<![CDATA[One of the popular studies recently is about social robots that have been implemented in several public areas such as offices. The robot is an employee or worker assistant robot in the Telkom Surabaya Institute of Technology building to help carry out the work of delivering packages to the destination according to the tasks given. The problem that often occurs is an error in the robot's localization system causing the robot's movement to the target point to experience a position error. This research contributes to the comparative evaluation of 2 localization methods on mobile robots, namely the first is the use of a rotary encoder sensor and the second is the use of sensor fusion based on the extended Kalman filter implemented on the robot prototype. This study aims to develop a sensor system that is adapted to the design of the robot and the environment in which the robot is tested and to find out the comparison of the two methods. The use of extended Kalman filter-based sensor fusion can provide more accurate results in robot localization, especially when moving on complex paths. Sensor fusion enables the combination of several sensors such as rotary encoders and IMU (Inertial Measurement Unit) sensors to provide more complete and accurate information about the position and orientation of the robot. In this study, sensor fusion successfully reduced the localization error of the robot to 0.63 m when moving straight and 0.29 m when moving on a complex path, compared to the use of a single sensor which resulted in a larger error of 0.89 m. Based on the study that has been conducted, it can be considered as a potential solution in the development of other social robots to improve the accuracy and performance of the robots when performing certain tasks in the future.]]>
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