James P. Bailey

Papers

Total Citations

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About

James P. Bailey is a researcher whose work lies at the intersection of robotics, path planning, and computational geometry. His most-cited paper, "Path-length analysis for grid-based path planning" (2021), has garnered 39 citations and provides a rigorous theoretical framework for understanding the efficiency of grid-based algorithms—a cornerstone of autonomous navigation. Bailey’s key contributions include developing analytical models that quantify path-length suboptimality in discrete environments, offering both practitioners and theorists a clearer lens through which to design and benchmark motion-planning systems. His work is particularly notable for bridging the gap between abstract algorithmic guarantees and real-world robotic performance, making it essential reading for students and engineers working on mobile robots or video game AI. While his citation count is modest, the depth and precision of his analysis have established him as a rising voice in the field, with his findings directly informing more efficient pathfinding heuristics. Bailey’s research continues to shape how we understand the trade-offs between computational cost and path quality in grid-based planning.