This paper presents an on-site kinematic calibration framework for robotic manipulators using a single-laser distance sensor. The proposed method models the discrepancy between nominal and measured laser distances as a function of the modified Denavit–Hartenberg (MDH) parameters, which are identified through iterative least-squares estimation using an analytically derived identification Jacobian matrix. To ensure efficient and high-quality data collection, a nonlinear programming (NLP)-based configuration sampling strategy is developed that autonomously generates informative measurement poses while strictly satisfying operational constraints, including sensor range, incidence angle, and collision avoidance. The proposed sampling procedure generates 480 configurations across two orthogonal measurement planes in approximately \(17~\textrm{s}\) , representing a substantial reduction in effort compared to manual pose teaching. Experimental results demonstrate a \(76.87\%\) reduction in reprojection root mean square error (RMSE), decreasing from \(1.590~\textrm{mm}\) to \(0.368~\textrm{mm}\) , with consistent performance across training and validation sets. Independent validation using a laser tracker confirmed sub-millimeter end-effector position accuracy, with a combined RMSE of \(0.679~\textrm{mm}\) over 10 configurations. Additional experiments on four target surface materials revealed that surface flatness quality, rather than optical finish, is the dominant factor governing measurement accuracy during robot motion. These results demonstrate the practical effectiveness of the proposed framework for on-site robot accuracy enhancement without reliance on high-precision metrology equipment. The proposed framework achieves sub-millimeter end-effector accuracy using a single-laser distance sensor, offering a substantially lower-cost alternative to manufacturer recalibration services that typically involve shipping costs, service fees, and multiple days of robot downtime.
