Sohaib Akbar

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About

Sohaib Akbar is a researcher whose work lies at the intersection of robotics, motion planning, and computational geometry, with a particular emphasis on solving high-dimensional problems through multilevel abstractions. His major contribution is the formalization of multilevel motion planning using fiber bundles—a mathematical framework that describes lower-dimensional projections of complex systems. This approach allows for significantly faster solutions to high-dimensional motion planning challenges by decomposing them into more tractable subproblems. His most-cited paper, "Multilevel Motion Planning: A Fiber Bundle Formulation" (2023), has garnered 19 citations, demonstrating growing recognition in the field. An earlier version of this work (2020) laid the groundwork with 4 citations. Akbar's research is notable for bridging abstract mathematical theory with practical algorithmic design, offering a powerful new lens for tackling the curse of dimensionality in robotics. His work holds promise for advancing autonomous systems, from robotic manipulation to autonomous navigation, by enabling more efficient and scalable planning in complex, high-dimensional spaces.