Design and Analysis of Mobile Locomation Approach

Shokendra Dev  Verma; Kirti  Bhatia; Shalini  Bhadola; Rohini  Sharma

Shokendra Dev Verma Student, Sat Kabir Institute of Technology and Management, Haryana, India
Kirti Bhatia Assistant Professor, Sat Kabir Institute of Technology and Management, Haryana, India
Shalini Bhadola Assistant Professor, Sat Kabir Institute of Technology and Management, Haryana, India
Rohini Sharma Assistatnt Professor and corresponding Author, GPGCW, Rohtak, India

Keywords: Robot Navigation, Localization and Mapping, Kalman filter

Abstract

One 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.

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