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About

Lars Blackmore is a pioneering researcher in robotics and autonomous systems, best known for his groundbreaking work in stochastic predictive control and state estimation under uncertainty. His research focuses on enabling robotic systems to make robust decisions in unpredictable environments, addressing challenges such as uncertain state estimation, disturbances, and stochastic mode transitions like component failures. Blackmore’s most cited paper, "A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control" (2010, 342 citations), introduced a novel particle-control framework that allows robots to plan control actions while satisfying probabilistic safety constraints—a critical advancement for real-world deployment. He also contributed to hybrid estimation techniques, combining stochastic and greedy search methods for robot monitoring and diagnosis, as seen in his 2005 and 2007 works. Beyond Earth, Blackmore explored the implications of wind-assisted aerial navigation for Titan mission planning, showcasing his interdisciplinary impact. His work has profoundly influenced the fields of robotics, aerospace, and control theory, providing foundational tools for autonomous systems operating under uncertainty. With over 350 citations across his key publications, Blackmore remains a key figure in advancing robust, intelligent robotic systems.