Design and Analysis of Mobile Locomation Approach

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dc.creatorVerma, Shokendra Dev
dc.creatorBhatia, Kirti
dc.creatorBhadola, Shalini
dc.creatorSharma, Rohini
dc.date2022-06-30
dc.date.accessioned2023-08-21T08:00:19Z
dc.date.available2023-08-21T08:00:19Z
dc.descriptionOne of the most difficult tasks for a robotic system is to determine the best path through the workspace. The main purpose is to prevent obstructions and create an optimum path. As a result, a mobile robot's free configuration space must be managed very carefully for course planning and navigation. The path planning work will be easier, faster, and more efficient if the configuration space is partitioned. In addition, the data perceived by the sensor contains some noise. As a result, we construct an approach to produce an optimal prediction state in order to build a map that aids in the effective management of the environment in order to locate the most efficient paths to target. We use the modified Kalman Filter (MKF) to determine the most reliable sensor data prediction, and then the K-means clustering method to identify the subsequent landmarks while evading barriers.en-US
dc.formatapplication/pdf
dc.identifierhttps://journals.researchparks.org/index.php/IJOT/article/view/3308
dc.identifier.urihttp://dspace.umsida.ac.id/handle/123456789/16055
dc.languageeng
dc.publisherResearch Parks Publishing LLCen-US
dc.relationhttps://journals.researchparks.org/index.php/IJOT/article/view/3308/3214
dc.sourceInternational Journal on Orange Technologies; Vol. 4 No. 6 (2022): IJOT; 110-117en-US
dc.source2615-8140
dc.source2615-7071
dc.source10.31149/ijot.v4i6
dc.subjectRobot Navigationen-US
dc.subjectLocalization and Mappingen-US
dc.subjectKalman filteren-US
dc.titleDesign and Analysis of Mobile Locomation Approachen-US
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typePeer-reviewed Articleen-US
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