The paper proposes a systematic literature review to investigate the research field from the controller design perspective. Therefore, the main objective is to analyze the published researches of the past seven years on the position control of AGVs to recognize research patterns, gaps, and tendencies, outlining the research field. Despite the increasing number of papers in AGVs and mobile robots’ position control research area, there is a lack of curatorial work presenting and analyzing the control strategies applied in the problem domain. Improving the position accuracy along the AGV trajectory allows the vehicle to work on narrower aisles with lower error tolerance. Rong, 2D range flow-based odometry fusing LiDAR and IMU, in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), 6–, pp.Automated Guided Vehicles (AGVs) are essential elements of manufacturing intralogistics and material handling. Meskin, Actuator fault detection and isolation of differential drive mobile robots using multiple model algorithm, in 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), 5–, pp.

Tan, Development of AGV as test bed for fault detection, in 2020 6th International Conference on Control, Automation and Robotics (ICCAR), 20–, pp. Cho, Sensor data fusion using unscented Kalman filter for accurate localization of mobile robots, in ICCAS 2010, 27–, pp. Havens, Sensor fused three-dimensional localization using IMU, camera and LiDAR, in 2016 IEEE SENSORS, 30 Oct–, pp. Paarkavi, Comparison of fault detection and isolation methods: a review, in 2016 10th International Conference on Intelligent Systems and Control (ISCO), 7–, pp. Ramdane, Mobile robot localization using extended Kalman filter, in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS), 19–, pp. Almajali, A systematic review on fusion techniques and approaches used in applications. Jianmin, A data fusion method applied in an autonomous robot (2008) Hancke, A review on challenges of autonomous mobile robot and sensor fusion methods.

With the usage of lidar, wheel slip was compensated. UKF generates better odometry estimation than EKF with 24.07% better accuracy. Different sensors were implemented along with sensor fusion. The performance of the EKF and UKF was compared to each other. AGV localization was tested with EKF and UKF on three different test tracks with different turn conditions. This paper implements extended Kalman filter (EKF) and unscented Kalman filter (UKF) for robot localization on AGV. Indoors, however, are more suitable with light detection and ranging (lidar) device. Inertial measurement unit (IMU) and global positioning unit (GPS) are usually implemented to improve robot localization but are susceptible to noise and are effective outdoors. With more sensors fused together, the more environmental information can be collected by the AGV, which helps with the localization of AGV. Robot localization is vital for the operation of an automated guided vehicle (AGV) but is susceptible to problems such as wheel slip.

Agv robot controller