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About

Amir Shahidi is a robotics researcher whose work lies at the intersection of motion planning, human-robot interaction, and the Internet of Production. His most significant contribution is the development of a kinematic graph—a novel data structure that structures sampling data to adapt sampling-based motion planning algorithms specifically for robotic manipulators. This innovation addresses the positioning problem in robotics, offering a more efficient and kinematically aware approach to motion planning. Shahidi has also proposed a framework for classifying human-robot interactions within the Internet of Production, a key step toward integrating collaborative robots into smart manufacturing ecosystems. His research extends to real-time motion planning in dynamic environments through enhanced velocity obstacle methods and the study of redundantly actuated DELTA-type parallel kinematic mechanisms. With his most cited work accumulating 7 citations since 2022, Shahidi’s contributions are gaining traction in the robotics community. His work on Robotik 4.0 further underscores his commitment to advancing automation for Industry 4.0, making him a promising voice in the future of intelligent, adaptive robotic systems.