This paper aims to address the “last millimetre” problem in mobile robot manipulation, where current mobile manipulators can only align to workplaces with limited accuracy, particularly in angular alignment. The authors introduce a novel self-reflective visual servoing system that significantly improves alignment repeatability, enabling mobile manipulators to perform tasks with precision comparable to fixed robots.
The authors developed a visual servoing system called “Selfie Aligner” that uses a camera tool mounted on a robot arm and a calibration tag with integrated mirror. The system uses optical principles to achieve high-precision alignment without requiring complex camera calibration. The methodology uses transformation matrices to determine the relative position between robot and workplace, using centre-point detection of circular patterns and self-reflection for precise angular alignment.
Experimental results demonstrate that the Selfie Aligner improves angular alignment accuracy significantly compared to traditional methods. The system achieves a repeatability of less than 0.1 mm in position and 0.3 mRad in orientation. A practical test with markings done post alignment at 1 m from the tag showed no observable difference in the marks left after three individual calibrations.
The presented approach introduces three novel concepts: a self-reflective visual servoing method that eliminates the need for complex camera calibration, a robust alignment strategy using circular patterns that is inherently resistant to lens distortion and a simplified yet highly accurate pose estimation method using mirror-based self-reflection. This system enables mobile manipulators to achieve fixed-robot precision levels while maintaining simplicity and robustness in industrial environments.
