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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM29802

AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM29802

Volume:

8 |

3 |

Mar 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM29802

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AF

Aleena Francis

APCB

Amrutha Priya C B

AKS

Arjun K S

DPS

Divya P S

DNCV

Dr. Nitha C Velayudhan

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Abstract

This paper presents a groundbreaking approach to data collection in hazardous or inaccessible environments, presenting the design, development, and implementation of an innovative autonomous robot. The robot is designed to navigate and collect valuable data from locations too dangerous or remote for human exploration, enabling scientific research and exploration in unprecedented ways. The AI-powered drone is equipped for precise human identification, controlled through a user-friendly mobile app. The software analyzes live drone footage to detect human presence using models like YOLO, with high accuracy in real-time human detection tasks. The robot is equipped with an array of sensors, including cameras, and uses image processing technology for processing images. GPS tracking technology is used for device tracking. The proposed autonomous robot promises to revolutionize data collection in unreachable environments, opening new avenues for scientific discovery, resource assessment, and environmental monitoring.

KEYWORDS

YOLOV8 UAV Python Flask Computer vision AI.

Cite This Article

Aleena Francis, Amrutha Priya C B, Arjun K S, Divya P S, Dr. Nitha C Velayudhan, "AI Enabled Robot for Data Collection in Unreachable and Extreme Environments: A Review," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 3, Mar 2024. https://doi.org/10.55041/IJSREM29802

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Publication Date

12/31/45380

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Double-Blind Peer Review

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The authors declare no conflict of interest related to this research work.

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