Purpose

This paper aims to present a hierarchical motion planner for planning the manipulation motion to repose long and heavy objects considering external support surfaces.

Design/methodology/approach

The planner includes a task-level layer and a motion-level layer. This paper formulates the manipulation planning problem at the task level by considering grasp poses as nodes and object poses for edges. This paper considers 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.

Findings

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.

Originality/value

The authors’ contribution is that they 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.

中文翻译：

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考虑重新抓握和约束下垂的组合，计划放置长而重的物体

目的

本文旨在提出一种分层运动规划器，用于规划操纵运动以在考虑外部支撑表面的情况下放置长而重的物体。

设计/方法/方法

规划器包括任务级层和运动级层。本文通过将抓取姿势视为节点，将对象姿势视为边缘，在任务级别制定了操作规划问题。本文在构建图时考虑了重抓和约束手滑（下垂），并通过搜索图找到混合的重抓和下垂序列。生成的序列自动分配手臂和支撑面之间的物体重量并避开配置障碍。笛卡尔规划用于机器人运动级别，以在任务级别层发现的序列的相邻关键抓握姿势之间生成运动。

发现

进行了各种实验以检查所提出的规划器的性能。结果表明，使用所提出的规划器，机器人手臂操纵长而重的物体的能力得到了提高。

原创性/价值

作者的贡献是他们最初开发了一个基于图形的规划系统，该系统考虑外部支持来推理手部和重新抓握操作运动。一方面，规划器将重抓和下垂相结合，在外部支持下实现手内操控。另一方面，当物体稳定放置时，它通过释放和重新抓取物体来切换状态。搜索图的节点可以从提供大量预注释数据的远程云服务器中检索，以实现网络情报。