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

Wilson, A. N. and Kumar, Abhinav and Jha, Ajit and et al, . (2022) Embedded Sensors, Communication Technologies, Computing Platforms and Machine Learning for UAVs: A Review. Institute of Electrical and Electronics Engineers Inc..

[img] Text
IEEE_Sensors_Journal.pdf - Published Version
Restricted to Registered users only

Download (3MB) | Request a copy


Unmanned aerial vehicles (UAVs) are increasingly becoming popular due to their use in many commercial and military applications, and their affordability. The UAVs are equipped with various sensors, hardware platforms and software technologies which enable them to support the diverse application portfolio. Sensors include vision-based sensors such as RGB-D cameras, thermal cameras, light detection and ranging (LiDAR), mmWave radars, ultrasonic sensors, and an inertial measurement unit (IMU) which enable UAVs for autonomous navigation, obstacle detection, collision avoidance, object tracking and aerial inspection. To enable smooth operation, UAVs utilize a number of communication technologies such as wireless fidelity (Wi-Fi), long range (LoRa), long-term evolution for machine-type communication (LTE-M), etc., along with various machine learning algorithms. However, each of these different technologies come with their own set of advantages and challenges. Hence, it is essential to have an overview of the different type of sensors, computing and communication modules and algorithms used for UAVs. This paper provides a comprehensive review on the state-of-the-art embedded sensors, communication technologies, computing platforms and machine learning techniques used in autonomous UAVs. The key performance metrics along with operating principles and a detailed comparative study of the various technologies are also studied and presented. The information gathered in this paper aims to serve as a practical reference guide for designing smart sensing applications, low-latency and energy efficient communication strategies, power efficient computing modules and machine learning algorithms for autonomous UAVs. Finally, some of the open issues and challenges for future research and development are also discussed. © 2001-2012 IEEE.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Kumar, Abhinavhttps://orcid.org/0000-0002-5880-4023
Item Type: Other
Uncontrolled Keywords: and wireless fidelity (Wi-Fi); embedded sensors; light detection and ranging (LiDAR); long-term evolution for machine-type communication (LTE-M); longrange (LoRa); mmWave radar; thermal cameras; ultrasonicsensors; Unmanned aerial vehicles (UAVs); vision-based sensors
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Department of Engineering Science
Depositing User: . LibTrainee 2021
Date Deposited: 13 Jul 2022 11:40
Last Modified: 13 Jul 2022 11:40
URI: http://raiith.iith.ac.in/id/eprint/9678
Publisher URL: http://doi.org/10.1109/JSEN.2021.3139124
OA policy: https://v2.sherpa.ac.uk/id/publication/3570
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 9678 Statistics for this ePrint Item