  We introduce a new approach to tackle the mobile manipulator task sequencing
problem. We leverage computational geometry, graph theory and combinatorial
optimization to yield a principled method to segment the task-space targets
into clusters, analytically determine reachable base pose for each cluster, and
find task sequences that minimize the number of base movements and robot
execution time. By clustering targets first and by doing so from first
principles, our solution is more general and computationally efficient when
compared to existing methods.
