OARMIC – Obstacle Avoidance based Autonomous Robotics Movements with Interference Less Multi-Channel Underwater Sensor Network
J. Premalatha1, P.M. Joe Prathap2
1J. Premalatha, Research Scholar, Department of CSE, Sathyabama University, Chennai (Tamil Nadu), India.
2P.M. Joe Prathap, Department of IT, RMD Engineering College, R.S.M. Nagar, Kavaraipettai, Tiruvallur (Tamil Nadu), India.
Manuscript received on 24 April 2019 | Revised Manuscript received on 02 May 2019 | Manuscript Published on 08 May 2019 | PP: 430-434 | Volume-7 Issue-5S3 February 2019 | Retrieval Number: E11770275S19/19©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Underwater (UW) sensor networks have been drawing additional attention investigation interests newly due to their diverse specialized submissions. Autonomous robots (ARs) function as unidentified UW surroundings must be able to keep away from flooded obstacle, for instance rock face, snow obstacle, and oceanic changes. The use of AR for data gathering presents significant recompense including elasticity in sensor deployment, substantial energy savings, and minimized collisions, hidden node issues, interference, and conflicts. We propose the use of AR to move along deep-sea segments and accumulate data onto the sensors. Moreover the methodology for obstacle prevention by ARs that are built with advanced cameras (AC). The data accumulated from the support of two ACs placed in vertical and horizontal directions are functioned in actual time to give obstacle discovery coordination as per the locations. Obstacle detection and avoidance in a different direction, computed based on fuzzy logic optimization using border detection, segments of route and curves. By using horizontal and vertical obstacle detection create a route to reach the destination without obstacle interruption. AR has the knowledge to justify the sea floor and angular changes equal to 20 meters ahead of its movement. We also present dissimilar AR mobility’s and inspect the result of diverse network size parameters on network outputs such as delay throughput and packet delivery ratio. Each AR then transports the received data to the outside base station (BS). In order, the outside BS transmits the received data from AR to the network tower control server. Also, we focus on the condition based channel allocation for UW in order to avoid the transmission issues. We design an active and stretchy channel reuse plan for the condition channel allocation, and prepare the interference situation as a flexible nosiness free chart as per the sensors current location sharing and a predetermined threshold of interference. Because of heavy interference the optimal output may not be possible for more data transmission due to its high computational cost. By using this proposed achievement we overcome all the issues.
Keywords: Autonomous Robots; Obstacles; Interference; Collision; Condition Channel Allocation; Noise Free.
Scope of the Article: Robotics