International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 2 (March-April 2025) Submit your research before last 3 days of April to publish your research paper in the issue of March-April.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 2
March-April 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

RAY:-Underwater Robot for Environmental Safety and Monitoring

Author(s) Akhil Vinod V, Archa R, Vinaya V S, Swetha P R, Reshmi Krishna Prasad
Country India
Abstract In recent years, the demand for advanced human detection technologies has surged, driven by applications
in security, search and rescue operations, and human-robot interaction. This paper presents an innovative
approach to human detection using multiple sensor modalities, including infrared (IR), ultrasonic, and
visual imaging, integrated into a mobile robotic platform. The proposed system aims to enhance detection
accuracy, reduce false positives and improve response times in diverse environments.
The robot is equipped with an array of sensors: infrared sensors detect heat signatures, ultrasonic sensors
measure distance through sound waves, and visual cameras provide real-time image processing
capabilities. The fusion of these data sources is achieved through a multi-layered machine learning
algorithm, allowing the robot to make informed decisions based on the contextual information it gathers.
This approach not only enhances the robot's ability to detect humans in various conditions such as low
light or obstructed views but also enables adaptive learning, allowing the system to improve its accuracy
over time through exposure to diverse scenarios. Testing in real-world environments demonstrates the
effectiveness of the multi-sensor framework. Results indicate a significant reduction in false detection
rates compared to single-sensor systems, highlighting the robustness of the approach. The implementation
of this multi-sensor human detection robot has the potential to transform applications in surveillance,
safety monitoring, and autonomous assistance, paving the way for more intelligent and responsive robotic
systems in the future.
Field Engineering
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-31
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.34339
Short DOI https://doi.org/g8xgkc
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com