ASSESSING NEO-M8N GPS-BASED WAYPOINT NAVIGATION PERFORMANCE FOR AN UNMANNED SURFACE VEHICLE

Unmanned surface vehicles (USVs) are crucial in various applications such as search and rescue, water irrigation, and border surveillance. This study proposes a specific and achievable method to enhance USV navigation precision by integrating a navigation sensory array system with GPS implementation...

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Bibliographic Details
Published in:Journal of Engineering Science and Technology
Main Author: Thamrin N.M.; Misnan M.F.; Nizam M.M.D.; Saaid M.F.
Format: Article
Language:English
Published: Taylor's University 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85217827167&partnerID=40&md5=3f49430f5f36ca6a96768dc655de961a
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Summary:Unmanned surface vehicles (USVs) are crucial in various applications such as search and rescue, water irrigation, and border surveillance. This study proposes a specific and achievable method to enhance USV navigation precision by integrating a navigation sensory array system with GPS implementation. Evaluations conducted in a controlled pond setting demonstrated the effectiveness of the integrated sensory system, with the USV achieving measurable results. The system showcased an average error of 0.6 m to 2.5 m in longitude and 0.6 m to 4.5 m in latitude, along with mean distance errors of 2.298 m and 2.5 m for rectangular and square waypoint navigation translating to percentage errors of 0.97% and 0.77%, respectively. These results not only highlight the feasibility of enhancing navigation accuracy but also emphasize the practicality of the proposed sensor integration approach in real-world scenarios. This refined GPS utilization aptitudes a dependable and efficient approach for ensuring accuracy and precision in USV navigation across various applications for future advancements in sensor integration to further enhance navigation capabilities. Furthermore, augmenting navigation precision by integrating cameras, sonars, and gyroscopes as additional sensors, which is relevant to improving overall navigation performance. © School of Engineering, Taylor’s University.
ISSN:18234690