  With the field of rigid-body robotics having matured in the last fifty years,
routing, planning, and manipulation of deformable objects have recently emerged
as a more untouched research area in many fields ranging from surgical robotics
to industrial assembly and construction. Routing approaches for deformable
objects which rely on learned implicit spatial representations (e.g.,
Learning-from-Demonstration methods) make them vulnerable to changes in the
environment and the specific setup. On the other hand, algorithms that entirely
separate the spatial representation of the deformable object from the routing
and manipulation, often using a representation approach independent of
planning, result in slow planning in high dimensional space.
  This paper proposes a novel approach to routing deformable one-dimensional
objects (e.g., wires, cables, ropes, sutures, threads). This approach utilizes
a compact representation for the object, allowing efficient and fast online
routing. The spatial representation is based on the geometrical decomposition
of the space into convex subspaces, resulting in a discrete coding of the
deformable object configuration as a sequence. With such a configuration, the
routing problem can be solved using a fast dynamic programming sequence
matching method that calculates the next routing move. The proposed method
couples the routing and efficient configuration for improved planning time. Our
simulation and real experiments show the method correctly computing the next
manipulation action in sub-millisecond time and accomplishing various routing
and manipulation tasks.

Robotics;Artificial Intelligence