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

TL;DR

This paper introduces a hierarchical motion planner that combines regrasping and drooping strategies to enable robots to manipulate long, heavy objects using external supports, improving their handling capabilities.

Contribution

We develop a graph-based planning system that integrates in-hand regrasping and drooping with external support considerations, enabling autonomous manipulation of heavy objects.

Findings

The planner successfully manipulates long, heavy objects in experiments.

It autonomously divides object weight between arm and support surface.

The system improves robot arm capabilities in object manipulation tasks.

Abstract

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.