Stephanie Kamarry

Papers

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About

Stephanie Kamarry is a robotics researcher whose work focuses on advancing path planning and environment representation for mobile robots. Her key contributions lie in improving the efficiency of the Rapidly-exploring Random Tree (RRT) algorithm, a cornerstone technique in autonomous navigation. In her highly cited 2015 paper, "Compact RRT," she introduced a novel guided sampling approach that significantly reduces node redundancy, leading to a more compact environmental representation and lower computational costs during tree growth. Building on this foundation, her 2017 work, "RRT-Edge," further enhanced node dispersion through a new node-tree connection method, optimizing path planning performance. Collectively, these papers have garnered over a dozen citations, demonstrating their influence on the robotics community. Kamarry’s innovations directly address critical challenges in mobile robotics—balancing exploration efficiency with computational economy—making her research valuable for students and engineers developing autonomous systems. Her compact RRT variants offer practical solutions for real-time navigation in complex environments, cementing her role as a contributor to efficient, scalable robotic motion planning.