Alla Safonova

California University of Pennsylvania, University of Pennsylvania

Papers

Total Citations

111

H-Index

About

Alla Safonova is a leading researcher in robotics and motion planning, specializing in algorithms that overcome the computational challenges of high-dimensional state-spaces. Her major contributions center on developing methods to make path planning for complex robotic systems—such as multi-jointed arms and humanoid robots—both faster and more practical. Safonova pioneered the concept of "adaptive dimensionality," a technique that strategically reduces the complexity of planning problems by focusing computational resources only where they are needed. Her seminal work, "High-dimensional planning on the GPU" (42 citations), demonstrated how to leverage graphics hardware to accelerate optimal searches like A*, while her "Path Planning with Adaptive Dimensionality" (37 citations) introduced a framework that dynamically switches between high- and low-dimensional planning. This approach is further refined in her "Incremental Planning with Adaptive Dimensionality" (20 citations), which enables real-time replanning. Notably, her research on motion planning for manipulators with independent wrist joints (12 citations) addresses the specific challenges of humanoid robots. With a combined citation count exceeding 110, Safonova’s work has profoundly influenced the efficiency of robotic motion planning, enabling more dexterous and autonomous systems.