Keyvan Majd

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About

Keyvan Majd is a researcher at the forefront of autonomous robotics, specializing in safe motion planning, control theory, and human-robot collaboration. His work addresses critical challenges in enabling robots to operate reliably alongside humans in dynamic environments. Majd’s most influential contribution is a stable analytical solution for car-like robot trajectory tracking and control, which guarantees global exponential stability of the tracking error—a foundational advance cited 39 times. He further extended this work by integrating Control Barrier Functions (CBFs) with sampling-based Rapidly-exploring Random Trees (RRTs) to generate provably safe motion plans in crowded, pedestrian-filled spaces, a paper that has garnered 26 citations. More recently, Majd has explored the underexamined intersection of communication and motion planning for collaborative robots (cobots), addressing the complex safety and efficiency challenges of co-working scenarios. His research is notable for its rigorous theoretical grounding and practical applicability, bridging the gap between formal guarantees and real-world deployment. Majd’s work is essential reading for anyone interested in the future of safe, autonomous navigation in human-occupied environments.