Topological Motion Planning

Classical motion planners often treat the robot and its environment as abstract geometric objects, ignoring rich structure available from the robot’s geometry and perception data. This leads to slow planning in cluttered scenes and poor scalability as complexity grows.

We are interested in developing planning frameworks that exploit robot geometry and perception data to build compact, structured representations of the collision-free space, and use these representations to guide search efficiently and with formal correctness guarantees.