Uncertainty-aware Cartesian error compensation in industrial robots via point interval prediction and residual calibration
Year: 2026
Type: article
Abstract: Accurate Cartesian positioning in industrial robots remains challenging under drift, friction, and regime changes. This paper presents an uncertainty-aware, two-stage compensation pipeline for a four-degree-of-freedom (4-DOF) serial robot using only joint telemetry. Stage I combines a long short-term memory (LSTM) point predictor with an auxiliary network that learns per-axis prediction intervals (PIs) via a loss balancing prediction-interval coverage probability (PICP) and mean prediction-interval width (MPIW). Stage II performs residual calibration from three features: point value, PI width, and point midpoint skew, using compact models (ridge, two-regime linear, and adaptive neuro-fuzzy inference system, ANFIS) with safety gates. Evaluation on a synthetic dataset containing slow drifts, a large step change, and strong periodic content shows calibrated or near-calibrated uncertainty on two axes and under-coverage on the stepped axis, consistent with probability integral transform (PIT) diagnostics. Translating PIs into residuals yields axis-dependent root-mean-square error (RMSE) changes: $$+4.7\%$$ ( $$x$$ ), $$+8.1\%$$ ( $$z$$ ), and $$-2.7\%$$ ( $$y$$ ). The results indicate that coupling point predictions with calibrated uncertainty enables conservative compensation near regime shifts and tighter corrections in smooth or periodic regimes, offering a transparent and deployable baseline for uncertainty-aware robot error compensation.
Source: Discover Robotics
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128094881