MPDrone: FPGA-based Platform for Intelligent Real-time Autonomous Drone Operations

Kovari, Balint Barna; Ebeid, Emad

doi:10.1109/ssrr53300.2021.9597857

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…3. Interoperability: ANNs are often developed using specialized software and hardware, which can make it difficult to integrate them with existing security platforms [12].…”

Section: Challenges Of Combining Anns and Security Platformsmentioning

confidence: 99%

Perspective Chapter: Artificial Intelligence in Security Platform

Helmy,

Diasty Sherif,

Hazem

2024

Artificial Intelligence

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Artificial intelligence (AI) has revolutionized numerous industries and cybersecurity is no exception. AI-powered security platforms are becoming increasingly popular as they provide enhanced protection against cyber threats. These platforms use machine learning algorithms to analyze and learn from data, enabling them to detect and respond to threats more effectively than traditional security systems. In this answer, we will explore the role of AI in security platforms, their benefits, and the future of cybersecurity.

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“…3. Interoperability: ANNs are often developed using specialized software and hardware, which can make it difficult to integrate them with existing security platforms [12].…”

Section: Challenges Of Combining Anns and Security Platformsmentioning

confidence: 99%

Perspective Chapter: Artificial Intelligence in Security Platform

Helmy,

Diasty Sherif,

Hazem

2024

Artificial Intelligence

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“…In 2020, Moreac et al [23] proposed FPGA based computer vision for drones by hardware-in-the-loop simulation. Finally in 2021 at our research group, Kövari [24] demonstrated the use of FPGA accelerated deep learning inference for autonomous drone navigation, also based on PYNQ.…”

Section: Related Workmentioning

confidence: 99%

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

Nyboe

Malle

Ebeid

2022

2022 International Conference on Unmanned Aircraft Systems (ICUAS)

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Autonomous drones are facing a tough efficiency challenge due to limitations on the utilized processing hardware units. Among these limitations is the tradeoff between fast computing and low power consumption; between functional complexity and flight time. Recent progressions point to FPGAs for accelerating heavy processing. In this work, we present the MPSoC4Drones Framework; a novel framework for organizing FPGA-design and OS build projects. The framework combines tools for creating bootable images for the Ultra96-V2 board.We show how MPSoC4Drones organizes the build, combining the latest well-known tools for research and industrial drone development, Ubuntu 20.04, PX4 autopilot, and ROS2 middleware. We validate the framework through a computationally intensive deep learning use case implemented in the MPSoC4Drones framework. We show the superior throughput and low power consumption of FPGA processing compared to classical CPU and GPU approaches. Finally, we offer the full framework as open-source.

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“…Another challenge of UAVs in the SAR operation is the moving target detection. The authors of [37,38] included speed estimates and object segmentation algorithms to identify real-time moving objects using an area-based image registration method in the SoC-FPGA-based CP.…”

Section: Object Detection Tracking and Environment Monitoringmentioning

confidence: 99%

A Survey on UAV Computing Platforms: A Hardware Reliability Perspective

Ahmed

Jenihhin

2022

Sensors

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This study describes the Computing Platforms (CPs) and the hardware reliability issues of Unmanned Aerial Vehicles (UAVs), or drones, which recently attracted significant attention in mission and safety-critical applications demanding a failure-free operation. While the rapid development of the UAV technologies was recently reviewed by survey reports focusing on the architecture, cost, energy efficiency, communication, and civil application aspects, the computing platforms’ reliability perspective was overlooked. Moreover, due to the rising complexity and diversity of today’s UAV CPs, their reliability is becoming a prominent issue demanding up-to-date solutions tailored to the UAV specifics. The objective of this work is to address this gap, focusing on the hardware reliability aspect. This research studies the UAV CPs deployed for representative applications, specific fault and failure modes, and existing approaches for reliability assessment and enhancement in CPs for failure-free UAV operation. This study indicates how faults and failures occur in the various system layers of UAVs and analyzes open challenges. We advocate a concept of a cross-layer reliability model tailored to UAVs’ onboard intelligence and identify directions for future research in this area.

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MPDrone: FPGA-based Platform for Intelligent Real-time Autonomous Drone Operations

Cited by 13 publications

References 12 publications

Perspective Chapter: Artificial Intelligence in Security Platform

Perspective Chapter: Artificial Intelligence in Security Platform

MPSoC4Drones: An Open Framework for ROS2, PX4, and FPGA Integration

A Survey on UAV Computing Platforms: A Hardware Reliability Perspective

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