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About

Emily A. Doucette is a researcher specializing in multi-robot systems, autonomous navigation, and human-robot interaction, with a focus on cooperative mapping, target tracking, and system stability. Her most influential work, "Map merging of rotated, corrupted, and different scale maps using rectangular features" (2016), addresses the critical challenge of integrating environmental maps from multiple ground robots, proposing algorithms that handle orientation, corruption, and scale variations—a foundational contribution to collaborative robotics with 25 citations. She has also advanced distributed guidance for mobile sensors in urban target search (2017) and developed a hybrid estimation algorithm for tracking adversarial teams (2017), with applications in network security and robotic soccer. Her research on passive switched system analysis of semi-autonomous systems (2016) provides stability guarantees for intermittently teleoperated systems, enhancing safety in human-robot collaboration. Collectively, her work spans 33 citations, demonstrating impact in robotics and control theory. Doucette’s contributions are notable for addressing real-world complexities in multi-agent coordination and autonomous decision-making, making her a key figure in advancing cooperative robotics and system resilience.