Autonomous Mobile Robot Localization by Using IMU and Encoder Data Fusion Technique by Kalman Filter

The Autonomous Mobile Robot (AMR) has been an essential technical tool in the digital transformation of traditional industries as automated transportation logistics systems. For AMRs to meet the requirements of industrial practice, the localization system plays a vital role in the motion control of the AMR along different routes. Up to now, there have been many different localization solutions using modern tools with expensive sensors, which leads to an increase in initial investment costs, making it difficult for small and medium enterprises with limited funds to access. Therefore, simple localization solutions with low-cost sensors that require low hardware architecture for navigation and guidance for AMRs while still meeting practice requirements are essential. In this research, we present an Inertial Measurement Unit (IMU) and encoder data fusion solution to locate AMR. First, the estimation of the robot’s posture and position from the IMU and encoder data is presented. Next, the IMU and encoder data fusion algorithm based on the Kalman filter is applied to eliminate noise and improve the AMR’s localization. The study results were installed on an industrial AMR sample fabricated by the research team to verify the effectiveness of the proposed method. Based on the experimental results, the position error varies from 0% to 1.48%, which shows the possibility of industrial application with low cost.