Vision-Guided Precision Tool Alignment and Target Contact for a Mobile Manipulator Using YOLO Detection and Depth-Based 3D Localization

Precision alignment and target contact are critical tasks for mobile manipulators in industrial inspection and flexible manufacturing. However, achieving high accuracy after navigation remains challenging due to accumulated errors from mobile base localization, perception noise, and calibration uncertainty. This paper proposes a vision-guided precision alignment framework for mobile manipulators using a single front-facing RGB-D camera. The method integrates YOLO-based target detection, AR marker-assisted plane depth estimation, and depth-based 3D localization within a coarse-to-fine alignment strategy. After navigation, the manipulator first moves to a predefined pre-alignment pose, followed by visual localization and iterative refinement to compensate for residual errors before executing precise target contact. The proposed system is implemented and evaluated in a Gazebo-based simulation environment using a mobile manipulator platform model. In a static touch panel experiment with 50 trials, the system achieves a success rate of 98%, with positioning errors maintained within a millimeter-level range. Simulation results demonstrate that the proposed method provides stable alignment performance in the simulation environment without relying on external sensing devices such as force sensors or multi-camera systems. The proposed approach shows promising potential for precision contact tasks in mobile manipulation.