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This paper presents a hierarchical motion planner for planning themanipulation motion to repose long and heavy objects considering externalsupport surfaces. The planner includes a task level layer and a motion levellayer. We formulate the manipulation planning problem at the task level byconsidering grasp poses as nodes and object poses for edges. We considerregrasping and constrained in-hand slip (drooping) during building graphs andfind mixed regrasping and drooping sequences by searching the graph. Thegenerated sequences autonomously divide the object weight between the arm andthe support surface and avoid configuration obstacles. Cartesian planning isused at the robot motion level to generate motions between adjacent criticalgrasp poses of the sequence found by the task level layer. Various experimentsare carried out to examine the performance of the proposed planner. The resultsshow improved capability of robot arms to manipulate long and heavy objectsusing the proposed planner. Our contribution is we initially develop agraph-based planning system that reasons both in-hand and regrasp manipulationmotion considering external supports. On one hand, the planner integratesregrasping and drooping to realize in-hand manipulation with external support.On the other hand, it switches states by releasing and regrasping objects whenthe object is in stably placed. The search graphs' nodes could be retrievedfrom remote cloud servers that provide a large amount of pre-annotated data toimplement cyber intelligence.

Planning to Repose Long and Heavy Objects Considering a Combination of Regrasp and Constrained Drooping