  This paper presents a hierarchical motion planner for planning the
manipulation motion to repose long and heavy objects considering external
support surfaces. The planner includes a task level layer and a motion level
layer. We formulate the manipulation planning problem at the task level by
considering grasp poses as nodes and object poses for edges. We consider
regrasping and constrained in-hand slip (drooping) during building graphs and
find mixed regrasping and drooping sequences by searching the graph. The
generated sequences autonomously divide the object weight between the arm and
the support surface and avoid configuration obstacles. Cartesian planning is
used at the robot motion level to generate motions between adjacent critical
grasp poses of the sequence found by the task level layer. Various experiments
are carried out to examine the performance of the proposed planner. The results
show improved capability of robot arms to manipulate long and heavy objects
using the proposed planner. Our contribution is we initially develop a
graph-based planning system that reasons both in-hand and regrasp manipulation
motion considering external supports. On one hand, the planner integrates
regrasping and drooping to realize in-hand manipulation with external support.
On the other hand, it switches states by releasing and regrasping objects when
the object is in stably placed. The search graphs' nodes could be retrieved
from remote cloud servers that provide a large amount of pre-annotated data to
implement cyber intelligence.
