Papers
2
Total Citations
8
H-Index
2
About
Jacqueline Jermyn is a robotics researcher specializing in sampling-based path planning algorithms for mobile robot navigation. Her work focuses on improving the efficiency and reliability of autonomous navigation through comparative evaluations of Rapidly Exploring Random Tree (RRT) and Probabilistic Roadmap (PRM) methods. Her most cited paper, "A Comparison of the Effectiveness of the RRT, PRM, and Novel Hybrid RRT-PRM Path Planners" (2021, 6 citations), introduces a hybrid approach that combines the strengths of both single-query and multi-query techniques, offering a more versatile solution for complex environments. Jermyn also contributed a systematic evaluation of five RRT variants in "Path Planning Algorithms: An Evaluation of Five Rapidly Exploring Random Tree Methods" (2021, 2 citations), providing valuable benchmarks for practitioners. Her work is notable for its practical focus on real-world applicability, helping to bridge the gap between theoretical algorithm design and implementation in autonomous systems. While her citation counts are modest, her contributions are foundational for researchers and students exploring efficient path planning in robotics, particularly those seeking clear comparisons of established and novel methods.
Research Focus
Key Achievements
Top Papers
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