Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation

Hariprasath Manoharan; Shitharth Selvarajan; Rajanikanth Aluvalu; Maha Abdelhaq; Raed Alsaqour; Mueen Uddin

doi:10.7717/peerj-cs.1709

Diagnostic structure of visual robotic inundated systems with fuzzy clustering membership correlation

PeerJ Comput Sci. 2023 Dec 19:9:e1709. doi: 10.7717/peerj-cs.1709. eCollection 2023.

Authors

Hariprasath Manoharan¹, Shitharth Selvarajan², Rajanikanth Aluvalu³, Maha Abdelhaq⁴, Raed Alsaqour⁵, Mueen Uddin⁶

Affiliations

¹ Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India.
² Department of Computer Science, Kebri Dehar University, Ethiopia.
³ Department of IT, Chaitanya Bharathi Institute of Technology, Hyderabad, India.
⁴ Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
⁵ Department of Information Technology, College of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia.
⁶ College of Computing and IT, University of Doha for Science and Technology, Qatar.

Abstract

The process of using robotic technology to examine underwater systems is still a difficult undertaking because the majority of automated activities lack network connectivity. Therefore, the suggested approach finds the main hole in undersea systems and fills it using robotic automation. In the predicted model, an analytical framework is created to operate the robot within predetermined areas while maximizing communication ranges. Additionally, a clustering algorithm with a fuzzy membership function is implemented, allowing the robots to advance in accordance with predefined clusters and arrive at their starting place within a predetermined amount of time. A cluster node is connected in each clustered region and provides the central control center with the necessary data. The weights are evenly distributed, and the designed robotic system is installed to prevent an uncontrolled operational state. Five different scenarios are used to test and validate the created model, and in each case, the proposed method is found to be superior to the current methodology in terms of range, energy, density, time periods, and total metrics of operation.

Keywords: Errors; Fuzzy clustering; Robot; Underwater.

Grants and funding

This research was supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2023R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.