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About

David A. Wilkie is a leading researcher in robot motion planning and human-robot interaction, best known for his foundational work on collision avoidance in dynamic environments. His most influential contribution is the development of **Generalized Velocity Obstacles (244 citations)**, which extended the classic velocity obstacle concept to car-like robots, enabling real-time navigation among moving obstacles. This work has become a cornerstone for autonomous driving and mobile robot navigation. Wilkie further advanced the field with **BRVO (113 citations)**, a novel method for predicting pedestrian trajectories using velocity-space reasoning, directly improving the safety and fluidity of human-robot interaction. He also introduced **LQG-Obstacles (87 citations)**, a groundbreaking framework that combines linear-quadratic feedback control with guaranteed collision avoidance under motion and sensing uncertainty. This work bridges control theory and motion planning, addressing a critical gap for robots operating in real-world, uncertain conditions. Wilkie’s research is widely cited across robotics, autonomous vehicles, and human-robot collaboration, and his methods are now standard tools for researchers tackling safe, real-time navigation in crowded, dynamic spaces.