This paper presents the design and experimental validation of an intelligent tire alignment and lifting control system for an under-vehicle autonomous parking robot. The proposed system enables the robot to autonomously enter beneath a vehicle, recognize tire positions using a LiDAR-based sensing module, and perform precise lifting through a fork-type mechanism. A YOLOv8 instance segmentation algorithm is employed to detect tire regions from LiDAR point cloud data and estimate their geometric centers. The detected tire positions are then matched with a vehicle database to determine the correct alignment for lifting. Experiments were conducted on three different vehicle types under various surface conditions. The results show that the proposed system achieved a tire recognition accuracy exceeding 95%, a lifting success rate of 100%, and an average lifting operation time of 12.3 seconds. These results demonstrate the reliability and practicality of the proposed method for real-world autonomous parking applications.

In bridge lifting apparatus by using the existing pressure control, unequal lifting inevitably occur due to non-uniform load of the upper structure. In order to solve this problem safety of bridge lifting apparatus using flow control system was evaluated. As a result, the load of each supports for bridge upper structure is measured at the range of 1,098 ~ 2,362 kN and lifting displacement uncertainty was within 0.5 mm observing not any cracks.