Joonkeol Song

Papers

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About

Joonkeol Song is a robotics researcher whose work focuses on advancing sensorless force control and external force estimation for robotic manipulator systems. His key contributions lie in developing algorithms that eliminate the need for expensive and fragile force-torque or joint torque sensors, instead leveraging momentum observer techniques to estimate external forces induced by contact between robots and their environments. This approach enhances the robustness and cost-effectiveness of robotic systems, particularly in applications requiring precise interaction with surroundings. His most-cited paper, "Sensorless Force Control Algorithm based on Momentum Observer Technique" (2021), has garnered 3 citations and represents a significant step toward simplifying force control in robotics. By reducing hardware dependencies, Song’s work opens doors for more accessible and reliable robotic automation in industries such as manufacturing, healthcare, and service robotics. His research aligns with broader trends in safe human-robot collaboration and adaptive control, making it a valuable reference for students and engineers seeking efficient, sensor-minimal solutions for dynamic robotic tasks.