Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review

Wilson, Aaron; Kumar, Abhinav; Jha, Ajit; Cenkeramaddi, Linga Reddy

doi:10.1109/jsen.2021.3139124

Cited by 86 publications

(29 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2) comprises of two parts: (i) a UAV or drone, and (ii) a controller (also called remote controller or ground controller). A drone is typically equipped with a power source, an onboard controller, multiple sensors (including inertial sensors, magnetometer, gyroscope, Global Navigation Satellite System (GNSS) e.g., Global Positioning System (GPS) module, pressure sensor and visual sensors (i.e., camera)), and a wireless transceiver [24]. These sensors enable the drone to maintain smooth flight, safe landing, path following, and object detection.…”

Section: A Architecture Of Uasmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

View full text Add to dashboard Cite

The market size of civilian drones is tremendously increasing and is expected to reach 1.66 million by the end of 2023. The increase in number of civilian drones poses several privacy and security threats. To safeguard critical assets and infrastructure and to protect privacy of people from the illegitimate uses of commercial drones, a drone detection system is inevitable. In particular, there is a need for a drone detection system that is efficient, accurate, robust, costeffective and scalable. Recognizing the importance of the problem, several drone detection approaches have been proposed over time. However, none of these provides sufficient performance due to the inherited limitations of the underlying detection technology. More specifically, there are trade-offs among various performance metrics e.g., accuracy, detection range, and robustness against environmental conditions etc. This motivates an in-depth study and critical analysis of the existing approaches, highlighting their potential benefits and limitations. In this paper, we provide a rigorous overview of the existing drone detection techniques and a critical review of the state-of-the-art. Based on the review, we provide key insights on the future drone detection systems. We believe these insights will provide researchers and practicing engineers a holistic view to understand the broader context of the drone detection problem.

show abstract

Section: A Architecture Of Uasmentioning

confidence: 99%

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The autonomous control model is alliteratively updated using feedback from a human agent who corrects undesirable model output as part of the reinforcement learning methodology.On other hand, a thorough evaluation of the most recent embedded sensors, communication technologies, computer platforms, and machine learning algorithms utilized in autonomous UAVs is also included in certain studies. [12], [13], [13]- [16].…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted-Smart Farming for Agricultural Monitoring in a Large Scale WSN area

Leranso¹

2022

Preprint

View full text Add to dashboard Cite

<p>With the aid of multiple autonomous unmanned aerial vehicles (UAVs), data collection from the large-scale Wireless Sensor Network (WSN) is a highly efficient, but challenging solution. In this paper, to minimize the total energy consumption of both UAVs, and the effective use of sink nodes' power, we optimize both the number of sink nodes and the trajectories of multiple UAVs in WSN. To do this, the UAV can start from the docking station and come back to the same dock station after the completion of the mission. To increase the lifetime WSN, we specifically use the Genetic Algorithm to choose the number of sink nodes. Then, we utilize a Deep Q-network-based method providing a Markov decision pr to establish all trajectory paths. We can show that our method can expedite the process of identifying the best answers by simulating its performance and contrasting it with other traditional heuristic benchmark techniques.</p>

show abstract

“…They also enumerate specific applications in which UAVs make a contribution: search and rescue [6][7][8][9], remote sensing [10][11][12], construction and infrastructure inspections [13][14][15], and precision agriculture [16][17][18]. There is a growing level of technological advancement in the areas of sensors [19][20][21], generated computing power [22], materials, as well as in the area of aggregation and data processing during the flight [23]. Reliability and a high level of safety are required for autonomously controlled flying robots-especially in transport and entertainment applications.…”

Section: Introductionmentioning

confidence: 99%

UAV Fault Detection Methods, State-of-the-Art

Puchalski

Giernacki

2022

Drones

View full text Add to dashboard Cite

The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table.

show abstract

Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review

Cited by 86 publications

References 75 publications

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review

UAV-Assisted-Smart Farming for Agricultural Monitoring in a Large Scale WSN area

UAV Fault Detection Methods, State-of-the-Art

Contact Info

Product

Resources

About