Kathleen Keogh
Papers
1
Total Citations
2
H-Index
1
About
Kathleen Keogh is a researcher advancing the field of robotic motion planning, with a particular focus on enhancing the efficiency and reliability of memoryless local planners. Her most-cited work introduces a novel two-stage planning approach that leverages depth-based sampling and steering constraints to overcome key limitations in environments where robots lack prior maps. By intelligently filtering out problematic sampling regions using only depth information, Keogh’s method significantly reduces computational overhead while improving planning success rates. Her contributions are especially valuable for real-time applications in unknown or dynamic spaces, where traditional planners often struggle. Though her citation count is still growing—reflecting the recency of her work—her 2023 paper has already garnered attention for its practical, data-efficient solution to a persistent challenge in robotics. Keogh’s research sits at the intersection of perception and control, offering a streamlined path toward more autonomous and responsive robotic systems. Her work is a promising step forward for students and engineers seeking to build smarter, faster, and more resource-conscious navigation algorithms.
Research Focus
Key Achievements
Top Papers
- 1