Erik Hedberg

Papers

Total Citations

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About

Erik Hedberg’s research lies at the intersection of robotics, sensor fusion, and intelligent control, with a focus on improving the precision and adaptability of industrial manipulators. His most cited work, “Industrial Robot Tool Position Estimation using Inertial Measurements in a Complementary Filter and an EKF” (2017, 14 citations), addresses a critical challenge in manufacturing: refining tool position estimates beyond what motor-angle forward kinematics alone can provide. By integrating an Inertial Measurement Unit (IMU) with both a Complementary Filter and an Extended Kalman Filter, Hedberg demonstrated a practical, sensor-driven approach to enhancing accuracy for robots like the ABB IRB 4600. This work has been foundational for researchers exploring low-cost sensor augmentation in industrial settings. In a complementary vein, his 2018 study “A Learning Approach for Feed-Forward Friction Compensation” (4 citations) tackles the persistent problem of friction-induced tracking errors. By comparing a model-based LuGre approach with a data-driven B-spline network, Hedberg showed that learned compensation can adapt to real-world nonlinearities, paving the way for more responsive robotic systems. Though his citation counts are modest, Hedberg’s contributions are notable for their hands-on experimental rigor and direct relevance to industrial automation—offering a bridge between classical control theory and modern learning-based methods.