Antonios Angelidis

Papers

Total Citations

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About

Antonios Angelidis is a researcher at the forefront of industrial robotics and human-robot collaboration, with a focus on precision motion control and immersive manufacturing environments. His most-cited work, "Prediction and compensation of relative position error along industrial robot end-effector paths" (2014, 60 citations), addresses a critical challenge in automation: mitigating trajectory inaccuracies that degrade performance in tasks like machining and assembly. By developing models to predict and correct positional errors in real time, Angelidis has contributed to making industrial robots more reliable for high-precision applications. More recently, his work on "An open extended reality platform supporting dynamic robot paths for studying human–robot collaboration in manufacturing" (2024) pioneers the integration of augmented and virtual reality with adaptive robotic systems. This platform enables researchers to safely study and optimize collaborative workflows where humans and robots share dynamic workspaces. Angelidis’s contributions bridge the gap between theoretical error compensation and practical, interactive manufacturing solutions, offering tools that enhance both robot accuracy and human-robot teamwork. His research is particularly valuable for students and engineers seeking to improve industrial automation through smarter, more adaptable robotic systems.