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About

Brian Mirtich is a pioneering researcher whose work bridges robotics, motion planning, and probabilistic analysis. His key research areas include nonholonomic path planning, robotic part feeding, and sensor-based navigation. Mirtich’s most influential contribution is a practical path planner for nonholonomic robots that uses a one-dimensional, maximal clearance skeleton through configuration space, enabling efficient navigation among obstacles—a paper that has garnered 84 citations. He also made significant strides in automated assembly lines by developing methods to estimate pose statistics for robotic part feeders using CAD models, with two related papers accumulating 53 and 51 citations respectively. These works provide fundamental probabilistic insights into part behavior, akin to analyzing dice and coins, but applied to industrial robotics. Mirtich’s notable achievements include deriving off-tracking bounds for car-trailer systems with kingpin hitching, demonstrating exponential convergence, and extending Reeds and Shepp’s shortest-path results to manifolds in configuration space. His sensor-based planning work for rod-shaped robots using the hierarchical generalized Voronoi graph further showcases his versatility. With over 275 total citations across his top papers, Mirtich’s research has profoundly impacted both theoretical motion planning and practical robotic systems.