Planning Grasps for Assembly Tasks

TL;DR

This paper presents a model-based grasp planning approach for industrial assembly tasks, capable of generating high-quality, stable grasps with minimal dependency on CAD model quality, using a novel segmentation technique.

Contribution

It introduces a superimposed segmentation method for mesh pre-processing and develops algorithms for stable grasp planning tailored to industrial end-effectors.

Findings

High precision and stability in grasp planning demonstrated

Algorithms work effectively with both simulated and real-world experiments

Tunable parameters allow adaptation to various assembly requirements

Abstract

This paper develops model-based grasp planning algorithms for assembly tasks. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering CAD models of target objects. The developed algorithms are able to stably plan a large number of high-quality grasps, with high precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which pre-processes a mesh model by peeling it into facets. The algorithms use superimposed segments to locate contact points and parallel facets, and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters were prepared to adapt the algorithms to meet various requirements. The experimental section demonstrates the advantages of the algorithms by analyzing the cost and stability of the algorithms, the precision of the planned grasps, and the tunable parameters with both simulations and real-world experiments. Also, some examples of robotic assembly systems using the proposed algorithms are presented to demonstrate the efficacy.