Reasonable Grasping Based on Hierarchical Decomposition Models of Unknown Objects

Abstract

Reasonable grasping for unknown objects is an interesting and important problem for autonomous robots in unstructured environment. Current grasping methods for unknown objects mostly focus on precision and stability. Until now there are few specific studies or reports describing reasonable grasp of unknown objects for service robots such as home service robots and nursing robots. In the paper we proposed a reasonable grasping method for unknown objects based on hierarchical decomposition point cloud models using a vision sensor. As a complex task, vision-based grasp is composed of a series solution of a mixture of subproblems. Therefore, we adopt an improved superquadrics fitting algorithm with the improved cuckoo search strategy (S-ICS) to achieve restoration and segmentation of incomplete point cloud data of unknown objects in a single visual angle. Then a reasonable region decision method based on hierarchical decomposition models is proposed to evaluate the reasonableness of grasped positions of unknown objects. Finally, we use a simulation to verify the effectiveness of the proposed method. Moreover, we also perform an extensive real-world grasping experiment on a set of unknown objects in daily use. The results also verify the effectiveness of our approach.