyeonsu

doi:10.1109/ictc.2018.8539587

Cited by 18 publications

(8 citation statements)

References 6 publications

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“…Relevant work has been done into implementing diverse type of sensors to enable the detect and avoid action in drones. [22][23][24][25][26][27][28][29] However, the main constraint is identifying how many sensors can a drone handle based on their payload capabilities. An initial need that can be implemented is a drone-to-drone detection.…”

Section: Problem Statementmentioning

confidence: 99%

IoT-enabled unmanned traffic management system with dynamic vision-based drone detection for sense and avoid coordination

Rangel,

Tardif,

Mehrubeoglu

et al. 2024

Real-Time Image Processing and Deep Learning 2024

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This paper showcases the integration of several technologies to develop an Unmanned Traffic Management System that enables the centralized coordination of unmanned ground and aerial vehicles. By addressing the need for safe and efficient autonomous vehicle operations, this system contributes to improved safety and reliability in various applications, from civilian to military contexts. Furthermore, the exploration of dynamic vision-based drone detection methods adds valuable insights into the field of real-time image processing and deep learning. In that perspective, a more in-depth computer vision development is been presented. The system's core components include the Swarmie, an unmanned ground vehicle (UGV) guided through a wireless mesh network through radio frequency enabled (RF) markers. Simultaneously, an unmanned aircraft vehicle (UAV) is controlled by an IoT cloud platform that sends coordinates to an embedded system. The integration of wireless communication and navigation markers is a proof to the importance of circuitry and microcontrollers in developing RF markers to enhance navigation.One of the primary objectives of this research is the development of a dynamic vision-based drone detection system for sense and avoid actions. Two different methods are explored for drone detection. The first method utilizes the Viola & Jones algorithm. The second method involves the You Only Look Once (YOLO) Real-Time Object Detection algorithm. The performance of these methods is evaluated, providing insights into the effectiveness of each approach in real-time drone detection.

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Section: Problem Statementmentioning

confidence: 99%

IoT-enabled unmanned traffic management system with dynamic vision-based drone detection for sense and avoid coordination

Rangel,

Tardif,

Mehrubeoglu

et al. 2024

Real-Time Image Processing and Deep Learning 2024

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show abstract

“…We can realize such a system with a conventional radar, i.e., an Airborne Collision Avoidance System (ACAS) [78], Automatic Dependent Surveillance Broadcasting (ADS-B) [79] and vision-based or sound-based detection systems e.g., Light Detection And Ranging (LIDAR) [80] or ultrasonic [81]. DAA establishes A2A links to communicate with the conflicting AVs, and the A2A reachability is an essential metric to ensure that the onboard system can detect an unknown object well in advance to avoid collisions.…”

Section: B Evtolsmentioning

confidence: 99%

A Survey of Wireless Networks for Future Aerial COMmunications (FACOM)

Baltaci,

Dinc,

Ozger

et al. 2021

Preprint

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Electrification turned over a new leaf in aviation by introducing new types of aerial vehicles along with new means of transportation. Addressing a plethora of use cases, drones are gaining attention in the industry and increasingly appear in the sky. Emerging concepts of flying taxi enable passengers to be transported over several tens of kilometers. Therefore, unmanned traffic management systems are under development to cope with the complexity of future airspace, thereby resulting in unprecedented communication needs. Moreover, the long-term increase in the number of commercial airplanes pushes the limits of voice-oriented communications, and future options such as single-pilot operations demand robust connectivity. In this survey, we provide a comprehensive review and vision for enabling the connectivity applications of aerial vehicles utilizing current and future communication technologies. We begin by categorizing the connectivity use cases per aerial vehicle and analyzing their connectivity requirements. By reviewing more than 500 related studies, we aim for a comprehensive approach to cover wireless communication technologies, and provide an overview of recent findings from the literature toward the possibilities and challenges of employing the wireless communication standards. After analyzing the proposed network architectures, we list the open-source testbed platforms to facilitate future investigations by researchers. This study helped us observe that while numerous works focused on cellular technologies to enable connectivity for aerial platforms, a single wireless technology is not sufficient to meet the stringent connectivity demands of the aerial use cases, especially for the piloting operations. We identified the need of further investigations on multi-technology heterogeneous network architectures to enable robust and real-time connectivity in the sky. Future works should consider suitable technology combinations to develop unified aerial networks that can meet the diverse quality of service demands of the aerial use cases.

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“…Experiments show how both sensor fusion configurations can achieve higher accuracy compared to the images of the single Bayer-pattern color camera. Also, a 2D LiDAR with a stereo camera for safe navigation in an autonomous flight of a multi-copter UAV has been implemented [ 135 ], showing that the research is giving a lot of attention to this promising area.…”

Section: Low and Medium Heightsmentioning

confidence: 99%

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

et al. 2020

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The spread of Unmanned Aerial Vehicles (UAVs) in the last decade revolutionized many applications fields. Most investigated research topics focus on increasing autonomy during operational campaigns, environmental monitoring, surveillance, maps, and labeling. To achieve such complex goals, a high-level module is exploited to build semantic knowledge leveraging the outputs of the low-level module that takes data acquired from multiple sensors and extracts information concerning what is sensed. All in all, the detection of the objects is undoubtedly the most important low-level task, and the most employed sensors to accomplish it are by far RGB cameras due to costs, dimensions, and the wide literature on RGB-based object detection. This survey presents recent advancements in 2D object detection for the case of UAVs, focusing on the differences, strategies, and trade-offs between the generic problem of object detection, and the adaptation of such solutions for operations of the UAV. Moreover, a new taxonomy that considers different heights intervals and driven by the methodological approaches introduced by the works in the state of the art instead of hardware, physical and/or technological constraints is proposed.

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Detect and avoid system based on multi sensor fusion for UAV

Cited by 18 publications

References 6 publications

IoT-enabled unmanned traffic management system with dynamic vision-based drone detection for sense and avoid coordination

IoT-enabled unmanned traffic management system with dynamic vision-based drone detection for sense and avoid coordination

A Survey of Wireless Networks for Future Aerial COMmunications (FACOM)

A Survey of Computer Vision Methods for 2D Object Detection from Unmanned Aerial Vehicles

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