Graduation Year
2023
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Yu Y. Sun, Ph.D.
Committee Member
Shaun S. Canavan, Ph.D.
Committee Member
Xiaoning Qian, Ph.D.
Keywords
Bin-Picking, Deep Learning, Grasping, Logistics, Manipulation
Abstract
Picking up multiple objects at once is a grasping skill that makes a human worker efficient in many domains. However, the State of Arts Robot Grasping skill has not developed such ability to compete with human.This paper presents a system to pick a requested number of objects by only picking once (OPO). The proposed Only-Pick-Once System (OPOS) contains several graph-based algorithms that convert the layout of objects into a graph, cluster nodes in the graph, rank and select candidate clusters based on their topology. OPOS also has a multi-object picking predictor based on a convolutional neural network for estimating how many objects would be picked up with a given gripper location and orientation. This paper presents four evaluation metrics and three protocols to evaluate the proposed OPOS. The results show OPOS has very high success rates for two and three objects when only picking once. Using OPOS can significantly outperform two to three times single object picking in terms of efficiency. The results also show OPOS can generalize to unseen size and shape objects.
Scholar Commons Citation
Ye, Zihe, "Only Pick Once-Multi-Object Picking Algorithms for Picking Exact Number of Objects Efficiently" (2023). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10777
