Open AccessRobotic Forklift for Stacking Multiple Pallets with RGB-D Cameras
Author(s) -
Ryosuke Iinuma,
Yusuke Hori,
Hiroyuki Onoyama,
Yukihiro Kubo,
Takanori Fukao,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.257
H-Index - 19
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2021.p1265
Subject(s) - pallet , rgb color model , stacking , computer science , task (project management) , computer vision , position (finance) , artificial intelligence , orientation (vector space) , path (computing) , engineering , mechanical engineering , mathematics , physics , geometry , systems engineering , finance , nuclear magnetic resonance , economics , programming language
We propose a robotic forklift system for stacking multiple mesh pallets. The stacking of mesh pallets is an essential task for the shipping and storage of loads. However, stacking, the placement of pallet feet on pallet edges, is a complex problem owing to the small sizes of the feet and edges, leading to a complexity in the detection and the need for high accuracy in adjusting the pallets. To detect the pallets accurately, we utilize multiple RGB-D (RGB Depth) cameras that produce dense depth data under the limitations of the sensor position. However, the depth data contain noise. Hence, we implement a region growing-based algorithm to extract the pallet feet and edges without removing them. In addition, we design the control law based on path following control for the forklift to adjust the position and orientation of two pallets. To evaluate the performance of the proposed system, we conducted an experiment assuming a real task. The experimental results demonstrated that the proposed system can achieve a stacking operation with a real forklift and mesh pallets.